Intermittent fasting inhibits Tp53-driven glioma through gut microbiota-mediated methionine-m^6A regulation

Domain: biology
Published: 2026-01-20
Editorial outcome: minor_revision

Abstract
Intermittent fasting (IF) has emerged as a potential cancer treatment modality, although its tumor-suppressive effects are limited. Glioblastoma (GBM) can be classified into CDKN2A subtype and TP53 subtype. Here, we discover that the efficacy of IF is correlated with tumor subtypes of GBM. IF significantly inhibit GBM progression in mice with the Tp53 GBM model, whereas its inhibitory effect is not significant in the Cdkn2a GBM model. Multi-omics sequencing is performed in the IF-responsive Tp53 GBM mouse model, delineating a comprehensive molecular profiling of IF that including the spatial transcriptome, spatial metabolome, single-cell transcriptome, single-cell RNA methylation, metabolome, and microbiome. Through systematic biological analysis and rescue experiments conducted in IF-responsive Tp53 GBM mice model, we demonstrate that the efficacy of IF is primarily mediated by alterations in the gut microbiota, which subsequently modulate the production of the microbial metabolite methionine sulfoxide. Methionine sulfoxide, by regulating m6A modification, inhibits the TGF-β signaling pathway, resulting in suppressing GBM progression. This study proposes a genotype-based hypothesis for the therapeutic effects of IF on tumors, and elucidates the potential RNA modification-related molecular mechanisms underlying the effective suppression of GBM by IF.

Introduction
Glioblastoma (GBM) ranks among the most aggressive types of tumors, yet current treatment options are limited in effectiveness and costly, highlighting the urgent need for exploration into new therapeutic approaches1. Acknowledging the heterogeneity among glioma patients, treatment strategies should be informed by GBM classification. Presently, there are diverse classification methods for GBM, with the most straightforward and recognized approach involving categorization based on alterations in the CDKN2A and TP53 genes2. As we reported before, patients with alterations in CDKN2A or TP53 exhibit markedly distinct expression profiles, various clinical characteristics and responses for glioma treatments2. Alterations in CDKN2A are mutually exclusive to those in TP53. Approximately 65% of GBM patients are identified as the CDKN2A subtype, while the other 35% of them are categorized under the TP53 subtype2.

Intermittent fasting (IF) represents an ancient and cost-effective approach to human health3–5. Recent studies have revealed that IF not only promotes health but also holds promise as a cancer treatment6. Animal experiments have demonstrated significant improvements in the prognosis of GBM mice following IF7. Furthermore, clinical studies have indicated that IF notably enhances several favorable prognostic indicators for different cancer3,8–11. Analysis of patient data from The Cancer Genome Atlas (TCGA)12 suggested that dietary restriction (DR)-related therapeutic targets hold significant potential for treating GBM13. Nonetheless, IF was reported exerting a limited inhibitory effect on tumors10. Therefore, we hypothesized that the effects of IF may vary among different GBM patients, considering the heterogeneity of GBM. Here, our study found that the effect of IF on Tp53 GBM mice model was more significant than Cdkn2a GBM model. Thus, this study will primarily investigate the molecular mechanisms underlying the beneficial effects of IF on specific GBM subtypes.

Recent studies have suggested that IF can modulate or reshape the gut microbiota, resulting in modulation of various microbiota metabolites, with implications for brain function. For instance, it was reported that IF can elevate the level of indole-3-propionic acid (IPA)14. IPA can traverse the blood-brain barrier and augment the regenerative and recuperative abilities of neurons15. Furthermore, IF downregulates methionine16, an essential amino acid implicated in tumor promotion17. Methionine is also a recognized substrate for N6-methyladenosine (m6A), which can promote the expression of the m6A methyltransferase METTL318. Being the most prevalent RNA modification, m6A can influence a wide array of RNA-related processes, including degradation, splicing, and translation, thereby exerting an impact on diseases such as cancer19. Suppression of METTL3 expression to lower m6A levels can inhibit the activity of several oncogenes, including those involved in GBM development20,21.

In this work, we construct a mouse model of GBM stratified by Tp53 and Cdkn2a genotypes, simulating the corresponding TP53 and CDKN2A subtypes of human GBM. We observe a significantly enhanced inhibitory effect on GBM of IF on Tp53 models compared to Cdkn2a mouse models. Subsequently, we perform single-cell RNA sequencing (scRNA-seq), bulk RNA sequencing (RNA-seq), spatial transcriptomics and spatial metabolomics on Tp53 GBM mice before and after IF to assess the impact of IF on the global metabolic and transcriptional regulation of GBM. Additionally, we conduct metagenomic analysis of the gut microbiome and metabolomic analysis of blood samples on Tp53 and Cdkn2a GBM mice to identify alterations in metabolites and gut microbes. Through multi-omics sequencing and molecular experiments, we propose that IF reshapes the gut microbiota in Tp53 GBM mice, subsequently leading to a decrease in methionine metabolism in part of the gut microbiota, which further regulates methionine and m6A levels in the GBM. The downregulation of key cancer pathways associated with m6A lead to improved prognosis of GBM. Finally, we validate our conclusion through multiple rescue experiments in GBM mice. In summary, this study delineates the comprehensive effects of IF on the GBM using multi-omics data, and proposes a mechanism by which IF influences TP53 subtype GBM through gut microbiota metabolite methionine, impacting m6A regulation.

Results
IF targets METTL3 to modulate TGF-β signalisng pathway and improve outcomes in TP53-driven glioma
Utilizing a computational approach to quantify DR-related molecular activities13, previous analysis of patient data from TCGA indicates that DR-related therapeutic targets hold significant potential for the treatment of GBM. We applied this method separately to CDKN2A subtype and TP53 subtype GBM, and observed that TP53 GBM patients exhibit higher DR scores compared to CDKN2A group patients, indicating that TP53 group GBM are more susceptible to the effects of DR (Fig. 1a). Then, we developed Cdkn2a and Tp53 GBM mouse models, respectively recapitulating the human CDKN2A subtype and TP53 subtype (See the “Methods” section for details). For Cdkn2a and Tp53 GBM mouse models, we randomly divided the mice into IF and ad libitum (AL) diet groups for different intervention (Fig. 1b). Experimental findings indicated that IF did not confer benefits to Cdkn2a GBM mice (Fig. 1c). Conversely, mice with Tp53 GBM in the IF group exhibited a markedly improved prognosis compared to those in the AL group (Fig. 1d), consistent with the conclusion of previous studies that IF contributes to the prognosis of GBM7. The results of hematoxylin and eosin (H&E) staining indicated that in Tp53 GBM model, there was a downward trend in tumor size, but a lack thereof in the Cdkn2a GBM model (Fig. 1e, f and Supplementary Fig. 1).

Subsequently, differentially expressed genes (DEGs) analysis of bulk transcriptomics on Tp53 GBM mice revealed 1443 up-regulated genes and 1952 down-regulated genes in the Tp53-IF group, compared to the Tp53-AL group (Fig. 1g). Notably, Mettl3, a well-known RNA-binding protein that regulates RNA methylation, was one of the down-regulated genes (Fig. 1g). Moreover, KEGG analysis revealed that these DEGs were enriched in several tumor-related pathways, such as the TGF-β signaling pathway, MAPK signaling pathway, and proteoglycans in cancer, among others (Fig. 1h). Notably TGF-β is widely acknowledged as a core pathway in tumor progression, with studies demonstrating that inhibition of TGFB2 can suppress GBM22. Enrichment analysis of differential gene sets between CDKN2A and TP53 GBM patients, revealed that the TGF-β signaling pathway was significantly altered between CDKN2A and TP53 groups, suggesting differential activation of TGF-β signaling in patients with Tp53 versus Cdkn2a subtypes (Supplementary Fig. 2a, b). And Tgfb2 is core regulator in TGF-β pathway situated upstream in cancer23 (Supplementary Fig. 3b). Furthermore, differential analysis between Tp53-IF and Cdkn2a-IF groups revealed a significant decrease of Mettl3 in Tp53-IF group (Supplementary Fig. 3c). Enrichment analysis further demonstrated significant involvement of the TGF-β signaling pathway among the DEGs (Supplementary Fig. 3d, e), suggesting that Cdkn2a and Tp53 GBM model respond differently to the METTL3-mediated TGF-β signaling pathway.

Moreover, we analyzed human transcriptomic data from 41 individuals following IF and controls (GEO DataSets: GSE196335)24. We observed significant enrichment of DEGs related to tumor pathways, notably the TGF-β pathway (Supplementary Fig. 3f). Subsequently, molecular functional analysis of gene ontology (GO) of DEGs revealed significant enrichment in protein binding, nucleotide binding, and RNA binding (Fig. 1i). Additionally, given previous reports indicating that inhibiting METTL3 can suppress GBM25. Network analysis revealed METTL3 as a key hub gene among these DEGs (Supplementary Fig. 3g), suggesting that IF induces alterations in pivotal pathways like METTL3 and TGF-β at the population level.

Further investigation of the Chinese Glioma Genome Atlas database26 unveiled a notable correlation between the expression of TGFB2 and METTL3 (Fig. 1j)26. Inhibiting METTL3 led to the downregulation of TGFB2 (Fig. 1k, Supplementary Fig. 3h–j), indicating that downregulation of METTL3 inhibits TGFB2 expression. Furthermore, it was reported that m6A modification of TGFB2 contributes to GBM pathogenesis25, and TGF-β signaling pathway regulating by METTL3 is associated with tumor development27. Our findings suggest that IF regulates RNA binding protein METTL3, which may serve as crucial downstream regulatory mechanisms of IF.

The interplay between m6A and transcriptomic landscapes modulated by IF in GBM from single-cell and bulk analyses
METTL3 generally regulates downstream genes like TGFB2 through m6A27. Therefore, we performed m6A-seq on GBM tissues from both the Tp53-AL and Tp53-IF groups of GBM mice, and found slightly different m6A motifs between the Tp53-AL group and the Tp53-IF group (Fig. 2a). Our analysis also revealed a decreased number of m6A modifications in the Tp53-IF group (Fig. 2b), suggesting that Tp53-IF may inhibit m6A modification. Furthermore, these modification sites were typically found in regions related to gene expression regulation (Fig. 2c). Additionally, a notable reduction in m6A modification on Tgfb2 was observed in the Tp53-IF group compared to the Tp53-AL group, as depicted in the tracks (Fig. 2d). We found that these significantly altered m6A sites are enriched with the Mettl3 binding motif28, further confirming the reported role of Mettl3 in regulating Tgfb2 and downstream genes (Fig. 2d). Even though the association between the METTL3-TGF-β pathway and tumor development has been previously reported27, our findings provide further evidence that Mettl3 may bind to Tgfb2 and that IF induces a decrease in m6A abundance on Tgfb2.

To precisely determine the single-cell level atlas under the influence of IF, we conducted scRNA-seq on GBM tissues from Tp53 GBM mice in both the AL and IF groups. We visualized 64,918 cells using Uniform Manifold Approximation and Projection (UMAP) and identified 37 clusters (Supplementary Fig. 4a). Subsequently, we identified 20 major cell populations in the GBM tissues of GBM mice from both the Tp53-AL and Tp53-IF groups (Fig. 2e, f). The clusters were manually annotated with their respective cellular identities using highly expressed genes and cell-specific markers from each cluster (Fig. 2g, Supplementary Fig. 4b, Supplementary Table 1, methods). According to single-cell transcriptomic profiling of the whole mouse brain29, the cell populations can be divided into excitatory neurons, inhibitory neurons, medium spiny neurons, tumor cells, oligodendrocytes, endothelial cells, microglia and macrophages. Excitatory neurons included ExN−L2/3−IT, ExN−L5−IT, ExN−L5−ET, ExN−Olf, ExN−L6−CT, and ExN−L5/6−NP; inhibitory neurons included InN−Chat, InN−Sst, InN−Syt6, InN−Olf, InN−Adarb2, and InN−Lhx6; medium spiny neurons included MSN−D1 and MSN−D2; tumor cells included OPC−like, AC−like, and MES−like (Fig. 2h).

Pseudotime analysis indicated that AL- and IF-treated tumor cells were distributed across the two branches of the trajectory (Fig. 2i, Supplementary Fig. 4c). Moreover, IF-treated tumor cells in elderly individuals were projected at the end of one branch along the developmental trajectory, suggesting that these cells were in the terminal differentiated state in elderly individuals (Fig. 2i). Additionally, expression patterns of Mettl3 and Tgfb2 in tumor cells were observed gradually changing from AL to IF (Fig. 2i). This suggested again that IF was associated with the downregulation of Mettl3 and Tgfb2 expression in Tp53 GBM tumor cells. Employing our single-cell m6A (scm6A) detection method30, we assessed the m6A abundance in single cell level of two groups (Fig. 2j). Our findings revealed a decrease in m6A abundance in cells from the Tp53-IF group. (Fig. 2k). Once again, we elucidated the regulatory profile of m6A and TGF-β signaling pathway at the single-cell level.

IF regulates methionine metabolism influencing m6A-regulated genes in brain regional heterogeneity using spatial omics data
It is well known that fasting primarily affects metabolites. Herein, we conducted metabolomic analysis of blood samples from Tp53 GBM mice of both the AL and IF groups. We found distinct metabolic profiles between the Tp53-IF and Tp53-AL groups (Supplementary Fig. 5a), with the majority of metabolites showing significant downregulation in the Tp53-IF group compared to the Tp53-AL groups (Fig. 3a), notably methionine and S-adenosylmethionine (SAM) (Fig. 3b). Furthermore, differential metabolites could be enriched in methionine metabolism-related pathways (Fig. 3c). Methionine and SAM serve as the substrate for m6A, thus the reduction in methionine and SAM caused by IF results in decreased m6A levels18,31. Consequently, this SAM-mediated m6A modification downregulates the TGF-β signaling pathway, thereby inhibiting GBM. Additionally, supplementation with methionine has been reported to raise SAM levels in various regions of the central nervous system32. These results indicated that the reduction in methionine caused by IF can inhibit the TGF-β pathway through the regulation of SAM-m6A axis.

To further investigate the correlation between gene expression and metabolism in different spatial regions of the whole brain, we conducted spatial metabolomic and spatial transcriptomic sequencing on samples from Tp53 GBM models treated with AL and IF. The Tp53-IF group and the Tp53-AL group exhibited distinct spatial metabolic profiles across the whole brain (Supplementary Fig. 5b, c), and within the same sample, different brain regions, including tumor sites, also displayed distinct metabolic characteristics (Fig. 3d). In Fig. 3e, the results showed that the tumor region in the Tp53-AL group was enriched with SAM, while the tumor region in the Tp53-IF group has lower SAM content. Additionally, spatial transcriptomic data also show higher expression levels of Mettl3 and Tgfb2 in the tumor region of the Tp53-AL group compared to the tumor region of the Tp53-IF group (Fig. 3f, Supplementary Fig. 5d). Furthermore, the Tp53 GBM tissues from the IF and AL groups exhibit distinct spatial transcriptomic characteristics across different regions (Supplementary Fig. 5e). We conducted an integrated analysis of spatial metabolomic data and spatial transcriptomic data to investigate the differences between the Tp53-IF group and the Tp53-AL group (Fig. 3g). According to differential gene expression features in different regions, the differential genes in spatial transcriptomic data were divided into 24 clusters, enriched in different brain regions (Fig. 3g, h). Similarly, based on the differential metabolite features between different regions of Tp53-AL and Tp53-IF, all differential metabolites were clustered into 17 clusters, with each cluster enriched in different regions possessing distinct differential metabolite characteristics (Fig. 3g, i). We found significant difference of enrichment of SAM, Mettl3, and Tgfb2 in the tumor region (Fig. 3g), indicating that the SAM-METTL3-TGF-β pathway plays an important role in IF.

