Inhibition of NAT10 Enhances the Antitumor Immunity by Increasing Type I Interferon Responses

doi:10.21203/rs.3.rs-4352052/v1

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Inhibition of NAT10 Enhances the Antitumor Immunity by Increasing Type I Interferon Responses

https://doi.org/10.21203/rs.3.rs-4352052/v1

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Posttranslational modifications add tremendous complexity to cancer progression; however, gaps remain in knowledge regarding the function and immune regulatory mechanism of newly discovered mRNA acetylation modification. Here, we discovered an unexpected role of N4-acetylcytidine (ac4C) RNA acetyltransferase-NAT10 on reshaping tumor immune microenvironment. Based on analyses of patient datasets, we found that NAT10 was upregulated in tumor tissues, and negatively correlated with overall survival and immune cells infiltration. Inhibition of NAT10 significantly suppressed tumor growth in vivo and in vitro. NAT10 deficiency in cancer cells significantly upregulated immune cells infiltration and stimulated tumor-specific cellular immune responses, leading to the establishment of robust anti-tumor immunity. Mechanistically, we identified MYC as a key downstream target of NAT10, and then induced CDK2-DNMT1 expression. Meanwhile, inhibition of NAT10 down-regulated MYC-CDK2-DNMT1 expression, which enhanced double-stranded RNAs (dsRNA) formation to induce type I IFN (IFN-I) and trigger immune responses of CD8⁺ T cells. In terms of clinical significance, we demonstrated that inhibition of NAT10 using Remodelin or PEI/PC7A/siRNA nanoparticles combined with anti-PD1 treatment synergistically improved tumor immune microenvironment and repressed tumor progression in vivo. Therefore, inhibition of NAT10 in cancer cells improve tumor immunogenicity, resulting in tumor suppression by enhancing anti-tumor immune responses. Our study uncovers a crucial role of NAT10 in re-modulating tumor immunogenicity and demonstrates a novel concept for targeting NAT10 in cancer immunotherapy.

Biological sciences/Cancer/Tumour immunology

Biological sciences/Cancer/Cancer therapy

Cancer represents a considerable global public health challenge. Compared to traditional treatment modalities like chemotherapy, radiotherapy, and surgery, cancer immunotherapy has significantly improved both patient survival and quality of life. The success of immunotherapies has spurred interest in the development of effective antitumor medications, including immune checkpoint inhibitors (ICIs) [1]. However, its effectiveness is primarily hindered by the limited infiltration and activation of immune cells within the tumor microenvironment [2].

Interferons (IFNs) are crucial components of the immune response against infections and malignancies. IFNs play a significant role in promoting the anti-tumor response through the activation and functioning of immune cells, facilitating the elimination of malignant cells [3]. IFNs are primarily released by immune and stromal cells, with cancer cells also contributing to the release of IFNs. Various genotoxic anticancer treatments, such as radiation and chemotherapy, small molecule kinase inhibitors, function by inducing DNA damage or, producing double-stranded RNAs (dsRNA), and then activating IFN pathways to stimulate an immune response against cancer [4–6].

Recent advanced transcriptomic studies have revealed that metazoan cells express diverse types of endogenous dsRNA, including endogenous retroviral elements (ERV), repetitive RNA elements, mitochondrial dsRNAs, mRNAs with inverted Alu-containing 3′ UTRs, and structural dsRNAs with long dsRNA stems [7, 8]. Malignant cells may exhibit increased levels of dsRNAs due to the loss of suppressive epigenetic modifications in repetitive elements, genomic instability, or mitochondrial damage induced by oxidative stress, thereby increasing the dsRNA load in cancer cells [9]. For instance, ERVs make up more than 8% of the human genome, with the majority being silenced in normal somatic cells through promoter DNA methylation. In some cancers, loss of ERV DNA methylation leads to aberrant overexpression of ERVs, and bidirectional transcription of ERVs has been demonstrated to enhance dsRNA formation [10, 11].

Growing evidence indicates that the accumulation of intracellular dsRNA produced by cancer cells stimulates the production of type I IFN, thereby enhancing antitumor immunity [12]. Abnormal accumulation of endogenous dsRNAs triggers an innate antiviral and antitumor immune response by activating dsRNA-sensing pathways, including retinoic acid-inducible gene I (RIG-I), which may lead to chronic inflammation and associated human diseases [13]. Further investigations into these gene signatures and associated changes in the anti-tumor immune response are likely to significantly contribute to predicting patient outcomes and their responses or resistance to chemotherapy or immunotherapy.

Some modifying enzymes, such as DNA modifying enzymes, and mRNA regulators, can influence tumor immunity [14]. In a recent study, Arango and colleagues identify N4-acetylcytidine (ac4C) as a new mRNA acetylation modification catalyzed by N-acetyltransferase 10 (NAT10) that increases the stability and translation efficiency of transcripts [15]. NAT10 has recently been shown to regulate tumor progression, however its impact on tumor immunity remains understudied. In this study, we demonstrate that inhibiting NAT10 effectively enhances the type I IFN response, stimulates adaptive immune responses, and inhibits tumor progression. Combining NAT10 inhibitors with a novel delivery system or ICIs therapy could offer effective means to boost immunity and halt tumor progression, providing targets for clinical treatment.

High expression of NAT10 correlates with poor survival and low immune cell infiltration in cancer

NAT10, the only writer for ac4C modification on mRNAs, has been reported to have many important functions, such as affecting stem cells differentiation, promoting glycolysis addiction. Additionally, the potential role of NAT10 in pro-tumor effect has been uncovered, but the anti-tumor immunity of cancer-intrinsic NAT10 has not been reported.

To determine the role of NAT10, we utilized GEPIA database to explore the influence of NAT10 in lung adenocarcinoma (LUAD) [16]. The expression of NAT10 was increased in TCGA-LUAD cohort with the stage increase of the disease (Fig. 1A), which indicated that NAT10 was related with the progression of cancer. Moreover, we download the raw data and survival information from TCGA-LUAD cohort to obtain a Kaplan-Meier survival curve. The results showed that higher NAT10 expression was correlated significantly with shorter overall survival (Fig. 1B). Concurrently, we collected 38 pathology slides of lung cancer patients and performed immuno-histochemical staining to obtain immuno-histochemistry stain score for survival analysis. The results was in accordance with TCGA database, as shown by higher NAT10 expression with lower survival (Fig. 1C). Moreover, ROC curve was utilized to validate the ability of the prognostic efficiency of NAT10, which indicated that the NAT10 has a good predictive value in lung cancer (Fig. 1D). Altogether, these results suggest that NAT10 is a cancer-promoting gene, which may be an important risk factor for the development of lung cancer.

Given our findings above, we further explored the oncogenic effect of NAT10 in vivo. Remodelin, a well-established inhibitor for NAT10 [17], was used for the further study. The immunocompetent (C57BL/6N) mice were used to establish syngeneic tumor models. Mice were injected subcutaneously with murine lung cancer cells (TC1) or murine fibrosarcoma cells (MCA205). The tumor masses on the flank of the mice indicated the successful establishment of the tumor after 14 days. Importantly, the tumors in mice exposed to low-dose Remodelin were significantly reduced in weight and size compared to the saline group (Fig. 1E, 1F). On the other hand, Immunodeficient (Nude/Nude) mice injected subcutaneously with TC1 or MCA205 cells were established. Our results showed that there was no difference in tumor size and weight whether exposed to saline or low-dose Remodelin in the nude mice (Fig. 1G, 1H). Additionally, we further explored the effect of NAT10 on cancer cell in vitro. Colony formation assay was conducted to detect the role of NAT10 in the cell proliferation. The results showed that Remodelin significantly suppressed the proliferation of cancer cells (Fig. S1A, S1B). The above results indicated that the effect of NAT10 on tumor growth was partly related to host immunity.

