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METTL3 preferentially enhances non-m6A translation of epigenetic factors and promotes tumourigenesis

Abstract

METTL3 encodes the predominant catalytic enzyme to promote m6A methylation in nucleus. Recently, accumulating evidence has shown the expression of METTL3 in cytoplasm, but its function is not fully understood. Here we demonstrated an m6A-independent mechanism for METTL3 to promote tumour progression. In gastric cancer, METTL3 could not only facilitate cancer progression via m6A modification, but also bind to numerous non-m6A-modified mRNAs, suggesting an unexpected role of METTL3. Mechanistically, cytoplasm-anchored METTL3 interacted with PABPC1 to stabilize its association with cap-binding complex eIF4F, which preferentially promoted the translation of epigenetic factors without m6A modification. Clinical investigation showed that cytoplasmic distributed METTL3 was highly correlated with gastric cancer progression, and this finding could be expanded to prostate cancer. Therefore, the cytoplasmic METTL3 enhances the translation of epigenetic mRNAs, thus serving as an oncogenic driver in cancer progression, and METTL3 subcellular distribution can assist diagnosis and predict prognosis for patients with cancer.

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Fig. 1: METTL3 repression delays gastric tumourigenesis.
Fig. 2: METTL3 acts as an oncogene independently of its m6A methyltransferase activity.
Fig. 3: Cytoplasmic METTL3 enables its oncogenic activity.
Fig. 4: METTL3 preferentially promotes the translation of non-m6A-modified transcripts.
Fig. 5: METTL3 interacts with PABPC1 to promote the RNA looping.
Fig. 6: METTL3 interacts with PABPC1 to promote the translation of non-m6A-modified oncogenic mRNAs.
Fig. 7: Cytoplasmic accumulation of METTL3 correlates with a poor outcome in patients with GC or PC.

Data availability

RNA-seq, MeRIP–seq, RIP-seq and eCLIP–seq data that support the findings of this study have been deposited in the Gene Expression Omnibus (GEO)45 under accession codes GSE163310 and GSE191170. The human cancer data were derived from the TCGA Research Network: http://cancergenome.nih.gov/. The dataset derived from this resource that supports the findings of this study is available in https://portal.gdc.cancer.gov/projects. Source data are provided with this paper.

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Acknowledgements

This work was funded by National Key Research and Development Program of China (2019YFA0801800 to J.Y., 2021YFA1102400 to F.W. and 2019YFA0111700 to X.W.); the National Natural Science Foundation of China (81530007 and 31725013 to J.Y., 82022001 and 81970103 to F.W., 82100135 to Y.G. and 82073129 to D.Z.); the CAMS Innovation Fund for Medical Sciences (2021-I2M-1-019 to J.Y. and 2021-I2M-1-040 to F.W.); the Fundamental Research Funds for the Core Facility (3332019001), the CAMS (2016GH310001 to J.Y., 2017-I2M-B&R-04 to J.Y. and 2018RC310013 to F.W.) and the Medical Epigenetics Research Center, CAMS (2017PT31035); Outstanding Youths Development scheme of Nanfang Hospital, Southern Medical University (2020J001 to S.R.) and Outstanding Youths Development program, Southern Medical University (2019YQPY006 to S.R.). The open-access charge was funded by the National Key Research and Development Program of China (2019YFA0801800 to J.Y.).

Author information

Authors and Affiliations

Authors

Contributions

J.Y., F.W., J.J. and D.Z. conceived and designed the project. J.Y. and F.W. supervised the experiments. X.W. and M.H. prepared all the samples for next-generation sequencing. Y.H., Y.G. and Q.W. analysed all the data, and G.H. and J.X. helped to interpret data. J.P., Y.C. and D.L. performed the clinical and mouse experiments. X.W., J.P., M.H., P.S., L.X., H.W., W.S. and J.L. constructed all of the plasmids and stable cell lines, and performed the cell proliferation, migration and invasion assays, protein purification, western blotting, Co-IP, m6A quantification assay, MeRIP–qPCR, RIP–qPCR, IF and PLA. X.W. and Y.S. constructed cell fraction and polysome profiling. X.W. and Y.G conducted the in vitro transcription, in vitro translation and in vitro binding assays. X.W., Y.M. and S.Y. contributed with reagents and discussions. J.Y., F.W., S.R., X.W., Y.H. and Y.G. wrote and edited the manuscript, and all authors commented on the manuscript.

Corresponding authors

Correspondence to Dongling Zou, Jing Jin, Fang Wang or Jia Yu.