The gut microbiota, modulated by IF, regulates methionine levels to regulate m6A modification
Given that the overall food intake during our breeding was essentially the same, with the only distinction being the feeding pattern, we posit that this metabolic disparity is not attributed to dietary intake but rather to other factors. Methionine is an essential amino acid that cannot be synthesized in the body; however, it can be extensively produced by intestinal bacteria33,34. Thus, we conducted metagenomic analysis of feces from Tp53 and Cdkn2a GBM mice of both AL and IF groups, aiming to explore why IF would cause a decrease of methionine levels. We found significant differences in the gut microbiota between the IF and AL groups of Tp53 and Cdkn2a GBM mice (Fig. 4a, Supplementary Fig. 6a). To be specific, we found that bacteroidota (phylum level) and bacteroidia (class level) seemed to be more abundant in the AL group at the levels of phylum and class (Fig. 4b, c, Supplementary Fig. 6b, c). Then, we applied linear discriminant analysis (LDA) effect size (LEfSe) method to explore the differences in bacterial taxa composition between IF and AL group of Tp53 and Cdkn2a GBM mice. Our results showed that Tp53-IF mice had a decreased abundance of g.Alistipes, s.Prevotella sp. CAG:873, etc. (Fig. 4d), while families Rikenellaceae and Eggerthellaceae were identified as major contributors to the gut microbiota in the Tp53-AL group compared to the Tp53-IF group (Supplementary Fig. 6d). In addition, LDA of Cdkn2a-AL group and Cdkn2a-IF also revealed that s__Alistipes ihumii was significantly abundance in AL (Supplementary Fig. 6e). It is worth noting that many bacteria involved in methionine synthesis are at the genus level of Alistipes35, which belong to f. Rikenellaceae, such as ihumii, montrealensis, communis, senegalensis. And Prevotella sp. CAG:873 also has the function of synthesizing methionine36 (https://www.uniprot.org/uniprotkb/R7J0K0/entry). We conducted differential analysis of gut microbiota abundant between IF and AL groups on Tp53 GBM mice and used MetOrigin37 to annotate these various microbial metabolites and their metabolic functions. We found that the microbiota related to methionine synthesis were all down regulated in Tp53-IF group (Fig. 4e). Furthermore, KEGG enrichment analysis suggested that the significantly differential metabolites were mainly involved in the cancer related pathways, Ras signaling pathway, glioma pathway and Cysteine and methionine metabolism (Fig. 4f). We also annotated the differentially represented metagenomic data using eggNOG and identified significant associations with methionine metabolism (Supplementary Fig. 6f). We conducted a correlation analysis between the abundance of microbial communities, which associated with methionine synthesis, and the abundance of differentially present metabolites across all samples. This analysis revealed significant correlations between many differential metabolites and microbial communities, including methionine (Fig. 4g, h). The relative abundances for methionine-producing bacteria were tracked across multiple time points in both Cdkn2a-IF and Tp53-IF groups. The results reveal dynamic temporal changes and further support the association between these taxa and IF response in Tp53-mutant GBM (Supplementary Fig. 7). Overall, the downregulation of methionine is explained to be regulated by the gut microbiota restructured by IF, as previous studies have shown that IF can decrease methionine levels through microbiota16.

Validation of IF’s GBM inhibition via microbiota-methionine-m6A-Tgfb2 axis
To further validate our conclusions, we injected mice intracranially with shRAS/shp53 and dual luciferase-GFP expression vectors. These mice consistently developed large, invasive tumor masses, which were confirmed through bioluminescence imaging (Fig. 5a). The Tp53 GBM mice were divided into nine groups: the Tp53-AL group, the Tp53-IF group, the Tp53-AL group treated with STM2457 (METTL3 inhibitor) (Tp53-STM2457-AL), the Tp53-AL group subjected to fecal microbiota transplant from IF mice (Tp53-FMT-AL), the Tp53-IF group with Mettl3 gene over-expression (Tp53-M3OE-AL), the Tp53-IF group with enhanced methionine intake (Tp53-MET-IF), and the Tp53-IF group injected with entacapone (Tp53-ENT-IF), an FTO inhibitor aimed at enhancing m6A modifications, the Tp53-AL group with broad-spectrum antibiotics (Tp53-ABX-AL), the Tp53-IF group with broad-spectrum antibiotics (Tp53-ABX-IF). The Cdkn2a GBM mice were divided into six groups: the Cdkn2a-AL group, the Cdkn2a-IF group, the Cdkn2a-AL group treated with STM2457 (Cdkn2a-STM2457-AL), the Cdkn2a-AL group subjected to fecal microbiota transplant from Cdkn2a-IF mice (Cdkn2a-FMT-AL), the Cdkn2a-AL group with broad-spectrum antibiotics (Cdkn2a-ABX-AL), the Cdkn2a-IF group with broad-spectrum antibiotics (Cdkn2a-ABX-IF). The results demonstrated that both IF, METTL3 inhibition even AL (Tp53-STM2457-AL), and the alteration of microbiota associated with IF (Tp53-FMT-AL) in Tp53 GBM mice groups slowed tumor growth (Fig. 5a–c) and significantly improved prognosis (Fig. 5d), but no significance in microbiota-inhibition groups (Tp53-ABX-AL and Tp53-ABX-IF) and IF-non-response groups (Cdkn2a-) (Supplementary Figs. 8a–e, h–k, 9c–e), indicating that IF inhibits Tp53 subtype GBM progression via the gut microbiota and METTL3. Concurrently, we observed significant reductions in the abundance of bacteria involved in methionine synthesis (Supplementary Fig. 8f), SAM and methionine levels in feces and serum (Fig. 5e–h), m6A levels (Fig. 5i), and protein levels of TGFB2 and METTL3 (Fig. 5j–l). These results further validated that IF can downregulate methionine, m6A, and TGFB2.

However, augmenting methionine intake or increasing m6A levels in mice from the Tp53-IF group led to tumor sizes and prognosis comparable to those of the Tp53-AL group (Fig. 5a–d), suggesting the inhibitory effects of IF can be reversed by replenishing methionine or m6A levels, further indicating that methionine and m6A are the principal mechanisms through which IF influences GBM. The abundance of bacterium ihumii in the Tp53-IF group, Tp53-FMT-AL group, Tp53-MET-IF group, and Tp53-ENT-IF group significantly decreased compared to the Tp53-AL group, indicating that the abundance of bacterium ihumii is regulated by IF (Fig. 5d). Moreover, we further discovered that the Tp53-IF Group, Tp53-FMT-AL group, Tp53-MET-IF group, and Tp53-ENT-IF group exhibited analogous overall gut microbiota profiles (Supplementary Fig. 8g). Meanwhile, the abundance of SAM and methionine was regulated by IF and IF fecal microbiota in both Tp53 and Cdkn2a subtype GBM (Fig. 5e–h, Supplementary Fig. 8h–k), suggesting that IF-regulated gut microbiota suppresses methionine. The decrease in m6A levels caused by IF or IF fecal microbiota suppression in Tp53 GBM mice, can be restored by methionine and FTO inhibitors (Fig. 5i). But in microbiota-inhibition groups (Tp53-ABX-AL and Tp53-ABX-IF) and IF-non-response groups (Cdkn2a-AL and Cdkn2a-IF), there was no significance for m6A levels (Supplementary Fig. 9a, b). These results further highlighting the reliability of the IF-gut microbiota-methionine-m6A signaling axis. Additionally, Western blot results demonstrated that the m6A suppressed by IF or FMT ultimately regulated the protein level of TGFB2 and the entire TGF-β signaling pathway (Fig. 5j–m). Overall, the results indicated that the biological effect of IF depends on m6A, which is the downstream of the gut microbiota-methionine pathway.

Methods
This study protocol, including the use of mice, was reviewed and approved by the Institutional Review Board of Guangxi Medical University (Ethics: 202307007).

RNA-sequencing analysis
Fresh tissue was processed using Qiagen RNALipid Tissue Kit. Quality of the RNA was determined using TapeStation. The sequencing strategy was paired-end 150 bp for NovaSeq 6000. The raw sequence reads have been stored at Genome Sequence Archive (GSA: https://bigd.big.ac.cn/gsub/) (Accession Number: CRA015912). We used FastQC package (https://www.bioinformatics.babraham.ac.uk/projects/fastqc/) (v0.11.9) to perform the sequencing quality control, and reads were aligned to the mouse reference genome (mm10) using Hisat249 (v2.2.1) with default parameters. Then, StringTie50 (v1.3.3b) was used for assembly and quantification of counts matrix and TPM (transcripts per kilobase of exon model per million mapped reads) of each annotated gene.

We used DESeq251 (v1.42.0) to detect significantly DEGs, with a threshold of |log fold change (FC)|>1.2 and a Benjamini-Hochberg corrected P<0.01. Subsequently, we utilized DAVID52 (https://david.ncifcrf.gov/) for KEGG enrichment analysis to identify significantly enriched pathways.

The population RNA-seq data of the AL and IF groups were obtained from Gene Expression Omnibus (GEO: https://www.ncbi.nlm.nih.gov/geo/) (Accession Number: GSE196335)24. We identified DEGs and performed Gene Set Enrichment Analysis (GSEA) by Hallmark database and Gene Ontology (GO) enrichment analysis by DAVID database. Then, DEGs were used to construct PPI network through GeneMANIA web tools53,54, and all parameters were set to default values.

m6A-sequencing analysis
Likewise, m6A-sequencing was performed on two groups of Tp53 GBM mice tissues, and the raw data were available through GSA (Accession Number: CRA015927). The reads were mapped to mouse reference genome (mm10) using Hisat249 (v2.2.1). The resulting BAM files were used for peak calling and differential Peak analysis with the R package exomePeak255 (v1.14.3). In order to explore the characteristics of m6A, motif enrichment analysis was conducted using HOMER software56, by RNA backgrounds with randomly permutated sequences (HOMER parameter: line=1000, size=200). Then, distributions of m6A peaks were plotted by the R package Guitar (v2.18.0), using mm10 as referenced sequence57, and all parameters were default. We employed bamCoverage to generate coverage tracks in bigWig format, using mm10 as the reference genome. We then analyzed Tgfb2 read coverage and Mettl3 binding motifs in m6A-seq data from Tp53-AL and Tp53-IF groups, using IGV58 (v2.8.13) as the visualization tool.

Single-cell transcriptome analysis
We performed single-cell RNA sequencing (scRNA-seq) of Tp53 GBM mice tissue of AL and IF groups, with raw data available through GSA (Accession Number: CRA016372). Data alignment and gene quantification were conducted using the official 10X Genomics CellRanger (v5.0.0) platform after sequencing. Based on the quantification results provided by CellRanger, a secondary screening and cell quality control were performed using Seurat59 (v5.0.2), adhering to the following criteria: (1) nCounts < 50,000; (2) nFeature > 500 and nFeature < 7000; (3) Mitochondrial gene proportion < 1%; (4) Red-blood-cell gene proportion < 1%. Then, the selected cells were processed by batch effect inspection, normalization and clustering (nFeature: 3000, dims: 1/20, resolution: 0.5, other parameters were default). UMAP60 were used to visualize. And, characteristic genes were used to define each cell type (Supplementary Table 1).

Monocle2 (v2.24.0) was utilized for pseudotime-based ordering of tumor cells, followed by visualization using the UMAP method. Subsequently, estimation of cell size factors and dispersions was conducted. Utilizing the top 10 DEGs, the cellular trajectory was delineated, employing the setOrderingFilter function for standardization. After that, dimensionality reduction was performed using the DDRTree method. In addition, cells were ordered and depicted along a pseudotime trajectory using the orderCells function to accurately represent the developmental dynamics of the cells. Finally, we used scm6A30 (v1.0) with default parameters to predict m6A abundance at the single-cell level for all cells and conducted statistical analysis on cells in the AL and IF groups using T-tests.

Spatial transcriptomics analysis
The tissues were obtained from Tp53 GBM mice after resection, fixed by formalin, and embedded in paraffin. After RNA quality control (DV200 > 30%), 5-micron thick sections were mounted onto Visium Gene Expression slides. The samples were stained with H&E and stained slides were scanned under a microscope, followed by decrosslinking using 0.1 N HCl and TE Buffer (pH 9.0) to release RNA. Three pairs of specific probes (5′ containing Small RNA Read 2S and 3′ containing poly-A) were used for probe hybridization overnight. After hybridization, the cDNA libraries were constructed according to the 10X protocol, and sequencing was performed on NovaSeq 6000 (Illumina). Raw imaging data were processed using SCiLS Lab software, applying smoothing and RMS normalization to adjust peak intensities66. A average mass spectra were derived from selected tissue regions, plotting m/z ratios against the RMS-normalized average peak intensities. Prominent peaks were subjected to MS/MS analysis, and the resulting spectra were matched against databases for identification. Low-intensity peaks were searched in databases within a 10 ppm mass accuracy. Utilizing the data gleaned from substance identification, we engaged in spatially-aware nearest shrunk centroids clustering to delineate regional information based on all targeted peaks67,68. The spatial mapping of identified peaks was executed to visualize the distribution of substances across the tissue microenvironment. Prior to clustering, the dataset underwent z-score normalization to standardize inter-sample variability. Subsequently, a hierarchical clustering heatmap analysis was conducted on the entire sample collection, employing the ComplexHeatmap (v2.20.0) application for sophisticated visualization of the metabolite distribution patterns69. Data were available in OMIX database (Accession Number: OMIX012365).

Metabolomics analysis
For each group of Tp53 and Cdkn2a GBM mice, we selected blood and feces samples for metabolomic sequencing. Fecal samples were collected at multiple time points, including before the initiation of fasting, during fasting (15 days), and at the terminal stage (30 days) of the experiment. In contrast, brain tissues and blood samples were harvested only at the terminal stage. After homogenizing and incubation on ice, the samples were centrifuged and injected into the LC-MS/MS system for analysis62. The mobile phases consisted of eluent, with a gradient profile optimized for comprehensive metabolite coverage. The mass spectrometer was operated in both positive and negative polarity modes.

Raw data were available in MassIVE database (Accession Number: MSV000100119), and were processed using Compound Discoverer 3.3 (Thermo Fisher Scientific) for peak alignment, picking, and quantitation. Data were normalized against total spectral intensity. Molecular formulas were predicted, and peaks were matched against databases like mzCloud and mzVault for qualitative and quantitative results. Then, these metabolites were annotated using the KEGG, HMDB, and LIPIDMaps databases. Principal Component Analysis and Partial Least Squares Discriminant Analysis (PLS-DA) were conducted using metaX63. Univariate analysis (t-test) was employed to calculate statistical significance (P values). Metabolites with variable importance in prediction (VIP) > 1, P < 0.05 and |log FC| > 1 were considered differentially expressed metabolites64. For cluster heatmaps, data were standardized by the z-scores of the intensity areas of differential metabolites and plotted using the pheatmap (v1.0.12) package in R (v4.3.2). And, KEGG enrichment analysis for metabolites was performed by the MetaboAnalyst 6.0 online web65 (https://www.metaboanalyst.ca/).

Spatial metabolomics analysis
Tissues on Tp53 GBM mice were cryosectioned at 10µm thickness. Then, sections were transferred to prechilled ITO slides and warmed until transparent, followed by drying using friction on the slide’s reverse. 2,5-dihydroxybenzoic acid (DHB) matrix was sprayed onto the tissue sections using a TM-Sprayer. ITO slides were mounted on a mass spectrometer target plate, and tissue regions were outlined using DataImaging software. The tissue was scanned by a laser to induce ionization, and the released molecules were analyzed to obtain mass-to-charge ratio (m/z) and peak intensity data. Raw data were processed in SCiLS Lab software for smoothing and Root Mean Square (RMS) normalization, generating relative intensity values for m/z ratios, which were then visualized as pixels on an imaging heatmap.