Analysis of immune cell infiltration was performed using the CIBERSORT algorithm between high and low NAT10 expression groups in TCGA-LUAD cohort [18]. The results showed that LUAD patients with lower expression of NAT10 appeared to have higher proportions of immune cells, include CD8⁺ T cells and DCs (Fig. 1I). The expression analysis of NAT10 within individual patients was conducted based on the scRNAseq data (GSE148071) [19], whereby the summation of NAT10 expression levels across all cells within each patient was followed by division by the total number of sequenced cells within the same patient. The results also indicated a positive relationship between NAT10 expression and malignant cells percentage while a negative correlation with the proportion of T cells or DCs (Fig. S1C). Furthermore, our immunohistochemical staining results in lung cancer samples (n = 37) showed the same negative association between NAT10 and CD8⁺ T cells (Fig. 1J). Collectively, these results suggest that NAT10 is a proto-oncogene and maybe affect tumor growth in an immune-dependent manner.

NAT10 deficiency suppresses tumor growth via eliciting immunological protection

Given the observed phenomenon, it is imperative to further investigate the mechanisms underlying NAT10 in anti-tumor immunity. CRISPR/Cas9 technology utilizing Nat10-specific sgRNA (sgNAT10) pairs was employed to knockout NAT10 in TC1 and MCA205 cells (Figs. 2A, S2A). Wild-type (WT) cells transfected with an empty vector served as controls. To evaluate whether NAT10 inhibition within cancer cells triggers immune responses, we established syngeneic tumor models in immunocompetent (C57BL/6N) mice and immunodeficient (Nude/Nude) mice transplanted with either WT or sgNAT10 cancer cells. All C57BL/6N mice bearing with WT TC1 or MCA205 cells had developed substantial tumor masses post-transplantation, whereas the tumor masses disappeared quickly in those bearing with sgNAT10 TC1 or MCA205 cells (Fig. 2A, 2B), indicating that NAT10 deficiency might impede subcutaneous tumor growth in immunocompetent mice. Subsequently, we established transplant tumor models in immunocompetent Nude mice. The results demonstrated that Nude mice in both WT and sgNAT10 groups developed apparently substantial tumors though with a significant difference (Fig. 2C, 2D), further suggesting that NAT10 deficiency may suppress tumors in an immune-dependent manner. As the mice receiving transplants of NAT10-deficient TC1 and MCA205 cells exhibited slightly smaller tumors compared to those with WT cells in Nude/Nude mice, we speculated that NAT10 might have additional suppressive effects on tumor cell proliferation besides of eliciting adaptive immune responses. Consequently, we performed colony formation assays and CCK8 assays. Our findings demonstrated that NAT10 deficiency significantly impeded colony formation (Figs. S2B, S2C) and proliferation (Figs. S2D, S2E) in both TC1 and MCA205 cells.

Moreover, we further investigated whether NAT10 could influence the survival via the host immune system. C57BL/6N mice bearing NAT10-deficient TC1 or MCA205 cell transplants exhibited significantly prolonged survival compared to mice with WT cell transplants; however, they succumbed to the tumor within 60 days. In contrast, all C57BL/6N mice receiving NAT10-deficient cancer cell transplants survived until day 60 (Fig. 2E, 2F). In the xenograft model using Nude/Nude mice, no significant difference of survival between WT and sgNAT10 groups was found and all mice died within 40 days (Fig. 2G, 2H). These differing outcomes between immunocompetent and immunodeficient mice further support the notion that NAT10 inhibition may enhance mouse survival by activating immunity.

Considering the possible nonspecific effects of the CRISPR/Cas9 system, we restored NAT10 expression in the NAT10-deficient TC1 cells. We first constructed a plasmid VP64-NAT10-GFP, in which the base sequence corresponding to the NAT10 sgRNA position in the VP64-NAT10-GFP plasmid was modified, with the encoded amino acids unchanged to avoid cleavage by the CRISPR CAS9 enzyme. Our findings demonstrated that NAT10 restoration enabled NAT10-deficient TC1 cells to successfully develop tumors (Fig. S2F), providing clear evidence of the pro-oncogene effect of NAT10.

For the assessment of possible involved immune memory, we subcutaneously immunized C57BL/6N mice with either sgNAT10 cancer cells or freeze-thawed WT cancer cells on the left side, followed by re-challenge with comparable numbers of live WT cancer cells on the right side after 2 weeks (Fig. 2I, left panel). Intriguingly, each mouse immunized with sgNAT10 cancer cells completely inhibited WT tumor growth on the right side, resulting in tumor-free mice, whereas those immunized with freeze-thawed WT cancer cells showed a significantly weaker effect (Fig. 2I). These results suggested that NAT10 deficiency elicited immunological protection.

NAT10 deficiency triggers immune responses of CD8 ⁺ T cells in vivo

Based on our previous findings suggesting that NAT10 deficiency may impede tumor growth by activating anti-immune mechanisms, we conducted transcriptomic RNA-sequencing (RNA-seq) analysis to comprehensively investigate whether NAT10 has impacts on immune-response signaling in vivo. Tumor tissues inoculated with WT or sgNAT10 cancer cells were harvested on day 8, and total RNA was extracted for RNA sequencing. Gene Set Enrichment Analysis (GSEA) revealed upregulation of "hallmark" signatures including "Interferon-gamma (IFN-γ) response", "Interferon-alpha (IFN-α) response", and "Inflammatory response" in sgNAT10 TC1 tumor tissues [20]. Heatmaps depicting differentially regulated genes from the GSEA analysis in WT and sgNAT10 TC1 tumor tissues showed increased expression of numerous cytokines and chemokines, such as C-X-C motif chemokine ligands 9, 10 and 11 (CXCL9, 10, 11) (Fig. 3A), which contribute to robust anti-tumor immunity. CXCL9/10/11 are responsible for recruiting and activating T cells via binding with CXCR3 [21]. Moreover, our results found that genes associated with the antigen presentation machinery (APM) and CD8⁺ Teff were upregulated in sgNAT10 tumor tissues (Fig. 3B), while cell cycle-related genes linked to proliferation were downregulated (Fig. 3C). Collectively, these findings indicate that NAT10 deficiency plays a crucial role in anti-tumor immunity through regulating immunological response factors especially genes associated with CD8⁺ Teff cells.

To further clarify the detailed immune cells involved in NAT10 deficiency-induced anti-tumor immunity, we applied multicolor immunofluorescence experiments. Our results demonstrated a notable elevation in CD8⁺ T cells and DCs in sgNAT10 group compared to the WT group, while no noticeable difference was initially observed in Tregs (Fig. 3D). Considering that CD8⁺ T cells play a crucial role in anti-tumor immunity, we conducted antibody-based depletion of CD8⁺ T cells prior to in vivo transplantation of sgNAT10 TC1 cancer cells. The results showed that depletion of CD8⁺ T cells markedly impeded NAT10-deficient-induced tumor regression, suggesting deletion of NAT10 primarily exerts anti-tumor immune effects via CD8⁺ T cells (Fig. 3E).

