METTL3 abrogation promotes glioma progression through regulating the ISG15-FASN axis-mediated lipid metabolism in macrophages

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

Abstract
Tumor-associated macrophages (TAMs) constitute a pivotal cellular component within the intricate tumor microenvironment (TME). However, the relationship between the N6-methyladenosine (m6A) and metabolic pattern of TAMs remains poorly understood. Here we show that the m6A methyltransferase METTL3 is conspicuously downregulated in monocyte-derived macrophages from glioblastoma (GBM) patients. Conditional knockout of Mettl3 in myeloid cells augments lipid metabolism and accelerates glioma progression. Furthermore, we found that METTL3 deficiency facilitates fatty acid synthase (FASN) expression, while compromising CD8+ T cell response. Mechanistically, ISG15 is highly expressed in METTL3-deficient macrophages. ISG15 interacts with FASN and regulates FASN ISGylation and its stability through impeding FASN ubiquitination. Notably, the suppression of ISG15 in METTL3-deficient macrophages reverses the enhanced FASN expression and restores CD8+ T cell functionality. Furthermore, we demonstrate that FASN knockout or FASN inhibitor treatment in myeloid cells abrogates the exaggerated tumor progression triggered by METTL3 knockout. Collectively, this study highlights the pivotal role of m6A in regulating macrophage metabolism and identifies potential targets in controlling GBM progression.

Introduction
Glioblastoma (GBM) is the most common and aggressive type of primary adult malignant tumor in the central nervous system1. Owing to tumor microenvironment (TME) heterogeneity, GBM is a challenging cancer to treat2. Among the TME components, tumor-associated macrophages are the most abundant population of immune cells, comprising a staggering 50% of all viable cells in the whole GBM tumor mass3. Emerging evidence demonstrates that TAMs are critical for promoting tumor progression and orchestrating an immunosuppressive landscape in GBM4. However, therapeutic strategies for depleting TAMs have not been well translated into the clinic5, suggesting that our understanding of this cell population is still limited.

The TAM compartment is highly dynamic and heterogeneous, both within individual tumors and across distinct tumor types6,7. In recent years, cellular metabolism has emerged as a key regulator for macrophage activation. Notably, an elevated degree of TAM metabolism heterogeneity has indeed been revealed not merely among diverse cancer patients but also within the various malignant lesions of a single patient, as well as within a specific neoplastic lesion8,9. The TAM compartment evolves over time as well as in space through an extensive remodeling of core energy metabolism10. Once macrophages are recruited into the TME, they have to be reprogrammed in the metabolic pathways in order to adapt to the special environment11. Among the metabolic alterations in TAMs, lipid metabolism has been associated with the tumoricidal capability12. Noteworthily, an increased lipid accumulation in TAMs is positively and significantly associated with the identification of hypoxic macrophages in GBM with therapeutic potential for vasculature normalization9,13. The reprogrammed lipid metabolism not only determines their secretory profile, but also further influences the immune status of TME and ultimately the progression of tumors. Thus, elucidating lipid metabolic reprogramming-associated signaling pathways in TAMs holds significant promise for the development of other therapeutic targets in cancer treatment.

In recent years, N6-methyladenosine (m6A), the most diverse and reversible post-transcriptional modification of eukaryotic messenger RNAs (mRNAs), has been recognized as a strong regulator of mRNA splicing, stability, localization and translation14,15. This intricate process is orchestrated by the MTases complex (writer), demethylases (erasers), and mRNA-binding proteins (readers). The writer proteins act as methyltransferases for m6A formation in cells and mainly include METTL316, METTL1417, METTL1618, WTAP19, VIRMA20, RBM15/15B21, ZC3H1322,23, and HAKAI20. Conversely, the erasers (demethylases: FTO and ALKBH5) remove m6A modification from RNA molecules24,25. Meanwhile, the readers (including YTHDF1/2/3, YTHDC1/2, IGF2BPs, FMR1, and HNRNPA2B1) specifically recognize m6A-modified RNA25, influencing its fate through mechanisms such as degradation, stabilization, or modulation of translation26. m6A has been regarded as a major regulator on various tumor-related processes, including proliferation, apoptosis, migration, invasion, energy processing, and response to radiotherapy. However, research on m6A modification in tumors is still in its infancy, the unknown participants still need to be further explored and identified.

In the study, we demonstrated that Mettl3 knockout in myeloid cells significantly accelerates glioma progression. Further analysis implied that METTL3 deficiency facilitates ISG15-mediated upregulation of FASN expression and subsequent alterations in lipid metabolism. Mechanistically, we identified an interaction between ISG15 and FASN, which modulates the ISGylation status and stability of FASN. Furthermore, we showed that either the knockout of FASN or the treatment of myeloid cells with a FASN inhibitor effectively abolishes the enhanced tumor progression phenotype observed in mice with METTL3 depletion, underscoring the vital role of this pathway in glioma progression.

Results
Lower expression of METTL3 in TAMs derived from GBM
The GBM microenvironment is highly heterogeneity and employ huge of TAMs. To investigate the m6A modification in TAMs involved during GBM progression, we explored the TCGA datasets and discovered that the expression of METTL3 was diminished in mesenchymal subtype with a poorer prognosis and strong radiation resistance than other glioma subtypes (Supplementary Fig. 1a). Analysis of public single-cell RNA-seq (scRNA-seq) data indicated that the expression of METTL3 in TAMs was lower in high-grade gliomas than that in low-grade gliomas (Fig. 1a). Using expression data (ESTIMATE) algorithm27, which calculates the abundance of stromal and immune cells in malignant tumor tissues based on expression data, we found a negative correlation between the expression of METTL3 mRNA and immune scores in GBM (Supplementary Fig. 1b). By leveraging the TIMER 2.0 website, we found that the expression of METTL3 was negatively correlated with the infiltration of TAMs (Supplementary Fig. 1c). We then investigated the relationship of METTL3 in TAMs with tumor progression in mice. The results also showed that METTL3 expression was suppressed in TAMs from GL261 tumor-beared mice compared with PMs from healthy mice by immunofluorescence staining (Fig. 1b). Moreover, western blot analysis also confirmed the downregulation of METTL3 protein expression in TAMs compared with PMs from healthy mice (Fig. 1c). Meanwhile, we also analyzed and demonstrated that the expression of METTL3 in TAMs was markedly decreased in GBM patients compared with microglia from healthy control (Fig. 1d). Meanwhile, we observed a generalized reduction in METTL3 expression among myeloid populations within tumors relative to their circulating counterparts, and a similar down-regulation in tumor-infiltrating microglia compared with microglia in healthy brain tissue (Supplementary Fig. 1d). In summary, our comprehensive data underscore a consistent pattern of inhibited METTL3 expression in TAMs within the context of GBM, highlighting its potential importance in tumor progression.

Mettl3 deficiency in myeloid cells enhances glioma progression
To elucidat the functional role of METTL3 in TAMs involved in glioma progression, we employed Mettl3fl/flLyz2Cre conditional knockout mice and their littermates Mettl3fl/fl in the C57BL/6 genetic background for tumor growth experiments. We firstly confirmed the depletion of METTL3 in BMDMs by western blotting and m6A dot blot (Supplementary Fig. 2a, b). In the syngeneic murine GL261 tumor model, we observed a significant increase in tumor volume in Mettl3fl/flLyz2Cre mice compared to that in Mettl3fl/fl mice (Fig. 1e and Supplementary Fig. 2c). HE and ki67 staining also demonstrated that GL261 tumor cell proliferated more rapidly in Mettl3fl/flLyz2Cre mice than that in Mettl3fl/fl mice (Fig. 1f, g and Supplementary Fig. 2d). The survival of these conditional Mettl3 knockout mice with GL261 tumor was shortened (Fig. 1h). Further analysis showed that co-injection of GL261 cells with STM2457-treated BMDMs resulted in significantly accelerated tumor growth (Fig. 1i). To further validate the pathological role of METTL3 in GBM, we reconstituted irradiated Wild Type (WT) mice with total bone marrow-derived cells from Mettl3fl/flLyz2Cre or Mettl3fl/fl mice, and analyzed the contributions of bone marrow-derived macrophages in the chimaeras after reconstitution. The results showed that chimeric mice lacking Mettl3 exhibited an elevated tumor burden (Fig. 1j–l). Moreover, we employed a patient-derived xenograft model of GBM and discovered that Mettl3fl/flLyz2Cre myeloid cells could enhance U87 tumor growth (Fig. 1m, n). To further confirm whether macrophages with METTL3 depletion are required for the increased tumor burden in Mettl3fl/flLyz2Cre mice, the mice were administered an intravenous injection of clodronate liposome 24h prior to tumor induction to destroy microglia and macrophages (Supplementary Fig. 2e, f). The results showed that clodronate-treated mice were unresponsive to Mettl3 depletion, relative to control mice, as characterized by an indistinguishable influence on tumor growth (Fig. 1o, p). In addition, we found that treatment with the CCR2 antagonist RS504393 abrogated the difference in tumor growth between Mettl3fl/fl or Mettl3fl/flLyz2Cre mice (Supplementary Fig. 3), indicating that the phenotype depends on monocyte-derived macrophages. Collectively, these results imply that Mettl3 ablation in TAMs promotes glioma progression, thereby emphasizing the importance of METTL3 in modulating macrophage-mediated tumor suppression.

Knockout of Mettl3 in myeloid cells inhibited CD8+ IFN-γ+ cell formation
To determine whether the myeloid deficiency of METTL3 reshapes tumor immune microenvironment, we conducted a thorough analysis of the frequency of tumor-infiltrating immune cells. The results showed no apparent differences in the total number of macrophage between Mettl3fl/flLyz2Cre and Mettl3fl/fl mice (Fig. 2a and Supplementary Fig. 4a, b). However, we observed that the frequency of M1 macrophages (F4/80+NOS2+) and M2 macrophage (F4/80+CD206+ or F4/80+ARG1+), was increased in METTL3 deficient mice (Fig. 2a and Supplementary Fig. 4a, b). Moreover, immunofluorescence staining also demonstrated that CD206 and ARG1 exhibited increased expression in METTL3 knockout macrophages compared with that in wild-type cells (Supplementary Fig. 4c). We further investigated the effects of METTL3 deficient macrophage on other tumor infiltrating immune cells. Most of the immune-suppressive cell populations, including myeloid-derived suppressor cells (MDSCs) and neutrophils (PMN-MDSCs) exhibited increased cell numbers in tumors from METTL3-deficient mice relative to wild-type mice (Fig. 2b and Supplementary Fig. 4d). In contrast, the presence of anti-tumor CD8+ T cells was diminished in these tumors (Fig. 2c and Supplementary Fig. 4e). In addition, we examined the CD8+ cell functionality and observed the decreased numbers of CD8+ IFN-γ+ and CD8+ Granzyme B+ cells and the increased number of CD8+ PD-1+, CD8+ LAG-3+, CD8+ TIM-3+ and CD8+ CD39+ cells (Fig. 2d, e). In vitro coculture experiments also exhibited that METTL3-deficient BMDMs could inhibit the function of CD8+ tumor killer cells as demonstrated by reduced expression of IFN-γ (Fig. 2f, g). These results indicate that METTL3 knockout substantially remodels the TME from anti-tumor to pro-tumor by altering the composition and function of key immune cell populations.

Knockout of Mettl3 in myeloid cells enhances lipid metabolism and fatty acid synthase (FASN) expression
TAMs and their impact on the overall metabolic profile of the TME have a major influence on tumor progression8. To explore whether METTL3 ablation influences the metabolic profile of macrophage, we conducted an unbiased metabolomic analysis comparing Mettl3fl/flLyz2Cre with their Mettl3fl/fl BMDMs counterparts. Metabolite analysis revealed 254 metabolites that were differentially regulated (125 upregulated, 129 downregulated) between Mettl3fl/flLyz2Cre and Mettl3fl/fl BMDMs (Fig. 3a). Enrichment analysis revealed that the changed metabolites were predominantly lipid and lipid like moleculars (Fig. 3b and Supplementary Fig. 5), suggesting that METTL3 participated in lipid metabolism pathway. So we tested the protein expression of key proteins related to lipid metabolism in BMDMs, and confirmed that FASN expression was significantly increased in Mettl3fl/flLyz2Cre BMDMs (Fig. 3c), accompanied with elevated lipid accumulation and expression of lipid laden macrophage-related gene Gpnmb (Fig. 3d, e). The gene expression analysis clearly revealed that FASN downstream genes involved in fatty acid elongation were significantly increased in Mettl3fl/flLyz2Cre BMDMs (Fig. 3f). However, qRT-PCR analysis showed that the expression of Fasn mRNA was unchanged (Fig. 3g). The mRNA decay assays also demonstrated that METTL3 had no effect on the stability of Fasn mRNA (Fig. 3h). Integrative genomics viewer (IGV) analysis showed that m6A peaks on the transcript of Fasn mRNAs remained unchanged in Mettl3fl/flLyz2Cre BMDMs (Supplementary Fig. 6a). These results implied that FASN was not directly regulated by METTL3-mediated modification. Previous studies have shown that METTL3 can bind to the gene promoters and influence their expression28. Our results indicated an absence of direct METTL3 binding to the Fasn gene promoter region (Supplementary Fig. 6b), further supporting the notion that METTL3 does not directly regulate FASN expression. Next, we reanalyzed the genes expression profiles in the single cell sequencing data GSE15915629 and discovered that 3 genes (ISG15, Lyz1, Plac8) were obviously upregulated in Mettl3fl/flLyz2Cre TAMs compared with Mettl3fl/fl TAMs (Fig. 3i). qRT-PCR results demonstrated that the expression of Isg15 was noticeably increased in the Mettl3fl/flLyz2Cre BMDMs, while the expression of Lyz1 and Plac8 remained unaffected (Fig. 3j). Western blot analysis also verified the increased expression of ISG15 in Mettl3fl/flLyz2Cre BMDMs (Fig. 3k). We next investigated the potential direct regulation of ISG15 by METTL3. However, IGV analysis showed that m6A peaks on the transcript of Isg15 remained unaltered in Mettl3fl/flLyz2Cre BMDMs (Supplementary Fig. 6c). In addition, METTL3 could not directly bind to the promoter region of Isg15 (Supplementary Fig. 6d). Given that ISG15 is an IFN-inducible protein30, we explored this avenue and found that IFN-β was upregulated in Mettl3fl/flLyz2Cre BMDMs and TAMs (Fig. 3l), aligning with previous reports of enhanced type I IFN expression following METTL3 ablation in monocytes31. In addition, we found that Mettl14 knockdown in BMDMs also robustly increased Ifnb1 expression (Fig. 3m). meRIP-qPCR assays further confirmed that Ifnb1 mRNA is indeed m⁶A-modified (Fig. 3n, o). Absence of Mettl3 enhanced the stability of Ifnb1 mRNAs (Fig. 3p). Furthermore, we demonstrated that IFN-β directly stimulated the expression of both ISG15 and FASN (Fig. 3q), while blocking IFN-β could abrogate the effects of METTL3 on FASN expression (Fig. 3r). In addition, treatment with IFN-β robustly elevated FASN levels in Mettl3fl/fl and Mettl3fl/flLyz2Cre BMDMs (Fig. 3s). These results suggested that METTL3 may regulate FASN expression and lipid metabolism through IFN-β/ISG15 axis.