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Extended data

Extended Data Fig. 1 METTL3 repression delays gastric tumorigenesis.

a, Correlation between METTL3 expression and perineural invasion (left panel) and between METTL3 expression and venous invasion (right panel) in GC patients (n = 113). The box plot indicates the median, the 25th and 75th percentiles (bounds of box), interquartile range, and the whiskers extending to the minima and maxima without considering outliers. b, Relative METTL3 expression in GC and normal tissues from the TCGA GC cohorts. c, Kaplan-Meier survival analysis of the overall survival of GC patients with high and low METTL3 expression from the TCGA GC cohorts. d, Heatmap of differential expressed genes between METTL3 high and low expressing groups. e, Gene ontology (GO) enrichment of 1,223 differential expressed genes in d. The bar chart shows the GO terms for pathways, ranked by -log10(p value). f, Somatic mutation patterns of GC patients from the TCGA dataset. The patients are divided into two groups based on METTL3 expression. g, Western blot analysis of METTL3 expression in MGC-803 cells (n = 3). h, Representative images of MGC-803-engrafted tumors treated with NTC or shMETTL3 at day 36 (n = 4). i, Representative images of H&E staining and immunostaining of METTL3, Ki67 and caspase-3 in the CDX model (n = 3). Scale bar, 100 μm. j, Representative images of PDX models treated with siMETTL3 or NTC (si-control) for 24 days (n = 4). Scale bar, 1 cm. k, Representative images of H&E staining and immunostaining of METTL3, Ki67 and caspase-3 in the PDX model (n = 3). Scale bar, 100 μm. The box plot (a) indicates the median (central mark), 25th and 75th percentiles (bounds of box), interquartile range, and the whiskers extending to the minima and maxima without considering outliers. The p values in (a, b) were determined by a Student’s t test (unpaired two-tailed). The p values in (c) were determined by log-rank with Mantel-Cox test. All data are shown as mean ± s.d.

Source data

Extended Data Fig. 2 METTL3 acts as an oncogene independently of its m6A methyltransferase activity.

a, Western blot analysis of METTL3 expression in METTL3_wt- or METTL3_cyto-transduced MGC-803 cells (left panel), and in METTL3_wt (middle panel) or METTL3_cyto (right panel) eCLIP precipitates (n = 3). b, A pie chart depicting the regional distribution of m6A sites identified by MeRIP-seq. c, Distribution of m6A locations across mRNA segments as identified by MeRIP-seq. d, Distribution of wild-type METTL3 binding sites across mRNA segments as identified by eCLIP-seq. e, The m6A peaks and METTL3 bound sites in KIAA1191, AASDHPPT and SUN1 transcripts belonging to the ‘Both’ group (upper panel). The m6A peaks in MYC, PKM and CDK1 transcripts belonging to the ‘m6A-only’ group (lower panel). The blue peaks represented the m6A peaks on the transcript, and the red peaks represented the accumulation of input. f, IF staining analysis of subcellular localization of METTL3 (green) in METTL3_wt and METTL3_mut-overexpressing MGC-803 and HGC-27 cells. Scale bar, 100 μm. g, The cellular proliferation rate of METTL3_wt- or METTL3_mut-transduced HGC-27 cells (n = 3). h, Transwell migration (left panel) and invasion (right panel) assays in METTL3_wt- or METTL3_mut-transduced HGC-27 cells (n = 3). i, Representative images of MGC-803-engrafted tumors; MGC-803 cells were treated with NTC or METTL3 constructs at day 36 (n = 4). The p values in (g, h) were determined by the Student’s t test (unpaired two-tailed). All data are shown as mean ± s.d.

Source data

Extended Data Fig. 3 Cytoplasmic METTL3 enables its oncogenic activity in GC cells.

a, Western blot analysis of fibrillarin and Hsp90 expression in cytoplasmic and nuclear fractions from MGC-803 cells treated with lysis buffer containing different NP-40 concentrations. From this result, 0.16% NP-40 (shown in the dotted box) was chosen for cytoplasmic and nuclear fractionation (n = 3). b, IF staining analysis of subcellular localization of METTL3 (green) in METTL3_cyto or METTL3_mut_cyto overexpressed MGC-803 and HGC-27 cells (n = 3). Scale bar, 100 μm. c, Western blot analysis of METTL3 expression in cytoplasmic and nuclear fractions from MGC-803 and HGC-27 cells treated by METTL3 sgRNA (M3 KO) and M3_wt or M3_cyto respectively (n = 3). d, Cell proliferation of MGC-803 and HGC-27 cells treated by different combination of METTL3 constructs as indicated (n = 3). e, Transwell migration (left panel) and invasion (right panel) assays of MGC-803 and HGC-27 cells treated by different combination of METTL3 constructs as indicated (n = 3). f, Cytoplasmic and nuclear METTL3 interactomes revealed by Co-IP analysis coupled with MS analysis. Proteins are sorted by ‘protein abundance’. Cytoplasmic PABPC1, and the nuclear partners WTAP and METTL14 are indicated by the red arrow. The p values in (d, e) were determined by the Student’s t test (unpaired two-tailed). All data are shown as mean ± s.d.