Given that CD8 antibodies reversed the protective effect of NAT10 deficiency, we further investigated CD8⁺ T cell infiltration and functionality. Our results of immunofluorescence assay revealed increased tumor-infiltrating CD8⁺ T cells in NAT10-deficient tumor tissues (Fig. S3A). Moreover, flow cytometry results quantified higher CD8⁺ T cell frequencies in NAT10-deficient tumor tissues, consistent with immunofluorescence results (Fig. S3B). For the functional assessment, we further demonstrated the elevated IFN-γ and Granzyme B (GZMB) levels in tumor-infiltrating CD8⁺ T cells in the sgNAT10 tumor group (Fig. 3F, 3G). Importantly, inguinal lymph nodes, critical for anti-tumor immunity, were also used in our study to investigate the CD8⁺ T cells. Our results also showed the upregulation of IFN-γ⁺CD8⁺ T cells in the NAT10-deficient group (Fig. S3C, S3D). Additionally, gene expression analysis by real-time PCR method confirmed the upregulation of CD8a, IFN-γ, Granzyme A (GZMA), GZMB, CXCL9, and CXCL10 in sgNAT10 group (Fig. 3H). IFN-γ secretion was increased in the sgNAT10 group, as observed in IFN-γ ELISpot assay (Fig. 3I). Moreover, T-cell proliferation assay indicated enhanced proliferation of both CD4⁺ and CD8⁺ T cells in NAT10-deficient cancer cells (Fig. 3J, 3K and S3E). These findings collectively suggest adaptive immune responses, particularly CD8⁺ T cell-mediated antitumor immunity, have been activated induced by NAT10 deficiency in vivo.

NAT10 deficiency induces IFN-I responses in cancer cells

The above results showing an enhanced IFN response and an increased infiltration of tumor-infiltrating lymphocytes (TIL) in the NAT10 deficient tumor microenvironment suggest a possible link between IFN-mediated tumor cell chemokine expression and increased TIL infiltration, which may be responsible for the enhanced antitumor immune responses. To test this hypothesis, RNA-seq was performed with total mRNA extracted from WT and sgNAT10 TC1 or MCA205 cells. GSEA analysis showed that these pathways were mainly involved in the “IFN-I” signaling pathway (Fig. S4A, S4B). Compared to WT cancer cells, NAT10 deletion induced the expression of genes related to IFN-I response (Fig. 4A, 4B). By RT-qPCR analysis, we further confirmed the increased expression of some of these genes in sgNAT10 cancer cells, including the type I IFN gene Ifnb1 itself, the transcription factor Stat1, the antiviral gene Mx2, the pattern recognition receptor genes Tlr3 and Ddx58, the antigen presentation related gene Tap1, as well as the chemokine-encoding genes Ccl5 and Ccl7 (Fig. 4C, 4D).

NAT10 deficiency could induce IFN-I responses in cancer cells, which can play key roles in the activation of cellular components of the immune response, such as dendritic cells and T cells. To verify that IFN-I responses underlined the outcomes, sgNAT10 cancer cells were transplanted into type I IFN receptor KO (Ifnar1 KO) mice. The results showed that both WT and sgNAT10 cancer cells developed apparently substantial tumors in Ifnar1 KO mice, suggesting the effects favoring anti-tumor immune responses triggered by NAT10 deficiency were significantly abolished on an Ifnar1 KO background (Fig. 4E, 4F). These data indicated that NAT10 deficiency in cancer cells may drive IFN-I responses to promote protective anti-tumor CD8⁺ T cell immunity.

NAT10 increases MYC expression through regulating mRNA acetylation

Next, we explore the mechanism by which NAT10 deletion induces interferon production. To identify whether the acetyltransferase NAT10 directly mediated antitumor immune response, acRIP-seq analysis was performed. The sequential analysis of ac4C peaks showed that typical GAGGAGA motifs were highly enriched within ac4C sites of mRNA (Fig. 5A). Further analytic results showed that the ac4C peaks predominantly occurred within coding sequences (CDS) and 3’untranslated regions (3’UTR) (Fig. 5B, 5C). As reported, the acetyltransferase NAT10 can confer enhanced mRNA stability, and ac4C peaks within wobble sites can stimulate translation efficiency [22]. We therefore investigated potential targets using a combination of acRIP-seq and Label-free quantitative proteomics. We identified 7 candidate genes (Phf2, Myc, Wwc2, Kmt2a, Gigyf1, Timeless, and Nufip2) that showed concomitant decreased mRNA acetylation and reduced protein levels in sgNAT10 cancer cells (Fig. 5D, S5A).

Among the 7 candidate genes, MYC has been reported to be related to both cell proliferation and antitumor immunity [23]. We then performed Western blot, and our results showed that NAT10 deficiency resulted in decreased protein expression of MYC in cancer cells (Fig. 5E). To identify the key ac4C sites that regulate mRNA stability, we further analyzed the acetylation peaks of MYC mRNA. AcRIP-seq data showed that the ac4C peaks were distributed in the CDS and 3’/5’UTR region of MYC mRNA (Fig. 5F). Interestingly, the 3’UTR region of MYC mRNA contains a nucleic acid sequence consistent with the typical GAGGAGA motifs (Fig. 5A), suggesting that this ac4C site may be more dynamic in regulating MYC mRNA stability. Subsequently, we constructed 3’UTR reporters containing wild type or mutant MYC 3’UTR after the firefly luciferase reporter gene (Fig. 5G). The dual-luciferase assay showed significantly attenuated fluorescence activity in the mut-3’UTR groups compared to WT-3’UTR groups, mirroring reduced mRNA stability due to the loss of acetylated position (Fig. 5H). Moreover, the acRIP-PCR results confirmed that NAT10 may bind to the 3'UTR of MYC (Fig. 5I). Furthermore, our results also showed that the half-life of MYC mRNA was ≈ 16 hours for WT cells and significantly decreased in sgNAT10 cells, meaning reduced ac4C enrichment was accompanied by increased decay of MYC mRNA (Fig. 5J). Overall, NAT10 promoted MYC mRNA stability and translation efficiency via ac4C modification, and the ac4C peak within the 3’UTR region was responsible for mRNA stability.

Considering the important expression-regulating role of NAT10 on MYC, we investigate whether NAT10 modulates anti-tumor immunity via MYC. Firstly, CRISPR/Cas9 technology utilizing MYC-specific sgRNA (sgMYC) pairs was employed to knockout MYC in TC1 (Fig. S5B). To evaluate whether intrinsic MYC deficiency inhibits tumor growth by triggering an immune response, we established syngeneic tumor models in C57BL/6N mice transplanted with either WT or sgMYC cancer cells. The results showed that sgMYC TC-1 tumors exhibited a significant reduction in tumor growth as compared with their WT parental cells (Fig. S5C). At day 10 after subcutaneous transplantation, a considerably higher percentage of CD8⁺ T cells were observed in sgMYC tumors than in WT tumors (Fig. S5D), indicating that adaptive immunity might be involved in MYC-deficient induced tumor reduction. The MYC protein restored in sgNAT10 TC1 cells was significantly abolished NAT10-deficient induced tumor regression (Fig. S5E, S5F). The elevated IFN-γ secretion induced by NAT10 deficiency in vivo were also significantly abolished in Myc-overexpressed sgNAT10 cells (Fig. S5G). These data suggest that NAT10 might modulate anti-tumor immunity via regulating MYC expression.