ISG15 interacts with FASN and regulates FASN ISGylation and stability through inhibiting FASN ubiquitination
To explore intricate regulatory mechanisms by which ISG15 modulates FASN expression. we searched the BioGRID (https://thebiogrid.org/) websites and found that ISG15 is a potential interacted protein of FASN32,33. To test the interactions between FASN and ISG15, reciprocal coimmunoprecipitation assays were performed. The results showed that endogenous FASN co-immunoprecipitated with ISG15, which was verified by coimmunoprecipitation of Myc-tagged ISG15 with endogenous FASN from transiently transfected HEK293T cells (Fig. 4a). Moreover, ISG15 and FASN that were epitope tagged and expressed in HEK293T cells were also able to interact with each other in co-immunoprecipitation experiments (Fig. 4b). Co-immunoprecipitation assays further confirmed endogenous ISG15 and FASN interaction in BMDMs (Fig. 4c). In vitro His pull-down assay also further definitively confirmed the direct interaction between FASN and ISG15 (Fig. 4d). To further corroborate this result, we performed immunofluorescence staining and observed strong colocalization of endogenous ISG15 and FASN in BMDMs (Fig. 4e). Ectopic expression of FLAG-FASN was also associated with Myc-ISG15 in 293T cells, providing compelling evidence that ISG15 is indeed in a protein complex with FASN (Fig. 4e). These data demonstrated that ISG15 indeed interacted with FASN. We then investigated the mechanism governing how ISG15 regulates FASN. We tested the dependence of FASN expression on ISG15. The results showed that ISG15 knockdown reduced FASN expression (Fig. 4f). Reversely, ISG15 overexpression enhanced FASN expression (Fig. 4g). Re-expression of ISG15 in ISG15 knockdown BMDMs reversed the reduced expression of FASN (Fig. 4f). Furthermore, we assessed the stability of FASN and observed the accelerated decay of FASN in BMDMs lacking ISG15 after cycloheximide (CHX) treatment (Fig. 4h). Re-expression of ISG15 restored the FASN protein stability in ISG15 knockdown BMDMs (Fig. 4h). Given that ISG15 is known to modify cellular proteins through a process called ISGylation30, we next searched the potential ISGylation sites of FASN protein by analyzing the ISGylome data previously published34. The results showed that there are 4 potential ISGylation sites in FASN protein (Fig. 4i). We then mutated these Lys (K) residues to Arg (R) residues and examined the mutant FASN expression. The results demonstrated that mutations at Lys 1582, Lys 1591 and Lys 1866 inhibited FASN protein expression (Fig. 4j), whereas the mRNA levels of FASN mutants were unchanged significantly (Fig. 4k). Co-immunoprecipitation assays further demonstrated that mutations at Lys 1582, Lys 1591 and Lys 1866 inhibited FASN and ISG15 interaction (Fig. 4l). The results indicated that Lys 1582, Lys 1591 and Lys 1866 truly served for FASN protein expression. We then investigated whether ISGylation affected the ubiquitination status of FASN. As shown in Fig. 5a, mutation at Lys 1582, Lys 1591 and Lys 1866 inhibited FASN ISGylation, but enhanced its ubiquitination. Furthermore, we demonstrated that inhibition of ISG15 led to a marked increase in FASN ubiquitination (Fig. 5b), while ISG15 overexpression impeded FASN ubiquitination (Fig. 5c). Notably, the ISG15 mutant lacking the C-terminal glycine residues (ISG15 △GG) could not inhibit FASN ubiquitination effectively (Fig. 5c). In addition, we demonstrated that the K48-linked ubiquitination of FASN was obviously downregulated upon ISG15 overexpression (Fig. 5d). These findings collectively suggest that ISG15 and ISGylation play a crucial role in regulating the ubiquitination-mediated degradation of FASN, thereby modulating its expression and stability.

The aforementioned data convincingly indicated that FASN was precisely modulated by ISG15, and METTL3 could inhibit ISG15 expression. To investigate whether ISG15 was a critical regulator for increased expression of FASN induced by METTL3 deficiency, we performed a rescue experiment to test the effect of ISG15 inhibition on FASN expression in METTL3-deficient BMDMs. The results showed that ISG15 knockdown inhibited the increased expression of FASN protein and FASN ISGylation while concurrently intensifying its ubiquitination upon METTL3 ablation (Fig. 6a). CHX assay also demonstrated that ISG15 inhibition abrogated the effect of METTL3 deficiency on FASN protein stability (Fig. 6b). ISG15 knockdown inhibited the M1-associated genes and the M2-associated genes expression in Mettl3fl/flLyz2Cre BMDMs (Fig. 6c). Notably, lipid accumulation was also significantly inhibited by ISG15 knockdown in Mettl3fl/flLyz2Cre BMDMs (Fig. 6d). Our previous results demonstrated that METTL3 deficiency in macrophages inhibited CD8+ IFN-γ+ cell differentiation (Fig. 2d, g). We also tested the role of ISG15 in CD8+ IFN-γ+ cell differentiation regulated by METTL3 ablation. The results displayed that ISG15 knockdown abrogated the inhibitory effect of METTL3 ablation on CD8+ IFN-γ+ cell differentiation (Fig. 6e). Moreover, tumors from mice receiving CpG-siRNA Isg15 exhibited a markedly increased fraction of IFN-γ-producing CD8+ T cells compared with those from control-treated animals (Fig. 6f). Silencing ISG15 by targeted delivery of CpG-siRNA Isg15 to TAMs also reduced the expression of GPNMB (Fig. 6g). In summary, these data suggested that METTL3 regulated FASN expression and lipid metabolism through ISG15 during macrophage activation.

FASN knockout or FASN inhibitor treatment abrogate the enhanced tumor progression induced by METTL3 knockout
To investigate whether FASN was a key regulator for enhanced tumor progression induced by METTL3 deficiency, we generated double knockout mice for METTL3 and FASN specifically in myeloid cells. Utilizing immunoblotting, we confirmed the successful depletion of METTL3 and FASN in BMDMs in Mettl3fl/flFasnfl/flLyz2Cre mice (Fig. 7a). Then the GL261 cells were injected into the brain of WT, Mettl3fl/flLyz2Cre and Mettl3fl/flFasnfl/flLyz2Cre mice. The results displayed that METTL3 and FASN double knockout inhibited the increased tumor growth observed in METTL3-deficient mice (Fig. 7b, c). Immunofluorescence staining also showed that METTL3 and FASN double knockout hindered M2 macrophage polarization caused by METTL3 deficiency, as demonstrated by reduced expression of CD206 (Supplementary Fig. 7a). Moreover, Nile red staining revealed that the macrophage lipid content was also limited to a lower level by FASN knockout in Mettl3fl/flLyz2Cre BMDMs (Supplementary Fig. 7b). In vitro experiments exhibited that Mettl3fl/flFasnfl/flLyz2Cre BMDMs enhanced the CD8+ IFN-γ+ cell differentiation compared with Mettl3fl/flLyz2Cre BMDMs (Fig. 7d). In addition, we observed that Mettl3fl/flLyz2Cre BMDMs with FASN knockdown increased IFN-γ production in CD8+ T cells, contrasting sharply with those co-cultured with METTL3 deficient BMDMs (Supplementary Fig. 7c, d). FASN knockdown inhibited the M1-associated genes and the M2-associated genes expression in Mettl3fl/flLyz2Cre BMDMs (Supplementary Fig. 7e). The lipid content analysis further confirmed the suppressive effect of FASN knockdown on lipid accumulation in Mettl3fl/flLyz2Cre BMDMs (Supplementary Fig. 7f). To confirm the role of FASN in tumor progression in Mettl3fl/flLyz2Cre mice, we also examined the tumor progression in Mettl3fl/fl and Mettl3fl/flLyz2Cre mice treated with a well-proved FASN inhibitor C75. The results showed that pharmacological inhibition of FASN with C75 largely abolished the aggravative effect of METTL3 depletion on tumor progression (Fig. 7e–g). Consistently, C75 treatment impeded F4/80+CD206+ M2 macrophage polarization and lipid accumulation associated with METTL3 depletion (Supplementary Fig. 8a, b). The reduced IFN-γ production in CD8+ T cells co-cultured with METTL3 deficient BMDMs was also significantly reversed upon FASN inhibition (Fig. 7h and Supplementary Fig. 8c). These data suggested that the effects of METTL3 on tumor growth are largely dependent on FASN expression. Clinical specimens analysis also showed that the glioma patients who had lower METTL3 expression in TAMs tended to have higher levels of ISG15 and FASN in tumor section, consistent with the results obtained in the study (Fig. 7i). To interrogate the prognostic impact of METTL3 expression within the tumor-associated macrophage compartment, we performed immunofluorescence co-staining for CD68 and METTL3 on tissue microarrays. Quantitative image analysis revealed that low METTL3 levels in CD68+ macrophages are strongly and independently associated with diminished overall survival (Fig. 7j). In summary, we found that inhibition of METTL3 regulates ISG15-FASN axis and lipid metabolism to promote glioma progression (Fig. 7k).

Methods
Clinical specimens
Frozen and paraffin-embedded primary brain tumor tissues were obtained from Chinese patients at Xijing Hospital (Xi’an, China), a subsidiary hospital of the Fourth Military Medical University. The use of clinical specimens in this study was approved by the Animal Experiment Administration Commission in Fourth Military Medical University (KY20253615-1). All adult participants provided written informed consent. No participant compensation was provided. Sex and gender were not considered in the study, as the primary focus of this study was unrelated to sex or gender.

Mice
Lyz2Cre mice (catalog 004781, The Jackson Laboratory) were crossed with floxed Mettl3 mice (Beijing Biocytogen Co., Ltd) to generate Mettl3fl/flLyz2-/- and Mettl3fl/flLyzcre/- mice. Mettl3fl/flLyzcre/- mice were crossed with Fasnfl/fl mice (Gempharmatech Co., Ltd) to generate Mettl3fl/flFasnfl/flLyzcre/- mice. All experimental mice were bred and maintained under specific pathogen-free conditions, fed standard laboratory chow, and kept on 12 h light to 12 h dark cycles. The temperature and humidity were set at 22 ± 1 °C and 55% ± 5%, respectively. Six- to twelve-week-old mice of both sexes were used for the experiments. Cohoused Cre-negative littermate mice were used as controls. All experimental procedures were approved by the Institutional Animal Care and Use Committee of Xinxiang Medical University (XYLL-20230041).

Cell lines
Mouse glioma cell line GL261 and human embryonic kidney cell line HEK293T were cultured in Dulbecco’s Modified Eagle’s Medium supplemented with 10% FBS, 1% penicillin/streptomycin, and 1% L-glutamine, and maintained at 37 °C and 5% CO2.

Plasmid construction
The FASN and ISG15 promoter was amplified and cloned into the PGL-3.0 vector. FLAG-FASN was constructed by cloning human full-length FASN cDNA into the p3×FLAG-CMV-10 vector. FLAG-FASN mutants were generated by homologous recombination method based on FLAG-FASN plasmid. Myc-ISG15 was generated by cloning full-length ISG15 cDNA into the pCMV-Myc vector. Myc-ISG15ΔGG was generated by depleting the two C-terminal glycine residues.

Metabolite extraction and mass-spectrometry-based metabolomic analysis
The cell culture media samples were thawed on ice, and metabolites were extracted from 20µL of each sample using 120µL of precooled 50% methanol buffer. Then the mixture of metabolites was vortexed for 1min and incubated for 10min at room temperature, and stored at −20°C overnight. The mixture was centrifugated at 4000×g for 20min, subsequently the supernatant was transferred to 96-well plates. The samples were stored at −80°C prior to the LC-MS analysis. Pooled quality control sample were also prepared by combining 10µl of each extraction mixture. All samples were analyzed using a Triple TOF 5600 Plus high resolution tandem mass spectrometer (SCIEX, Warrington, UK) with both positive and negative ion modes. Chromatographic separation was performed using an ultra performance liquid chromatography (UPLC) system (SCIEX, UK). An ACQUITY UPLC T3 column (100mm*2.1mm, 1.8µm, Waters, UK) was used for the reversed-phase separation. The TripleTOF 5600 Plus system was used to detect metabolites eluted from the column.

Tumor cell implantation
GL261-Luc cells were grown in Dulbecco’s modified eagle medium (DMEM) containing glucose, L-glutamine, and 10% FBS, at 37°C with 5% CO2 and maintained under normal adherent culture conditions. Prior to tumor cell injection, use a sterile 25 gauge sharp needle to puncture the skull at 2mm to the right of the bregma and 1mm anterior to the coronal suture, thereby creating an opening for the injection of tumor cells. A Hamilton syringe (22 gauge needle) containing 300,000 GL261-Luc cells in 3μl DMEM was inserted into the hole to a depth of 3mm, and the cells were injected over 5min. The needle was left in place for 2min to reduce backflow before the wound was closed with staples.

GBM xenografts
3×10⁵ U87 GBM cells were prepared in PBS for stereotactic injection into 6–8 weeks old Nu/Nu mice. Then 5×106 Mettl3fl/fl or Mettl3fl/flLyz2Cre myeloid cells mixed with 5×106 lymphocytes were injected into Nu/Nu mice via tail vein every week. The mice were sacrificed for HE staining to monitor the tumor growth after 3 weeks. Mice were sacrificed at the indicated time point after tumor implantation and xenografts were harvested for further analyses.

In vivo bioluminescence imaging
For in vivo bioluminescence imaging, mice were weighted, injected intraperitoneally with D-luciferin at 150mg/kg, and anesthetized with isoflurane. The mice were imaged using an IVIS® Spectrum In Vivo Imaging System (PerkinElmer, Waltham, MA). The Living Image software was used to draw an ROI around the tumor signal and measure the size of each tumor.

Generation of bone marrow derived macrophages (BMDMs)
Bone marrow was isolated from femurs and tibia of mice, and red blood cells were lysed. The bone marrow cells were cultured in DMEM supplemented with 10% fetal bovine serum, penicillin (100U/ml), streptomycin (100μg/ml), and M-CSF (40ng/ml). Cells were cultured for 7 days to generate BMDMs. Cultured BMDMs were used for qRT-PCR analysis, western blotting, and other experiments.

Transfection
For siRNA transfection, cells were seeded 1 day before transfection. Transfection was performed using Lipofectamine RNAiMAX (Thermo Fisher Scientific) according to the manufacturer’s instructions. The siRNA duplex oligonucleotides used are listed in S1 Table. The plasmids were transfected by Lipofectamine 2000 (Thermo Fisher Scientific) following the manufacturer’s instructions.

qRT-PCR
Total RNA was isolated from cells using TRIzol (Thermo Fisher Scientific). For qRT-PCR analysis of mRNAs, cDNA was generated using a PrimeScript RT reagent kit (TaKaRa). mRNA expression levels were analyzed using SYBR Green PCR master mix (TaKaRa). The primers used for qRT-PCR are listed in S2 Table.

Co-Immunoprecipitation
HEK293T cells were lysed in NP-40 buffer containing 50mM Tris-HCl (pH 7.5), 150mM NaCl, 0.3% NP-40, and protease inhibitor cocktail. Cell lysates were incubated with specific antibodies or control IgG for 3h at 4°C. The dynabeads protein G (10004D, Invitrogen) were washed three times with NP-40 buffer and mixed with the cell lysates, followed by incubation at 4˚C overnight. Beads were then washed four times using NP-40 buffer. The precipitate samples were subjected to immunoblotting analysis.

