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m6A RNA methylation orchestrates transcriptional dormancy during paused pluripotency

Nature Cell Biology volume 25, pages 1279–1289 (2023)Cite this article

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Abstract

Embryos across metazoan lineages can enter reversible states of developmental pausing, or diapause, in response to adverse environmental conditions. The molecular mechanisms that underlie this remarkable dormant state remain largely unknown. Here we show that N6-methyladenosine (m6A) RNA methylation by Mettl3 is required for developmental pausing in mouse blastocysts and embryonic stem (ES) cells. Mettl3 enforces transcriptional dormancy through two interconnected mechanisms: (1) it promotes global mRNA destabilization and (2) it suppresses global nascent transcription by destabilizing the mRNA of the transcriptional amplifier and oncogene N-Myc, which we identify as a crucial anti-pausing factor. Knockdown of N-Myc rescues pausing in Mettl3−/− ES cells, and forced demethylation and stabilization of Mycn mRNA in paused wild-type ES cells largely recapitulates the transcriptional defects of Mettl3−/− ES cells. These findings uncover Mettl3 as a key orchestrator of the crosstalk between transcriptomic and epitranscriptomic regulation during developmental pausing, with implications for dormancy in adult stem cells and cancer.

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Fig. 1: The m6A methyltransferase Mettl3 is essential for paused pluripotency.
Fig. 2: Mettl3 regulates hypotranscription in paused pluripotency.
Fig. 3: The methyltransferase activity of Mettl3 sustains paused pluripotency.
Fig. 4: Mettl3 promotes RNA destabilization during pausing.
Fig. 5: The transcription factor N-Myc is a key mediator of the pausing defects of Mettl3−/− ES cells.
Fig. 6: Mettl3 regulates pausing through m6A-mediated destabilization of Mycn mRNA.

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Data availability

Sequencing data have been deposited into the NCBI Gene Expression Omnibus (GEO) repository (http://ncbi.nlm.nih.gov/geo) under accession number GSE202848. Mass spectrometry data have been deposited into the Metabolights database (https://www.ebi.ac.uk/metabolights) under the identifier MTBLS8041. Published RNA-seq and ChIP–seq data used in this study are available under the accession numbers E-MTAB-2958 (early mouse embryos), E-MTAB-3386 (Myc/Mycn DKO ES cells) and GSE11431 (N-Myc ChIP). Examples of FACS gating have been deposited into the Figshare repository (https://doi.org/10.6084/m9.figshare.23551986). Mouse and human reference genomes (mm10 and hg19, respectively) were downloaded from the UCSC browser (https://genome.ucsc.edu/). All other data supporting the findings of this study are available from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

Code supporting this study are available at a dedicated GitHub repository (https://github.com/EvelyneCollignon/Mettl3_pausing) and at Zenodo (https://doi.org/10.5281/zenodo.8068381).

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Acknowledgements

We thank members of the Santos Laboratory, D. Schramek, A. Bulut-Karslioglu, T. Macrae, J. Jeschke and F. Fuks for feedback on the manuscript; members of the Hanna Laboratory for providing cells; members of the UCSF Center for Advanced Technology and the LTRI Sequencing Core for next-generation sequencing; staff at the LTRI Flow Cytometry Facilities, A. Bulut-Karslioglu and S. Biechele for advice on diapause; M. Percharde and T. Macrae for bioinformatics guidance; and staff at the TCP Transgenic Core for providing timed pregnant animals and the TCP Animal Resources for colony management. E.C. was supported by a fellowship from the Belgian American Educational Foundation. P.A.L. is funded by the US National Institutes of Health (NIH, GM58843). This work was supported by grant R01GM113014 from the NIH, a Canada 150 Research Chair in Developmental Epigenetics, the Great Gulf Homes Charitable Foundation, and project grants 165935 and 178094 from the Canadian Institutes of Health Research (to M.R.-S.).