NAT10 depletion induces dsRNA-mediated RIG-I-dependent IFN-I signaling via Myc/CDK2/DNMT1 pathway

As shown above that NAT10 enhanced mRNA stability and translation efficiency of MYC, we then aimed to elucidate IFN-I signaling induced by NAT10 inhibition. Considering the ability of NAT10 to promote cell proliferation, we reanalyzed the RNA-seq data and revealed several differentially-expressed genes associated with proliferation, in which CDK2, a member of the cyclin-dependent kinases family [24], was the most significantly down-regulated in NAT10 deficient cells (Fig. 6A, S6A). It has been reported that MYC could directly regulate CDK2 expression [25], and our western blot results also showed that MYC deletion significantly inhibited the expression of CDK2 in cancer cells (Fig. S6B). Moreover, our results showed that knocking out CDK2 (sgCDK2) led to the inhibition of tumor growth (Fig. S6D), consistent with the effect of siNAT10. Next, we explored the effects of CDK2 on antitumor immune response. The results showed that CDK2 deficiency in cancer cell elevated CD8⁺ T cells infiltration and IFN-γ expression, which is consistent with the phenomenon caused by NAT10 deletion (Fig. S6E, S6F). These findings suggest that NAT10 deficiency might enhance anti-tumor immunity via Myc-mediated regulation of CDK2 expression.

How does CDK2 deletion induce IFN-I responses? CDK2-deficient cells have been proven to inhibit the activity of DNMT, and loss of its activity can induce IFN-I responses by increasing production of dsRNA [26]. We then reanalyzed our RNA-seq data and found that DNMT1 has the highest expression in cancer cells (Fig. 6B, S6C). Western blot analysis also showed that the protein levels of CDK2 and DNMT1 were significantly reduced in NAT10-deficient cells compared to WT cells (Fig. 6C). Furthermore, DNMT1 expression was restored by overexpressing the CDK2 in NAT10 deficient cells (Fig. S6G). More importantly, overexpression of CDK2 in sgNAT10 cells could promote the development of tumors (Fig. S6G). Correlation analysis between NAT10 and several downstream genes performed with the GEPIA website [34] revealed statistically positive correlations between NAT10 and MYC, CDK2, DNMT1 (Fig. S6H). These data suggest the critical role of NAT10 in maintaining the expression of CDK2 and DNMT1 through MYC.

Increased IFN-I response in cancer cells has been shown to occur in response to DNA demethylation caused by 5-azacytidine, which inhibits the activity of DNMT1 [27]. DNMT1 inhibition could trigger IFN-I response by inducing dsRNA. In our study, quantification of dsRNA performed by immunofluorescence using the dsRNA-specific J2 antibody showed a significantly higher abundance of dsRNA within sgNAT10 and sgCDK2 cells than those within WT cells. Restored MYC and CDK2 significantly abolished NAT10 deficiency-induced dsRNA production (Fig. 6D, 6E). It has been reported that dsRNA could be sensed by RIG-I and MDA-5, which triggers IFN-I response [28]. Therefore, our next objective was to investigate whether NAT10 deletion-induced dsRNA production predominantly activates IFN via the RIG-I or MDA-5 signaling pathway. GSEA enrichment analyzed by RNA-seq data showed that “RIG-I like receptor signaling pathway” were upregulated in sgNAT10 cancer cells compared to WT cells (Fig. S6I, S6J). Therefore, we silenced RIG-I in sgNAT10 TC1 cells and assessed the functionality of the IFN-I signaling pathway. Our results showed that deletion of RIG-I partially negated the elevated expressions of IFN stimulated genes (ISGs) induced by NAT10 deletion (Fig. 6F) [29]. Together, our results demonstrate that NAT10 modulates the IFN-I signaling pathway via RIG-I-mediated dsRNA sensing.

Inhibition of NAT10 with Remodelin enhances response to ICIs therapy

The above data indicate that NAT10 deletion enhances intratumoral IFN-I production and T cell infiltration, two biomarkers associated with sensitivity to ICIs therapy. Previous studies also reported that activating the IFN-I pathway and enhancing T cell infiltration could promote the therapeutic effect of ICIs therapy [30]. Therefore, we next investigated cooperation between NAT10 inhibitor and PD-1 treatment using syngeneic tumor models. Mice were gavaged with Remodelin once a day for 7 consecutive days. On day 7, we treated mice with isotype control (vehicle), or anti-PD-1 mAb (10 mg/kg, intraperitoneally (ip), twice a week for 2 weeks), and a humane endpoint was reached in a vehicle group mouse on day 29 (Fig. 7A). The results showed that either PD-1mAb or Remodelin effectively inhibited tumor growth compared to control group. Importantly, the tumor size of the combined treatment was much smaller than either of the other two groups (Fig. 7B, 7C), suggesting combining inhibition of NAT10 and PD-1 synergistically suppresses cancer growth in vivo.

Furthermore, we investigated the immunological changes and our results present with a significantly increased number of tumor-infiltrating CD8⁺ T cells after single treatment of Remodelin or anti-PD-1 mAb compared to control, while the combination group has the much higher CD8⁺ T cells infiltration (Fig. 7D, 7E). Moreover, a remarkable increase in the number of IFN-γ-positive active CD8⁺ T cells was seen in the Remodelin single treatment group; the effects were significantly enhanced by the combination treatment (Fig. 7F, 7G). Importantly, the combination treatment secreted more IFN-γ in the tumor microenvironment (Fig. 7H, 7I). Considering the clinical setting, we further detected and analyzed the relationship of NAT10 and PD-L1 in lung cancer samples. Our results demonstrate a positive correlation between the expression levels of PD-L1 and NAT10 (Fig. S7A). Overall, these data suggest that inhibition of NAT10 enhances the efficacy of PD-1 blockade therapy in suppressing tumor growth.

Intratumoral delivery of siNAT10-lipid nanoparticles (LNPs) for cancer immunotherapy

Considering the limited absorption of Remodelin that could mitigate its therapeutic effect on tumors, we developed two commonly-used delivery systems, SM102 and PEI/PC7A nanoparticles, to enhance inhibitory efficiency of NAT10 expression both in vivo and in vitro. SM102, a cationic amino lipid approved for mRNA delivery in the Moderna COVID-19 vaccine, also functions as an ionizable component in LNPs for RNAi-based therapeutics [31]. Additionally, PEI/PC7A nanoparticle, composed of polyethyleneimine (PEI) and a pH-responsive PC7A polymer, is developed for efficient siRNA transfection [32]. The particle size of siNAT10 was determined using dynamic light scattering (DLS, Malvern) and confirmed to be approximately 160 nm (Fig. 8A, S8A). Confocal laser scanning microscopy (CLSM) analysis demonstrated the overlap of fluorescence signals representing lysosomes (red fluorescence) with siRNAs (green fluorescence) within 4 hours. Moreover, a significant amount of green fluorescence was observed outside the lysosomes, indicating the escape of siRNA from the lysosomes (Fig. 8B, S8B). Successful release of siRNA from endosomes and lysosomes indicated the formation of the RNA-induced silencing complex in the cytosol.

RT-qPCR analysis was applied to assess the inhibitory efficiency of nanoparticles on NAT10 expression. The results demonstrated a significant reduction in NAT10 mRNA expression levels with both SM102 and PEI/PC7A/siNAT10 nanoparticles compared to only siNAT10 transfection (Fig. 8C, S8C). Furthermore, our in vivo experiments revealed that PEI/PC7A/siNAT10 has more effective inhibition on tumor growth than SM102 (Fig. S8D, S8E). Consequently, we employed PEI/PC7A/siNAT10 nanoparticles to evaluate its tumor inhibitory effect for the following study.