Immunoblotting
For immunoblotting, cells were lysed in RIPA buffer and protein concentrations were determined with a BCA assay. Proteins were separated by SDS–PAGE, electrotransferred to PVDF membranes (Millipore), and probed with the indicated antibodies. The primary antibody information is as follows: rabbit anti-FASN (1:1000, 3180S, Cell Signaling Technology), rabbit anti-ACLY (1:1000, 4332S, Cell Signaling Technology), rabbit anti-Phospho-ACLY (1:1000, 4331T, Cell Signaling Technology), rabbit anti-Lipin 1 (1:1000, 14906S, Cell Signaling Technology), rabbit anti-ACSL1 (1:1000, 9189S, Cell Signaling Technology), rabbit anti-C/EBPα (1:1000, 8178S, Cell Signaling Technology), rabbit anti-Perilipin-1 (1:1000, 9349S, Cell Signaling Technology), rabbit anti-PPARγ (1:1000, 2435S, Cell Signaling Technology), rabbit anti-AceCS1 (1:1000, 3658S, Cell Signaling Technology), Mouse anti-ISG15 (1:1000, sc-166755, Santa Cruz Biotechnology), mouse anti-FLAG (1:2000, Sigma), rabbit anti-FLAG (1:1000, 20543-1-AP, Proteintech), mouse anti-Myc (1:2000, M192-3, MBL), rabbit anti-METTL3 (1:1000, ab18810, Abcam), and mouse anti-β-actin (1:5000, Sigma).

mRNA stability analysis
BMDMs were treated with actinomycin D (Sigma) at a final concentration of 5μg/ml and collected at the indicated time. The total RNA was extracted, then the mRNA transcript levels of interest were determined by qRT-PCR.

Bone marrow chimeras
The bone marrow of recipient mice was ablated with 9.5 Gy irradiation. Meanwhile, donor bone marrow cells were collected from the femurs and tibias of mice by flushing with cold DMEM. The bone marrow was filtered through a 70µm filter to produce single cell suspensions. Cells were counted and re-suspended in cold PBS. After irradiation, 1×107 cells of donor bone marrow cells in 200µl PBS were injected into recipient mice through the tail vein for reconstruction.

Depletion of macrophages
Macrophages were depleted using Clodronate Liposomes. Briefly, 200μL Clodronate Liposomes were intraperitoneally injected into mice on days −1, 1, 3, and 5 during tumor growth. Immunofluorescence staining for F4/80+ cells was used to confirm macrophages depletion.

Flow cytometry
To obtain a single cell suspension, brain tumors were finely chopped and digested for 30min using dispase and collagenase Ⅰ in 1640 medium with intermittent shaking at 37°C, and then passed through a 70μm filter. Cell pellets were washed twice in PBS and resuspended in FACS buffer. For surface staining, cells were incubated with the antibodies diluted in FACS buffer on ice for 30min. To perform intracellular immuno-staining, cells were fixed and permeabilized with a Staining Buffer (Thermo Fisher Scientific) according to the manufacturer’s protocol. Samples were run on a BD Caliper (BD) or a CytoFLEX S (Beckman Coulter), and all subsequent compensation and gating design was performed using FlowJo software. The following antibodies were used for flow cytometry: CD45 (30-F11, Biolegend), CD11b (M1/70, Biolegend), Gr1 (RB6-8C5, Biolegend), CD3 (145-2C11, eBioscience), CD8 (clone 53-6.7, Biolegend), IFN-γ (XMG1.2, Biolegend), CD4 (GK1.5, Biolegend), CD49b (HMα2, Biolegend), F4/80 (BM8, Biolegend), Ly6G (1A8, Biolegend), CD11b (clone M1/70, Biolegend), LAG-3 (C9B7W, Biolegend), CD39 (Duha59, Biolegend), TIM-3 (B8.2C12, Biolegend), CX3CR1 (SA011F11, Biolegend), ARG1 (A1exF5, eBioscience), NOS2 (CXNFT, eBioscience).

Immunofluorescence staining
Immunofluorescence was performed using the standard protocol in cells and tissues. Antibodies specific to F4/80 (1:500, ab6640, Abcam), CD68 (1:2000, 66231-2-Ig, Proteintech), FLAG (1:1000, 20543-1-AP, Proteintech), Myc (1:2000, M192-3, MBL), METTL3 (1:1000, ab18810, Abcam), ISG15 (1:1000, sc-166755, Santa Cruz Biotechnology), NOS2 (1:100, PA1-036, Thermo Fisher Scientific) and CD206 (1:100, 64693, Abcam) were used. Images were acquired using a microscope (Olympus).

In vitro T cell activation and co-culture with BMDMs
Spleens were collected under sterile conditions. The spleen was smashed on a 70μm cell strainer and single cells were collected by centrifugation. Red blood cells were lysed by Red Blood Cell Lysis Buffer. Lymphocytes were activated with anti-CD3 (eBioscience, 5mg/ml) and anti-CD28 (eBioscience, 2.5mg/ml) for 5 days47. The activated Lymphocytes were cocultured with BMDMs for the indicated time. The cells were then subjected to surface marker and intracellular cytokine staining and analyzed by FACS.

Oligonucleotide design treatment
The phosphothioate ODN48,49 and sense strands (SS) of siRNAs were joined using six units of C3 carbon chain linker, (CH2)3 (Glen Research). The resulting constructs were annealed to complementary siRNA antisense (AS) strands to generate CpG–siRNA conjugates (asterisks indicate phosphothioation sites). Sequences of single-stranded constructs are listed below: CpG-siRNA Isg15: 5′-T*C*C*A*T*-G*A*C*G*T*T*C*C*T*G*A*T*G*C*T/linker-G*C*GGGACCUAAAGGUGAA-GAU−3′; In vivo delivery of CpG-siRNA Isg15 for treatment began every other day on day 7 post-inoculation. Mice were sacrificed at the indicated time points after tumor implantation and xenografts were harvested for further analyses.