Source data

Extended Data Fig. 4 METTL3 promotes the translation of non-m6A-modified transcripts.

a, METTL3 distribution on different gene types revealed by wild-type (M3_wt) and the cytoplasmic (M3_cyto) METTL3 eCLIP-seq. b, The overlaps between m6A-modified mRNA detected by MeRIP-seq and METTL3-bound mRNA identified by the cytoplasmic METTL3 eCLIP-seq. c, Volcano plots of differentially expressed genes (DEGs) in METTL3_wt, METTL3_mut, shMETTL3 transcripts and their corresponding controls (NTC). Grey shading indicates genes with unchanged expression (log2 fold change < =1 and > = −1, or p > 0.05) compared with METTL3_wt, METTL3_mut, shMETTL3 transcripts and their corresponding controls (NTC). Red shading indicates up-regulated genes (log2 fold change>1 and p < 0.05) in METTL3_wt, METTL3_mut, shMETTL3 transcripts and their corresponding controls (NTC). Blue shading indicates down-regulated genes (log2 fold change < -1 and p < 0.05) in METTL3_wt, METTL3_mut, shMETTL3 transcripts and their corresponding controls (NTC). d, A Venn diagram illustrating target genes that are translationally activated by METTL3.

Extended Data Fig. 5 METTL3 interacts with PABPC1 to promote RNA looping.

a, Co-IP analysis using a METTL3-specific antibody under three concentrations of NP-40 (n = 3). b, Co-IP analysis using a METTL3-specific antibody in cells in which PABPC1 expression was knocked-down (n = 3).

Source data

Extended Data Fig. 6 METTL3 interacts with PABPC1 to promote the translation of oncogenic mRNAs.

a, The cellular proliferation rate of NTC or PABPC1-overexpressing MGC-803 cells (n = 3). b, Transwell cell migration (left panel) and invasion (right panel) assay of NTC or PABPC1-overexpressing MGC-803 cells (n = 3). c, The cellular proliferation rate of NTC or PABPC1-knockdown MGC-803 cells (n = 3). d, Transwell cell migration (left panel) and invasion (right panel) assay of PABPC1-knockdown and NTC MGC-803 cells (n = 3). e, Representative images of MGC-803-engrafted tumors in PABPC1-knockdown mice and PABPC1-competent control mice (day 36) (n = 4). f, Quantification of tumor growth (left panel) and tumor weight (right panel) in the CDX model following NTC or shPABPC1 treatment (n = 4). g, Left panel: rescue assay by transfection with NTC, PABPC1 (P1), shMETTL3 (shM3) or shMETTL3 (shM3) and PABPC1 (P1) (rescue) and western blot (n = 3). Right panel: Cellular proliferation rate of NTC, P1, shM3 or shM3 and P1 (rescue) MGC-803 cells. h, The number of migratory cells (left panel) and invading cells (right panel) of NTC, P1, shM3 or shM3 plus P1 (rescue) MGC-803 cells (n = 3). The p values in (a, b, c, d, f, g, h) were determined by the Student’s t test (unpaired two-tailed). All data are shown as mean ± s.d.

Source data

Extended Data Fig. 7 Cytoplasmic accumulation of METTL3 correlates with a poor outcome in GC and PC patients.

a, Relative METTL3 expression in GC and adjacent normal tissues. b, Left panel: correlations between total METTL3 expression and clinical stage in GC cancer patients. Right panel: Kaplan-Meier survival analysis of the overall survival probability of GC patients with low and high total METTL3 expression. c, Relative METTL3 expression in PC tissues and adjacent normal tissues. d, Left panel: the correlation between total METTL3 expression and clinical cancer stage in PC patients. Right panel: the correlation between METTL3 expression and Gleason grade in PC patients. e, Relative METTL3 expression in EC and adjacent normal tissues. f, Left panel: correlations between total METTL3 expression and clinical tumor stages in EC patients. Right panel: Kaplan-Meier survival analysis of the overall survival rates of EC patients with high and low METTL3 expression. g, Upper panels: correlations between the cytoplasmic to nuclear ratio of METTL3 and clinical tumor stages in EC patients. Lower panels: Kaplan-Meier survival analysis of overall survival rates of EC patients with high and low cytoplasmic to nuclear ratio of METTL3. h, Western blot analysis of METTL3 expression in cytoplasmic and nuclear fractions from DU145 (left panel) and KSYE510 (right panel) cells treated by M3_wt or M3_cyto respectively. i, Cell proliferation of DU145 (left panel) and KSYE510 (right panel) cells treated by different combination of METTL3 constructs as indicated (n = 3). j, Transwell migration and invasion assays of DU145 (upper panel) and KSYE510 (lower panel) cells treated by different combination of METTL3 constructs as indicated (n = 3). The p values in (a-g, i, j) for two-sample comparisons were determined by the Student’s t test (unpaired two-tailed). The p values in (b, f, g) for Kaplan-Meier survival analysis were determined by log-rank with Mantel-Cox test. For each violin in (b, d, f, g), the box plot indicates the median (white dot), 25th and 75th percentiles (bounds of box), interquartile range, and the whiskers extending to the minima and maxima without considering outliers. All data are shown as mean ± s.d.

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Wei, X., Huo, Y., Pi, J. et al. METTL3 preferentially enhances non-m6A translation of epigenetic factors and promotes tumourigenesis. Nat Cell Biol 24, 1278–1290 (2022). https://doi.org/10.1038/s41556-022-00968-y

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