Moreover, western blotting results further confirmed significant NAT10 protein expression suppression by PEI/PC7A/siNAT10 nanoparticles (Fig. 8D). And intratumoral delivery of PEI/PC7A/siNAT10 nanoparticles treatment significantly reduced TC1 tumor growth in C57/BL6N mice, showing much more superior efficacy compared to Remodelin (Fig. 8E). Subsequently, we combined PEI/PC7A/siNAT10 nanoparticles with ICIs therapy to enhance the effect of inhibiting tumors. Our results demonstrated the effective tumor growth inhibition with both PD-1mAb and PEI/PC7A/siNAT10 nanoparticles, with the combined treatment resulting in much smaller tumor sizes compared to other groups (Fig. 8F). Furthermore, we observed a significant increase in tumor-infiltrating CD8⁺ T cells following PEI/PC7A/siNAT10 nanoparticle treatment or combination therapy, with the combination group exhibiting substantially higher CD8⁺ T cell levels (Fig. 8G). Additionally, a notable rise in IFN-γ-positive active CD8⁺ T cells was observed in the PEI/PC7A/siNAT10 nanoparticle single treatment group, with significantly enhanced effects noted in the combination therapy group (Fig. 8H). Overall, these findings suggest that NAT10 suppression by nanoparticles enhances the therapeutic effects of ICIs in controlling tumor growth.

mRNA ac4C writer NAT10 may reshape the tumor immune microenvironment. Our results reveal that targeting NAT10 not only controls tumor growth but stimulates an antitumor immune response to achieve maximal therapeutic effects. This strategy, when combined with an ICI, could approach a cure. Our approach specifically targets NAT10 to ablate tumors by downregulating mRNA stability and translation efficiency. Additionally, targeting NAT10 induces ERV-mediated dsRNA, thereby cross-priming T cell activation through a type I IFN response. Moreover, we identify that the PEI/PC7A/siRNA nanoparticles as a potent inhibitor of NAT10 expression promote robust anti-tumor activity (Fig. 9).

NAT10, the only known ac4C “writer” protein and a predominantly nuclear protein, is characterized by a unique RNA cytosine acetyltransferase domain [33]. In this study, we used an acRIP-seq method to profile the changes in global mRNA acetylation modification patterns on NAT10 deletion and identified undefined typical GAGGAGA motifs. Specifically, acetylation modification at mRNA 3’UTR enables the Myc stability, promoting translation. Myc methylation at 3’UTR may allow upregulate CDK2-DNMT1 to prevent the ERV associated with the role of dsRNA structure. Indeed, NAT10 or CDK2 inhibition in cancer cells promoted dsRNA formation which underlies RIG-I activation in cancer cells.

Our study demonstrates that tumor elimination induced by NAT10 deletion relies on IFN-I responses. Multichannel imaging and Image analysis revealed that the changes in T cell subpopulations seen after NAT10 deletion are associated with immunological memory, which effectively protected the host from challenge with the corresponding WT cancer cells. Current treatment options used high-dose cytotoxic chemotherapies that dampen immune responses. Interestingly, we found that neither NAT10 deletion nor inhibitor treatment perturbed T cell function.

How does NAT10 inhibition in cancer cells elicit a distinct response in T cells? In this study, we showed that RIG-I-dependent dsRNA sensing by cancer cells is critical for the effects of T cell priming. Notably, cancer cells express higher levels of NAT10 relative to normal counterparts from healthy donors. On NAT10 deficiency, cancer cells accumulate cytosolic dsRNA, providing abundant substrate for RIG-I signaling. Such changes in dsRNA are partially due to DNA demethylation induced by the loss of DNMT1 seen after NAT10 inhibition. Interestingly, GSEA of RNA-seq from NAT10 deficiency versus control cancer cells showed significant downregulation of CDK2 and DNMT1, confirming an association between NAT10 inhibition and DNA demethylation.

Additionally, effective inhibition of NAT10 in vivo can enhance the tumor immune response. Remodelin hydrobromide, an orally active and selective NAT10 inhibitor, has been identified as such [34]. Upon intragastric administration, Remodelin inhibits tumor growth by activating host immunity. However, its oral bioavailability is low, resulting in limited efficacy against tumors. Lipid nanoparticles (LNPs) have gained clinical approval as carriers for siRNA and mRNA. Among LNPs' critical components, ionizable lipids are pivotal in determining RNA delivery efficiency. We developed two delivery systems: SM-102 and PEI/PC7A/siNAT10 nanoparticles. Our findings indicate that PEI/PC7A/siNAT10 effectively penetrates cell membranes, inhibits NAT10 expression, and suppresses tumor growth. Notably, PEI/PC7A/siNAT10 outperforms SM-102 and Remodelin. Combined with ICIs therapy, PEI/PC7A/siNAT10 stimulates potent anti-tumor immunity, effectively suppressing tumor growth.

Collectively, we demonstrate a biological role for NAT10 in cancer. We develop a PEI/PC7A/siNAT10 nanoparticles blocking NAT10 activity in vitro and in vivo. Our study also prompts an appraisal of anticancer drugs with consideration of their impact on immune cells within the tumor microenvironment and provides a rationale for further evaluation of NAT10 inhibition combined with a PD-1/PD-L1 inhibitor against “cold” tumor.

Analysis of tumor-infiltrating immune and prognostic model

We analyzed the patterns of immune cell infiltration according to the immune cell biomarker previously reported [18]. The algorithm was operated with the R-package and the data were visualized with R package ggplot2. TCGA-LUAD (n = 1,082) were applied to illustrate the potential prognostic significance of NAT10. According to the expression of NAT10, patients were divided into high- or low-expression group. The CIBERSORT algorithms was used to calculate the proportion of immune cell infiltration of different groups. The diagram was drawn by using the ggplot2 package. Additionally, survival analysis was performed using R 'survival' package. The ggplot2 and survminer packages were used to create survival curves between different groups. In addition, the ROC curve was generated using the R package survival ROC to detect the prognostic value for NAT10 expression.

Cell culture

The MCA205 murine fibrosarcoma, TC1 murine lung epithelial, and HEK293 human embryonic kidney cell lines were cultured in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal bovine serum (FBS, Gibco) and 1% penicillin/streptomycin (Gibco). Cultures were maintained in a humidified atmosphere containing 5% CO₂.

Construction of stable cell lines with CRISPR/Cas9 system

Deletion of NAT10, MYC, RIG-I or CDK2 was achieved using LentiCRISPR v2 (Addgene, Cambridge, MA, USA), which carries expression cassettes for Streptococcus pyogenes CRISPR-Cas9 and a chimeric guide RNA selected from the Guide Design Resources at http://crispr.mit.edu. HEK293 cells were co-transfected with three plasmids: pMD2.G (Addgene, cat#12259), psPAX2 (Addgene, cat#12260), and either LentiCRISPR v2 or a control vector, using Lipofectamine 3000 (Thermo Fisher Scientific) for 48 hours. Viral stocks generated were used to infect target cells. Post-infection, cells were cultured in puromycin (4 µg/ml, InvivoGen, cat#ant-pr-1) for at least seven days. Monoclonal cells obtained using FACSAria™ III cell sorter (Becton Dickinson, San José, CA, USA) were plated in a 96-well plate. Sequences synthesized in this study are provided in Supplementary Table S1. NAT10 heterozygous knockout, MYC, CDK2 and RIG-I knockout, and control cell lines were further validated through Western blot analysis of NAT10 expression.