Statistical analysis
Statistical significance was determined by two-paired t test or ANOVA, and differences were considered statistically significant when P<0.05.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Line 78 : Add a reference to the following article : https://www.sciencedirect.com/science/article/pii/S0092867424008249?via=ihub\nSection : Mettl3 deficiency in myeloid cells enhances glioma progression\n• Line 141 : No validation of the model. The figure should start with a western blot showing that the model was validated for the abundance of METTL3 as well as the level of m6A. Furthermore, the abundance of METTL3 should also be investigated in other immune cell types via flow cytometry.\n• Figure 1 :\no B : Missing the time point in the legend\no C : Missing the time point in the legend & the number of replicates\no D : Data on the patients available ?\no F & G : Missing the time point in the legend & quantification for G & the number of replicates\no H : Missing the time point in the legend. Furthermore, quantification should be available in addition to the representative images & the number of replicates\no I : Add the number of injections necessary for the reconstitution, and end point of the experiment post-injection of the GL261 cancer cells\no J : Time point & quantification & number of replicates\no K : Time point & quantification & number of replicates\n• Line 156 : On Figure 1G : the section encircled for the cKO compared to the WT is quite different from the Figure 1K - Control. Shouldn’t both of these Figures be quite similar ? If not, why – different time points ? If there is variability, it would be nice to highlight it through quantification. Furthermore, it seems that the region which is encircled is bigger in the clodronate cKO than in the clodronate WT in similar fashion to the cKO & WT which did not receive clodronate. Thus, clodronate doesn't seem to have an effect while in the text, the following information is written : “the results showed that clodronate-treated mice were unresponsive to Mettl3 depletion, relative to control mice. Moreover, to avoid any mistake : can these images be quantified ? Or perform the same experiment as in Figure 1E with quantification of the total photon flux.\n• How does clodronate affect the brain resident macrophages ? Is the effect observed due to the absence of Mettl3 in monocyte-derived macrophages ? How would the tumor behave if the recruitment of macrophages is prevented through Ccr2 antagonist, for example ?\n• To confirm the results : the following experiment would be nice : Tumor growth curve with GL261 cancer cells + DMSO treated BMDMs versus GL261 cancer cells + STM2457 (inhibitor against Mettl3) treated BMDMs\n• Is survival also affected between WT and cKO ?\nSection : Knockout of Mettl3 in myeloid cells inhibited CD8+ IFNg+ cell formation\n• Line 167 : “the frequency of M2 macrophage, identified by F4/80+NOS2+ and F4/80+ARG1” : iNOS as a marker of M2 macrophages ?\n• Figure 2 : Time point is again missing\no A : Quantification of total, M1 or M2 macrophages (but in the text – line 167 – they don’t indicate that NOS2 = M1 macrophages)\n• Line 168 : ”immunofluorescence staining also demonstrated that CD206 and ARG1” : Could you explain why iNOS was not check via immunofluorescence. Furthermore, why CD206 was not check via flow cytometry ?\n• The following experiment would be nice : add experiments on co-inhibitory receptors known as hallmarks of an exhausted T phenotype, such as LAG-3, TIGIT, TIM-3, and CD39 in WT and cKO to see if an increase can be observed in the Mettl3 cKO as it was the case in the Dong et al. paper.\nSection : Knockout of Mettl3 in myeloid cells enhances lipid metabolism and fatty acid synthase (FASN) expression\n• As some of the Figure 3 was performed on BMDMs, it’s not clear what’s happening in the context of TAMs : is the number of lipid laden macrophages increase in the absence of Mettl3 in myeloid cells ?\n• Line 223 : it’s written that IFNb was upregulated in Mettl3 cKO in line with the results of Qiu et al. How does Mettl3 regulate IFNb through m6A ? Did you check if depletion of Mettl14 in BMDMs led to an increase in IFNb /or/ if depletion of Fto in BMDMs led to a decrease in IFNb or is the phenotype described specific to Mettl3 ? Moreover, in BMDMs, is the absence of Mettl3 link to the stability of IFNb ? Also, knowing that ISG15 is primarly induced by Type I IFNs, did you check the level of IFNalpha in WT and cKO animals ?\n• To confirm the results : Similarly to Figure 3i, it would be nice to see if the increase in IFNb is also observed in TAMs isolated from WT and cKO animals.\n• Figure 3 :\no C : Representative Figure : how many repeats ?\no L : Representative Figure : how many repeats ?\no J : Representative Figure : how many repeats ?\no M : Representative Figure : how many repeats ?\nSection : ISG15 interacts with FASN and regulates FASN ISGylation and stability through inhibiting FASN ubiquitination\n- Figure 4 :\no A+B+C+D+E+F+G+I : Representative Figure : how many repeats ?\no D : Quantification would be nice to add\n- Figure 5\no How many repeats ?\n- Figure 6 :\no A+B : How many repeats ?\n- Supplementary experiments : Figure 6C is on BMDMs, can the effect be check on TAMs ? And same thing for Figure 6D using CpG-siRNAs, for example and/or using co-injection of BMDMs untreated or treated with the siRNAs ?\nSection : FASN knockout or FASN inhibitor treatment abrogates the enhanced tumor progression induced by METTL3 knockout\n- Line 295 : The Figure regarding the depletion of METTL3 should be at the start of the paper, thus in Figure 1. Number of repeats ?\n- Figure 7 :\no A : Should remove the blot of METTL3\no B+C : Repeats ? Time point ? Quantification to add.\no F : Quantification\no H : Quantification\nDiscussion\n- Line 339 : Does not mention the following study : “Lactylation-driven METTL3-mediated RNA m6A modification promotes immunosuppression of tumor-infiltrating myeloid cells” which found that Mettl3 cKO decreased tumor growth in MC38. Here, it seems that the current papers have only described that cKO of Mettl3 is going to enhance tumor growth. It would be nice to discuss conflicting results.\n- Discuss the different population of macrophages : resident and recruited and how the absence of Mettl3 affect these populations.\nSupplementary\n- Extended Fig 6 : Indicate the number of replicates\nGeneral remark\n- P values were determined via t-test while almost everywhere there is more than 2 groups\n- Provide a schematic to summarize the findings", "reviewer_2": "This manuscript by Yin et al. investigates the role of METTL3 in regulating lipid metabolism in tumor-associated macrophages (TAMs) and its impact on tumor progression in glioblastoma (GBM). The study is well-written and logically structured, with compelling genetic and molecular evidence supporting the function of the METTL3-ISG15-FASN axis. The use of METTL3 knockout models and in vivo FASN inhibition provides strong support for the proposed mechanisms and highlights potential therapeutic avenues. These findings address critical gaps in our understanding of immunotherapy resistance in GBM and offer novel mechanistic insights into TAM reprogramming. However, a few additional experiments and clarifications would strengthen the manuscript and improve the overall narrative.\n- Ext Data Fig 1: Please provide METTL3 expression levels in other immune cell types to clarify whether the observed regulation is specific to TAMs or broader across myeloid populations, especially microglia population.\n- Fig1b: The use of BMDMs as a control is informative, but a more physiologically relevant comparison, such as peritoneal macrophages from tumor-bearing mice or macrophages/microglia isolated from normal brain, would better highlight TME-induced METTL3 downregulation.\n- Fig1d: CD68⁺ cells in the healthy brain are most likely microglia rather than infiltrating macrophages. The authors should clarify this point.\n- Fig1g-k: These data are presented as representative images. Please indicate the number of animals used and consider including quantification or composite data to support the rigor of these results.\n- Fig3j: Please label gene names directly on the plot to aid interpretation. It is unclear with the current presentation which genes are indicated.\n- The interplay between IFN-β and the METTL3-ISG15-FASN axis remains somewhat unclear. The manuscript shows that METTL3 knockout increases IFN-β and that IFN-β knockdown reduces FASN, yet IFN-β is abundant in the TME. Could IFN-β upregulate FASN through METTL3? Would exogenous IFN-β treatment of METTL3 KO BMDMs restore FASN expression? Clarifying the transcriptional regulation and directionality of these signals would help define the role of IFN-β in TAM reprogramming.\n- Fig6-7: While CD8⁺ T cell activation is a useful readout of TAM function, further phenotypic analysis of TAMs or BMDMs after ISG15 or FASN modulation (e.g., activation markers, cytokines) would provide more direct evidence of functional reprogramming. Reference to the authors’ previous work (Yin et al., 2021) supports this point.\n- The study relies exclusively on the GL261 glioma model, which has well-recognized limitations in accurately representing human gliomas. To strengthen the translational relevance and generalizability of the findings, validation in an additional, more representative glioma model would be beneficial.\n- Fig7h: The correlation with patient data is compelling but remains associative. Could the authors replicate key findings (e.g. ISG15/FASN modulation) in human macrophages to strengthen translational relevance?\n- A schematic summarizing the METTL3-ISG15-FASN pathway and its role in TAM function would greatly aid reader comprehension.", "reviewer_3": "The manuscript entitled: “METTL3 abrogation promotes glioma progression through regulating the 1 ISG15-FASN axis-mediated lipid metabolism in macrophages” investigates how METTL3, an m⁶A RNA methyltransferase, regulates lipid metabolism and immune suppression in glioblastoma (GBM) via tumor-associated macrophages (TAMs). The authors report that METTL3 is downregulated in TAMs in high-grade gliomas, and that myeloid-specific deletion of METTL3 promotes tumor progression. Mechanistically, METTL3 loss increases ISG15 levels, stabilizing FASN by blocking its ubiquitination, leading to lipid accumulation and suppression of CD8⁺ T cell function. Targeting FASN pharmacologically or genetically restores anti-tumor immunity. This study proposes a METTL3–ISG15–FASN axis as a critical regulator of TAM-mediated immunosuppression in GBM.\nThe conceptual advance is significant, but the current manuscript lacks sufficient mechanistic validation in physiologically relevant systems and needs clearer quantitative support for in vivo claims. With additional experiments and clearer data presentation, this work could meet the bar for Nature Communications.\nMajor Points\nFig 1- The claim that METTL3 is diminished in the mesenchymal subtype is only partially supported (Extended Data Fig. 1a) it appears lower than in classical tumors, but differences with neural and proneural subtypes are less clear. The authors should clarify whether the reduction is statistically significant across all subtype comparisons.\nFigure 1d presents immunofluorescence staining of human glioma tissue and suggests that METTL3 is downregulated in CD68⁺ macrophages within the tumor microenvironment compared to normal brain. However, this figure does not clarify METTL3 expression in glioma cells themselves. This is an important omission given that multiple prior studies have reported pro-tumorigenic roles for METTL3 in glioma, including its ability to enhance MYC expression and glioblastoma stem cell self-renewal via m⁶A modification. The authors should clarify whether METTL3 expression is also altered in tumor cells and reconcile these findings with the published literature showing that METTL3 is often elevated in GBM. The authors should include higher-magnification images to allow better resolution of METTL3 staining in CD68⁺ cells and include and specify the number and type of glioma specimens examined, including tumor grade and IDH status.\nFigures 1e–f show tumor growth using the Mettl3^fl/fl Lyz2-Cre model and an intracranial GL261 injection. However, key comparisons (e.g., tumor burden, immune infiltration) appear to use only n = 4 mice per group, which may limit statistical power. The authors should justify the sample size (e.g., via power analysis) and clarify the tumor harvesting timepoint. Given the expected 100% tumor take in GL261 models by day 21, it would be helpful to confirm tumor take and include survival or longitudinal growth data to ensure the model behaved as expected.\nFigures 1j and 1k show H&E-stained sections to support METTL3-dependent tumor progression and the effect of clodronate-mediated macrophage depletion. However, the number of mice is not reported, and no tumor burden quantification or confirmation of macrophage depletion is provided. Including these data would strengthen the conclusions.\nFigure 2: Since tumor size can impact immune infiltration and T cell function, the authors should clarify whether tumor burden was comparable across groups at the time of immune profiling. If METTL3-deficient mice had larger tumors, this could confound interpretation of reduced CD8⁺ T cell activity. Including tumor volume data or performing immune analysis at matched tumor sizes would strengthen the mechanistic link between TAM phenotype and T cell suppression.\nIn Figure 3c, the authors present Western blot analysis of lipid metabolism proteins in Mettl3^ΔLyz2 and control BMDMs. To ensure that the observed changes are consistent and not derived from a single sample, the authors should include independent replicates and across multiple mice.\nIn Figure 4a–c, all interaction assays between ISG15 and FASN are performed in HEK293T cells using overexpression or recombinant proteins. While these support a physical interaction, no endogenous co-IP is shown even in HEK293 cells or any interaction at all in primary macrophages or TAMs. This limits the physiological relevance of the proposed mechanism\nIn Figures 4e–f, the authors use ISG15 knockdown in BMDMs to show that ISG15 supports FASN protein stability, but the re-expression experiment is performed only in HEK293T cells. To strengthen the mechanistic conclusion, ISG15 should be reintroduced in the knockdown background in BMDMs to confirm that it rescues FASN levels in the relevant system.\nFigures 4g–j aim to link ISGylation to FASN stabilization through site-specific mapping and mutational analysis. However, several key issues limit the strength of these conclusions. In Figure 4g, knockdown of ISG15 shows reduced FASN stability, but no rescue experiment is performed. In Figures 4i–j, ISGylation site mutants are analyzed only in HEK293T cells, and it is not shown whether these mutants actually disrupt ISG15 conjugation. Including ISG15 conjugate blots and validating these findings in macrophages would make the mechanistic link between ISGylation and FASN stability more robust and physiologically relevant.\nFigure 5 provides detailed analysis of how ISG15 suppresses FASN ubiquitination, including lysine site mapping, ISG15 mutant function, and ubiquitin chain specificity. However, all experiments are conducted in HEK293T cells with overexpression constructs. The lack of validation in primary macrophages limits the physiological relevance of these findings.\nWhile Figure 6 convincingly shows that ISG15 knockdown reverses METTL3-dependent changes in FASN expression, lipid accumulation, and T cell suppression, it does not establish a direct mechanistic link (e.g., via ISGylation or ubiquitination of FASN) in this system.\nFigures 7b and 7c present important visual evidence linking FASN deletion to tumor suppression in METTL3-deficient mice. However, the number of animals used in these experiments is not stated, and no quantitative analysis of tumor burden is provided to support the conclusion. The authors should specify the number of biological replicates and include quantitative metrics (e.g., tumor volume or area) to substantiate these findings.\nIn Figure 7h, the authors present immunofluorescence from human glioma cases showing variability in METTL3, ISG15, and FASN expression. While this supports relevance in human disease, it lacks quantitative analysis and clinical context. Importantly, no cell-type–specific assessment is provided to support TAM-specific METTL3 downregulation in patient tumors. The authors could strengthen the translational relevance by analyzing METTL3 expression in TAMs using published GBM single-cell RNA-seq datasets and correlating it with clinical outcomes or immune signatures.\nThe authors show that METTL3 expression in TAMs is lower in high-grade gliomas based on single-cell RNA-seq data. However, no clinical outcome data are provided to determine whether patients with low TAM-specific METTL3 expression have worse survival or altered immune profiles. Incorporating survival analysis from TCGA or stratification of single-cell datasets by METTL3 expression in TAMs would greatly enhance the translational relevance of these findings. At minimum, the authors should discuss this limitation.\nIn Figure 7e–g, the rationale for choosing day 13 as the terminal time point for imaging and immune profiling is not provided. Given that the authors previously followed tumor growth to 21 days (Fig. 1e), please clarify why the study was ended at 13 days. A longer follow-up would strengthen the interpretation of FASN inhibitor efficacy and allow assessment of survival benefit or sustained immune modulation.