Metagenomic sequencing and analysis
The feces of the mice were collected for subsequent testing. We constructed the DNA libraries by fragmenting DNA, end repair, adapter ligation, PCR amplification, and purification. Sequencing on the Illumina platform generated raw data, which was cleaned by deleting adapter contamination, low-quality bases (Q < 20) and removing human host DNA using the Bowtie2 software70,71. And, raw data was available in GSA (Accession Number: CRA015939). MEGAHIT software was used for assembly analysis of Clean Data72, then the clean data was aligned to the initial gene catalog by using Bowtie2 to calculate the number of reads of the genes on each sample alignment. Gene profiles were derived by mapping high-quality sequencing reads to a reference gene catalog. The relative abundance profiles of microbial communities were then inferred from the gene-level relative abundances.

The dissimilarities between two groups were analyzed by the nonredundant genes, and visually represented by the Bray-Curtis distances based principal coordinate analysis (PCoA). Metastats and LEfSe analysis73 (LDA Score is 4 by default) were conducted to identify species-level differences between groups. To elucidate the function of differential species, we performed functional enrichment analysis by KEGG74,75 (https://www.genome.jp/kegg/) and eggNOG76 (http://eggnog5.embl.de/#/app/home) databases. Subsequently, we identified 29 bacterial groups that synthesized methionine, and Pearson correlation analysis was used to calculate the correlation between bacterial groups and differential metabolites to further explore the function of bacterial groups.

Lentivirus construction
The study employed the methodology by utilizing lentiviruses carrying HrasG12V, sh-p53, or sh-cdkn2a/sh-cdkn2b to induce a GBM model2. To be specific, the lentiviral vector used to induce mouse GBM contained an HRASV12 fragment along with either a shp53 or shCDKN2A and shCDKN2B fragment. The co-expression of HRASV12 and luciferase was facilitated by Internal Ribosome Entry Site (IRES). The lentiviral vector is co-transfected with packaging plasmids pCMVΔ8.9 and pMD2.G into human embryonic kidney 293T cells. The supernatant containing lentiviral particles is collected and centrifuged at 4°C and 130,000 × g for 2.5 h. After centrifugation, the lentiviral particles are resuspended in phosphate-buffered saline containing 0.1% bovine serum albumin. Finally, the lentiviral particles are quantified using qPCR. All shRNA target sequences used in this study have been provided in Supplementary Table 2. These sequences were synthesized by BGI Genomics Co., Ltd. All plasmids, oligonucleotides, and viral vectors newly generated in this study are available from the corresponding author.

Animal models
The 7–8-week-old female C57BL/6J used in this study were sourced from Gempharmatech Co., Ltd (Jiangsu, China). The prepared lentivirus, as described in lentivirus construction section, was stereotactically injected into the hippocampus of the mice using a stereotaxic instrument. In summary, the anesthetized mice were secured with ear bars and a nose clamp. A 0.8 mm cranial drill bit was employed to thin the skull, following which 2 µL of purified lentivirus particles (1×109/mL) were drawn into a 5 µL syringe and administered at a rate of 1 µL/min. The injection was performed using specific coordinates: 1.6 mm lateral and 1.2 mm anterior to the bregma, and 1.75 mm below the skull surface. Post-injection, the needle remained in place for 3 min before being gradually withdrawn over a period of 1 min2. The procedure was concluded with suturing of the injection site on the mouse’s head. All procedures conducted in this research were approved by the animal ethics committee of the Guangxi Medical University (202307007), and all mice were maintained under pathogen-free conditions. According to our approved protocol, mice were monitored daily, and were euthanized immediately if they exhibited neurological deficits, persistent lethargy, reduced mobility, seizures.

Animal treatment and group
Mice were housed in a specific pathogen–free facility under a 12-h light/12-h dark cycle, with controlled ambient temperature (22 ± 2°C) and humidity (50–60%). Animals had free access to food and water unless specified otherwise in the experimental design. All animals were fed a standard rodent chow diet (Nanning Yancheng Biotechnology Co., Ltd, YC-6680). The diet composition was provided by the manufacturer and appropriate for standard laboratory rodent maintenance.

The GBM-bearing mice (Tp53 or Cdkn2a) were randomly divided into several experimental groups: For the control group (AL), mice were given ad libitum diet. For the Intermittent Fasting (IF) group, the mice underwent a fasting regimen where their food was removed for a 24-h period (Supplementary Fig. 3a). The total food intake was maintained at the same level across the groups, and the mice in this group had free access to water throughout the fasting period. For Tp53- and Cdkn2a-STM2457-AL mice, STM2457 (TargtMol, T9060) was dissolved in 20% (w/v) 2-hydroxyproply β-cyclodextrin vehicle (Sigma, H107). And then, indicated doses77 of STM2457 or vehicle were delivered to the mice via intraperitoneal injection on day 10 after injection of lentivirus, once daily for total 2 weeks (14 treatments). For the FMT (Cdkn2a- and Tp53-FMT-AL) group, A broad-spectrum antibiotic cocktail (including neomycin, ampicillin, metronidazole: 200 mg/L, and vancomycin: 100 mg/L) was added to the drinking water of mice. After 2 weeks of treatment to deplete the microbiota, FMT was performed. The collected fresh feces from the IF group mice were then mixed with appropriate amounts of sterilized water, ground, and added to 32 ml of sterilized water. Then, the FMT mice began consuming water that was prepared from the fresh feces of the IF group mice. For the Mettl3 over-expression mice group, the lentivirus for mettl3 overexpression was injected along with the GBM-inducing lentivirus. For the Intermittent Fasting plus Methionine (Tp53-MET-AL) group, the mice underwent a fasting schedule as IF group. The water provided to these mice contained 400 mg/kg of DL-Methionine (Shanghai Wankang Biotechnology Co., Ltd). For the Intermittent Fasting plus entacapone (Cdkn2a-ENT-AL) group, the mice underwent a fasting regimen and received intraperitoneal injections of entacapone78, at a dosage of 10 mg/kg, administered once weekly.

Bioluminescence imaging
All tumor cells were transduced with a lentiviral construct coding for firefly luciferase. At the terminal stage of the experiment, Brain tumor size was monitored by in vivo luciferase assay, by intraperitoneal injection of 15 mg/ml of Beyotime D-Luciferin potassium salt, and detecting brain luminescence with PerkinElmer IVIS Lumina XRMS (PerkinElmer Corporation, USA). Following image acquisition, the radiance values (photons) were obtained from the selected ROI areas for quantification.

Western blotting
The cells were lysed with RIPA lysate and centrifuged at 16,000 × g for 15 min at 4°C. The supernatants were collected, and total protein content was measured using the BCA assay. The mice brain tissues were lysed with RIPA lysate (P0013) and then further disruption by using a homogenizer, finally centrifuged to collect the supernatants. Lysates were separated with 10% SDS-PAGE, transferred to membrane filters, and subjected to immunoblotting using anti-TGF-β2 (1:1000, Proteintech, Cat#19999-1-AP), anti-METTL3 (1:1000, Abcam, ab195352), anti-GAPDH (D16H11) XP® Rabbit mAb (1:1000, Cell Signaling, #5174), and anti-Goat anti-Rabbit IgG H&L (1:10000, Abcam, ab216773). Cell lines T98G (Serial: SCSP-5274) were obtained from the National Collection of Authenticated Cell Cultures (NCACC).

LC-MS/MS conditions
The levels of methionine and m6A levels were measured in the brain tissue of AL (n=5 samples) and intermittent fasting (n=5 samples) mice by liquid chromatograph-mass spectrometer (LC-MS). Pooled quality-control samples were intermittently injected to assess analytical stability. Approximately 100 mg of tissue was homogenized in liquid nitrogen, extracted with pre-chilled 80% methanol, vortexed, incubated on ice for 5 min, and centrifuged at 15,000 × g for 20 min at 4°C62. The supernatant was diluted to 53% methanol with LC-MS–grade water, re-centrifuged, and the clarified extract was used for LC-MS/MS measurement.

The LC-MS/MS analysis was performed using a Vanquish UHPLC system (Thermo Fisher, Germany) coupled with an Orbitrap Q ExactiveTM HF-X mass spectrometer (Thermo Fisher, Germany). Samples were injected onto a Hypersil Gold column (100 × 2.1 mm, 1.9 μm) using a 12-min linear gradient at a flow rate of 0.2 mL/min. The eluents for the positive and negative polarity modes were eluent A (0.1% FA in Water) and eluent B (Methanol). The solvent gradient was set as follows: 2% B, 1.5 min; 2–85% B, 3 min; 85-100% B, 10 min；100–2% B, 10.1 min；2% B, 12 min. Q ExactiveTM HF mass spectrometer was operated in positive/negative polarity mode with spray voltage of 3.5 kV, capillary temperature of 320°C, sheath gas flow rate of 35 psi and aux gas flow rate of 10 L/min, S-lens RF level of 60, Aux gas heater temperature of 350°C. The raw data files generated by LC-MS/MS, as analyzed in Metabolomics analysis section.