Overexpression vectors and transfection

To achieve overexpression of NAT10, MYC-HA, and CDK2-GFP, the dCAS9-VP64-GFP plasmid (Addgene, cat#61422) was digested with BamH I (NEB, cat#R0136S) and Nhe I (NEB, catalog no. R0131), and the VP64 sequence was replaced with the cDNA sequences corresponding to the genes of interest. Subsequently, 293T cells were transfected with the dCAS9-VP64-GFP plasmid, along with packaging plasmids psPAX2 (Addgene, plasmid cat#12260) and envelope pMD2.G (Addgene, cat#12259), using Lipofectamine 3000 (Invitrogen, catalog no. L3000-015) according to the manufacturer’s instructions. After 48 hours, lentivirus was harvested from the cell culture medium, followed by collection of lentiviral particles via centrifugation (5,000 rpm/10 minutes) and filtration through a 0.45 µm sterile filter (Merck Millipore Ltd. PR05543). The collected lentivirus was stored at -80°C. Transfected cells were subsequently isolated using fluorescence-activated cell sorting (FACS).

Tumor models

C57BL/6J background mice and athymic nude BALB/c mice (nu/nu) aged 6–8 weeks, sourced from Vital River Laboratory Animal Technology Company (Beijing, China), were housed under specific pathogen-free (SPF) conditions at the Laboratory Animal Center of Shandong University. Following grouping, cells (2×10^⁶ cells per mouse) were subcutaneously implanted. Tumor dimensions were measured daily using vernier calipers, and tumor size was calculated by multiplying the length by the width. After 9–13 days, tumors were harvested for RNA sequencing, flow cytometric analysis, tissue immunofluorescence staining, and ELISpot analysis. Survival analysis involved intravenous injection of cancer cells, was conducted with daily recording of mouse mortality. In the CD8⁺ cell blocking assay, anti-CD8 antibodies (200 µg/mouse, BE0004-1, BioXCell) were intravenously injected at the specified time point. Tumor growth curves were presented with error bars representing mean ± SD at each time point. Kaplan–Meier survival curves were generated. Animals were euthanized with CO₂ when tumor volume reached 300 mm².

Multichannel imaging and Image analysis

Multichannel imaging was conducted using a Vectra Polaris Imaging System (Akoya Biosciences). Slides were captured at 200× magnification. Image analysis was performed using QuPath version 0.4.3 (Queen’s University) [35]. Tissue sections were divided into tumor and stroma regions based on pan-CK staining. Cell segmentation employed an algorithm based on nuclear DAPI staining. Fluorescence intensity of cells was quantified for each marker. Cells were classified into distinct phenotypic classes using positivity thresholds for individual markers, determined by cytoplasmic or nuclear staining intensity, and evaluated across all samples. Cell count, density, and percentage in different regions were calculated for each phenotype.

RNA sequencing and data analysis

Cells and tumor tissues were lysed directly after grinding, and total RNA extraction was carried out using the RNeasy Mini Kit (QIAGEN, cat#74104). Six hundred nanograms of total RNA were reversely transcribed into cDNA using ProtoScript II Reverse Transcriptase (New England BioLabs, cat#E7420L). The resulted double-stranded cDNA was purified with Agencourt AMPure XP Beads (Beckman, cat#A63881) and then ligated with paired-end adaptors using Multiplex Oligos for RNA sequencing. Sequencing was conducted on an Illumina HiSeq 10X platform, and data analysis was performed using the Linux system. Differentially expressed genes (DEGs) were identified using the R language, including the “edgeR” and “gplots” packages.

Gene set enrichment analysis (GSEA)

GSEA analysis was conducted using GSEA 4.1.0 software following the guidelines provided on the official website. The complete normalized RNA expression count matrix, including all genes rather than just differentially expressed ones, was utilized as input. The matrix was partitioned into two groups: (1) KO-High group; (2) WT-Low group. Hallmarks were chosen from the gene sets database, and 1,000 permutations were performed based on default weighted enrichment statistics.

RNA extraction and RT-qPCR

Following the manufacturer's protocol, cell pellets were collected and subjected to total RNA extraction using NucleoZol (MNG, Cat#740404.200). The extracted RNA was then reversely transcribed into cDNA using the One Step PrimeScript RT-PCR kit (TaKaRa, Cat#36110A) for subsequent qPCR analysis. Gene-specific primers listed in Table S2 and SYRB Green qPCR mix (Bimake. cn, Cat#B21202) were employed for PCR amplification and detection on the Light Cycler Real-Time PCR System (Roche). RT-qPCR data were normalized to GAPDH and presented as fold changes in gene expression relative to the control sample.

Protein extraction and Western blot analysis

Cells, tumor tissues, or paired adjacent tissues were collected and lysed using the lysis buffer from Bestbio Company (China). Protein concentration was determined with the BCA kit (Beyotime, cat# P0011). Equal amounts of protein from each sample were loaded onto SDS-PAGE gels and subsequently transferred to PVDF membranes. The PVDF membranes were then blocked in 5% non-fat milk for 1 hour at room temperature. After being washed with PBST, the membranes were incubated with the primary antibodies as follow: anti-NAT10 (1:1000, Abcam, cat#ab194297 ), anti-Myc (1:1000, CST, cat#18583), anti-CDK2 (1:1000, CST, cat#2546), anti-DNMT1 (1:1000, CST, cat#5032), anti-RIG-I (1:1000, CST, cat#3743), anti-HA-tag (1:1000, CST, cat#3724), anti-Tubulin (1:1000, CST, cat#2146), anti-β-Actin (1:1000, CST, cat#4970). On the next day, the members were washed with TBST three times and incubated with anti-rabbit IgG, HRP-linked antibody (1:2000, CST, cat#7074) at room temperature for 50 minutes. Membranes were imaged using the ChemiDoc XRS + system (Bio-Rad, USA).

CCK8 and colony formation assay

After chemical inhibition of NAT10 by Remodelin (10 µM or 20 µM, MCE, cat#HY-16706A ) or genomic depletion, the proliferation assays of cancer cells were detected by CCK8 and colony formation assays. In brief, 10000 cancer cells were seeded. And the CCK8 detection reagent was added in 96-well plates. After 4 hours, the absorbance was detected by a microplate reader (Biotek, HIMFD, USA) according to the manufacturers’ instructions (Bestbio Company, China). For the colony formation assays, 2000 cells were plated in the six-well plates. Severn days later, colonies were fixed with 4% PFA, stained with a 0.5% crystal violet staining solution (Beyotime Company, China) for 30 min and counted with microscopy.

Flow cytometry

Mice were sacrificed at appropriate time, and the tumors were collected and separated into single cells. Briefly, tumors were excised and minced. Then Liberase TL Research Grade 10 (2 µg/mL, Roche, cat#05401020001) and DNase I (Roche, cat#70271500) was used for the digestion. After being filtered with strainer, the cell suspensions were stimulated with brefeldin A (BFA, PeproTech, 10 mg/mL), phorbol myristate acetate (PMA, PeproTech, 100 µg/mL) and ionomycin (PeproTech, 1mg/mL) at 37℃for 4 h. For the cell surface staining, cells were stained for cell markers including cell death dye (1:300, Invitrogen, eBioscience™ Fixable Viability Dye eFluor™ 780, cat#2633409), CD45.2 (1:100, BioLegend, cat#109814), CD8a (1:100, BioLegend, cat#B373965), CD11c (1:100, Invitrogen, cat#2400633), and IA/IE (1:100, BioLegend, cat#107608) at 4℃ for 30 min. As for intracellular staining, cells were stained with IFN-γ (1:100, Invitrogen, cat#2481435) and Granzyme B (1:100, BioLegend, cat#515406) after being treated with fixation/permeabilization kit at 4℃ for 30 min. Cells were analyzed using a Gallios flow cytometer (Beckman Coulter, USA) and the results were analyzed by Flowjo.