\nMinor Points\n• Figure 1a: The labels “low-grade” and “high-grade glioma” on the graph require correction for clarity and formatting.\n• Figure 1a legend: Clarify that the left panel shows per-cell METTL3 expression and the right panel shows per-sample averages. The current description is vague and may be misinterpreted.\n• Line 226: Correct “may regulated” to “may regulate.”\n• Line 227: Consider rewording to “through the IFN-β–ISG15 signaling axis” for clarity and grammar.\n• Line 355: Add spacing between transcription factors and citations: “SREBP-1 (Choi et al., 2008), USF (Wong et al., 2009), and LXR (Na et al., 2009).”"}, "author_response": "1. Summary of major comments from Reviewers.\nBased on the comments from the reviewers, we have performed additional\nexperiments and analyses. The details of our revision are summarized in the following\nTable 1.\nTable 1. Revision for major comments from the reviewers\nKey Questions Revi New experiments New supporting data\newe\nrs\nabundance of #2: abundance of METTL3 expression is downregulated in\nMETTL3 in other immune cell immune cells within the tumor\nshould be types via flow cytometry. microenvironment compared to\ninvestigated those in peripheral blood\nin other (Response Figure 1c; revised\nimmune cell Fig. S1d).\ntypes.\nsection #2: depicting tumor area are bioluminescence imaging results\nshould be #3: provided. In addition, we with quantification of total photon\nquantifed. have added in vivo flux (Response Figure 5; revised\nbioluminescence imaging Fig. 1o). Additionally, tumor areas\ndata together with the were quantified from\ncorresponding total H&E-stained brain sections\nphoton flux (Response Figures 2, 4, 16, 17;\nquantification. Figs. 1g, 1l, 1n, 1p, 7c, 7g ).\nMETTL3-def additional experiments to antagonist RS504393 abrogated\nicient explore the effect of the difference in tumor growth\nmonocyte-der METTL3 in between Mettl3fl/fl and\nived monocyte-derived Mettl3fl/flLyz2Cre mice (Response\nmacrophages macrophages on glioma Figure 6b; revised Fig. S3),\nin glioma. progression in Mettl3fl/fl indicating that the phenotype\nand Mettl3fl/flLyz2Cre depends on monocyte-derived\nmice. cells.\nMETTL3 experiments to examine expression of GPNMB is\ndeficiency the expression of significantly higher in TAMs from\ninfluence the lipid-laden macrophage Mettl3fl/flLyz2Cre mice than that in\nlipid-laden maker GPNMB 1 in those from Mettl3fl/fl mice\nmacrophages TAMs isolated from (Response Figure 11; revised\nwithin tumors of Mettl3fl/fl and Fig. 3e)\ntumors? Mettl3fl/flLyz2Cre mice.\nMETTL3 #2: decay assays were mRNA is m6A-modified\nregulate IFNb employed to explored the (Response Figure 12a; revised\nthrough mechanisms underlying Fig. 3n), and METTL3 deletion\nm6A ? how METTL3 regulate markedly reduced its modification\nIFNb. (Response Figure 12b; revised\nFig. 3o)\nthe relevance and extend the receiving METTL3-deficient\ntranslational generalizability of our myeloid cells developed\nrelevance and findings, we employed a significantly larger tumors\ngeneralizabili human glioblastoma (Response Figure 24, revised\nty of the cell-derived xenograft Fig. 1m, 1n).\nfindings, model to specifically\nvalidation in assess how\nan additional, METTL3-deficient\nmore macrophages influence\nrepresentativ tumor progression.\ne glioma\nmodel would\nbe beneficial.\ncorrelation #3: the prognostic revealed that low METTL3\nwith patient significance of METTL3 expression in CD68⁺ macrophages\ndata. in tumor-associated serves as a robust, independent\nmacrophages, we predictor of shorter overall\nconducted dual survival (Response Figure 25c;\nimmunofluorescence revised Fig. 7j), underscoring the\nstaining for CD68 and clinical relevance of\nMETTL3 on tissue macrophage-intrinsic METTL3\nmicroarrays. activity.\non the co-immunoprecipitation observation that ISG15 physically\nregulation of and expression analyses associates with FASN in BMDMs\nMETTL3-IS to verify the (Response Figure 31; revised\nG15-FASN ISG15-FASN interaction Fig. 4c). We have now extended\nshould be and its regulation in this observation by demonstrating\nperformed in THP-1-derived human the same interaction in human\nmacrophage. macrophages. Rescue THP-1–derived macrophages\nRescue assays were additionally (Response Figure 34; revised\nexperiments performed to consolidate Fig. 5). Further biochemical\nshould be the mechanistic analysis established that ISG15\nperformed to conclusions. conjugation or ubiquitination of\nstrength the FASN directly governs its protein\nmechanistic stability (Response Figure 35;\nconclusion. revised Fig. 6a), while rescue\nassays demonstrated that\nre-introduction of ISG15 alone is\nsufficient to restore FASN levels\n(Response Figure 32; revised\nFig. 4f).\n2. REVIEWER COMMENTS\nReviewer #1 (Remarks to the Author):\nResponse：We would like to thank you for your supportive and constructive\ncomments and are pleased that you find our work important and interesting. We\nbelieve these questions and advice have helped us improve the manuscript.\nLine 78 : Add a reference to the following article :\nhttps://www.sciencedirect.com/science/article/pii/S0092867424008\n249?via=ihub\nResponse： Thank you for this valuable suggestion. We absolutely agree with the\nsuggestion and have added the reference (2024, Cell, Macrophage-mediated myelin\nrecycling fuels brain cancer malignancy) in our revised manuscript (Page 3, Line\n75,).\nSection : Mettl3 deficiency in myeloid cells enhances glioma\nprogression\n• Line 141 : No validation of the model. The figure should start with a\nwestern blot showing that the model was validated for the abundance\nof METTL3 as well as the level of m6A. Furthermore, the abundance of\nMETTL3 should also be investigated in other immune cell types via\nflow cytometry.\nResponse： Thank you for this helpful comment. We have performed experiments to\ntest the expression of METTL3 as well as the level of m6A in Mettl3fl/fl and\nMettl3fl/flLyz2Cre BMDMs. The results show that the METTL3 protein expression and\nm6A level are significantly reduced in Mettl3fl/flLyz2Cre BMDMs compared with that\nin Mettl3fl/fl BMDMs (Response Figure 1a, b; revised Fig. S2a, b). These results\nconfirm the successful establishment of the METTL3-knockout mouse model.\nWe further quantified METTL3 abundance in other immune subsets by flow\ncytometry. The results demonstrate that METTL3 levels are markedly lower in\ntumor-infiltrating immune cells than that in their circulating counterparts. A parallel\nreduction was observed in glioma-associated microglia relative to microglia from\nhealthy brain tissue (Response Figure 1c; revised Fig. S1d). Collectively, these data\nreveal a tumor-wide suppression of METTL3 within the immune cell compartment of\nGBM and underscore its potential functional relevance to disease progression.\nResponse Figure 1 (a,b) The knockout efficiency of METTL3 in Mettl3fl/fl and\nMettl3fl/fllLyz2Cre BMDMs was analysed by western blot (a) and m6A dot blot (b).\n(n=3 independent experiments). (c) Flow cytometry analysis of METTL3 expression\nin distinct immune subsets from GL261 tumors, healthy blood, and brain tissue.\n• Figure 1 :\no B : Missing the time point in the legend\no C : Missing the time point in the legend & the number of replicates\nResponse：Thank you sincerely for pointing this out. We have now included the time\npoint and the number of replicates in Figure legend (Pages 31-32, Lines 828-833,).\no D : Data on the patients available ?\nResponse：Thank you for this helpful comment. We have now provided the patients\ninformation in source data.\no F & G : Missing the time point in the legend & quantification for G &\nthe number of replicates\nResponse：Thank you sincerely for pointing this out. We have now included the time\npoint and the number of replicates in Figure legend (Page 32, Lines 837-840). The\nquantitative chart is also provided (Response Figure 2; revised Fig. 1g).\nResponse Figure 2 Systematic quantification of the tumor area was performed on\nHE-stained images of brain sections.\no H : Missing the time point in the legend. Furthermore, quantification\nshould be available in addition to the representative images &\nthe number of replicates\nResponse：Thank you for this helpful comment. We have now included the time point\nand the number of replicates in Supplementary Figure legend (Page 4, Lines 47-49).\nThe quantitative statistical chart is also provided (Response Figure 3; revised Fig.\nS2d).\nResponse Figure 3 Ki67⁺ cells within the tumor were systematically quantified.\no I : Add the number of injections necessary for the reconstitution,\nand end point of the experiment post-injection of the GL261 cancer\ncells\no J : Time point & quantification & number of replicates\no K : Time point & quantification & number of replicates\nResponse： Thank you sincerely for pointing this out. We have now added them in\nFigure legend (Page 32, Lines 847-850；Page 33, Lines 856-860). The tumor area\nwas quantified (Response Figure 4; revised Fig. 1l, p).\nResponse Figure 4 (a) WT mice were irradiated and reconstituted for 4 weeks with\n1× 106 Mettl3fl/fl or Mettl3fl/flLyz2Cre bone marrow cells and then implanted with 3×\narea was quantified.\n(n=7 mice per group). (b) Mettl3fl/fl or Mettl3fl/flLyz2Cre mice\nimplanted with GL261 cells were injected with control or clodronate liposomes.\nMice were sacrificed on day 21 post-tumor implantation. The tumor area was quantified.\n(n=7 mice per group).\n• Line 156 : On Figure 1G : the section encircled for the cKO compared\nto the WT is quite different from the Figure 1K - Control. Shouldn’t\nboth of these Figures be quite similar ? If not, why – different time\npoints ? If there is variability, it would be nice to highlight it through\nquantification. Furthermore, it seems that the region which is\nencircled is bigger in the clodronate cKO than in the clodronate WT in\nsimilar fashion to the cKO & WT which did not receive clodronate. Thus,\nclodronate doesn't seem to have an effect while in the text, the\nfollowing information is written : “the results showed that\nclodronate-treated mice were unresponsive to Mettl3 depletion,\nrelative to control mice. Moreover, to avoid any mistake : can these\nimages be quantified ? Or perform the same experiment as in Figure\n1E with quantification of the total photon flux.\nResponse： We sincerely appreciate your careful observation and apologize for any\nconfusion the initial presentation may have caused. The subtle discrepancies between\nFigure 1K and Figure 1G may reflect the expected variability following liposome or\nclodronate administration. Recognizing that the original images could be interpreted\nas showing a residual difference, we have now supplemented our data with in vivo\nbioluminescence imaging and quantitative HE staining analyses. The overall results\nshowed that the difference in tumor size is almostly abolished by clodronate liposome\ntreatment (Response Figures 4b, 5; revised Fig. 1o, p). We apologize for any\nconfusion arising from the initial image selection. To ensure clarity, we have now\nreplaced the representative HE images of brain sections with more typical examples\nthat better reflect the overall dataset.\nResponse Figure 5 Mettl3fl/fl or Mettl3fl/flLyz2Cre mice implanted with GL261 cells\nwere injected with control or clodronate liposomes. Bioluminescence was measured at\nthe indicated time points and quantified.\n• How does clodronate affect the brain resident macrophages ? Is the\neffect observed due to the absence of Mettl3 in monocyte-derived\nmacrophages ? How would the tumor behave if the recruitment of\nmacrophages is prevented through Ccr2 antagonist, for example ?\nResponse：We appreciate your insightful comment. We have performed experiments\nto test the brain resident macrophages (CD11b+CX3CR1+) 2 after clodronate treatment.\nThe results showed that the number of brain resident macrophages was diminished\nafter clodronate liposomes treatment consistent with the previous study 3, 4 (Response\nFigure 6a; revised Fig. S2e). Given that we focused on METTL3 depletion in the\nmonocyte-derived macrophage, the destroy of brain resident macrophages in both\nMettl3fl/fl and Mettl3fl/flLyz2Cre mice does not influence the conclusions we draw.\nIn addition, we evaluated the tumor growth in Mettl3fl/fl or Mettl3fl/flLyz2Cre mice\nfollowing CCR2 blockade with the monocyte-infiltration inhibitor RS504393 5.\nTreatment with the CCR2 antagonist RS504393 abrogated the tumor-growth disparity\nbetween Mettl3fl/fl and Mettl3fl/flLyz2Cre mice (Response Figure 6b; revised Fig. S3),\ndemonstrating that monocyte-derived macrophages are indispensable for the\nMETTL3-mediated effects.\nResponse Figure 6 (a) Mettl3fl/fl or Mettl3fl/flLyz2Cre mice were injected with\nclodronate liposome. Flow cytometry analysis were performed to test the percentages\nof CD11b+CX3CR1+ cells in brain. (n=6 mice per group). (b) The tumor growth in\nMettl3fl/fl or Mettl3fl/flLyz2Cre mice treated with the CCR2 antagonist RS504393 (2\nmg/kg) was monitored by serial in vivo bioluminescence imaging. (n=7 mice per\ngroup).\n• To confirm the results : the following experiment would be nice :\nTumor growth curve with GL261 cancer cells + DMSO treated BMDMs\nversus GL261 cancer cells + STM2457 (inhibitor against Mettl3)\ntreated BMDMs\nResponse：Thank you for the valuable suggestion. According to your suggestions, we\nperformed intracranial co-injections of GL261 cells with DMSO- or\nSTM2457-pretreated BMDMs into wild-type mice and monitored tumor progression\nby in vivo bioluminescence imaging. Consistent with our previous observations,\nexpanded significantly faster when seeded in the presence of STM2457-conditioned\nmacrophages (Response Figure 7; revised Fig. 1i). These data further support the\nconclusion that METTL3 deficiency in BMDMs accelerates GL261 tumor\nprogression.\nResponse Figure 7 WT mice were injected with 4×104 GL261 cells mixed with\n1×104 DMSO- or STM2457-pretreated BMDMs. Bioluminescence was measured at\nthe indicated time points and quantified. (n=10 mice per group).\n• Is survival also affected between WT and cKO ?\nResponse：Thank you for this meaningful comment. We have already included\nsurvival curve analysis for Mettl3fl/fl and Mettl3fl/flLyz2Cre mice. The results showed\nthat Mettl3fl/flLyz2Cre mice bearing GL261 tumors experience a modest yet consistent\nreduction in lifespan compared with Mettl3fl/fl mice (Response Figure 8; revised Fig.\n1h).\nResponse Figure 8 The survival of Mettl3fl/fl and Mettl3fl/flLyz2Cre injected with\nGL261 tumor cells was documented (n=8 mice per group).\nSection : Knockout of Mettl3 in myeloid cells inhibited CD8+ IFNg+\ncell formation\n• Line 167 : “the frequency of M2 macrophage, identified by\nF4/80+NOS2+ and F4/80+ARG1” : iNOS as a marker of M2\nmacrophages ?\nResponse：Thank you sincerely for pointing this out. We are very sorry for missing\nthe text “M1”. We have corrected the description as following: “the frequency of M1\nmacrophage (F4/80+NOS2+) and M2 macrophage (F4/80+CD206+ or F4/80+ARG1+)”\n(Page 7, Lines 173, 174).\n• Figure 2 : Time point is again missing\nResponse：Thank you for this valuable comment. We have now included the time\npoint in Figure legend (Pages 34-35, Lines 869-877).\no A : Quantification of total, M1 or M2 macrophages (but in the text –\nline 167 – they don’t indicate that NOS2 = M1 macrophages)\nResponse：We greatly appreciate you pointing this out. We sincerely apologize for\nomitting “M1” in the original text. The description has now been revised to read: “the\nfrequency of M1 macrophages (F4/80+NOS2+) and M2 macrophages (F4/80+CD206+\nor F4/80+ARG1+)” (Page 7, Lines 173,174).\n• Line 168 : ”immunofluorescence staining also demonstrated that\nCD206 and ARG1” : Could you explain why iNOS was not check via\nimmunofluorescence. Furthermore, why CD206 was not check via\nflow cytometry ?\nResponse： We appreciate your careful observation and your thoughtful question.\nAlthough we had initially attempted iNOS(NOS2) immunofluorescence, the resulting\nsignal in brain tissue was too sparse to support reliable quantification (Response\nFigure 9a, only for reviewer), so we ultimately set this approach aside.\nWe sincerely apologize for the oversight in failing to detect the critical M2\nmacrophage marker CD206 through flow cytometry. In the revised manuscript, we\nhave rectified this omission by incorporating CD206 staining to comprehensively\nevaluate M2-polarized macrophages within tumor tissues derived from both Mettl3fl/fl\nand Mettl3fl/flLyz2Cre mice (Response Figure 9b; revised Fig. 2a and S4a). The\nresults showed that the percentages of CD206+ M2 macrophages is increased in\ntumors of Mettl3fl/flLyz2Cre mice, aligning with our proposed model.\nResponse Figure 9 (a) Immunofluorescence analysis was performed with anti-F4/80,\nand NOS2 antibodies. Scale bars: 30 μm. (b) Representative plots of F4/80+CD206+\nTAMs in GL261 tumors from Mettl3fl/fl or Mettl3fl/flLyz2Cre mice. Quantitative analysis\nwas subsequently performed to determine the percentages of F4/80+CD206+ M2\nmacrophages. (n=5 mice per group).\n• The following experiment would be nice : add experiments on\nco-inhibitory receptors known as hallmarks of an exhausted T\nphenotype, such as LAG-3, TIGIT, TIM-3, and CD39 in WT and cKO to\nsee if an increase can be observed in the Mettl3 cKO as it was the case\nin the Dong et al. paper.