Hematoxylin and eosin (H&E) staining
Freshly dissected tissues were fixed in 4% paraformaldehyde, processed routinely, and embedded in paraffin. Sections were cut at a thickness of 4–5 μm. Deparaffinized and rehydrated sections were stained with hematoxylin, differentiated, and blued following standard protocols. Subsequently, sections were counterstained with eosin, dehydrated, cleared, and mounted with a synthetic resin.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "The manuscript by Lin, Meng et al. proposes a pathway whereby intermittent fasting inhibits TP53-driven but not Cdkn2a-driven gliomas through gut microbiome mediated Methionine-m6A regulation. If proven, this pathway would enhance the understanding how the gastrointestinal tract contributes to antitumor effects of intermittent fasting. The manuscript contains many phenotypic and correlation analyses, however, the mechanisms of under the phenotypes remain largely unconfirmed.\nMajor comments:\n1.In Figure 1g, the authors compared the AL and IF groups, but the response of IF was different in the TP53 and Cdkn2a groups, so the authors should compare the differential genes of IF and AL in the Tp53 mutant group. And in the whole manuscript is a comparison of AL and IF groups, it is unknown whether the author made the comparison in the IF response group (Tp53) and IF-non-response (Cdkn2a).\n2.In Figure 1k, the authors observed down-regulation of TGF-β2 by using the inhibitor STM2457, but it was not known whether STM2457 would mimic the antitumor effects of IF in TP53 group, and Cdkn2a should also be added as the control group.\n3.In Figure 4, the authors should compare the differences in gut microbiota between IF-treated Cdkn2a and Tp53 GBM mice, and whether the gut microbiota of mice in CDKN2a group was unchanged. In the fecal microbiota transplantation experiment from IF mice, Cdkn2a mice treated with IF should also be added as the control group.\n4.To confirm that intermittent fasting is microbiome-dependent experiments in ABX-treated and germ-free setting should be perform.\n5.Additionally, please provide data on whether methionine is decreased in the tumor and fecal after IF-treated in Cdkn2a and Tp53 groups.\n6.16S rRNA seq has been performed in a cross-sectional study (same time, at the end of the experiments) with several biases (cag effect and tumor sizes). It is important to perform a longitudinal study of the taxonomic microbial shifts in a paired manner, and compare the effects of various diets at each time points and overtime.\n7.The reference “Methionine deficiency facilitates antitumour immunity by altering m6A methylation of immune checkpoint transcripts” (The authors did not cited this reference) had demonstrated Methionine deficiency facilitates antitumour immunity by altering m6A methylation, and the mechanism is explained in more detail than this manuscript. So in this manuscript, the authors should address the mechanism in how IF-specific inhibition of TP53-mutated gliomas and the gut microbiota alter methionine methylation in brain.\nMinor comments:\n1.On Line 186, “AL and IF tumor cells…..” should be “AL- and IF-treated tumor cells…..”.\n2.In Figure 2k, the authors should use scatter plots and label n values.\n3.On Line 221, “Figure 3e showed……” should be revised as “ In Figure 3e, the results showed that ……:.\n4.On Line 273, the sentence “including methionine (Figure 5g, 5h)…..” should be verified.", "reviewer_2": "1. Limited novelty: The connection between METTL3 and Glioblastoma (GBM) has been extensively reported in multiple studies (Oncogene. 2018;37:522-533; Cancer Lett. 2021;511:36-46; Genes. 2019;10:141; Front. Oncol. 2021;11:702983; Cancer Res. 2019;79:5785–5798; Genome Biol. 2021;22:51.).\n2. The selection of METTL3 and TGFB signaling as the key downstream targets appears to be cherry-picked, as the authors did not provide a strong rationale for their choice.\n3. This study does not explore the contribution of METTL3 to TGFB2, and in Figure 1k, the METTL3 inhibitor STM2475 fails to repress TGFb2.\n4. The authors should further explore whether the METTL3 inhibitor STM2475 or TGF-beta inhibitor would achieve a similar protective effect against GBM as intermittent fasting (IF) treatment.\n5. Several key pieces of information are missing. For example, the sample size is missing in several figures. Regarding the FMT model, it is unclear whether antibiotics were administered to the mice before the FMT.\n6. Figure 1e and 1f do not provide quantitative results of tumor size.\n7. For Figure 5, please also conduct hematoxylin and eosin (H&E) staining to show the changes in tumor size among different groups.\n8. It is not explained whether and how the findings of the METTL3-m6A-TGF-β signaling axis could explain the differential response of Cdkn2a and Tp53 GBM mouse models to IF treatment.\n9. More than 40 methyl transfers from S-adenosylmethionine (SAM) are known to occur to various substrates such as nucleic acids, proteins, lipids, and secondary metabolites. It is unclear why the authors focused specifically on m6A.\n10. In Figure 5h, the sample size for the western blot (n=1 per group) is insufficient to draw any meaningful results.\n11. In Figure 5i, it is unclear how the authors performed the Pearson correlations, given the very limited sample size (n=2 per group). Also, the authors should explain what kind of experiment was conducted.\n12. It is not unclear how IF treatment leads to a decrease in methionine-related gut microbial species.\n13. In Figure 5f, please explore the stool level and serum level of methionine and S-adenosylmethionine (SAM) by targeted metabolomics.\n14. The authors should incorporate germ-free or antibiotic-treated mice models to provide additional evidence supporting the protective effect of IF treatment and its reliance on alterations in the gut microbiome.", "reviewer_3": "Reviewer Summary: In this manuscript, the authors examine the effect of intermittent fasting on the pathogenesis of GBM, stratified into groups driven by loss of TP53 and those with loss of CDKN2A. The authors found that IF only affected mice with tumors bearing TP53 deletion, which they found correlated with loss of methionine levels in tumor tissue. They found that this loss of methionine correlated to a loss of m6A, which they hypothesize leads to a reduction in TGFB signaling through METTL3.\nThe experiments performed in the study generally support the notion that intermittent fasting may regulate the gut microbiota and influence metabolism in orthotopic GBM in vivo. The methodology is sound and well described. The strengths of the manuscript are the mouse models used, the use of multiple omics platforms to measure gene- and metabolite-level data in tumor tissue, and rescue experiments to show the effects of methionine supplementation and entacapone in reversing the benefit of IF.\nThere are a few weaknesses in the manuscript. First, the mechanistic link between IF and METTL3’s effect on TGFB is unclear from these studies. The authors do not provide evidence for how IF specifically influences METTL3, other than stating that TGFB likely has a reduction in m6A in IF mice. Further experimentation would help strengthen the link between IF and TGFB’s effects on tumor growth in their models. Specifically, it would be useful to show the effects of METTL3 knockout in AL mice and METTL3 overexpression in IF mice. Second, while the authors show that FMT provides the same benefit as IF, they do not demonstrate that the microbiota (or lack of certain species) is necessary for the benefit of IF. This should be accomplished by treating AL mice with antibiotics to see if these mice have prolonged survival by limiting methionine-producing gut microbiota. Third, the authors do not measure the metabolic effects of IF in their mice – what happens to serum glucose, insulin levels, C-peptide, ketones – in serum of mice? Are mice adequately fasted? Is 24 hours fast required, or is less time of fasting (e.g. 18 hours) beneficial? This would be relevant to patients, where a 24-hour fast would be difficult to achieve. More typical fasting is overnight fast with first meal of the day being lunch. In addition, the methodology regarding sample harvest from mice is not described – when are samples harvested, at the end of IF before refeeding or somewhere in-between? These parameters should be specified in the methods for all samples taken from mice.\nUnique aspects: The use of intermittent fasting in genotype-stratified mouse models would be helpful in establishing targeted nutritional therapy in GBM\nStudy design: Basic translational investigation to identify the effects of IF in mouse models of GBM\nWriting style: The manuscript style is succinct and straight-forward, though there are a few typos that need correction.\nGeneral Considerations\nSpecific comments:\n1. In Figure 1E-F, only visual representation of H&E sections is shown. Quantification of tumor cells is not performed. If the authors wish to quantify tumor cells, then they should perform a proliferation stain like Ki67 and quantify that. Otherwise, the following sentence needs to be removed: The results of hematoxylin and eosin (H&E) staining indicated that in Cdkn2a GBM model, there were no significant changes in tumor size and tumor cell count before and after IF (Figure 1e). In Tp53 GBM model, however, tumor size significantly decreased in IF group compared to the AL group, with fewer tumor cells (Figure 1f).\n2. In line 134, the authors mention 41 individuals. They should clarify if they mean humans or mice. If the latter, they should say “individual mice.”\n3. In line 135, it is unclear what GSE196335 refers to. The authors should clarify.\n4. The authors should clarify why they focused on METTL3 and not any of the other significant hits from their transcriptomic screen.\n5. The western blot shown in Figure 1K is not convincing. The authors state that they inhibited METTL3, but do not mention the method of inhibition. In the figure, they mention a compound STM2457 and use it at 10 uM. They should confirm that this dose inhibits METTL3. Second, the authors should quantify the differences observed in the blot to show the effect of METTL3 on TGFB2 expression.\n6. In Figure 3D, it is apparent that there is tumor in the AL group, but in the IF image, there appears to be little to no tumor. How do the tumors know that the region selected in the IF group is tumor and not normal brain? Since tumors do not grow to a large extent in the IF group, the authors should allow tumors in the IF group to grow to a larger size and then randomize mice to AL or IF, which would allow better testing of metabolites in tumor tissue from both groups.\n7. In Figure 3, it is interesting to note that the authors report a reduction in all metabolites in the IF group. One would expect that certain metabolites and/or pathways, such as glucose uptake regulated by the AMPK pathway, might be increased under fasting conditions so the tumor cells can maintain growth. Did the authors see any metabolites enriched under IF conditions?\n8. In Figure 5C, the authors do not show survival for the IF group? What is their survival compared to the other groups?\n9. In Legend for Figure 5C, the authors state a difference between 5 groups, but there are only 4 groups in the figure. The authors should clarify this statement.\n10. When the authors mention methionine and m6A levels in Figure 5F and G, they should specify from where these metabolites are derived (e.g. tumor tissue, blood, etc.). In the methods they mention obtaining these metabolites from the tumor tissue, so they should reiterate that in the results section for this content.\n11. The authors do not seem to have measured methionine levels in the blood stream, which would be a confirmation that the differences in microbiota in IF versus AL groups corresponds to a difference in circulating methionine in mice.\n12. Figure 6 is not well schematized. There should be more representation of IF rather than an “X’ over the mouth, specific gut microbiota, and mention of METTL3.", "reviewer_4": "Lin et al. tried to link the genotype-specific tumor-inibiting effects of intermittent fasting (IF) on glioma development. However, a lack of experimental rigor, statistic testing, and key comparision across different genotypes makes their argument hardly convincing.\nThe experiments throughout lack a critical control group, Cdkn2a-mutant gliomas. IF should have similar effects on these mice, yet it is hard to explain why they are not nearly as responsive as Trp53-mutant mice.\nThroughout the manuscript, many statements are not backed up by quantification or do not provide the n value or the timepoints of sample collection/imaging for each experiment.The Methods/Figure legends lack critical experimental details.\nFig. 2k, the differences are very minor between the two groups.\nFig. 3d,e,f, the authors should show H&E images to clearly demarcate the tumor areas, and perform individual statistic testing to confirm differential expression of METTL3 and TGFB2 in tumor areas.\nFig. 4b,c, it is hard to appreciate the difference in bacteriodota given the signifcant cross-sample variations.\nKras overexpressing/mutation is uncommon in human GBMs, making these GBM models less relevant to human disease, which should be acknowledged.\nSome overstatements need to be tuned down. For example, contrary to the authors' claim, GBM stratification by TP53 and CDKN2a alone is hardly a well-recognized method; the decrease of excitotary neurons in scRNA-seq may not mean anything since it has many explanations."}, "author_response": "Response: Thanks for the insightful suggestion. We apologize for any lack of clarity in our original manuscript. Initially, we compared differences between intermittent fasting (IF) and ad libitum (AL) conditions within the TP53-mutant genotype and observed phenotypic improvement in TP53-mutant mice upon IF. To further investigate why IF is effective specifically in the TP53-mutant subtype of GBM, all experiments from Figure 1 onward focused exclusively in the Tp53-subtype genetic background. Since CDKN2A-deleted mice did not exhibit improvement after IF, they were not initially included as controls.\nAs recommended, we have now included a comparative analysis between CDKN2A-AL and CDKN2A-IF groups as a control to better delineate the IF response specific to TP53-mutant GBM. Differential gene expression analysis between CDKN2A- and TP53-IF groups identified 2,741 significantly dysregulated genes, including METTL3—a key regulator of m⁶A methylation—providing further support that METTL3-mediated m⁶A modification may underlie the efficacy of IF in GBM (SF3b, 3c). Additionally, enrichment analysis revealed significant involvement of the TGF-β signaling pathway (SF3d, 3e).\nThese new results reinforce that the transcriptomic changes induced by IF are specific to TP53-mutant tumors and are not universally present across genetic subtypes.\n(SF3b) Comparing Mettl3 and Tgfb2 gene expression in Tp53-AL, Tp53-IF, Cdkn2a-AL and Cdkn2a-IF group on GBM mice (Error bars: mean ± SD, t-test: ns: P>0.05; *P<0.05; **P<0.01).\n(SF3c) The differentially expressed genes (DEGs) analyzed by volcano plots between Tp53-IF and Cdkn2a-IF mice group. Red dots represent up-regulated genes, blue dots represent down-regulated genes, and gray dots represent non-differential genes.\n(SF3d) KEGG analysis of DEGs between Tp53-IF and Cdkn2a-IF mice group.\n(SF3e) Molecular Function by GO enrichment analysis of DEGs between Tp53-IF and Cdkn2a-IF mice group.\n2. In Figure 1k, the authors observed down-regulation of TGF-β2 by using the inhibitor STM2457, but it was not known whether STM2457 would mimic the antitumor effects of IF in TP53 group, and Cdkn2a should also be added as the control group.\nResponse: We feel great thanks for your professional review work. We supplemented CDKN2A- and TP53-STM2457-AL GBM mice. The results strongly align with those obtained under intermittent fasting, thereby significantly strengthening the robustness of our conclusions.\nIn TP53 GBM mice, administration of STM2457 markedly suppressed tumor growth (F5a, 5b, 5c) and improved survival outcomes (F5d), consistent with the effects observed in the IF group. Notably, however, no significant reduction in fecal or serum levels of SAM or methionine was detected. These findings support our proposed mechanism, as STM2457 directly targets the downstream effector METTL3, leading to reduced m6A levels and downregulation of TGF-β2 protein expression—effects mirroring those in the TP53-IF group and significantly differing from the TP53-AL group.\nIn contrast, within the CDKN2A GBM model, STM2457 treatment did not elicit significant antitumor effects or improve survival. No meaningful differences in tumor progression (SF8a, 8b, 8c), SAM (SF8e, 8f) or methionine levels (SF8j, 8h), or survival (SF9g) were observed compared to the CDKN2A-AL group.\nThese results further underscore the genotype-specific efficacy of both IF and METTL3 inhibition, reinforcing the conclusion that the responsiveness to metabolic and epigenetic intervention is dependent upon TP53 subtype.\n(F5a) Representative rainbow images of luminescence in GBM of live mice from different treatment groups (from left to right: Tp53-AL group, Tp53-IF group, Tp53-STM2457-A group, Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group).\n(F5b) Log10 quantification of total flux from mice in (F5a), each black dot represents a sample. (ns: P>0.05; *P<0.05; **P<0.01).\n(F5c) As in (F5b) but comparing the percentage of the tumor area to the total area in H&E strain staining images (ns: P>0.05; *P<0.05; **P<0.01).\n(F5d) Kaplan Meier curves (left) for survival probability in GBM mice stratified to Tp53-AL group, Tp53-IF group, Tp53-STM2457-AL group, Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group. Heatmap shows significant difference in survival probability among these 7 groups (log-rank test,**P＜0.01; ***P＜0.001;).\n(F5e-5h) As in (F5b) but comparing relative abundance of S-adenosylmethionine (SAM) levels in feces (F5e), in serum (F5f), and methionine levels in feces (F5g), in serum (F5h), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(F5i) As in (F5b) but comparing m6A (%) level between 7 groups (ns: P>0.05, *P<0.05). (F5j) In GBM mice, WB was adapted to identify the levels of METTL3 and TGF-β2 protein expression.\n(F5k,5l) Determination of gray value of target protein METTL3 (F5k) and TGF-β2 (F5l) between 7 groups (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001).