Immunohistochemical (IHC) analysis

The pathology sections of patients was obtained from Qilu Hospital of Shandong University. After dewaxing, dehydration, and antigen retrieval, paraffin-embedded slides (4 µm) were blacked and labeled with anti-NAT10 (1:250, Abcam, cat#ab182744), anti-CD8a (1:250, Abcam, cat#ab182744), and anti-PD-L1 (1:250, Abcam, cat#ab213524) antibody. The next day, the slides were incubated with the secondary antibody labeled with HRP (Shanghai Gene Company, cat#GK500705) for 1 hour, stained with DAB and counterstained with hematoxylin. The images were detected by a microscopy.

Immunofluorescence staining and imaging

Tumors were collected at the appropriate time and fixed in 4% paraformaldehyde for 24 hours. Subsequently, the tumors were embedded in OCT after dehydration in a 30% (wt/vol) sucrose solution. Following sectioning into 4.5 mm thick slices, the sections were blocked in 10% goat serum in PBS. Primary antibodies against CD8a (1:100, Abcam, cat#ab217344) were then used to incubate the tumor sections. The next day, secondary antibodies (1:500, Invitrogen, cat#A32732) were applied to the sections for 1 hour. Nuclei were stained with DAPI, and the results were visualized using a confocal microscope and analyzed with ImageJ software. dsRNA was detected by the J2 antibody [1:250 dilution, 4 mg/ml, English and Scientific Consulting Kft (SCICONS), cat#10010200].

T-cell proliferation assay

Freshly purified splenocytes were isolated from C57BL/6N mice. Splenocytes were labeled with cell proliferation Dye eFluor™ 670 (eBioscience, cat#65-0840-85) at 5 µM for 10 min at 37°C, and then resuspended in the RPMI media containing 10% FBS, 1% penicillin/streptomycin, 0.5 µg/ml purified anti-mouse CD3 Antibody (Biolegend, cat#100238) and 0.5 µg/ml anti-mouse CD28 Antibody (Biolegend, cat#102112). 3^10⁵ purified splenocytes were then co-cultured with 1^10⁴ WT or NAT10 deficient TC1/MCA205 cancer cells in 96-well round-bottom plates. The unstimulated splenocytes were used as a negative control, and those stimulated with CD3 and CD28 antibodies were used as a positive control. After 72 h, cells were collected and stained, and the dilution of cell proliferation Dye eFluor™ 670 in CD4⁺ T (Biolegend, cat#100428, 1:100) or CD8⁺ T (Biolegend, cat#100708, 1:100) cells was determined by flow-cytometric analysis.

Enzyme-linked immune spot (ELISpot) assay

IFN-γ secretion was assessed using BD ELISpot assay kits (BD Biosciences, cat#551881) according to the manufacturer's instructions. Tumors were aseptically harvested and processed into a single-cell suspension. Cells were then plated at a density of 2x10^⁶ per well in ELISpot plates precoated with capture antibodies and incubated in a humidified 5% CO₂ incubator at 37°C for 20 hours. After incubation, cells were removed, and the plate was washed three times. IFN-γ production was detected by incubating with a detection antibody for 2 hours, followed by three washes and incubation with an HRP-linked secondary antibody for 2 hours. Color development was achieved by adding 100 µL of Final Substrate Solution (AEC). Red dot signals were visualized using the CTL ImmunoSpot® S6 Analyzers (LLC, OH, USA).

Dual-luciferase reporter assay

The promoter activity of NAT10 in TC1 cells was assessed using a luciferase assay. In brief, pEZX-MT06-MYC-WT-Luc or pEZX-MT06-MYC–Mut-Luc were cloned into pEZX-MT06 Reporter Vector pGL4.0 (GeneCopoeia, cat#NM_001177354.1). Nat10 coding DNA sequence was cloned into dCAS9-VP64-GFP (Addgene, cat#61422). TC1 WT cells were seeded at a density of 2^10⁵ cells per well in 24-well plates and incubated overnight prior to transfection. Subsequently, cells were co-transfected with pEZX-MT06-MYC, Renilla luciferase plasmids, and either VP64-NAT10 plasmids or empty plasmids using Lipofectamine 3000 (Invitrogen, cat#L3000-015). After 24 hours, firefly luciferase and Renilla luciferase activities were assessed using the Dual-Luciferase reporter system (Promega, cat#E1960), and efficacy was determined by calculating the ratio of firefly luciferase to Renilla luciferase activity.

Acetylated RNA Immunoprecipitation Sequencing (acRIP-seq) and acRIP-qPCR

acRIP-seq and data analysis was conducted by Guangzhou Epibiotek Co., Ltd. The WT and sgNAT10 TC1 cells were subjected to acRIP-seq. Total RNA was extracted and purified from WT and sgNAT10 TC1 cells using TRIzol reagent (Invitrogen). One hundred micrograms of total RNA was fragmented into 100–200 nt RNA fragments using 10X RNA Fragmentation Buffer (100 mm Tris-HCl, 100 mm ZnCl₂ in nuclease-free H₂O), followed by termination of the reaction with 10XEDTA. Immunoprecipitated RNA fragments were obtained by incubating fragmented RNA with anti-ac4C monoclonal antibody for 3 h at 4°C, followed by incubation with protein A/G magnetic beads (Invitrogen, Cat#8880210002D/10004D) for 2 h at 4°C, as per the EpiTM ac4C immunoprecipitation kit protocol (Epibiotek, R1815). The library was prepared using the smart-seq method. Both the input samples without IP and the ac4C IP samples were subjected to 150-bp, paired-end sequencing on an Illumina NovaSeq 6000 sequencer.

The RIP-qPCR assay was conducted to confirm the interaction between NAT10 and Myc mRNA using the RIP Kit (BersinBio, Cat# Bes5101). TC1 cells were lysed using a polysome lysis buffer containing protease and RNase inhibitors. DNase was added to degrade the DNA at 37°C for 10 min. NAT10 or IgG antibodies were added to the samples and incubated at 4°C for 16 h in a vertical mixer. Subsequently, the samples were incubated with protein A/G beads for 1 h. Following the manufacturer’s instructions, the beads containing the immunoprecipitated RNA-protein complex were treated with proteinase K to remove proteins. The target RNAs were then extracted using the phenol-chloroform method, amplified by PCR (Table S3), and detected using DNA gel electrophoresis with normalization to their input group.

mRNA Stability Assay

TC1 cells were cultured in complete DMEM medium supplemented with 5 µg/ml actinomycin D (Sigma, Cat#A9415) for 0, 4, 8, 12, 16, and 24 hours. At the specified time points, cells were harvested, and total RNA was extracted following the protocol outlined in the "RNA extraction" section for subsequent real-time PCR analysis (Table S2).