\nResponse：Thank you for the valuable suggestions. According to your suggestions,\nwe have examined the exhausted CD8 T cell (LAG-3+, TIM-3+, or CD39+) via flow\ncytometry in tumors from both Mettl3fl/fl and Mettl3fl/flLyz2Cre mice. The results\nshowed that the numbers of CD8+LAG3+, CD8+TIM-3+ and CD8+CD39+ exhausted T\ncells were increased in tumors from Mettl3fl/flLyz2Cre mice compared with those from\nMettl3fl/fl control group (Response Figure 10; revised Fig. 2e).\nResponse Figure 10 Quantification of CD8+LAG3+, CD8+TIM-3+ and CD8+CD39+ T\ncells in GL261 tumor derived from Mettl3fl/fl or Mettl3fl/flLyz2Cre mice. (n=5 mice per\ngroup).\nSection : Knockout of Mettl3 in myeloid cells enhances lipid\nmetabolism and fatty acid synthase (FASN) expression\n• As some of the Figure 3 was performed on BMDMs, it’s not clear\nwhat’s happening in the context of TAMs : is the number of lipid laden\nmacrophages increase in the absence of Mettl3 in myeloid cells ?\nResponse：Thank you for your thoughtful question. In an effort to quantify\nGPNMBhigh lipid-laden macrophages 1 by flow cytometry, we first attempted to use an\nanti-GPNMB antibody on tumor cells from Mettl3fl/fl and Mettl3fl/flLyz2Cre mice.\nRegretfully, the antibody proved unsuitable for flow cytometry and yielded no\ndetectable signal. Alternatively, we isolated the TAMs and measured GPNMB\nexpression directly. The results showed that GPNMB levels were markedly elevated\nin TAMs from Mettl3fl/flLyz2Cre mice relative to those from Mettl3fl/fl mice (Response\nFigure 11; revised Fig. 3e), which implies that METTL3 depletion indeed enhances\nthe formation of lipid-laden macrophages.\nResponse Figure 11 qRT-PCR analysis of Gpnmb expression in Mettl3fl/fl or\nMettl3fl/flLyz2Cre TAMs isolated from tumor-bearing mice. (n=3 biological replicates).\n• Line 223 : it’s written that IFNb was upregulated in Mettl3 cKO in line\nwith the results of Qiu et al. How does Mettl3 regulate IFNb through\nm6A ? Did you check if depletion of Mettl14 in BMDMs led to an\nincrease in IFNb /or/ if depletion of Fto in BMDMs led to a decrease in\nIFNb or is the phenotype described specific to Mettl3 ? Moreover, in\nBMDMs, is the absence of Mettl3 link to the stability of IFNb ? Also,\nknowing that ISG15 is primarly induced by Type I IFNs, did you check\nthe level of IFNalpha in WT and cKO animals ?\nResponse: Thank you for this insightful comments. Qiu et al demonstrated that\nMettl3 deficiency enhance the phosphorylation of upstream kinase Tbk1, transcription\nfactors p65 and Irf3 and IFNb expression 6. Another paper demonstrated that IFNb\nmRNA is m6A modified and is more stable in METTL3-depleted cells 7. We also\nperformed meRIP-PCR assays and confirmed that Ifnb1 mRNA is indeed m6A\nmodified (Response Figure 12a; revised Fig. 3n). METTL3 knockout reduced the\nm6A modification of Ifnb1 mRNAs (Response Figure 12b; revised Fig. 3o).\nIn addition, we examined the stability of Ifnb1 mRNAs in Mettl3fl/fl and\nMettl3fl/flLyz2Cre BMDMs. The results showed that the absence of Mettl3 enhanced the\nstability of Ifnb1 mRNAs (Response Figure 12c; revised Fig. 3p). We also have\nperformed experiments to test Ifnb1 expression in Mettl14 knockdown BMDMs.\nThe results showed that the Ifnb1 expression is increased in Mettl14 knockdown BMDMs\ncompared with that in control group (Response Figure 12c; revised Fig. 3m). The\nresult implies that Ifnb1 expression is governed by m6A modification, rather than by\nthe individual writers METTL3 or METTL14. As suggested, we also check the level\nof Ifnα in TAMs isolated from tumors of Mettl3fl/fl and Mettl3fl/flLyz2Cre mice.\nThe results demonstrated that METTL3 depletion exerted no discernible influence on\nIFN-α abundance. (Response Figure 12e; revised Fig. 3l).\nResponse Figure 12 (a) m6A enrichment of Ifnb1 mRNA in BMDMs was determined\nby m6A-RIP-qPCR. (n=3 biological replicates). (b) m6A enrichment of Ifnb1 mRNA\nin Mettl3fl/fl or Mettl3fl/flLyz2Cre BMDMs was measured by m6A-RIP-qPCR. (n=3\nbiological replicates). (c) Ifnb1 mRNA stability in Mettl3fl/fl or Mettl3fl/flLyz2Cre\nBMDMs treated with actinomycin D at the indicated times determined by qRT-PCR.\n(n=3 biological replicates). (d) qRT-PCR analysis of genes expression in Mettl14\nknockdown BMDMs. (n=3 biological replicates). (e) qRT-PCR analysis of genes\nexpression in Mettl3fl/fl or Mettl3fl/flLyz2Cre TAMs. (n=3 biological replicates).\n• To confirm the results : Similarly to Figure 3i, it would be nice to see\nif the increase in IFNb is also observed in TAMs isolated from WT and\ncKO animals.\nResponse：Thank you for your professional advice. We have checked the level of\nIfnb1 in TAMs isolated from tumors of Mettl3fl/fl and Mettl3fl/flLyz2Cre mice.\nThe results demonstrated that the Ifnb1 expression is increased in Mettl3fl/flLyz2Cre TAMs\nrelative to Mettl3fl/fl group (Response Figure 13; revised Fig. 3l).\nResponse Figure 13 qRT-PCR analysis of genes expression in Mettl3fl/fl or\nMettl3fl/flLyz2Cre TAMs. (n=3 biological replicates).\n• Figure 3 :\no C : Representative Figure : how many repeats ?\no L : Representative Figure : how many repeats ?\no J : Representative Figure : how many repeats ?\no M : Representative Figure : how many repeats ?\nResponse：Thank you sincerely for pointing this out. We have now included the\nnumber of replicates in Figure legend (Pages 36-37, Lines 891,908, 920, 923,).\nSection : ISG15 interacts with FASN and regulates FASN ISGylation\nand stability through inhibiting FASN ubiquitination\n- Figure 4 :\no A+B+C+D+E+F+G+I : Representative Figure : how many repeats ?\nResponse：Thank you sincerely for pointing this out. We have now included the\nnumber of replicates in each Figure legend (Pages 39-40, Lines 934, 936, 940, 943,\n945, 948, 952, 955).\no D : Quantification would be nice to add\nResponse：Thank you for your advice. We have now included the quantitative\nstatistical chart (Response Figure 14; revised Fig. 4e).\nResponse Figure 14 The subcellular distributions of endogenous FASN and ISG15,\nas well as of exogenous FLAG-FASN and Myc-ISG15, in HEK293T cells were\nquantitatively evaluated. Scale bars: 10 μm. (n=3 independent experiments).\n- Figure 5\no How many repeats ?\nResponse：Thank you sincerely for pointing this out. We have now included the\nnumber of replicates in each Figure legend (Pages 41-42, Lines 966, 970, 973, 976).\n- Figure 6 :\no A+B : How many repeats ?\nResponse：Thank you sincerely for pointing this out. We have now included the\nnumber of replicates in each Figure legend (Page 43, Lines 986, 989).\n- Supplementary experiments : Figure 6C is on BMDMs, can the effect\nbe check on TAMs ? And same thing for Figure 6D using CpG-siRNAs,\nfor example and/or using co-injection of BMDMs untreated or treated\nwith the siRNAs ?\nResponse：Thank you for offering this valuable suggestion. We have performed\nexperiments to investigate the expression of GPNMB (a maker for lipid-laden\nmacrophage) 1 in TAMs after using CpG-siRNAIsg15 to knockdown ISG15 expression\nin Mettl3fl/fl and Mettl3fl/flLyz2Cre mice 8, 9. The results showed that silencing ISG15 by\ntargeted delivery of CpG-siRNAISG15 to TAMs reduced the expression of GPNMB\n(Response Figure 15a; revised Fig. 6g), underscoring the contribution of ISG15 to\nthe formation of lipid-rich macrophages. Accordingly, intratumoral CD8⁺ T cells\nobtained from CpG-siRNA Isg15 -treated mice exhibited a markedly elevated frequency\nof IFN-γ-producing cells relative to control-treated animals (Response Figure 15b;\nrevised Fig. 6f).\nResponse Figure 15 (a) Mice were engrafted with GL261 tumor cells and then\ninjected with 5 mg/kg CpG-siRNAIsg15 every other day beginning on day 7\npost-inoculation. The expression of Gpnmb expression in TAMs on day 21 were\nexamined by qRT-PCR. (b) Percentages of tumor-infiltrating IFNγ-producing CD8+ T\ncells on day 21 were calculated. (n=5 independent experiments).\nSection : FASN knockout or FASN inhibitor treatment abrogates the\nenhanced tumor progression induced by METTL3 knockout\n- Line 295 : The Figure regarding the depletion of METTL3 should be at\nthe start of the paper, thus in Figure 1. Number of repeats ?\nResponse：Thank you sincerely for pointing this out. We have added the western blot\nof METTL3 in Supplementary Figure 2a. The number of replicates was also included\nin Supplementary Figure legend (Page 4, Lines 43-44).\n- Figure 7 :\no A : Should remove the blot of METTL3\nResponse：Thank you for your advice. We have moved the western blot of METTL3\nto Supplementary Figure 2a.\no B+C : Repeats ? Time point ? Quantification to add.\nResponse： Thank you sincerely for pointing this out. We have now included the\nnumber of replicates and time point in Figure legend (Page 45, Lines 1013-1015).\nThe quantitative statistical chart on tumor area was also added (Response Figure 16;\nrevised Fig. 7c).\nResponse Figure 16 HE-stained was performed on brain sections from WT,\nMettl3fl/flLyz2Cre and Mettl3fl/flFasnfl/flLyz2Cre mice implanted with GL261 cells for 21\ndays. The tumor area was quantified. (n=6 mice per group).\no F : Quantification\nResponse：Thank you sincerely for pointing this out. We have now included the\nquantitative statistical chart (Response Figure 17; revised Fig. 7g).\nResponse Figure 17 Mice were engrafted intracranially with GL261 cells and then\nreceive intraperitoneal vehicle or C75. All animals were euthanized on day 21 for\nendpoint analysis. Brain sections were stained and the tumor area was quantified.\n(n=6 mice per group).\no H : Quantification\nResponse：Thank you sincerely for pointing this out. We have now included the\nquantitative statistical chart (Response Figure 18; revised Fig. 7i).\nResponse Figure 18 Immunofluorescence staining for CD68, METTL3, ISG15 and\nFASN in sections from patients with glioma tumours. Quantitative image analysis was\nperformed.\nDiscussion\n- Line 339 : Does not mention the following study :\n“Lactylation-driven METTL3-mediated RNA m6A modification\npromotes immunosuppression of tumor-infiltrating myeloid cells”\nwhich found that Mettl3 cKO decreased tumor growth in MC38. Here,\nit seems that the current papers have only described that cKO of\nMettl3 is going to enhance tumor growth. It would be nice to discuss\nconflicting results.\nResponse：Thank you sincerely for this valuable suggestion. We have now included\nthe discussion as following (Page 15, Lines 369-373): “By contrast, a separate\nstudy reported that TAM-specific Mettl3 deletion restrained MC38 tumour growth 10.\nThis apparent contradiction underscores the existence of a multifaceted,\ncontext-dependent m⁶A-regulatory network that governs myeloid cell behavior within\nthe tumour microenvironment and warrants further dissection”.\n- Discuss the different population of macrophages : resident and\nrecruited and how the absence of Mettl3 affect these populations.\nResponse：Thank you for this constructive comment. In murine and human gliomas,\nthe pool of TAMs is composed of brain-resident microglia (MG), which develop from\nfetal yolk sac progenitors, and peripherally recruited monocyte derived macrophages\n(MDMs), which originate from adult hematopoietic progenitors 11, 12. The number of\nTAMs infiltrates positively correlates with the glioma grading by the World Health\nOrganization (WHO) and negatively correlates with patient survival. Although the\nactivation states of GAMs are complex, it is often presented into two categories the\nsimplicity-classically activated macrophages or M1 macrophages and alternatively\nactivated macrophages or M2 macrophages, which exhibit\nantitumor/immunostimulatory and protumor/immunosuppressive effects, respectively.\nIn our study, conditional deletion of Mettl3 in the myeloid lineage reprogrammed\nrecruited monocyte-derived macrophages from a pro-inflammatory M1 to an\nanti-inflammatory M2 phenotype, thereby fostering an immunosuppressive tumor\nmicroenvironment and potentially modulating the activation state of brain-resident\nmicroglia in glioma progression. We have now added these statement in the revised\nmanuscript (Page 17, Lines 424-430).\nSupplementary\n- Extended Fig 6 : Indicate the number of replicates\nResponse：Thank you for your advice. In the revised manuscript, we have conducted\nthe experiments presented in Figure 5, which now includes the results previously\nshown in Extended Figure 6. To avoid duplication, we have removed Extended Figure\n6 from the manuscript.\nGeneral remark\n- P values were determined via t-test while almost everywhere there\nis more than 2 groups\nResponse：Thank you for this helpful comment. We have reanalysed the P value by\nANOVA with Tukey’s multiple comparisons test for more than 2 groups comparison.\nP values were also indicated in each Figure legend.\n- Provide a schematic to summarize the findings\nResponse：Thank you for this valuable suggestion. We have provided a graphical\nabstract in revised Figure 7k (Response Figure 19) for reader comprehension.\nResponse Figure 19 Schematic illustrating the mechanism by which\nMETTL3-deficient macrophages promote glioma progression through ISG15-FASN\naxis-mediated reprogramming of lipid metabolism.\nReviewer #2 (Remarks to the Author):\nThis manuscript by Yin et al. investigates the role of METTL3 in\nregulating lipid metabolism in tumor-associated macrophages (TAMs)\nand its impact on tumor progression in glioblastoma (GBM). The study\nis well-written and logically structured, with compelling genetic and\nmolecular evidence supporting the function of the\nMETTL3-ISG15-FASN axis. The use of METTL3 knockout models and in\nvivo FASN inhibition provides strong support for the proposed\nmechanisms and highlights potential therapeutic avenues. These\nfindings address critical gaps in our understanding of immunotherapy\nresistance in GBM and offer novel mechanistic insights into TAM\nreprogramming. However, a few additional experiments and\nclarifications would strengthen the manuscript and improve the\noverall narrative.\nResponse：We sincerely thank you for the encouraging and highly constructive\nfeedback, which has not only strengthened the scientific rigor of our study but also\nsignificantly improved the clarity and completeness of the manuscript. The insightful\nsuggestions prompted us to perform additional mechanistic experiments, expand the\nclinical relevance analysis, and refine the presentation of our data, all of which have\nbeen incorporated into the revised version.\n- Ext Data Fig 1: Please provide METTL3 expression levels in other\nimmune cell types to clarify whether the observed regulation is\nspecific to TAMs or broader across myeloid populations, especially\nmicroglia population.\nResponse： Thank you for this valuable suggestion. We have performed experiments\nto investigate the abundance of METTL3 in other immune cell types via flow\ncytometry. We observed a generalized reduction in METTL3 expression among\nmyeloid populations within tumors relative to their circulating counterparts, and a\nsimilar down-regulation in tumor-infiltrating microglia compared with microglia in\nhealthy brain tissue (Response Figure 1c; revised Fig. S1d).\n- Fig1b: The use of BMDMs as a control is informative, but a more\nphysiologically relevant comparison, such as peritoneal macrophages\nfrom tumor-bearing mice or macrophages/microglia isolated from\nnormal brain, would better highlight TME-induced METTL3\ndownregulation.\nResponse：Thank you for this insightful comment. According to your suggestion, we\nperformed additional experiments to examine the METTL3 expression using\nperitoneal macrophages from healthy mice as a control. The results showed that\nMETTL3 expression was suppressed in TAMs from GL261 tumor-beared mice\ncompared with that in peritoneal macrophages from healthy mice (Response Figure\n20; revised Fig. 1b, c).\nResponse Figure 20 (a) F4/80 and METTL3 staining and corresponding\nquantification in PMs from healthy mice or tumor tissues from GL261-tumor bearing\nmice on day 21 post-xenograft. Scale bars: 30 μm. (n=6 independent experiments). (b)\nImmunoblotting of METTL3 in PMs from healthy mice or TAMs isolated from tumor\ntissues of GL261-tumor bearing mice on day 21 post-xenograft. Scale bars: 10 μm.\n(n=3 independent experiments).\n- Fig1d: CD68⁺ cells in the healthy brain are most likely microglia\nrather than infiltrating macrophages. The authors should clarify this\npoint.\nResponse：Thank you sincerely for pointing this out. We are very sorry for this\nconfusion. We have clarified this point in our manuscript (Page 5, line 128).\n- Fig1g-k: These data are presented as representative images. Please\nindicate the number of animals used and consider including\nquantification or composite data to support the rigor of these results.\nResponse： Thank you for this helpful suggestion. We absolutely agree with the\nsuggestion and have including the number of animals used and quantification of the\ntumor area in HE staining images of brain sections (Response Figures 2, 4, 5;\nrevised Fig. 1g, l, o, p). The results showed that Mettl3 ablation in TAMs promotes\nglioma progression.\n- Fig3j: Please label gene names directly on the plot to aid\ninterpretation. It is unclear with the current presentation which genes\nare indicated.\nResponse：Thank you for this comment. We have carefully checked the Figure 3j\n(Figure 3k in revised manuscript) and found that no gene names were omitted. To\nenhance readability, we guessed that you may let us label gene names directly on the\nplot in Figure 3i to aid interpretation. We have now included the gene names in\nrevised Figure 3i (Response Figure 21) for clarify.