\n(SF8a) Representative rainbow images of luminescence in GBM of live mice from different treatment groups (from left to right: Tp53-ABX-AL group, Tp53-ABX-IF group, Cdkn2a-AL group, Cdkn2a-IF group, Cdkn2a-STM2457-AL group, Cdkn2a-FMT-AL group, Cdkn2a-ABX-AL group and Cdkn2a-ABX-IF group).\n(SF8b) Log10 quantification of total flux from mice in (SF8A), each black dot represents a sample. (ns: P>0.05).\n(SF8c) As in (SF8b) but comparing the percentage of the tumor area to the total area in H&E strain staining images (ns: P>0.05).\n(SF8h-k) As in (SF8b) but comparing relative abundance of S-adenosylmethionine (SAM) levels in feces (SF8h), in serum (SF8i), and methionine levels in feces (SF8j), in serum (SF8k), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(SF9g) Kaplan Meier curves for survival probability in GBM mice of various treatment: Cdkn2a-AL vs. Cdkn2a-IF vs. Cdkn2a-STM2457-AL vs. Cdkn2a-FMT-AL.\n3. In Figure 4, the authors should compare the differences in gut microbiota between IF-treated Cdkn2a and Tp53 GBM mice, and whether the gut microbiota of mice in CDKN2a group was unchanged. In the fecal microbiota transplantation experiment from IF mice, Cdkn2a mice treated with IF should also be added as the control group.\nResponse: Thank you for raising this important point. We have now performed metagenomic analysis comparing the gut microbiota of AL- and IF-treated CDKN2A and TP53 GBM mice (F4a, SF6a). Principal coordinate analysis (PCoA) and stacked bar plots at the phylum and class levels reveal distinct microbial community structures between these groups (F4a, 4b, 4c, SF6b, 6c). Additionally, LefSe analysis comparing CDKN2A-AL and CDKN2A-IF identified Alistipes as a genus enriched in the AL group (SF6e).\nAdditionally, in the fecal microbiota transplantation experiment from IF mice, Cdkn2a mice treated with IF also be added as the control group. We found that CDKN2A mice did not show improvement in GBM after fecal transplantation (SF8a, 8c, 8c, 9g), further confirming that the CDKN2A phenotype is no response. However, we found that the levels of SAM and methionine in the feces and serum of in the CDKN2A-IF and the CDKN2A-FMT-AL mice groups were significantly decreased (SF8h, 8i, 8j ,8k), indicating that the reduction of SAM and methionine levels caused by IF is also effective in the CDKN2A phenotype, and the changes in metabolites are caused by the gut microbiota. Thank you for your suggestions, which have made the mechanism of this research more complete.\n(F4a) Principal coordination analysis (PCoA) by Bray-Curtis distances based on species level of Cdkn2a-AL, Cdkn2a-IF, Tp53-AL and Tp53-IF groups.\n(F4b) The relative abundances of the top 10 microbial communities identified at the phylum level between Cdkn2a-AL, Cdkn2a-IF, Tp53-AL and Tp53-IF groups.\n(F4C) As in (F4b) but at the class level.\n(SF6a) Sample correlation heatmap of gene abundance in metagenomes.\n(SF6b) The relative abundances of the top 10 microbial communities identified at the phylum level between Cdkn2a-AL, Cdkn2a-IF, Tp53-AL and Tp53-IF samples.\n(SF6c) As in (SF6b) but at the class level.\n(SF6e) Linear Discriminant Analysis Effect Size (LEfSe) analysis identified the abundances of microbes in the Cdkn2a-AL group significantly differed from Cdkn2a-IF groups. The findings with regards to family, genus, and species are shown in the plot (LDA score > 4, p < 0.05). The red fonts indicate microbes associated with methionine.\n(SF6e) Linear Discriminant Analysis Effect Size (LEfSe) analysis identified the abundances of microbes in the Cdkn2a-AL group significantly differed from Cdkn2a-IF groups. The findings with regards to family, genus, and species are shown in the plot (LDA score > 4, p < 0.05). The red fonts indicate microbes associated with methionine.\n(SF8a) Representative rainbow images of luminescence in GBM of live mice from different treatment groups (from left to right: Tp53-ABX-AL group, Tp53-ABX-IF group, Cdkn2a-AL group, Cdkn2a-IF group, Cdkn2a-STM2457-AL group, Cdkn2a-FMT-AL group, Cdkn2a-ABX-AL group and Cdkn2a-ABX-IF group).\n(SF8b) Log10 quantification of total flux from mice in (SF8A), each black dot represents a sample. (ns: P>0.05).\n(SF8c) As in (SF8b) but comparing the percentage of the tumor area to the total area in H&E strain staining images (ns: P>0.05).\n(SF8h-k) As in (SF8b) but comparing relative abundance of S-adenosylmethionine (SAM) levels in feces (SF8h), in serum (SF8i), and methionine levels in feces (SF8j), in serum (SF8k), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(SF9g) Kaplan Meier curves for survival probability in GBM mice of various treatment: Cdkn2a-AL vs. Cdkn2a-IF vs. Cdkn2a-STM2457-AL vs. Cdkn2a-FMT-AL.\n4. To confirm that intermittent fasting is microbiome-dependent experiments in ABX-treated and germ-free setting should be perform.\nResponse: We agree that demonstrating the microbiome-dependency of the IF effect is crucial. To address this, we treated TP53 and CDKN2A GBM mice with broad-spectrum antibiotics (ABX) and performed RNA-seq and survival analyses. In ABX-treated TP53 mice, no significant difference in Mettl3 or Tgfb2 expression was observed between AL and IF groups (SF8d), and only 13 genes were differentially expressed (SF8e). Moreover, survival curves showed no significant difference between AL and IF groups in either TP53 or CDKN2A ABX-treated mice (all P > 0.05) (SF9f, 9h). These results confirm that the beneficial effects of IF are indeed microbiota-dependent.\n(SF8d) Comparing Mettl3 and Tgfb2 gene expression in Tp53-ABX-AL and Tp53-ABX-IF group on GBM mice (Error bars: mean ± SD, t-test: ns: P>0.05).\n(SF8e) The differentially expressed genes (DEGs) analyzed by volcano plots between Tp53-ABX-AL and Tp53-ABX-IF mice group. Red dots represent up-regulated genes, blue dots represent down-regulated genes, and gray dots represent non-differential genes.\n(SF9f, 9h) Kaplan Meier curves for survival probability in GBM mice of various treatment, Tp53-ABX-AL vs. Tp53-ABX-IF (SF9f), Cdkn2a-ABX-AL vs. Cdkn2a-ABX-IF (SF9h).\n5. Additionally, please provide data on whether methionine is decreased in the tumor and fecal after IF-treated in Cdkn2a and Tp53 groups.\nResponse: Thank you for your suggestion. We supplemented AL- and IF-treated GBM mice in Cdkn2a and Tp53 groups to detect SAM and methionine levels in feces and serum. Consistent with previous conclusions, IF leads to a significant reduction of SAM and methionine levels both in Cdkn2a and Tp53 groups, and the levels of the two metabolites in the FMT-AL group are also significantly lower than the AL group (F5e, 5f, 5g, 5h, SF8h, 8i, 8j, 8k). Meanwhile, we quantified the SAM levels of the spatial metabolome (F3e), further demonstrating the influence of dietary restriction on the levels of METTL3-related metabolites.\n(F5e-5h) Comparing relative abundance of S-adenosylmethionine (SAM) levels for Tp53-AL group, Tp53-IF group, Tp53-STM2457-A group,Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group in feces (F5e), in serum (F5f), and methionine levels in feces (F5g), in serum (F5h), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(SF8h-8k) Comparing relative abundance of S-adenosylmethionine (SAM) levels for Tp53-ABX-AL vs. Tp53-ABX-IF (left), Cdkn2a-AL vs. Cdkn2a-IF vs. Cdkn2a-STM2457-AL vs. Cdkn2a-FMT-AL (middle), Cdkn2a-ABX-AL vs. Cdkn2a-ABX-IF (right). in feces (F5e), in serum (F5f), and methionine levels in feces (F5g), in serum (F5h), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(F3e) Ion images for normalized S-adenosylmethionin (m/z 399.1434) abundance of Tp53-AL group (left) and Tp53-IF group (right middle), Scale bars: 4 mm, the red circle indicates the location of the tumor. Box plots (right) showing the S-adenosylmethionin (SAM) level of Tp53-AL and Tp53-IF groups’ GBM tumor area in spatial metabolome.\n6. 16S rRNA seq has been performed in a cross-sectional study (same time, at the end of the experiments) with several biases (cag effect and tumor sizes). It is important to perform a longitudinal study of the taxonomic microbial shifts in a paired manner, and compare the effects of various diets at each time points and overtime.\nResponse: We thank the reviewer for highlighting the importance of longitudinal analysis. We have now performed a time-course analysis of microbial abundance for three methionine-producing bacteria (t1 denotes the time point at fasting onset, t2: the 15-day fasting checkpoint, and t3: the 30-day fasting endpoint—when mice first exhibit pronounced phenotypic differences.) (SF7). Their relative abundances were tracked across multiple time points in both CDKN2A-IF and TP53-IF groups. The results reveal dynamic temporal changes and further support the association between these taxa and IF response in TP53-mutant GBM. We believe these additions have substantially strengthened the manuscript and thank the reviewer again for the valuable feedback.\n(SF7) Time-course analysis of relative microbial abundance for three methionine-producing bacteria (t1 denotes the time point at fasting onset, t2 denotes the 15-day fasting checkpoint, and t3 denotes the 30-day fasting endpoint)\n7. The reference “Methionine deficiency facilitates antitumour immunity by altering m6A methylation of immune checkpoint transcripts” (The authors did not cited this reference) had demonstrated Methionine deficiency facilitates antitumour immunity by altering m6A methylation, and the mechanism is explained in more detail than this manuscript. So in this manuscript, the authors should address the mechanism in how IF-specific inhibition of TP53-mutated gliomas and the gut microbiota alter methionine methylation in brain.\nResponse: Thanks for highlighting this important reference. The cited study elucidates the regulatory effect of methionine deficiency on m6A modification in Escherichia coli, which we have now incorporated into our revised manuscript. While our investigation focuses on glioblastoma (GBM) rather than bacterial models, both studies underscore the complex and diverse effects of Methionine deficiency on organisms.\nIn our previous analyses, we observed that Tp53-mutated and Cdkn2a-deleted subtypes exhibit distinct genomic backgrounds and transcriptomic profiles. Due to the inherent complexity of human regulatory networks and ethical constraints, precise genetic manipulations-as performed in prokaryotic systems-are not feasible in human patients. To examine the potential differential effects of intermittent fasting, methionine restriction, and TGF-β signaling between these two GBM subtypes, we conducted comparative transcriptomic analysis.\nDifferential expression analysis revealed significant dysregulation of the TGF-β signaling pathway between TP53- and CDKN2A-patient groups [1] (SF2a, 2b). Specifically, Tp53-mutated tumors demonstrated reduced basal TGF-β pathway activity. This divergence in TGF-β activation suggests a subtype-specific mechanism underlying the differential response to METTL3-mediated m6A modification within the TGF-β pathway, ultimately contributing to the distinct phenotypic outcomes observed.\nCollectively, these findings support a model in which the IF-methionine-TGF-β axis operates in a genotype-dependent manner in GBM, providing a mechanistic basis for subtype-specific vulnerabilities.\nReference\n[1] Zhou X, Li G, An S, Li WX, Yang H, Guo Y, Dai Z, Dai S, Zheng J, Huang J, Iavarone A, Zhao X. A new method of identifying glioblastoma subtypes and creation of corresponding animal models. Oncogene. 2018 Aug;37(35):4781-4791.\n(SF2a) The gsearank plot shows the significantly different signaling pathways between the CDKN2A and TP53 GBM patients.\n(SF2b) Ridgeplot shows the expression distribution of the core enriched genes in the enriched pathway.\nMinor comments:\n1. On Line 186, “AL and IF tumor cells…..” should be “AL- and IF-treated tumor cells…..”.\nResponse: Thank you for your suggestion. This modification is indeed more reasonable and standard. We have made the changes and also improved other related sentences.\n2. In Figure 2k, the authors should use scatter plots and label n values.\nResponse: Thank you for your comments. Considering the visualization suggestions of other reviewers for Figure 2k, we replotted to highlight that the m6A level was significantly lower in the TP53-IF group at the single-cell level.\n3. On Line 221, “Figure 3e showed……” should be revised as “ In Figure 3e, the results showed that ……:.\nResponse: We feel great thanks for your professional review work, and we have made modifications at the appropriate locations throughout the manuscript.\n4. On Line 273, the sentence “including methionine (Figure 5g, 5h)…..” should be verified.\nResponse: Thank you for your careful checks and we apologize for this mistake. In fact, this sentence describes the result of Figure 4g and 4h. We have made revisions in the manuscript.\nReviewer #2 (Remarks to the Author):\n1. Limited novelty: The connection between METTL3 and Glioblastoma (GBM) has been extensively reported in multiple studies (Oncogene. 2018;37:522-533; Cancer Lett. 2021;511:36-46; Genes. 2019;10:141; Front. Oncol. 2021;11:702983; Cancer Res. 2019;79:5785–5798; Genome Biol. 2021;22:51.).\nResponse: We sincerely appreciate the valuable comments. However, our primary innovation does not lie in reiterating the downstream role of METTL3 in glioblastoma (GBM), which has indeed been extensively characterized. Instead, the novelty of our study is in demonstrating that the upstream regulation of METTL3 is governed by a diet-sensitive, microbiota-mediated m6A axis, and that this process is modulated by patient-specific genetic backgrounds. This discovery not only provides a new dimension to the established link between METTL3 and GBM, but also offers a conceptual framework for exploring the role of METTL3 within the broader context of the gut–tumor axis across other malignancies. Thus, while previous studies—including those as early as 2018—have reported on METTL3 in GBM, our focus diverges substantially, as the mechanistic emphasis is shifted from the tumor-intrinsic function of METTL3 to its upstream dietary and microbial regulation, which represents a distinct and highly innovative contribution.\nEven though the regulatory role of METTL3 in GBM was first reported as early as 2018, studies related this topic continue to emerge, including reports published as recently as 2025 [1]. This once again highlights the sustained, pivotal role of METTL3 in glioblastoma biology, while also underscoring the distinct focus of each individual study.\nReference\n[1] Liu L, Wang R, Cheng K, Bai C, Ji Y, Zhang Y, Yang H, Gong M, Xie F, Zhao Y, Pan J, Yuan Y. Steroid receptor coactivator-1 facilitates METTL3-mediated m6A modification by coactivating NF-κB and promotes the malignant progression of glioblastoma. Oncogene. 2025 Sep;44(36):3333-3349.\n2. The selection of METTL3 and TGFB signaling as the key downstream targets appears to be cherry-picked, as the authors did not provide a strong rationale for their choice.\nResponse: Our selection of METTL3 and TGF-β signaling as the key downstream targets was not arbitrary. Bioinformatic analyses initially helped us narrow the range of candidate pathways, including the MAPK-ERK, TGF-β, and HIF-1 pathways (F1h). At the outset, we did not know which pathway would be most critical. We therefore systematically evaluated them in turn [1,2]. First, when we examined the MAPK pathway, we observed no significant changes in either protein levels or phosphorylation status before and after IF treatment, and consequently did not pursue this pathway further. These negative results were omitted from the main text for clarity but informed the refinement of our study.\n(F1h) Over-representation analysis based on the KEGG database for DEGs between the groups of AL- and IF-treated Tp53 mice.\nWestern blot analysis of P-ERK and ERK protein isolated from AL- and IF-treated mice.\nWhen we investigated the TGF-β pathway, however, we obtained consistent positive results across multiple experiments. Thus, our emphasis on TGF-β was the result of an iterative process of prediction, validation, and exclusion, rather than selective reporting.\nThis study was conducted over a six-year period. Guided by bioinformatic analyses, we systematically evaluated several candidate pathways. All but the METTL3-TGF-β axis produced negative results. Through iterative rescue and replication experiments, we consistently validated METTL3-TGF-β signaling as the operative mechanism. For clarity, the negative findings from other pathways were not included in the main manuscript.\nReference\n[1] Tessitore L, Bollito E. Early induction of TGF-beta1 through a fasting-re-feeding regimen promotes liver carcinogenesis by a sub-initiating dose of diethylnitrosamine. Cell Prolif. 2006 Apr;39(2):105-16.\n[2] Morikawa Y, Ueyama E, Senba E. Fasting-induced activation of mitogen-activated protein kinases (ERK/p38) in the mouse hypothalamus. J Neuroendocrinol. 2004 Feb;16(2):105-12.\n3. This study does not explore the contribution of METTL3 to TGFB2, and in Figure 1k, the METTL3 inhibitor STM2475 fails to repress TGFb2.\nResponse: We sincerely thank you for the valuable feedback that we have used to improve the quality of our manuscript. To address the reviewer’s concern, we have added five independent biological replicates and employed lentiviral shRNA-mediated METTL3 knockdown in glioma cell line. In contrast to the modest effect observed with the small-molecule inhibitor STM2457, shRNA-mediated depletion of METTL3 reproducibly and significantly decreased TGFB2 protein levels (F1k, SF3h, 3i, 3j). These data demonstrate that METTL3 directly contributes to TGFB2 regulation.\n(F1k) Western blot analysis of TGF-β2 protein isolated from T98G cells treated with DMSO (control), 10μM STM2457 (METTL3 inhibitor) (upper left) and METTL3 knockout (shMETTL3) (lower left), respectively. Determination of gray value of target protein TGF-β2 between control vs. STM2457 (upper right) and control vs. shMETTL3 (upper left) (**P＜0.01).\n(SF3h) Western blot analysis of TGF-β2 protein isolated from T98G cells treated with DMSO (control), 10μM STM2457 (METTL3 inhibitor), respectively.\n(SF3i) Western blot analysis of METTL3 and TGF-β2 protein isolated from T98G cells treated with DMSO (control), and METTL3 knockout (shMETTL3).\n(SF3j) Determination of gray value of target protein METTL3 between control vs. shMETTL3 (**P＜0.01).\n4. The authors should further explore whether the METTL3 inhibitor STM2475 or TGF-beta inhibitor would achieve a similar protective effect against GBM as intermittent fasting (IF) treatment.