Synthesis of SM-102

NAT10 siRNA and FAM-labeled siRNA-NC were synthesized by Atantares. Ionizable lipids SM-102 (Cat#O02010), 1,2-distearoyl-sn-glycero-3-phosphocholine (DSPC, Cat#S01005) and 1,2-dimyristoyl-rac-glycero-3-methoxypolyethylene glycol-2000 (DMG-PEG2000, Cat#O02005) were purchased from AVT (Shanghai) Pharmaceutical Tech Co., Ltd. Cholesterol (Cat#A90286) was purchased from Innochem. Polyetherimide (Cat#61128-46-9) was purchased from MACKLIN. 2-(azepan-1-yl) ethanol (Cat#35984E), Methacryloyl chloride (Cat#90100B) and 2-Bromoisobutyryl Bromide were purchased from Adamas.

Synthesis of PC7A

For synthesis of 2-(Hexamethyleneimino) ethyl Methacrylate Monomer (C7A-MA), 2-(azepan-1-yl) ethanol (5.0 g) and triethylamine (7.0 g) were dissolved in 100 mL of dried tetrahydrofuran and cooled to 0°C in an ice bath. Methacryloyl chloride (4.0 g) was dissolved in 15 mL THF and subsequently dropped into previous solution. This reaction conducted at room temperature under stirring for 8 h. For synthesis of PC7A polymer, 0.5 g C7A-MA, 8.5 g CuBr and 11.6 mg initiator were dissolved in 0.5 mL of dried THF. After undergoing three rounds of freeze − pump − thaw, 10.3 mg N,N,N′,N″,N″-pentamethyldiethylenetriamine was introduced. Subsequently, the polymerization process was conducted at a temperature of 70°C for a duration of 10 hours. The resulting reaction mixture was then dissolved in acidic water with a pH of 4 and dialyzed in distilled water, utilizing a cut-off molecular weight of 3500 Da, to eliminate any unreacted monomers and copper. Finally, the product was obtained through the process of lyophilization.

Preparation of Lipid Nanoparticles

siRNA was encapsulated lipid nanoparticles (LNPs) as described [36]. Briefly, siRNAs were dissolved in sodium acetate (pH = 4) and combined with a lipid solution at an amine-to-phosphate (N/P) ratio of 8. The lipid stock solutions were prepared with a total lipid concentration of 12.5 mM by dissolving SM102, DSPC, cholesterol, and DMG-PEG-2000 in ethanol at a molar ratio of 50:10:38.5:1.5. Ultrafiltration centrifugation (3500G, 40 min) was used to remove unentrapped siRNA from the LNPs.

Preparation of PEI/PC7A

PEI and PC7A were dissolved in sterile water. Subsequently, mix the PEI, PC7A and siRNA in a weight ratio of 1.3:1:1 to form nanoparticles through electrostatic interaction with the negatively charged siRNA. Leave the nanoparticle for 5 minutes before using.

Size distribution of nanoparticles

Size distribution and PDI were measured by Malvern Nano Sizer (Malvern Instruments Ltd) in double-distilled water.

LNPs and PEI/PC7A transfection

Cells were counted using trypan blue dye. For Real-time PCR and Western blotting, 2 x 10^⁵ cells were placed in 12-well plates overnight. Replace the medium with Opti-MEM and add LNPs or PEI/PC7A with a final concentration of siRNA of 20 nM. For immunofluorescence, cells were cultured in chamber slides overnight, and then added with 20 nM FAM-labeled siRNA for 4 h. Cells were stained with 50 nM Lyso-Tracker Red (Beyotime, Cat#C1046) and 10 µg/mL Hoechst (Beyotime, Cat#C1022) for 30 min. Immunofluorescence images were acquired on a Nikon A1 fluorescence microscope.

Ethics approval and consent to participate

Animal studies were approved by the Animal Ethics Committee of Qilu Hospital of Shandong University. Fresh tumor tissue and paired adjacent tissues were collected from patients at Qilu Hospital. All patients provided informed consent, and our study was sanctioned by the Medical Ethics Committee of Qilu Hospital (KYLL-202311-043).

Statistics

Statistical analyses were performed with the software GraphPad Prism 7. The continuous variables were presented as mean ± SD. Data with normal distribution were analyzed by one-way ANOVA or unpaired two-tailed Student’s t-tests, and tumor growth curves were compared by the Mann-Whitney U test or a two-way ANOVA test, and P values were indicated by * P < 0.05, ** P < 0.01, and *** P <0.001.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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There is NO Competing Interest.

supplementarymaterials.doc
SupplementaryTable.doc
Additional file 1: Table S1. Sequence of Guide RNA (sgRNA) oligonucleotides. Additional file 2: Table S2. Primers designed for real-time PCR. Additional file 3: Table S3. Primers designed for RIP-qPCR. Additional file 4: Table S4. NAT10 siRNA and the primers for RT-qPCR.
FigS1.tif
Additional file 5: Figure S1. Elevated NAT10 expression is associated with decreased survival and reduced infiltration of immune cells in cancer.
FigS2.tif
Additional file 6: Figure S2. NAT10 deficiency suppresses tumor growth via immune-dependent mechanisms.
FigS3.tif
Additional file 7: Figure S3. NAT10 deficiency triggers immune-response signaling and induces cellular immune responses in vivo..
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Additional file 8: Figure S4. NAT10 deficiency triggers IFN-I responses in cancer cells.
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Additional file 9: Figure S5. NAT10 regulates the stability and translation efficiency of MYC mRNA.
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Additional file 10: Figure S6. Depletion of NAT10 induces dsRNA-mediated RIG-I-dependent signaling through the Myc/CDK2/DNMT1 pathway.
FigS7.tif
Additional file 11: Figure S7. Expression of NAT10 is positively correlated with PD-L1 in human lung cancer.
FigS8.tif
Additional file 12: Figure S8. Intratumoral delivery of siNAT10-lipid nanoparticles (LNPs) for cancer immunotherapy.

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Inhibition of NAT10 Enhances the Antitumor Immunity by Increasing Type I Interferon Responses

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Abstract

Figures

Background

Results

High expression of NAT10 correlates with poor survival and low immune cell infiltration in cancer

NAT10 deficiency suppresses tumor growth via eliciting immunological protection

NAT10 deficiency induces IFN-I responses in cancer cells

NAT10 increases MYC expression through regulating mRNA acetylation

NAT10 depletion induces dsRNA-mediated RIG-I-dependent IFN-I signaling via Myc/CDK2/DNMT1 pathway

Inhibition of NAT10 with Remodelin enhances response to ICIs therapy

Intratumoral delivery of siNAT10-lipid nanoparticles (LNPs) for cancer immunotherapy

Discussion

Materials and methods

Analysis of tumor-infiltrating immune and prognostic model

Cell culture

Construction of stable cell lines with CRISPR/Cas9 system

Overexpression vectors and transfection

Tumor models

Multichannel imaging and Image analysis

RNA sequencing and data analysis

Gene set enrichment analysis (GSEA)

RNA extraction and RT-qPCR

Protein extraction and Western blot analysis

CCK8 and colony formation assay

Flow cytometry

Immunohistochemical (IHC) analysis

Immunofluorescence staining and imaging

T-cell proliferation assay

Enzyme-linked immune spot (ELISpot) assay

Dual-luciferase reporter assay

Acetylated RNA Immunoprecipitation Sequencing (acRIP-seq) and acRIP-qPCR

mRNA Stability Assay

Synthesis of SM-102

Synthesis of PC7A

Preparation of Lipid Nanoparticles

Preparation of PEI/PC7A

Size distribution of nanoparticles

LNPs and PEI/PC7A transfection

Declarations

References

Additional Declarations

Supplementary Files

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