\nResponse Figure 21 Scatter plots showing genes expression changes in cellular\ntranscript levels in Mettl3fl/flLyz2Cre TAMs versus Mettl3fl/fl TAMs.\n- The interplay between IFN-β and the METTL3-ISG15-FASN axis\nremains somewhat unclear. The manuscript shows that METTL3\nknockout increases IFN-β and that IFN-β knockdown reduces FASN,\nyet IFN-β is abundant in the TME. Could IFN-β upregulate FASN\nthrough METTL3? Would exogenous IFN-β treatment of METTL3 KO\nBMDMs restore FASN expression? Clarifying the transcriptional\nregulation and directionality of these signals would help define the\nrole of IFN-β in TAM reprogramming.\nResponse：Thank you for offering this valuable comment. In our study, IFN-β is a\ndownstream gene of METTL3. Loss of METTL3 up-regulated IFN-β and its\ndownstream target ISG15, which in turn stabilized FASN via ISGylation-dependent\nmechanisms. We also check the level of IFN-β in TAMs isolated from tumors of\nMettl3fl/fl and Mettl3fl/flLyz2Cre mice. The results demonstrated that the absence of\nMettl3 enhanced the expression of IFN-β in TAMs (Response Figure 13; revised Fig.\n3l), suggesting an increased IFN-β availability within the tumor microenvironment in\nMettl3fl/flLyz2Cre mice. Moreover, we also investigated whether exogenous IFN-β\ncould rescue FASN expression in METTL3-deficient BMDMs. The results showed\nthat treatment with IFN-β robustly elevated FASN levels (Response Figure 22;\nrevised Fig. 3s). These results further confirmed that METTL3 regulates FASN\nexpression through IFN-β.\nResponse Figure 22 Immunoblotting analysis of the indicated proteins in Mettl3fl/fl or\nMettl3fl/flLyz2Cre BMDMs treated with IFN-β protein. (n=3 independent experiments).\n- Fig6-7: While CD8⁺ T cell activation is a useful readout of TAM\nfunction, further phenotypic analysis of TAMs or BMDMs after ISG15\nor FASN modulation (e.g., activation markers, cytokines) would\nprovide more direct evidence of functional reprogramming. Reference\nto the authors’ previous work (Yin et al., 2021) supports this point.\nResponse：Thank you for this valuable suggestion. According to your suggestion, we\nperformed additional experiments to explore the expression of M1 macrophage and\nM2 macrophage marker genes in Mettl3fl/fl and Mettl3fl/flLyz2Cre BMDMs after ISG15\nor FASN modulation. Our previous work demonstrated that the expression of the\nM1-associated genes (Nos2, Il6 and Tnf-α) and the M2-associated genes (Arg1 and\nIl10) is significantly increased in Mettl3fl/flLyz2Cre BMDMs 13. Consistent with our\nprior observations, METTL3-deficient macrophages exhibited simultaneous\nup-regulation of signature genes associated with both M1 and M2 polarization states.\nNotably, ISG15 or FASN knockdown inhibited the M1-associated genes (Nos2, Il6\nand Tnf-α) and the M2-associated genes (Arg1 and Il10) expression in Mettl3fl/flLyz2Cre\nBMDMs (Response Figure 23; revised Figs. 6c and S7e). These results imply that\nISG15 or FASN knockdown could reverse the pro-tumor state of Mettl3fl/flLyz2Cre\nBMDMs.\nResponse Figure 23 (a) qRT-PCR analysis of genes expression in Mettl3fl/fl or\nMettl3fl/flLyz2Cre BMDMs transfected with Isg15 siRNAs. (n=3 biological replicates).\n(b) qRT-PCR analysis of genes expression in Mettl3fl/fl or Mettl3fl/flLyz2Cre BMDMs\ntransfected with Fasn siRNAs. (n=3 biological replicates).\n- The study relies exclusively on the GL261 glioma model, which has\nwell-recognized limitations in accurately representing human gliomas.\nTo strengthen the translational relevance and generalizability of the\nfindings, validation in an additional, more representative glioma\nmodel would be beneficial.\nResponse：Thank you for this constructive comment. To strengthen the translational\nrelevance and generalizability of the findings, we employed a xenograft model of\nhuman glioblastoma cells to explored the role of METTL3-deficient macrophage in\ntumor progression. In brief, 3×10⁵ U87 GBM cells were prepared in PBS for\nstereotactic injection into 6-8 weeks old Nu/Nu mice as previously described 14, 15.\nThen 5×106 Mettl3fl/fl or Mettl3fl/flLyz2Cre myeloid cells mixed with 5×106 lymphocytes\nwere injected into Nu/Nu mice via tail vein every week. The mice were sacrificed for\nHE staining to monitor the tumor growth after 3 weeks. The results demonstrated that\nmice received METTL3-defcient myeloid cells developed markedly larger tumors\n(Response Figure 24, revised Fig. 1m,n).\nResponse Figure 24 (a) 3×105 U87 GBM cells were implanted into the right striatum\nof nude mice for 3 weeks. Mettl3fl/fl or Mettl3fl/flLyz2Cre myeloid cells mixed with\nlymphocytes were injected into the mice via tail vein every week. The mice were\nsacrificed for HE staining to monitor the tumor growth after 3 weeks. Quantification\nof the tumor area was performed (b). (n=7 mice per group).\n- Fig7h: The correlation with patient data is compelling but remains\nassociative. Could the authors replicate key findings (e.g.\nISG15/FASN modulation) in human macrophages to strengthen\ntranslational relevance?\nResponse：Thank you for this thoughtful comment. We have performed experiments\nto examine the relationship of METTL3, ISG15 and FASN in human tumor\nmacrophages through double immunofluorescence staining. The results showed that\nlower METTL3 expression in CD68⁺ tumor-associated macrophages was associated\nwith elevated ISG15 and FASN levels in glioma patients, consistent with the results\nobtained in the study (Response Figure 25a, b; revised Fig. 7i). In addition, dual\nimmunofluorescence for CD68 and METTL3 on glioma tissue microarrays was\nemployed to specifically interrogate the prognostic impact of macrophage-intrinsic\nMETTL3 expression. Image-based quantification revealed that low METTL3\nexpression in CD68⁺ macrophages independently forecasts poorer survival (Response\nFigure 25c; revised Fig. 7j), highlighting the clinical relevance of\nmacrophage-intrinsic METTL3 activity.\nResponse Figure 25 (a,b) Immunofluorescence staining for CD68, METTL3, ISG15\nand FASN in sections from patients with glioma tumors (a). Quantitative image\nanalysis was performed (b). (c) Quantitative image analysis revealed that lower\nMETTL3 levels in CD68⁺ macrophages are strongly and independently associated\nwith diminished overall survival.\n- A schematic summarizing the METTL3-ISG15-FASN pathway and its\nrole in TAM function would greatly aid reader comprehension.\nResponse：Thank you for this valuable suggestion. We have included a schematic in\nrevised Figure 7k (Response Figure 19) for reader comprehension.\nReviewer #3 (Remarks to the Author):\nThe manuscript entitled: “METTL3 abrogation promotes glioma\nprogression through regulating the 1 ISG15-FASN axis-mediated lipid\nmetabolism in macrophages” investigates how METTL3, an m⁶A RNA\nmethyltransferase, regulates lipid metabolism and immune\nsuppression in glioblastoma (GBM) via tumor-associated\nmacrophages (TAMs). The authors report that METTL3 is\ndownregulated in TAMs in high-grade gliomas, and that\nmyeloid-specific deletion of METTL3 promotes tumor progression.\nMechanistically, METTL3 loss increases ISG15 levels, stabilizing FASN\nby blocking its ubiquitination, leading to lipid accumulation and\nsuppression of CD8⁺ T cell function. Targeting FASN pharmacologically\nor genetically restores anti-tumor immunity. This study proposes a\nMETTL3–ISG15–FASN axis as a critical regulator of TAM-mediated\nimmunosuppression in GBM.\nThe conceptual advance is significant, but the current manuscript\nlacks sufficient mechanistic validation in physiologically relevant\nsystems and needs clearer quantitative support for in vivo claims.\nWith additional experiments and clearer data presentation, this work\ncould meet the bar for Nature Communications.\nResponse：Thank you for acknowledging the conceptual significance of our study and\noffering invaluable, constructive guidance. Your insightful feedback has not only\nhelped us refine the clarity and precision of our manuscript but also prompted us to\nstrengthen the mechanistic depth and translational relevance of our findings. Through\nadditional experimental validations and more rigorous data presentation, we believe\nthat the revised version now fully addresses your concerns and significantly enhances\nthe overall quality and impact of the work.\nMajor Points\nFig 1- The claim that METTL3 is diminished in the mesenchymal\nsubtype is only partially supported (Extended Data Fig. 1a) it appears\nlower than in classical tumors, but differences with neural and\nproneural subtypes are less clear. The authors should clarify whether\nthe reduction is statistically significant across all subtype\ncomparisons.\nResponse：Thank you for this helpful query. To clarify whether the reduction of\nMETTL3 expression is statistically significant across all subtype comparisons. We\nperformed statistically analysis and discovered that METTL3 expression in the\nmesenchymal subtype is lower than that in classical and proneural subtypes\n(Response Figure 26; revised Fig. S1a). Previous studies have reported that GBM\nwith mesenchymal phenotype carried the worst prognosis 16, 17. These results implied\nthat diminished METTL3 levels may contribute to this adverse outcome. This\nobservation provides a compelling rationale for further investigating\nmacrophage-intrinsic METTL3 in glioma progression.\nResponse Figure 26 The METTL3 expression in all these GBM subtypes (Classical,\nMesenchymal, Neural, Proneural) from RNA-seq results of the TCGA database.\nFigure 1d presents immunofluorescence staining of human glioma\ntissue and suggests that METTL3 is downregulated in CD68⁺\nmacrophages within the tumor microenvironment compared to\nnormal brain. However, this figure does not clarify METTL3 expression\nin glioma cells themselves. This is an important omission given that\nmultiple prior studies have reported pro-tumorigenic roles for METTL3\nin glioma, including its ability to enhance MYC expression and\nglioblastoma stem cell self-renewal via m⁶A modification. The authors\nshould clarify whether METTL3 expression is also altered in tumor\ncells and reconcile these findings with the published literature\nshowing that METTL3 is often elevated in GBM. The authors should\ninclude higher-magnification images to allow better resolution of\nMETTL3 staining in CD68⁺ cells and include and specify the number\nand type of glioma specimens examined, including tumor grade and\nIDH status.\nResponse： We sincerely appreciate your insightful feedback. Indeed, prior studies\nhave established a tumor-promoting role for glioma-intrinsic METTL3 18, 19. Rather\nthan contradict these findings, our study was intentionally designed to complement\nthem by dissecting the macrophage-specific contribution of METTL3.\nWe regret that our original presentation did not adequately clarify METTL3\nexpression in the tumor cells themselves. We now have included higher-magnification\nimages to allow better resolution of METTL3 staining in CD68⁺ cells and other cells\n(Response Figure 27; revised Fig. 1d). We can observe that the expression of\nMETTL3 in tumor-infiltrating microglia is indeed reduced compared with that in\nhealthy brain tissue. This apparent contradiction underscores the existence of a\nmultifaceted, context-dependent m⁶A-regulatory network that governs myeloid cell\nbehavior within the tumour microenvironment and warrants further dissection. We\nhave included discussion on the inconsistent expression of METTL3 in tumor cells\nand macrophages in our manuscript (Page 15, Lines 371-373).\nMoreover, we have provided the number and type of glioma specimens examined,\nincluding tumor grade and IDH status and so on in source data file.\nResponse Figure 27 Representative fluorescence micrographs of brains from the\nnormal and glioma patient, immunostained with CD68 and METTL3 antibody. The\nintensity of the fluorescence signal is quantified. Scale bars: 10 μm.\nFigures 1e–f show tumor growth using the Mettl3^fl/fl Lyz2-Cre\nmodel and an intracranial GL261 injection. However, key comparisons\n(e.g., tumor burden, immune infiltration) appear to use only n = 4\nmice per group, which may limit statistical power. The authors should\njustify the sample size (e.g., via power analysis) and clarify the tumor\nharvesting timepoint. Given the expected 100% tumor take in GL261\nmodels by day 21, it would be helpful to confirm tumor take and\ninclude survival or longitudinal growth data to ensure the model\nbehaved as expected.\nResponse：Thank you for the helpful comment. We are very sorry for the confusion\ncaused by the four representative brain images in Figure 1f (Fig. S2c in revised\nversion). In fact, we used more mice to analyze the tumor burden, immune infiltration\nand other related experiments, as detailed in the revised Figure Legend (Page 32,\nLines 837-840). We also clarified the tumor harvesting time point in Figure legend\n(Page 32, Line 838). To confirm tumor take, we performed HE staining and\nquantified the tumor area on brain sections (Response Figure 2; revised Fig. 1g). In\naddition, we have already included survival curve analysis for Mettl3fl/fl and\nMettl3fl/flLyz2Cre mice harboring GL261 tumor. The results showed that\nMettl3fl/flLyz2Cre mice with GL261 tumors have shortened survival relative to Mettl3fl/fl\nmice (Response Figure 8; revised Fig. 1h).\nFigures 1j and 1k show H&E-stained sections to support\nMETTL3-dependent tumor progression and the effect of\nclodronate-mediated macrophage depletion. However, the number of\nmice is not reported, and no tumor burden quantification or\nconfirmation of macrophage depletion is provided. Including these\ndata would strengthen the conclusions.\nResponse：Thank you for your professional advice. We are very sorry for not\nproviding the number of mice used. We have added the information in Figure legend\n(Page 32, Line 850; Page 33, Line 860). In addition, the tumor areas in HE staining\nimages of brain sections were also quantified (Response Figure 4; revised Fig. 1l, p).\nAs required, macrophage depletion by clodronate was also confirmed (Response\nFigure 28; revised Fig. S2f).\nResponse Figure 28 Mettl3fl/fl or Mettl3fl/flLyz2Cre mice were injected with clodronate\nliposome. Immunofluorescence analysis were performed with anti-F4/80 (red)\nantibodies. The percentages of F4/80+ cells were quantified. Scale bars: 50 μm. (n=6\nindependent experiments).\nFigure 2: Since tumor size can impact immune infiltration and T cell\nfunction, the authors should clarify whether tumor burden was\ncomparable across groups at the time of immune profiling. If\nMETTL3-deficient mice had larger tumors, this could confound\ninterpretation of reduced CD8⁺ T cell activity. Including tumor volume\ndata or performing immune analysis at matched tumor sizes would\nstrengthen the mechanistic link between TAM phenotype and T cell\nsuppression.\nResponse：Thank you for raising this important point. Owing to the accelerated tumor\ngrowth in METTL3-deficient mice, tumor sizes were inevitably larger in the\nMETTL3-deficient cohort at the chosen time-points. To provide transparent context,\nwe have now documented the individual tumor volumes (Response Figure 29;\nrevised Fig. S4b), allowing readers to interpret the immune analysis with this\nvariability in mind.\nResponse Figure 29 The GL261 tumors were dissected from Mettl3fl/fl or\nMettl3fl/flLyz2Cre mice and the tumor volume was quantified.\nIn Figure 3c, the authors present Western blot analysis of lipid\nmetabolism proteins in Mettl3^ΔLyz2 and control BMDMs. To ensure\nthat the observed changes are consistent and not derived from a\nsingle sample, the authors should include independent replicates and\nacross multiple mice.\nResponse：Thank you for the valuable suggestion. According to your suggestions, we\nrepeated these experiments in Mettl3fl/fl and Mettl3fl/flLyz2Cre BMDMs across multiple\nmice (n=3 replicates, each replicate includes 3 mice). The new data corroborate our\ninitial finding that METTL3 deficiency elevates FASN expression in macrophages\n(Response Figure 30; revised Fig. 3c).\nResponse Figure 30 Western blot analysis of the indicated proteins in Mettl3fl/fl or\nMettl3fl/flLyz2Cre BMDMs. (n=3 replicates, each replicate includes 3 mice).\nIn Figure 4a–c, all interaction assays between ISG15 and FASN are\nperformed in HEK293T cells using overexpression or recombinant\nproteins. While these support a physical interaction, no endogenous\nco-IP is shown even in HEK293 cells or any interaction at all in primary\nmacrophages or TAMs. This limits the physiological relevance of the\nproposed mechanism\nResponse：Thank you for this thoughtful comment. To extend our earlier observations,\nwe performed endogenous co-immunoprecipitation assays using freshly isolated\nbone-marrow-derived macrophages (BMDMs). In brief, equal amounts of whole-cell\nlysate were incubated with antibodies against ISG15 or FASN, followed by\nimmunoblotting for the reciprocal target. Consistent with our previous findings,\nISG15 readily co-precipitated with FASN, and conversely, FASN was efficiently\nrecovered in ISG15 immunoprecipitates (Response Figure 31; revised Fig. 4c).\nThese data corroborate the specificity of the ISG15-FASN interaction under\nphysiological conditions.\nResponse Figure 31 Co-immunoprecipitation to detect the interaction of endogenous\nISG15 and FASN in BMDMs. (n=3 independent experiments).