\nResponse: We feel great thanks for your professional review work on our article. We supplemented CDKN2A- and TP53-STM2457-AL GBM mice. The results strongly align with those obtained under intermittent fasting, thereby significantly strengthening the robustness of our conclusions.\nIn TP53 GBM mice, administration of STM2457 markedly suppressed tumor growth (F5a, 5b, 5c) and improved survival outcomes (F5d), consistent with the effects observed in the IF group. Notably, however, no significant reduction in fecal or serum levels of SAM or methionine was detected. These findings support our proposed mechanism, as STM2457 directly targets the downstream effector METTL3, leading to reduced m6A levels and downregulation of TGF-β2 protein expression—effects mirroring those in the TP53-IF group and significantly differing from the TP53-AL group.\nThese results further underscore the genotype-specific efficacy of both IF and METTL3 inhibition, reinforcing the conclusion that the responsiveness to metabolic and epigenetic intervention is dependent upon TP53 subtype.\n(F5a) Representative rainbow images of luminescence in GBM of live mice from different treatment groups (from left to right: Tp53-AL group, Tp53-IF group, Tp53-STM2457-A group, Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group).\n(F5b) Log10 quantification of total flux from mice in (F5a), each black dot represents a sample. (ns: P>0.05; *P<0.05; **P<0.01).\n(F5c) As in (F5b) but comparing the percentage of the tumor area to the total area in H&E strain staining images (ns: P>0.05; *P<0.05; **P<0.01).\n(F5d) Kaplan Meier curves (left) for survival probability in GBM mice stratified to Tp53-AL group, Tp53-IF group, Tp53-STM2457-AL group, Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group. Heatmap shows significant difference in survival probability among these 7 groups (log-rank test,**P＜0.01; ***P＜0.001;).\n(F5e-5h) As in (F5b) but comparing relative abundance of S-adenosylmethionine (SAM) levels in feces (F5e), in serum (F5f), and methionine levels in feces (F5g), in serum (F5h), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(F5i) As in (F5b) but comparing m6A (%) level between 7 groups (ns: P>0.05, *P<0.05). (F5j) In GBM mice, WB was adapted to identify the levels of METTL3 and TGF-β2 protein expression.\n(F5k,5l) Determination of gray value of target protein METTL3 (F5k) and TGF-β2 (F5l) between 7 groups (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001).\n5. Several key pieces of information are missing. For example, the sample size is missing in several figures. Regarding the FMT model, it is unclear whether antibiotics were administered to the mice before the FMT.\nResponse: Thank you for your reminder. We marked the sample size on all the bar plots, with each black dot representing a sample. And antibiotics were indeed administered to the mice before the FMT. We have also made supplements in the method section: “A broad-spectrum antibiotic cocktail (including neomycin, ampicillin, metronidazole: 200 mg/L, and vancomycin: 100 mg/L) was added to the drinking water of mice. After two weeks of treatment to deplete the microbiota, fecal microbiota transplantation was performed.”\n6. Figure 1e and 1f do not provide quantitative results of tumor size.\nResponse: Thank you for your reminder. We have supplemented the quantitative results of Figure 1e and 1f tumor size (H&E) in Supplementary Figure 1.\n(SF1) Calculating the percentage of the tumor area to the total area in H&E strain for quantification of Figure 1E and 1K.\n7. For Figure 5, please also conduct hematoxylin and eosin (H&E) staining to show the changes in tumor size among different groups.\nResponse: Thanks for your advice. We used the preserved mice brain tissue samples for H&E staining. Compared with the previous submission, we supplemented extra groups and quantified the tumor size: Tp53-STM2457-AL, Tp53-M3OE-IF, Tp53-ABX-AL, Tp53-ABX-IF, Cdkn2a-AL, Cdkn2a-IF, Cdkn2a-STM2457-AL, Cdkn2a-FMT-AL, Cdkn2a-ABX-AL and Cdkn2a-ABX-IF. The results were consistent with our expectations. The tumor sizes in the TP53-IF group, TP53-STM2457-AL group and TP53-FMT-AL group were significantly reduced (F5c). The ABX treated and the CDKN2A subtype mice had no tumor suppressive effect (SF8c).\n(F5c, SF8c) Comparing the percentage of the tumor area to the total area in H&E strain staining images for different groups, each black dot represents a sample. (ns: P>0.05; *P<0.05; **P<0.01)\n8. It is not explained whether and how the findings of the METTL3-m6A-TGF-β signaling axis could explain the differential response of Cdkn2a and Tp53 GBM mouse models to IF treatment.\nResponse: This is a question fundamentally rooted in population genetics. Addressing it comprehensively requires multi-omic data from diverse human populations to elucidate the underlying mechanism. Our studies using Cdkn2a and Tp53 GBM mouse models reveal that these two genotypes exhibit fundamentally distinct gene expression profiles, particularly within the TGF-β signaling pathway. This divergence results in differential activation states of the TGF-β pathway, which ultimately drives the observed genotype-specific phenotypic responses.\n9. More than 40 methyl transfers from S-adenosylmethionine (SAM) are known to occur to various substrates such as nucleic acids, proteins, lipids, and secondary metabolites. It is unclear why the authors focused specifically on m6A.\nResponse: We focused on m6A because an a priori survey of alternative SAM-dependent methylation pathways did not yield convincing candidates, whereas multiple independent lines of evidence converged on RNA m6A.\nSpecifically, global DNA methylation in GBM mouse brains before and after intermittent fasting (IF) was assessed using a Global DNA Methylation Assay kit, which showed no significant change (P = 0.82), arguing against DNA methylation as a major regulatory mechanism (a).\nIn parallel, histone methylation was profiled by sequencing, which revealed differential signals near BCAM, CEP170, SAMM50, and VPS33B; however, literature review and prognostic analyses indicated that these alterations had limited relevance to GBM biology (b, c). By contrast, unbiased RNA-seq after IF highlighted significant modulation of the m6A writer METTL3, which led us to perform targeted m6A-seq. These data confirmed reproducible remodeling of m6A marks post-IF, and functional perturbation experiments demonstrated that METTL3/m6A was necessary for the IF-associated phenotypes. Thus, our emphasis on m6A is data-driven: among the many SAM-dependent methylation reactions, only the m6A pathway consistently linked expression changes, epitranscriptomic remodeling, and functional impact under IF.\n(a) Global DNA methylation in GBM mouse brains before and after intermittent fasting (t-test).\n(b) Volcano plot shows identified different expression genes by histone methylation sequencing.\n(c) Kaplan-Meier curves showing survival probability of GBM patients stratified on the basis of CEP170 (upper left), BCAN (upper right), SAMM50 (lower left) and VPS33B (lower right) expression levels.\n10. In Figure 5h, the sample size for the western blot (n=1 per group) is insufficient to draw any meaningful results.\nResponse: Thank you. For Figure 5h, we have re-added groups and made three repetitions. The grayscale images of the three repetitions are shown in the following figures.\n(F5j) In GBM mice, WB was adapted to identify the levels of METTL3 and TGF-β2 protein expression.\n(F5k, 5l) Determination of gray value of target protein METTL3 (F5k) and TGF-β2 (F5l) between 7 groups (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001).\n11. In Figure 5i, it is unclear how the authors performed the Pearson correlations, given the very limited sample size (n=2 per group). Also, the authors should explain what kind of experiment was conducted.\nResponse: Thank you for your careful review. We apologize for the misleading expressions in the manuscript. To clarify, the Pearson correlation analysis presented in Figure 5i was not performed between experimental groups, but rather between individual samples based on the expression profiles of genes within the TGF-β signaling pathway.\nSpecifically, we extracted the expression values of all TGF-β pathway-related genes from each of the 10 samples (5 groups×2 replicates). The correlation matrix was then calculated pairwise between samples using the rcorr() function from the R package Hmisc, which computes Pearson correlation coefficients and corresponding p-values. The resulting correlation matrix reflects the similarity in TGF-β pathway gene expression patterns between each pair of samples, irrespective of their group assignments.\nThe heatmap visualizes this sample-to-sample correlation matrix, with colors representing the strength of correlation. This approach is commonly used to assess reproducibility between replicates and similarity/dissimilarity across samples, even with limited replicate numbers per group. We are sorry for causing you to misunderstand.\n12. It is not unclear how IF treatment leads to a decrease in methionine-related gut microbial species.\nResponse: The gut microbiota is a highly complex ecosystem, comprising more than 5,000 species. In the colon of a 70-kg “reference man,” the total number of bacteria is estimated at approximately 3.8 × 1013. These microbial species also engage in intricate interactions with one another. Intermittent fasting (IF) reshapes this intestinal ecosystem through repeated substrate deprivation and ecological filtering, selectively constraining methionine-related microbial pathways and taxa [1]. Diet–microbiome studies have demonstrated that microbial methionine and sulfur metabolism is strongly substrate-driven [2]. Consistently, both IF and fasting-mimicking interventions remodel gut microbial composition and function in humans and mice. Together with our own antibiotic/FMT and rescue experiments, these findings support a model in which IF reduces the abundance of methionine-related microbes and methionine availability, thereby modulating METTL3-dependent m6A regulation and downstream TGFB2 expression.\nReference\n[1] Hu X, Xia K, Dai M, Han X, Yuan P, Liu J, Liu S, Jia F, Chen J, Jiang F, Yu J, Yang H, Wang J, Xu X, Jin X, Kristiansen K, Xiao L, Chen W, Han M, Duan S. Intermittent fasting modulates the intestinal microbiota and improves obesity and host energy metabolism. NPJ Biofilms Microbiomes. 2023 Apr 7;9(1):19.\n[2] Fang W, Jiang L, Zhu Y, Yang S, Qiu H, Cheng J, Liang Q, Tu ZC, Ye C. Methionine restriction constrains lipoylation and activates mitochondria for nitrogenic synthesis of amino acids. Nat Commun. 2023 May 2;14(1):2504.\n13. In Figure 5f, please explore the stool level and serum level of methionine and S-adenosylmethionine (SAM) by targeted metabolomics.\nResponse: Thank you for your suggestion. We supplemented AL- and IF-treated GBM mice in Cdkn2a and Tp53 groups to detect SAM and methionine levels in feces and serum. Consistent with previous conclusions, IF leads to a significant reduction of SAM and methionine levels both in Cdkn2a and Tp53 groups, and the levels of the two metabolites in the FMT-AL group are also significantly lower than the AL group (F5e, 5f, 5g, 5h, SF8h, 8i, 8j, 8k).\n(F5e-5h) Comparing relative abundance of S-adenosylmethionine (SAM) levels for Tp53-AL group, Tp53-IF group, Tp53-STM2457-A group,Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group in feces (F5e), in serum (F5f), and methionine levels in feces (F5g), in serum (F5h), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(SF8h-8k) Comparing relative abundance of S-adenosylmethionine (SAM) levels for Tp53-ABX-AL vs. Tp53-ABX-IF (left), Cdkn2a-AL vs. Cdkn2a-IF vs. Cdkn2a-STM2457-AL vs. Cdkn2a-FMT-AL (middle), Cdkn2a-ABX-AL vs. Cdkn2a-ABX-IF (right). in feces (F5e), in serum (F5f), and methionine levels in feces (F5g), in serum (F5h), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n14. The authors should incorporate germ-free or antibiotic-treated mice models to provide additional evidence supporting the protective effect of IF treatment and its reliance on alterations in the gut microbiome.\nResponse: We agree that demonstrating the microbiome-dependency of the IF effect is crucial. To address this, we treated TP53 and CDKN2A GBM mice with broad-spectrum antibiotics (ABX) and performed RNA-seq and survival analyses. In ABX-treated TP53 mice, no significant difference in Mettl3 or Tgfb2 expression was observed between AL and IF groups (SF8d), and only 13 genes were differentially expressed (SF8e). Moreover, survival curves showed no significant difference between AL and IF groups in either TP53 or CDKN2A ABX-treated mice (all P > 0.05) (SF9f, 9h). These results confirm that the beneficial effects of IF are indeed microbiota-dependent.\n(SF8d) Comparing Mettl3 and Tgfb2 gene expression in Tp53-ABX-AL and Tp53-ABX-IF group on GBM mice (Error bars: mean ± SD, t-test: ns: P>0.05).\n(SF8e) The differentially expressed genes (DEGs) analyzed by volcano plots between Tp53-ABX-AL and Tp53-ABX-IF mice group. Red dots represent up-regulated genes, blue dots represent down-regulated genes, and gray dots represent non-differential genes.\n(SF9f, 9h) Kaplan Meier curves for survival probability in GBM mice of various treatment, Tp53-ABX-AL vs. Tp53-ABX-IF (SF9f), Cdkn2a-ABX-AL vs. Cdkn2a-ABX-IF (SF9h).\nReviewer #3 (Remarks to the Author):\nReviewer Summary: In this manuscript, the authors examine the effect of intermittent fasting on the pathogenesis of GBM, stratified into groups driven by loss of TP53 and those with loss of CDKN2A. The authors found that IF only affected mice with tumors bearing TP53 deletion, which they found correlated with loss of methionine levels in tumor tissue. They found that this loss of methionine correlated to a loss of m6A, which they hypothesize leads to a reduction in TGFB signaling through METTL3.\nThe experiments performed in the study generally support the notion that intermittent fasting may regulate the gut microbiota and influence metabolism in orthotopic GBM in vivo. The methodology is sound and well described. The strengths of the manuscript are the mouse models used, the use of multiple omics platforms to measure gene- and metabolite-level data in tumor tissue, and rescue experiments to show the effects of methionine supplementation and entacapone in reversing the benefit of IF.\nThere are a few weaknesses in the manuscript.\nFirst, the mechanistic link between IF and METTL3’s effect on TGFB is unclear from these studies. The authors do not provide evidence for how IF specifically influences METTL3, other than stating that TGFB likely has a reduction in m6A in IF mice. Further experimentation would help strengthen the link between IF and TGFB’s effects on tumor growth in their models. Specifically, it would be useful to show the effects of METTL3 knockout in AL mice and METTL3 overexpression in IF mice.\nResponse: Our data suggest that IF influences METTL3 through remodeling of the gut microbiota. Specifically, IF alters microbial composition, leading to a reduced abundance of methionine-producing bacteria. This reduction in methionine subsequently affects METTL3-mediated m6A regulation. Importantly, TGFB2 is one of the downstream targets of METTL3-dependent m6A modification.\nAs recommended, we have now included mice injected with STM2457 (METTL3 inhibitor, imitating METTL3 knockout) but ad libitum diet group, and mice with METTL3 overexpression and IF group. The results demonstrated that METTL3 inhibition even AL (Tp53-STM2457-AL) in Tp53 GBM mice groups slowed tumor growth (F5a, 5b, 5c) and significantly improved prognosis (F5d), but no significance in METTL3 overexpression even IF group (Tp53-M3OE-IF). Concurrently, we observed significant differences in the SAM and methionine levels in feces and serum (F5e, 5f, 5g, 5h), m6A levels (Figure 5i), and protein levels of TGFB2 and METTL3 (Figure 5j, 5k, 5l). Therefore, STM2457 injected in AL treated mice and METTL3 overexpression in IF-treated mice indicated that the tumor-suppressive effect of IF requires METTL3-mediated regulation of TGFB2.\nAdditionally, our fluorescence imaging data and protein-level analyses of TGFB2 and METTL3 provide consistent evidence supporting this pathway (F5j, 5k 5l). Together, these repeated rescue experiments strongly support the mechanistic link we propose.\n(F5a) Representative rainbow images of luminescence in GBM of live mice from different treatment groups (from left to right: Tp53-AL group, Tp53-IF group, Tp53-STM2457-A group, Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group).\n(F5b) Log10 quantification of total flux from mice in (F5a), each black dot represents a sample. (ns: P>0.05; *P<0.05; **P<0.01).\n(F5c) As in (F5b) but comparing the percentage of the tumor area to the total area in H&E strain staining images (ns: P>0.05; *P<0.05; **P<0.01).\n(F5d) Kaplan Meier curves (left) for survival probability in GBM mice stratified to Tp53-AL group, Tp53-IF group, Tp53-STM2457-AL group, Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group. Heatmap shows significant difference in survival probability among these 7 groups (log-rank test,**P＜0.01; ***P＜0.001;).\n(F5e-5h) As in (F5b) but comparing relative abundance of S-adenosylmethionine (SAM) levels in feces (F5e), in serum (F5f), and methionine levels in feces (F5g), in serum (F5h), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(F5i) As in (F5b) but comparing m6A (%) level between 7 groups (ns: P>0.05, *P<0.05). (F5j) In GBM mice, WB was adapted to identify the levels of METTL3 and TGF-β2 protein expression.\n(F5k,5l) Determination of gray value of target protein METTL3 (F5k) and TGF-β2 (F5l) between 7 groups (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001).\nSecond, while the authors show that FMT provides the same benefit as IF, they do not demonstrate that the microbiota (or lack of certain species) is necessary for the benefit of IF. This should be accomplished by treating AL mice with antibiotics to see if these mice have prolonged survival by limiting methionine-producing gut microbiota.\nResponse: We agree that demonstrating the microbiome-dependency of the IF effect is crucial. To address this, we treated TP53 and CDKN2A GBM mice with broad-spectrum antibiotics (ABX) and performed RNA-seq and survival analyses. In ABX-treated TP53 mice, no significant difference in Mettl3 or Tgfb2 expression was observed between AL and IF groups (SF8d), and only 13 genes were differentially expressed (SF8e). Moreover, survival curves showed no significant difference between AL and IF groups in either TP53 or CDKN2A ABX-treated mice (all P > 0.05) (SF9f, 9h). These results confirm that the beneficial effects of IF are indeed microbiota-dependent.