\nIn Figures 4e–f, the authors use ISG15 knockdown in BMDMs to show\nthat ISG15 supports FASN protein stability, but the re-expression\nexperiment is performed only in HEK293T cells. To strengthen the\nmechanistic conclusion, ISG15 should be reintroduced in the\nknockdown background in BMDMs to confirm that it rescues FASN\nlevels in the relevant system.\nResponse：Thank you for this valuable suggestion. According to your suggestions, we\nhave performed experiments to re-express ISG15 via electroporation in ISG15\nknockdown BMDMs to evaluate FASN protein level. The results showed that\nre-expression of ISG15 in ISG15 knockdown BMDMs reversed the reduced\nexpression of FASN (Response Figure 32; revised Fig. 4f). The results provide\ncompelling evidence that ISG15 is necessary and sufficient to sustain FASN\nabundance in macrophages and further validate the specificity of our earlier\nobservations.\nResponse Figure 32 BMDMs transfected with NC siRNA, Isg15 siRNAs or Isg15\nsiRNAs and Myc-ISG15 for 48 h were lysed, followed by immunoblotting with the\nindicated antibodies. (n=3 independent experiments).\nFigures 4g–j aim to link ISGylation to FASN stabilization through\nsite-specific mapping and mutational analysis. However, several key\nissues limit the strength of these conclusions. In Figure 4g,\nknockdown of ISG15 shows reduced FASN stability, but no rescue\nexperiment is performed. In Figures 4i–j, ISGylation site mutants are\nanalyzed only in HEK293T cells, and it is not shown whether these\nmutants actually disrupt ISG15 conjugation. Including ISG15\nconjugate blots and validating these findings in macrophages would\nmake the mechanistic link between ISGylation and FASN stability\nmore robust and physiologically relevant.\nResponse：Thank you for this insightful comment. To directly address the requirement\nfor ISG15 in maintaining FASN stability, we re-introduced ISG15 by electroporation\nin ISG15 knockdown BMDMs to evaluate FASN protein level in revised Figure 4h\n(original Figure 4g). The results showed that re-expression of ISG15 restored the\nFASN protein stability in ISG15 knockdown BMDMs (Response Figure 33a;\nrevised Fig. 4h). The result suggests that ISG15 is necessary and sufficient to sustain\nFASN stability.\nIn addition, we performed co-immunoprecipitation assays to detect the interaction of\nMyc-ISG15 with FLAG-FASN mutants in human macrophage cells THP1. The\nresults showed that mutations at Lys 1582, Lys 1591 and Lys 1866 disrupt ISG15 and\nFASN interaction (Response Figure 33b; revised Fig. 4l), demonstrating that\nISGylation at these residues is essential for the ISG15-FASN association.\nResponse Figure 33 (a) Western blot to analyze FASN expression after ISG15 silence\nwith or without Myc-ISG15 overexpression combined with 100 ug/ml cycloheximide\n(CHX) at the 0, 3, 6 and 9 h. (n=3 independent experiments). (b)\nCo-immunoprecipitation to detect the interaction between Myc-ISG15 and\nFLAG-FASN mutants in human macrophage cells THP1. (n=3 independent\nexperiments).\nFigure 5 provides detailed analysis of how ISG15 suppresses FASN\nubiquitination, including lysine site mapping, ISG15 mutant function,\nand ubiquitin chain specificity. However, all experiments are\nconducted in HEK293T cells with overexpression constructs. The lack\nof validation in primary macrophages limits the physiological\nrelevance of these findings.\nResponse：Thank you for this meaningful comment. We now have performed the\nsame assay in primary macrophage cells THP-1. Consistently, the results indicated\nthat Lys 1582, Lys 1591 and Lys 1866 truly served for FASN protein expression\nthrough enhancing FASN ISGylation and inhibiting its ubiquitination (Response\nFigure 34a; revised Fig. 5a). Concordantly, genetic silencing of ISG15 markedly\nelevated global FASN ubiquitination (Response Figure 34b; revised Fig. 5b),\nwhereas enforced ISG15 expression suppressed it (Response Figure 34c; revised Fig.\n5c). Mechanistically, ISG15 overexpression specifically attenuated K48-linked\nubiquitination of FASN (Response Figure 34d; revised Fig. 5d). These findings\ncollectively suggest that ISG15 and ISGylation play a crucial role in regulating the\nubiquitination-mediated degradation of FASN, thereby modulating its expression and\nstability.\nResponse Figure 34 (a) FLAG-FASN mutants along with HA-unbiquitin were\nco-expressed in THP1 cells. Cell lysates and immunoprecipitates were detected with\nindicated antibodies. (n=3 independent experiments). (b) FLAG-FASN mutants\nco-expressed with HA-unbiquitin in ISG15 silenced THP1 cells treated with MG132.\nCell lysates and immunoprecipitates were examined with indicated antibodies.\n(n=3 independent experiments). (c) FLAG-FASN mutants, HA-unbiquitin and Myc-ISG15\nor Myc-ISG15ΔGG were co-expressed in THP1 cells treated with MG132.\nCell lysates and immunoprecipitates were detected with indicated antibodies.\n(n=3 independent experiments). (d) FLAG-FASN mutants, HA-unbiquitin and Myc-ISG15\nor Myc-ISG15ΔGG were co-expressed in ISG15 silenced THP1 cells treated with\nMG132. Cell lysates and immunoprecipitates were examined with indicated\nantibodies. (n=3 independent experiments).\nWhile Figure 6 convincingly shows that ISG15 knockdown reverses\nMETTL3-dependent changes in FASN expression, lipid accumulation,\nand T cell suppression, it does not establish a direct mechanistic link\n(e.g., via ISGylation or ubiquitination of FASN) in this system.\nResponse: Thank you for this helpful comment. According to your suggestion, we\nperformed additional experiments to explore the ISGylation and ubiquitination of\nFASN in Figure 6a. In METTL3-deficient BMDMs, we observed a marked increase\nin FASN ISGylation and its stability accompanied by a concomitant reduction in its\nubiquitination. ISG15 knockdown largely abolished the enhanced ISGylation and\nconcurrently increased FASN ubiquitination (Response Figure 35; revised Fig. 6a).\nThese data underscore that METTL3 loss tips the balance toward\nISGylation-dependent FASN stabilization, whereas ISG15 depletion reverses this\neffect by permitting increased ubiquitin-mediated degradation.\nResponse Figure 35 Mettl3fl/fl or Mettl3fl/flLyz2Cre BMDMs treated with NC siRNA or\nIsg15 siRNAs for 48 h were lysed. Cell lysates and immunoprecipitates were\nexamined with indicated antibodies. (n=3 independent experiments).\nFigures 7b and 7c present important visual evidence linking FASN\ndeletion to tumor suppression in METTL3-deficient mice. However, the\nnumber of animals used in these experiments is not stated, and no\nquantitative analysis of tumor burden is provided to support the\nconclusion. The authors should specify the number of biological\nreplicates and include quantitative metrics (e.g., tumor volume or\narea) to substantiate these findings.\nResponse：Thank you for this valuable suggestion. We have provided the information\non the number of animals in Figure legend (Page 46, Line 1015). Moreover, the\ntumor area in HE staining images of brain sections was also quantified (Response\nFigure 16; revised Fig. 7c).\nIn Figure 7h, the authors present immunofluorescence from human\nglioma cases showing variability in METTL3, ISG15, and FASN\nexpression. While this supports relevance in human disease, it lacks\nquantitative analysis and clinical context. Importantly, no cell-type–\nspecific assessment is provided to support TAM-specific METTL3\ndownregulation in patient tumors. The authors could strengthen the\ntranslational relevance by analyzing METTL3 expression in TAMs using\npublished GBM single-cell RNA-seq datasets and correlating it with\nclinical outcomes or immune signatures.\nThe authors show that METTL3 expression in TAMs is lower in\nhigh-grade gliomas based on single-cell RNA-seq data. However, no\nclinical outcome data are provided to determine whether patients with\nlow TAM-specific METTL3 expression have worse survival or altered\nimmune profiles. Incorporating survival analysis from TCGA or\nstratification of single-cell datasets by METTL3 expression in TAMs\nwould greatly enhance the translational relevance of these findings.\nAt minimum, the authors should discuss this limitation.\nResponse：Thank you for this important comment. We have performed experiments to\nexamine the relationship of METTL3, ISG15 and FASN in human tumor-associated\nmacrophage through double immunofluorescence staining. Consistent with our\nmurine data, patients with lower METTL3 expression displayed appreciably elevated\nISG15 and FASN signals in CD68⁺ macrophages, underscoring the translational\nrelevance of our findings (Response Figure 25a, b; revised Fig. 7i).\nIn Figure 1a, we have analyzed METTL3 expression in TAMs using published GBM\nsingle-cell RNA-seq datasets and found that the expression of METTL3 in TAMs was\nlower in high-grade gliomas than that in low-grade gliomas. Unfortunately, clinical\noutcomes or survival information on these patients is unavailable. Alternatively, we\nperformed immunofluorescence co-staining for CD68 and METTL3 on tissue\nmicroarrays to interrogate the prognostic impact of METTL3 expression within the\ntumor-associated macrophage compartment. Quantitative image analysis revealed that\nlow METTL3 expression in CD68⁺ macrophages is a robust, independent predictor of\nshortened survival (Response Figure 25c; revised Fig. 7j), underscoring the clinical\nrelevance of macrophage-intrinsic METTL3 activity. These results further consolidate\nour conclusion that the lower expression of METTL3 in macrophage is associated\nwith the poor prognosis of glioma patients.\nIn Figure 7e–g, the rationale for choosing day 13 as the terminal time\npoint for imaging and immune profiling is not provided. Given that the\nauthors previously followed tumor growth to 21 days (Fig. 1e), please\nclarify why the study was ended at 13 days. A longer follow-up would\nstrengthen the interpretation of FASN inhibitor efficacy and allow\nassessment of survival benefit or sustained immune modulation.\nResponse：Thank you for this thoughtful observation. In Figure 7e-g, we chose 21\ndays as the endpoint for H&E staining so that we could accurately evaluate tumor\nburden. Unfortunately, owing to a temporary unavailability of the in vivo imaging\nsystem on that day, we were unable to acquire in vivo images for the 21-day time\npoint. We sincerely apologize for any confusion this may have caused and have now\nincluded the 21-day images to ensure consistency throughout all Figures (Response\nFigure 36; revised Fig. 7e, f).\nResponse Figure 36 Representative bioluminescent images of Mettl3fl/fl or\nMettl3fl/flLyz2Cre mice bearing orthotopic murine glioma GL261 cell xenografts treated\ni.p. with vehicle or C75 and bioluminescent signal was quantified. (n=6 mice per\ngroup).\nMinor Points\n• Figure 1a: The labels “low-grade” and “high-grade glioma” on the\ngraph require correction for clarity and formatting.\nResponse：Thank you for your advice. We have corrected the labels “low-grade” and\n“high-grade glioma” on the graph for clarity.\n• Figure 1a legend: Clarify that the left panel shows per-cell METTL3\nexpression and the right panel shows per-sample averages. The\ncurrent description is vague and may be misinterpreted.\nResponse：Thank you sincerely for pointing this out. We have corrected Figure 1a\nlegend (Page 31, Lines 826-827) for clarity as “the left panel shows per-cell\nMETTL3 expression and the right panel shows per-sample averages”.\n• Line 226: Correct “may regulated” to “may regulate.”\nResponse：Thank you for pointing this out. We are very sorry for our carelessness and\nhave corrected “may regulated” to “may regulate” (Page 10, Line 241).\n• Line 227: Consider rewording to “through the IFN-β–ISG15\nsignaling axis” for clarity and grammar.\nResponse：Thank you for this helpful comment. We have corrected “through the\nIFN-β–ISG15 signaling axis” to “through the IFN-β/ISG15 axis” (Page 10, Lines\n241-242).\n• Line 355: Add spacing between transcription factors and citations:\n“SREBP-1 (Choi et al., 2008), USF (Wong et al., 2009), and LXR (Na et\nal., 2009).”\nResponse：Thank you for pointing this out. We have added space between\ntranscription factors and citations: “SREBP-1 (Choi et al., 2008), USF (Wong et al.,\n2009), and LXR (Na et al., 2009).” (Page 16, Line 389).\n.\nReferences\n1. Kloosterman, D.J. et al. Macrophage-mediated myelin recycling fuels brain cancer malignancy.\nCell 187, 5336-5356.e5330 (2024).\n2. Dissing-Olesen, L. et al. FEAST: A flow cytometry-based toolkit for interrogating microglial\nengulfment of synaptic and myelin proteins. Nat Commun 14, 6015 (2023).\n3. Ye, F. et al. Characteristics of activation of monocyte-derived macrophages versus microglia\nafter mouse experimental intracerebral hemorrhage. J. Cereb. Blood Flow Metab. 43,\n1475-1489 (2023).\n4. Torres, L. et al. Dynamic microglial modulation of spatial learning and social behavior. Brain.\nBehav. Immun. 55, 6-16 (2016).\n5. Huang, J. et al. Inhibition of aortic CX3CR1+ macrophages mitigates thoracic aortic aneurysm\nprogression in Marfan syndrome in mice. J. Clin. Invest. 135 (2025).\n6. Qiu, W. et al. N(6)-methyladenosine RNA modification suppresses antiviral innate sensing\npathways via reshaping double-stranded RNA. Nat Commun 12, 1582 (2021).\n7. Winkler, R. et al. m(6)A modification controls the innate immune response to infection by\ntargeting type I interferons. Nat. Immunol. 20, 173-182 (2019).\n8. Kortylewski, M. et al. In vivo delivery of siRNA to immune cells by conjugation to a TLR9\nagonist enhances antitumor immune responses. Nat. Biotechnol. 27, 925-932 (2009).\n9. Ma, S. et al. YTHDF2 orchestrates tumor-associated macrophage reprogramming and controls\nantitumor immunity through CD8(+) T cells. Nat. Immunol. 24, 255-266 (2023).\n10. Xiong, J. et al. Lactylation-driven METTL3-mediated RNA m(6)A modification promotes\nimmunosuppression of tumor-infiltrating myeloid cells. Mol. Cell 82, 1660-1677.e1610\n(2022).\n11. Akkari, L.A.-O.X. et al. Dynamic changes in glioma macrophage populations after radiotherapy\nreveal CSF-1R inhibition as a strategy to overcome resistance. Sci. Transl. Med. 12, eaaw7843\n(2020).\n12. Klemm, F. et al. Interrogation of the Microenvironmental Landscape in Brain Tumors Reveals\nDisease-Specific Alterations of Immune Cells. Cell 181, 1643-1660.e1617 (2020).\n13. Yin, H. et al. RNA m6A methylation orchestrates cancer growth and metastasis via\nmacrophage reprogramming. Nat. Commun. 12, 1394 (2021).\n14. Torrini, C. et al. Lactate is an epigenetic metabolite that drives survival in model systems of\nglioblastoma. Mol. Cell 82, 3061-3076.e3066 (2022).\n15. Randall, E.C., Lopez, B.G.C., Peng, S. & Regan, M.S. Localized Metabolomic Gradients in\nPatient-Derived Xenograft Models of Glioblastoma. Cancer Res. 80, 1258-1267 (2020).\n16. Noushmehr, H. et al. Identification of a CpG island methylator phenotype that defines a\ndistinct subgroup of glioma. Cancer Cell 17, 510-522 (2010).\n17. Phillips, H.S. et al. Molecular subclasses of high-grade glioma predict prognosis, delineate a\npattern of disease progression, and resemble stages in neurogenesis. Cancer Cell 9, 157-173\n(2006).\n18. You, J. et al. Transcription factor YY1 mediates self-renewal of glioblastoma stem cells\nthrough regulation of the SENP1/METTL3/MYC axis. Cancer Gene Ther. 30, 683-693 (2023).\n19. Chang, Y.Z. et al. METTL3 enhances the stability of MALAT1 with the assistance of HuR via\nm6A modification and activates NF-κB to promote the malignant progression of IDH-wildtype\nglioma. Cancer Lett. 511, 36-46 (2021).\nREVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors successfully addressed my concerns\nReviewer #2 (Remarks to the Author):\nThe authors adequately addressed all of my previous concerns and provided compelling\nadditional data in response to the other reviewers’ suggestions.\nReviewer #3 (Remarks to the Author):\nMy concerns have been satisfactorily addressed with new data, clearer quantification, and\nimproved explanations. The revisions strengthen the mechanistic and in vivo findings.\nResponse to REVIEWERS' COMMENTS\nReviewer #1 (Remarks to the Author):\nThe authors successfully addressed my concerns\nResponse: Thank you very much for your time and positive assessment.\nReviewer #2 (Remarks to the Author):\nThe authors adequately addressed all of my previous concerns and provided compelling\nadditional data in response to the other reviewers’ suggestions.\nResponse: Thank you very much for your recognition of our work.\nReviewer #3 (Remarks to the Author):\nMy concerns have been satisfactorily addressed with new data, clearer quantification, and\nimproved explanations. The revisions strengthen the mechanistic and in vivo findings.\nResponse: Thank you very much for your valuable feedback and positive assessment."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors successfully addressed my concerns", "reviewer_2": "The authors adequately addressed all of my previous concerns and provided compelling additional data in response to the other reviewers’ suggestions.", "reviewer_3": "My concerns have been satisfactorily addressed with new data, clearer quantification, and improved explanations. The revisions strengthen the mechanistic and in vivo findings."}, "author_response": "Response: Thank you very much for your time and positive assessment.\nResponse: Thank you very much for your recognition of our work.\nResponse: Thank you very much for your valuable feedback and positive assessment."}]

Decision letter