\n(SF8d) Comparing Mettl3 and Tgfb2 gene expression in Tp53-ABX-AL and Tp53-ABX-IF group on GBM mice (Error bars: mean ± SD, t-test: ns: P>0.05).\n(SF8e) The differentially expressed genes (DEGs) analyzed by volcano plots between Tp53-ABX-AL and Tp53-ABX-IF mice group. Red dots represent up-regulated genes, blue dots represent down-regulated genes, and gray dots represent non-differential genes.\n(SF9f, 9h) Kaplan Meier curves for survival probability in GBM mice of various treatment, Tp53-ABX-AL vs. Tp53-ABX-IF (SF9f), Cdkn2a-ABX-AL vs. Cdkn2a-ABX-IF (SF9h).\nThird, the authors do not measure the metabolic effects of IF in their mice – what happens to serum glucose, insulin levels, C-peptide, ketones – in serum of mice? Are mice adequately fasted? Is 24 hours fast required, or is less time of fasting (e.g. 18 hours) beneficial? This would be relevant to patients, where a 24-hour fast would be difficult to achieve. More typical fasting is overnight fast with first meal of the day being lunch.\nResponse: Thank you for this valuable suggestion. Based on your comments, we further stratified the mouse models according to different fasting durations. Subsequent survival analysis revealed that mice subjected to a 24-hour fasting regimen exhibited significantly improved prognosis compared to those in the 18-hour fasting group. And we also supplement key metabolites, such as serum glucose, insulin and C-peptide levels in the TP53 mice groups (SF9c, 9d, 9e). This refinement has substantially strengthened the rigor and completeness of our study.\n(SF3a) Kaplan Meier curves for survival probability in GBM mice of different feeding patterns (ad libitum diet, 18h and 24h intermittent fasting).\n(SF9c) Comparing blood glucose in Tp53 GBM mice groups (Error bars: mean ± SD, t-test: ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(SF9d) As in (SF9b) but comparing insulin.\n(SF9e) As in (SF9b) but comparing C-peptide.\nIn addition, the methodology regarding sample harvest from mice is not described – when are samples harvested, at the end of IF before refeeding or somewhere in-between? These parameters should be specified in the methods for all samples taken from mice.\nResponse: We appreciate the reviewer’s comment. Fecal samples were collected at multiple time points, including before the initiation of fasting, during fasting, and at the terminal stage of the experiment. In contrast, brain tissues and blood samples were harvested only at the terminal stage. These details have now been clarified and described more explicitly in the Methods section.\nUnique aspects: The use of intermittent fasting in genotype-stratified mouse models would be helpful in establishing targeted nutritional therapy in GBM\nResponse: We sincerely thank the editor and all reviewers for their valuable feedback that we have used to improve the quality of our manuscript.\nStudy design: Basic translational investigation to identify the effects of IF in mouse models of GBM\nResponse: We feel great thanks for your professional review work on our article.\nWriting style: The manuscript style is succinct and straight-forward, though there are a few typos that need correction.\nResponse: We were really sorry for our careless mistakes. Thank you for your reminder.\nGeneral Considerations\nSpecific comments:\n1. In Figure 1E-F, only visual representation of H&E sections is shown. Quantification of tumor cells is not performed. If the authors wish to quantify tumor cells, then they should perform a proliferation stain like Ki67 and quantify that. Otherwise, the following sentence needs to be removed: The results of hematoxylin and eosin (H&E) staining indicated that in Cdkn2a GBM model, there were no significant changes in tumor size and tumor cell count before and after IF (Figure 1e). In Tp53 GBM model, however, tumor size significantly decreased in IF group compared to the AL group, with fewer tumor cells (Figure 1f).\nResponse: Thank you for your suggestions. We have supplemented the quantitative results of Figure 1e and 1f tumor size (H&E) in Supplementary Figure 1. Moreover, we removed the sentence. Change to: The results of hematoxylin and eosin (H&E) staining indicated that in Tp53 GBM model, there was a downward trend in tumor size but a lack thereof in the Cdkn2a GBM model (Figure 1e, 1f, Supplementary Figure 1). This expression seems more rigorous.\n(SF1) Calculating the percentage of the tumor area to the total area in H&E strain for quantification of Figure 1E and 1K.\n2. In line 134, the authors mention 41 individuals. They should clarify if they mean humans or mice. If the latter, they should say “individual mice.”\nResponse: Thank you for your careful review. We apologize for the misleading expressions in the manuscript. In fact, data GSE196335 was sourced from the public database (Gene Expression Omnibus, GEO, https://www.ncbi.nlm.nih.gov/gds) and is human data, which has been further annotated in the manuscript\n3. In line 135, it is unclear what GSE196335 refers to. The authors should clarify.\nResponse: Thanks for your reminder. In GEO database, GES refers to Gene Expression Omnibus Series ID. Each dataset has a unique GSE number used to identify and retrieve the dataset. We further marked in the manuscript to help readers better understand.\n4. The authors should clarify why they focused on METTL3 and not any of the other significant hits from their transcriptomic screen.\nResponse: Thank you. Bioinformatic analyses initially helped us narrow the range of candidate pathways, including the MAPK-ERK, TGF-β, and HIF-1 pathways. At the outset, we did not know which pathway would be most critical. We therefore systematically evaluated them in turn [1,2]. First, when we examined the MAPK pathway, we observed no significant changes in either protein levels or phosphorylation status before and after IF treatment, and consequently did not pursue this pathway further. These negative results were omitted from the main text for clarity but informed the refinement of our study.\n(F1h) Over-representation analysis based on the KEGG database for DEGs between the groups of AL- and IF-treated Tp53 mice.\nWestern blot analysis of P-ERK and ERK protein isolated from AL- and IF-treated mice.\nWhen we investigated the TGF-β pathway, however, we obtained consistent positive results across multiple experiments. Thus, our emphasis on TGF-β was the result of an iterative process of prediction, validation, and exclusion, rather than selective reporting.\nThis study was conducted over a six-year period. Guided by bioinformatic analyses, we systematically evaluated several candidate pathways. All but the METTL3-TGF-β axis produced negative results. Through iterative rescue and replication experiments, we consistently validated METTL3-TGF-β signaling as the operative mechanism. For clarity, the negative findings from other pathways were not included in the main manuscript.\nReference\n[1] Tessitore L, Bollito E. Early induction of TGF-beta1 through a fasting-re-feeding regimen promotes liver carcinogenesis by a sub-initiating dose of diethylnitrosamine. Cell Prolif. 2006 Apr;39(2):105-16.\n[2] Morikawa Y, Ueyama E, Senba E. Fasting-induced activation of mitogen-activated protein kinases (ERK/p38) in the mouse hypothalamus. J Neuroendocrinol. 2004 Feb;16(2):105-12.\n5. The western blot shown in Figure 1K is not convincing. The authors state that they inhibited METTL3, but do not mention the method of inhibition. In the figure, they mention a compound STM2457 and use it at 10 uM. They should confirm that this dose inhibits METTL3. Second, the authors should quantify the differences observed in the blot to show the effect of METTL3 on TGFB2 expression.\nResponse: We sincerely thank you for the valuable feedback that we have used to improve the quality of our manuscript. The using of the 10uM STM2457 is the result of extensive literature research and comprehensive consideration [1-4]. To address the reviewer’s concern, we have added five independent biological replicates and employed lentiviral shRNA-mediated METTL3 knockdown in glioma cell lines (F1k, SF2h, 2i, 2j). The grayscale images of the three repetitions are shown in the following figures.\n(F1k) Western blot analysis of TGF-β2 protein isolated from T98G cells treated with DMSO (control), 10μM STM2457 (METTL3 inhibitor) (upper left) and METTL3 knockout (shMETTL3) (lower left), respectively. Determination of gray value of target protein TGF-β2 between control vs. STM2457 (upper right) and control vs. shMETTL3 (upper left) (**P＜0.01).\n(SF2h) Western blot analysis of TGF-β2 protein isolated from T98G cells treated with DMSO (control), 10μM STM2457 (METTL3 inhibitor), respectively.\n(SF2i) Western blot analysis of METTL3 and TGF-β2 protein isolated from T98G cells treated with DMSO (control), and METTL3 knockout (shMETTL3).\n(SF2j) Determination of gray value of target protein METTL3 between control vs. shMETTL3 (**P＜0.01).\nReference\n[1] Yankova E, Blackaby W, Albertella M, Rak J, De Braekeleer E, Tsagkogeorga G, Pilka ES, Aspris D, Leggate D, Hendrick AG, Webster NA, Andrews B, Fosbeary R, Guest P, Irigoyen N, Eleftheriou M, Gozdecka M, Dias JML, Bannister AJ, Vick B, Jeremias I, Vassiliou GS, Rausch O, Tzelepis K, Kouzarides T. Small-molecule inhibition of METTL3 as a strategy against myeloid leukaemia. Nature. 2021 May;593(7860):597-601.\n[2] Wu L, Du Y, Wang L, Zhang Y, Ren J. Inhibition of METTL3 ameliorates doxorubicin-induced cardiotoxicity through suppression of TFRC-mediated ferroptosis. Redox Biol. 2024 Jun;72:103157.\n[3] Wang L, Yang Q, Zhou Q, Fang F, Lei K, Liu Z, Zheng G, Zhu L, Huo J, Li X, Peng S, Kuang M, Lin S, Huang M, Xu L. METTL3-m6A-EGFR-axis drives lenvatinib resistance in hepatocellular carcinoma. Cancer Lett. 2023 Apr 10;559:216122.\n[4] Li S, Zou D, Chen W, Britz GW, Liu Z, Weng YL. METTL3 inhibition reduces N6 -methyladenosine levels and prevents allogeneic CD4+ T-cell responses. Immunol Cell Biol. 2022 Oct;100(9):718-730.\n6. In Figure 3D, it is apparent that there is tumor in the AL group, but in the IF image, there appears to be little to no tumor. How do the tumors know that the region selected in the IF group is tumor and not normal brain? Since tumors do not grow to a large extent in the IF group, the authors should allow tumors in the IF group to grow to a larger size and then randomize mice to AL or IF, which would allow better testing of metabolites in tumor tissue from both groups.\nResponse: We thank the reviewer for this thoughtful suggestion. All mice used in this study were age-matched, and tumor induction was performed under stereotactic guidance to ensure viral injection into the same hippocampal region. The same viral construct was used across animals. The injection was performed using specific coordinates: 1.6 mm lateral and 1.2 mm anterior to the bregma, and 1.75 mm below the skull surface. Post-injection, the needle remained in place for 3 minutes before being gradually withdrawn over a period of 1 minute. Based on our previous experience, this approach yields GBM tumors of relatively uniform size. In addition, in vivo fluorescence imaging was performed prior to randomization, which confirmed that tumors were of comparable size; mice were then randomly assigned to either the AL or IF group，the following image shows the tumor fluorescence photography of GBM mice randomly divided before experimental treatment. Histological examination (H&E) and fluorescence imaging further verified the presence and localization of tumors in both groups. We acknowledge the reviewer’s suggestion to allow tumors in the IF group to grow larger before randomization, but our study design was intended to minimize variability in tumor burden at baseline and to comply with ethical considerations.\nThe tumor fluorescence photography of GBM mice randomly divided before experimental treatment\n7. In Figure 3, it is interesting to note that the authors report a reduction in all metabolites in the IF group. One would expect that certain metabolites and/or pathways, such as glucose uptake regulated by the AMPK pathway, might be increased IF under fasting conditions so the tumor cells can maintain growth. Did the authors see any metabolites enriched under IF conditions?\nResponse: Thank you for your professional review. You raised an important point regarding potential metabolite enrichment under intermittent fasting (IF) conditions, particularly related to energy-sensing pathways such as those involving AMPK and glucose metabolism. In response to your question, we did specifically measure key metabolites and hormones related to glucose metabolism. Our results showed that blood glucose levels, as well as circulating insulin and C-peptide, were significantly reduced in the IF group compared to the control group (SF9c-e). These findings are consistent with the overall reduction in metabolites observed in the tumor tissue, and suggest a systemic metabolic adaptation to the fasting condition rather than a selective enrichment of glucose-related metabolites within the tumor. We appreciate your valuable input and hope these additional explanations address your question.\n(SF9c) Comparing blood glucose in Tp53 GBM mice groups (Error bars: mean ± SD, t-test: ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(SF9d) As in (SF9b) but comparing insulin.\n(SF9e) As in (SF9b) but comparing C-peptide.\n8. In Figure 5C, the authors do not show survival for the IF group? What is their survival compared to the other groups?\nResponse: Thank you for your careful review. We supplemented TP53-IF and other related groups for survival analysis and conducted comparisons (F5d). The results are shown in the following figure.\n(F5d) Kaplan Meier curves (left) for survival probability in GBM mice stratified to Tp53-AL group, Tp53-IF group, Tp53-STM2457-AL group, Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group. Heatmap shows significant difference in survival probability among these 7 groups (log-rank test,**P＜0.01; ***P＜0.001;).\n9. In Legend for Figure 5C, the authors state a difference between 5 groups, but there are only 4 groups in the figure. The authors should clarify this statement.\nResponse: We feel sorry for our carelessness. In our resubmitted manuscript, the mistake is revised. Thanks for your correction.\n10. When the authors mention methionine and m6A levels in Figure 5F and G, they should specify from where these metabolites are derived (e.g. tumor tissue, blood, etc.). In the methods they mention obtaining these metabolites from the tumor tissue, so they should reiterate that in the results section for this content.\nResponse: We apologize for the weaknesses in our statement. In the method, we further annotate that the sources of metabolites are blood and feces. And it has been indicated in the corresponding result figures and legends.\n11. The authors do not seem to have measured methionine levels in the blood stream, which would be a confirmation that the differences in microbiota in IF versus AL groups corresponds to a difference in circulating methionine in mice.\nResponse: Thank you for your suggestion. We supplemented AL- and IF-treated GBM mice in Cdkn2a and Tp53 groups to detect SAM and methionine levels in feces and serum. Consistent with previous conclusions, IF leads to a significant reduction of SAM and methionine levels both in Cdkn2a and Tp53 groups, and the levels of the two metabolites in the FMT-AL group are also significantly lower than the AL group (F5e, 5f, 5g, 5h, SF8h, 8i, 8j, 8k).\n(F5e-5h) Comparing relative abundance of S-adenosylmethionine (SAM) levels for Tp53-AL group, Tp53-IF group, Tp53-STM2457-A group,Tp53-FMT-AL group, Tp53-M3OE-IF group, Tp53-MET-IF group and Tp53-ENT-IF group in feces (F5e), in serum (F5f), and methionine levels in feces (F5g), in serum (F5h), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(SF8h-8k) Comparing relative abundance of S-adenosylmethionine (SAM) levels for Tp53-ABX-AL vs. Tp53-ABX-IF (left), Cdkn2a-AL vs. Cdkn2a-IF vs. Cdkn2a-STM2457-AL vs. Cdkn2a-FMT-AL (middle), Cdkn2a-ABX-AL vs. Cdkn2a-ABX-IF (right). in feces (F5e), in serum (F5f), and methionine levels in feces (F5g), in serum (F5h), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n12. Figure 6 is not well schematized. There should be more representation of IF rather than an “X’ over the mouth, specific gut microbiota, and mention of METTL3.\nResponse: We agree with the suggestions and have incorporated the recommended changes into the figure.\n(F6) Schematic diagram illustrating the effects of intermittent fasting on the molecular mechanisms mediating intestinal flora and tumor growth in TP53 and CDKN2A GBM subtypes.\nReviewer #4 (Remarks to the Author):\nLin et al. tried to link the genotype-specific tumor-inibiting effects of intermittent fasting (IF) on glioma development. However, a lack of experimental rigor, statistic testing, and key comparision across different genotypes makes their argument hardly convincing.\nThe experiments throughout lack a critical control group, Cdkn2a-mutant gliomas. IF should have similar effects on these mice, yet it is hard to explain why they are not nearly as responsive as Trp53-mutant mice.\nResponse: We feel great thanks for your professional review work.\nIn transcriptomics, we have now included a comparative analysis between CDKN2A-AL and CDKN2A-IF groups as a control to better delineate the IF response specific to TP53-mutant GBM. Differential gene expression analysis between CDKN2A- and TP53-IF groups identified 2,741 significantly dysregulated genes, including METTL3—a key regulator of m⁶A methylation—providing further support that METTL3-mediated m⁶A modification may underlie the efficacy of IF in GBM (SF2b, 2c). Additionally, enrichment analysis revealed significant involvement of the TGF-β signaling pathway (SF2d, 2e). These new results reinforce that the transcriptomic changes induced by IF are specific to TP53-mutant tumors and are not universally present across genetic subtypes.\nIn addition, we have performed metagenomic analysis comparing the gut microbiota of AL- and IF-treated CDKN2A and TP53 GBM mice (F4a, SF6a). Principal coordinate analysis (PCoA) and stacked bar plots at the phylum and class levels reveal distinct microbial community structures between these groups (F4a, 4b, 4c, SF6b, 6c). Additionally, LefSe analysis comparing CDKN2A-AL and CDKN2A-IF identified Alistipes as a genus enriched in the AL group (SF6e).\nMeanwhile, in the fecal microbiota transplantation experiment from IF mice, Cdkn2a mice treated with AL and IF also be added as the control group. We found that CDKN2A mice did not show improvement in GBM after fecal transplantation (SF8a, 8c, 8c, 9d), further confirming that the CDKN2A phenotype is no response. However, we found that the levels of SAM and methionine in the feces and serum of in the CDKN2A-IF and the CDKN2A-FMT-AL mice groups were significantly decreased (SF8h, 8i, 8j ,8k), indicating that the reduction of SAM and methionine levels caused by IF is also effective in the CDKN2A phenotype.\nImportantly, in our previous analyses, we observed that Tp53-mutated and Cdkn2a-deleted subtypes exhibit distinct genomic backgrounds and transcriptomic profiles. Due to the inherent complexity of human regulatory networks and ethical constraints, precise genetic manipulations-as performed in prokaryotic systems-are not feasible in human patients. To examine the potential differential effects of intermittent fasting, methionine restriction, and TGF-β signaling between these two GBM subtypes, we conducted comparative transcriptomic analysis.\nDifferential expression analysis revealed significant dysregulation of the TGF-β signaling pathway between TP53- and CDKN2A-patient groups [1] (SF2a, 2b). Specifically, Tp53-mutated tumors demonstrated reduced basal TGF-β pathway activity. This divergence in TGF-β activation suggests a subtype-specific mechanism underlying the differential response to METTL3-mediated m6A modification within the TGF-β pathway, ultimately contributing to the distinct phenotypic outcomes observed.\nCollectively, these findings support a model in which the IF-methionine-TGF-β axis operates in a genotype-dependent manner in GBM, providing a mechanistic basis for subtype-specific vulnerabilities.\nThank you for your suggestions, which have made the mechanism of this research more complete.\nReference\n[1] Zhou X, Li G, An S, Li WX, Yang H, Guo Y, Dai Z, Dai S, Zheng J, Huang J, Iavarone A, Zhao X. A new method of identifying glioblastoma subtypes and creation of corresponding animal models. Oncogene. 2018 Aug;37(35):4781-4791.\n(SF2b) Comparing Mettl3 and Tgfb2 gene expression in Tp53-AL, Tp53-IF, Cdkn2a-AL and Cdkn2a-IF group on GBM mice (Error bars: mean ± SD, t-test: ns: P>0.05; *P<0.05; **P<0.01).\n(SF2c) Hallmark Enrichment analysis of DEGs in GSE196335 cohort between AL and IF groups.\n(SF2d) The differentially expressed genes (DEGs) analyzed by volcano plots between Tp53-IF and Cdkn2a-IF mice group. Red dots represent up-regulated genes, blue dots represent down-regulated genes, and gray dots represent non-differential genes.\n(SF2e) KEGG analysis of DEGs between Tp53-IF and Cdkn2a-IF mice group.\n(F4a) Principal coordination analysis (PCoA) by Bray-Curtis distances based on species level of Cdkn2a-AL, Cdkn2a-IF, Tp53-AL and Tp53-IF groups.\n(F4b) The relative abundances of the top 10 microbial communities identified at the phylum level between Cdkn2a-AL, Cdkn2a-IF, Tp53-AL and Tp53-IF groups.\n(F4C) As in (F4b) but at the class level.\n(SF6a) Sample correlation heatmap of gene abundance in metagenomes.\n(SF6b) The relative abundances of the top 10 microbial communities identified at the phylum level between Cdkn2a-AL, Cdkn2a-IF, Tp53-AL and Tp53-IF samples.\n(SF6c) As in (SF6b) but at the class level.\n(SF6e) Linear Discriminant Analysis Effect Size (LEfSe) analysis identified the abundances of microbes in the Cdkn2a-AL group significantly differed from Cdkn2a-IF groups. The findings with regards to family, genus, and species are shown in the plot (LDA score > 4, p < 0.05). The red fonts indicate microbes associated with methionine.\n(SF8a) Representative rainbow images of luminescence in GBM of live mice from different treatment groups (from left to right: Tp53-ABX-AL group, Tp53-ABX-IF group, Cdkn2a-AL group, Cdkn2a-IF group, Cdkn2a-STM2457-AL group, Cdkn2a-FMT-AL group, Cdkn2a-ABX-AL group and Cdkn2a-ABX-IF group).\n(SF8b) Log10 quantification of total flux from mice in (SF8A), each black dot represents a sample. (ns: P>0.05).\n(SF8c) As in (SF8b) but comparing the percentage of the tumor area to the total area in H&E strain staining images (ns: P>0.05).\n(SF8h-k) As in (SF8b) but comparing relative abundance of S-adenosylmethionine (SAM) levels in feces (SF8h), in serum (SF8i), and methionine levels in feces (SF8j), in serum (SF8k), each black dot represents a sample (ns: P>0.05; *P<0.05; **P<0.01; ***P<0.001; ****P<0.0001).\n(SF9a) Comparing m6A (%) level in Cdkn2a-AL and Cdkn2a-IF group on GBM mice, each black dot represents a sample (Error bars: mean ± SD, ns: P>0.05).\n(SF9d) Kaplan Meier curves for survival probability in GBM mice of various treatment: Cdkn2a-AL vs. Cdkn2a-IF vs. Cdkn2a-STM2457-AL vs. Cdkn2a-FMT-AL.\n(SF2a) The gsearank plot shows the significantly different signaling pathways between the CDKN2A and TP53 GBM patients.\n(SF2b) Ridgeplot shows the expression distribution of the core enriched genes in the enriched pathway.\nThroughout the manuscript, many statements are not backed up by quantification or do not provide the n value or the timepoints of sample collection/imaging for each experiment.The Methods/Figure legends lack critical experimental details.\nResponse: We apologize for the weaknesses in our manuscript. We marked the sample size on all the bar plots, with each black dot representing a sample. Meanwhile, we supplemented the sampling time of mouse feces and blood as well as the time for fluorescence shooting. Thanks again for your reminder.\nFig. 2k, the differences are very minor between the two groups.\nResponse: Thank you for your comments. In order to better visualize, We applied the CDF curve to present the data distribution. The results highlight that the m6A level was significantly lower in the TP53-IF group at the single-cell level (P = 1.656×10-5).\n(F2k) Plot of cumulative fraction of single-cell m6A level of all cells in AL- and IF-treated Tp53 groups. (wilcox-test: P = 1.656×10-5).\nFig. 3d,e,f, the authors should show H&E images to clearly demarcate the tumor areas, and perform individual statistic testing to confirm differential expression of METTL3 and TGFB2 in tumor areas.\nResponse: Thanks for your suggestion. We tried our best to improve the figures. First, we quantified the SAM level in the spatial metabolome and used boxplot for quantification and marking of significant differences. Subsequently, we labeled the tumor areas of the spatial transcriptome sections based on the results of H&E staining. Finally, the average expressions of the key genes, METTL3 and TGFB2, in the tumor area were statistically analyzed and quantified. Your suggestions will be of great help to us in improving the quality of the manuscript\n(F3d) Clustering of spatial metabolomics spots by Tp53-AL group (left) and Tp53-IF group (right) metabolite abundance.\n(F3e) Ion images for normalized S-adenosylmethionin (m/z 399.1434) abundance of Tp53-AL group (left) and Tp53-IF group (right middle), Scale bars: 4 mm, the red circle indicates the location of the tumor. Box plots (right) showing the S-adenosylmethionin (SAM) level of Tp53-AL and Tp53-IF groups’ GBM tumor area in spatial metabolome.\n(F3f) Normalized gene expression of mouse brain (red circle indicates the location of the tumor). Mettl3 gene expression in Tp53-AL group (upper left); Mettl3 gene expression in Tp53-IF group (upper middle); Tgfb2 gene expression in Tp53-AL group (lower left); Tgfb2 gene expression in Tp53-IF group (lower middle), Box plots showing Mettl3 (upper right) and Tgfb2 (upper left) gene expression of Tp53-AL and Tp53-IF groups’ GBM tumor area in spatial transtranscriptome.\nFig. 4b,c, it is hard to appreciate the difference in bacteriodota given the signifcant cross-sample variations.\nResponse: We think this is an excellent suggestion. In order to appreciate the difference in bacteriodota given the signifcant variations, we combined the samples based on the abundance of enriched microbiota. And we replaced the sub-samples by sub-group and insert the the sub-samples into the supplementary figures.\n(F4b) The relative abundances of the top 10 microbial communities identified at the phylum level between Cdkn2a-AL, Cdkn2a-IF, Tp53-AL and Tp53-IF groups.\n(F4c) As in (F4b) but at the class level.\n(SF6b) As in (F4b) but separated by samples.\n(SF6c) As in (SF6b) but at the class level.\nKras overexpressing/mutation is uncommon in human GBMs, making these GBM models less relevant to human disease, which should be acknowledged.\nResponse: Thank you for your comments. Although mutations in the RAS gene itself are rare, alterations in the RTK/RAS pathway are detected in approximately 88% of glioblastoma (GBM) patients, and RAS occupies a central position within this pathway, regulating even PI3K [1] (see the 2008 TCGA landmark study entitled “Comprehensive genomic characterization defines human glioblastoma genes and core pathways”; Figure 5 of that paper provides a clear overview of the core pathways in GBM). Thus, dysregulation of the RAS pathway is a critical determinant for modeling clinical phenotypes of GBM. However, since it is not feasible to manipulate all genes within the RAS pathway, we focused on mutations in the central RAS gene itself to better recapitulate the clinical state of patients. Development of a novel mouse glioma model using lentiviral vectors\nThe same study also identified TP53 and CDKN2A as two additional core pathways. The TP53 pathway regulates senescence and apoptosis in downstream cells, whereas the CDKN2A pathway modulates G1/S phase progression. This differential regulatory mechanism may explain the distinct responses of these two pathways to TGFB2. Therefore, we suggest that this classification scheme for GBM deserves greater recognition.\nReference\n[1] Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008 Oct 23;455(7216):1061-8.\nPUBLISHED DATA FIGURE REDACTED\nSome overstatements need to be tuned down. For example, contrary to the authors' claim, GBM stratification by TP53 and CDKN2a alone is hardly a well-recognized method; the decrease of excitotary neurons in scRNA-seq may not mean anything since it has many explanations.\nResponse: We thank the reviewer for this insightful comment. We agree that the original wording may have overemphasized the clinical recognition of the TP53- and CDKN2A-based classification. We have therefore revised the text to more accurately reflect the current state of the field. The TP53 and RB (in which CDKN2A is a key component) pathways are indeed among the three core pathways definitively altered in the vast majority of glioblastomas, as established by the foundational TCGA study [1]. While this pathway-based framework is profoundly influential for understanding disease biology, we acknowledge that it is not currently a primary method for clinical stratification, which more commonly incorporates markers like IDH mutation and MGMT promoter status. Our intention is to highlight the mechanistic importance of these pathways and suggest that their role in defining disease subtypes deserves further investigation, not to present them as a standalone clinical classification system.\nFurthermore, in accordance with the reviewer's suggestion, we have removed the speculative statement regarding the decrease in excitatory neurons observed in scRNA-seq data, as its interpretation is indeed complex and not directly relevant to the central claims of our study.\nReference\n[1] Cancer Genome Atlas Research Network. Comprehensive genomic characterization defines human glioblastoma genes and core pathways. Nature. 2008 Oct 23;455(7216):1061-8."}, {"round": 2, "reviewer_comments": {"reviewer_1": "The revised manuscript by Lin et al. significantly enhanced the quality of their manuscript and the reliability of the data，and I still have some doubts.\n1. Thereference 31 merely indicates that gut microbiotacontributes to catabolism of methionine, but does not suggest that gut microbiota is involved in the synthesis of methionine.\n2. The value of TPM of gene expression of Mettl3 increased by approximately 30% after being treated with antibiotics compared to the untreated condition （SF 3b and SF 8d）.\n3. Why does the treatment of IF in CDKN2A mice result in a decrease in methionine and SAM levels, yet it still fails to exhibit anti-tumor effects?", "reviewer_2": "The authors have addressed all my concerns.", "reviewer_3": "The authors have adequately addressed all of my concerns. Thank you for the thorough review of concerns and new inclusion of additional data.", "reviewer_4": "The authors have been responsive to my comments and added the CDKN2A group in their analysis. I have a few minor concerns regarding the revised manuscript.\n1. Upregulation of TGFb signaling appears marginal in the GO enrichment. The author should test TGFb as well as downstream signaling targets through Western blot to confirm this is the core effector pathway.\n2. H&E images (alone) should be shown side by side with the gene experession data in spatial transcriptomics to highlight the tumor area.\n3. Discussions regarding the limitation of Ras as a driver gene should be incorporated into the main text/discussion section, not just in the rebuttal letter."}, "author_response": "Reviewer #1 (Remarks to the Author):\nThe revised manuscript by Lin et al. significantly enhanced the quality of their manuscript and the reliability of the data，and I still have some doubts.\n1. The reference 31 merely indicates that gut microbiota contributes to catabolism of methionine, but does not suggest that gut microbiota is involved in the synthesis of methionine.\nResponse: Thank you for your careful checks and we apologize for this mistake. We have addressed this concern by replacing the original reference 31 with more appropriate sources. We hope this can solve your doubts.\n2. The value of TPM of gene expression of Mettl3 increased by approximately 30% after being treated with antibiotics compared to the untreated condition （SF 3b and SF 8d）.\nResponse: Antibiotic treatment broadly suppresses all gut microbiota, and the gut microbial ecosystem is extremely complex. In fact, the gut microbiota consists of more than 5,000 species, and in the colon of a 70-kg “reference man,” the total bacterial load is estimated to be approximately 3.8 × 10¹³. These microbial species also engage in highly intricate ecological interactions.\nBecause of this complexity, it is very difficult to directly attribute specific host gene expression changes before and after antibiotic treatment to particular microbial taxa. The observed increase in Mettl3 expression is not necessarily caused by m6A-related or methionine-associated microbes; other microbial populations may also play important roles. Therefore, we do not consider a detailed comparison of pre- and post-antibiotic microbial contributions feasible within the scope of this study.\n3. Why does the treatment of IF in CDKN2A mice result in a decrease in methionine and SAM levels, yet it still fails to exhibit anti-tumor effects?\nResponse: Thanks for your review. In our previous analyses, we observed that Tp53-mutated and Cdkn2a-deleted subtypes exhibit distinct genomic backgrounds and transcriptomic profiles [1]. To examine the potential differential effects of intermittent fasting, methionine restriction, and TGF-β signaling between these two GBM subtypes, we conducted comparative transcriptomic analysis. Differential expression analysis revealed significant dysregulation of the TGF-β signaling pathway between TP53- and CDKN2A-patient groups [1] (SF2a, 2b). Specifically, Tp53-mutated tumors demonstrated reduced basal TGF-β pathway activity. This divergence in TGF-β activation suggests a subtype-specific mechanism underlying the differential response to METTL3-mediated m6A modification within the TGF-β pathway, ultimately contributing to the distinct phenotypic outcomes observed.\nOverall, the reason why CDKN2A mice did not show anti-tumor effects after IF treatment is that the activation degrees of the TGF-β pathway by Tp53-mutated and Cdkn2a-deleted subtypes are different.\nReference\n[1] Zhou X, Li G, An S, Li WX, Yang H, Guo Y, Dai Z, Dai S, Zheng J, Huang J, Iavarone A, Zhao X. A new method of identifying glioblastoma subtypes and creation of corresponding animal models. Oncogene. 2018 Aug;37(35):4781-4791.\n(SF2a) The gsearank plot shows the significantly different signaling pathways between the CDKN2A and TP53 GBM patients.\n(SF2b) Ridgeplot shows the expression distribution of the core enriched genes in the enriched pathway.\nReviewer #4 (Remarks to the Author):\nThe authors have been responsive to my comments and added the CDKN2A group in their analysis. I have a few minor concerns regarding the revised manuscript.\n1. Upregulation of TGFb signaling appears marginal in the GO enrichment. The author should test TGFb as well as downstream signaling targets through Western blot to confirm this is the core effector pathway.\nResponse: We appreciate the reviewer’s insightful comment. In addition to assessing TGF-β expression, we further examined downstream signaling to determine whether TGF-β activity is functionally altered. Specifically, we evaluated the AKT signaling pathway, which is one of the well-established downstream effectors of TGF-β. Our Western blot analysis showed that, compared with the TP53-AL group, intermittent fasting in the TP53-IF group not only reduced TGF-β expression (Fig. 5j) but also led to a marked decrease in AKT phosphorylation. These results support that intermittent fasting suppresses TGF-β signaling activity and its downstream pathway.\n2. H&E images (alone) should be shown side by side with the gene experession data in spatial transcriptomics to highlight the tumor area.\nResponse: Thank you for your suggestion. Considering the layout of the figures, we have placed the H&E images corresponding to spatial transcriptomics in the supplementary information (Supplementary Figure 5d).\n3. Discussions regarding the limitation of Ras as a driver gene should be incorporated into the main text/discussion section, not just in the rebuttal letter.\nResponse: Thanks for your reminder. We have incorporated the discussion on the limitations of Ras as a driver gene into the discussion section of the main text, as recommended."}]

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