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  1. Evelyne Collignon

    Present address: Laboratory of Cancer Epigenetics, Faculty of Medicine, ULB-Cancer Research Centre (U-CRC) and Institut Jules Bordet, Université Libre de Bruxelles, Brussels, Belgium

Authors and Affiliations

  1. Lunenfeld–Tanenbaum Research Institute and Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada

    Evelyne Collignon, Brandon Cho, Giacomo Furlan, Julie Fothergill-Robinson, Sylvia-Bryn Martin, Sarah A. McClymont & Miguel Ramalho-Santos

  2. Thermo Fisher Scientific, Franklin, MA, USA

    Robert L. Ross

  3. Rieveschl Laboratories for Mass Spectrometry, Department of Chemistry, University of Cincinnati, Cincinnati, OH, USA

    Patrick A. Limbach

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Contributions

E.C. and M.R.-S. conceived the project and designed experiments. E.C. performed the majority of the experiments and interpreted data. B.C. performed EU flow cytometry and embryo imaging. G.F. generated knockout ES cells and induced mouse hormonal diapause. J.F.-R. performed lentiviral infection and chromatin shearing. E.C., S.-B.M. and S.A.M. performed SLAM–seq. P.A.L. and R.L.R. performed mass spectrometry. M.R.-S. supervised the project. E.C. and M.R.-S. wrote the manuscript with feedback from all authors.

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Correspondence to Evelyne Collignon or Miguel Ramalho-Santos.

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Nature Cell Biology thanks Jacob Hanna, Baoming Qin, and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Extended data

Extended Data Fig. 1 Dissection of paused pluripotency in Mettl3−/− models.

a. m6A increase in paused ESCs was validated in an independent mass spectrometry experiment. Levels relative to control (Ctrl) for each replicate are shown (n = 3 biological replicates). b. Validation of Rptor and Rictor knockdown by RT-qPCR in paused ESCs grown in FBS/LIF/2i (n = 4 biological replicates). c. mTOR inhibition by Rptor and Rictor knockdown induces a paused phenotype with reduced cell proliferation and total RNA levels (n = 4 biological replicates). d. Dot blot showing an increase in m6A levels in ESCs upon knockdown of Rptor and Rictor. Levels of m6A are normalized to RNA loading control (methylene blue staining, n = 4 biological replicates). e. Design of Mettl3−/− ESCs models used in this study. f. Validation of Mettl3−/− ESCs, in control and pausing conditions, by western blot (representative of 3 biological replicates). g. Validation of Mettl3−/− #2–4 ESCs, in control and pausing conditions, by western blot (representative of 2 biological replicates). h. Mettl3−/− #2–4 ESCs also fail to suppress proliferation in paused conditions (n = 3 biological replicates). i. Mettl3-knockout mutant model in mice (Mettl3TCP−/−) and genotyping by PCR (left). Example of PCR genotyping of embryos resulting from Mettl3TCP+/− crossing, representative of all genotyping performed in this study [n(Mettl3TCP+/+) = 87, n(Mettl3TCP+/+) = 132, n(Mettl3TCP+/+) = 46]. +/+: wildtype, +/-: heterozygous, -/-: knockout. j. Validation of Mettl3TCP−/− in embryos by immunofluorescence. Representative staining images are shown. Number of embryos (n) as indicated. Scale bars = 30 µm. Data are mean ± SD (a-d) or mean ± SEM (h). P-values (as indicated on figure) by one-way ANOVA with Dunnett’s multiple comparison tests (a-c), two-tailed ratio paired Student’s t-tests (d), and linear regression test with interaction (h).

Source data

Extended Data Fig. 2 Paused Mettl3−/− ESCs acquire a distinct gene expression profile.

a. Quantification of total RNA per cell in Mettl3+/+ #2 and Mettl3−/− #2–4 ESCs, in control and paused conditions. Data are mean ± SD, n = 5 biological replicates. b. Decrease in nascent RNA per cell, as measured by EU incorporation with nuclear signal quantification, in wildtype ex vivo paused or hormonally diapaused blastocysts, compared to control E3.5 embryos. Number of embryos (n) as indicated. Scale bars = 30 µm. c. Strategy for RNA-seq with cell number-normalization using ERCC spike-in RNAs. d. Quantification of the number of expressed genes in Mettl3+/+ and Mettl3−/− ESCs, in control and paused conditions. Expressed genes are further defined as having high expression (log2 normalized reads > 5, n = 3 biological replicates). e. Number of differentially expressed genes (fold-change > 1.5 and adjusted P < 0.05) upon pausing, in Mettl3+/+ and Mettl3−/− ESCs. f. PCA plot for all expressed genes across all samples, showing across PC1 that Mettl3+/+ ESCs acquire a more divergent expression profile upon pausing than Mettl3−/− ESCs, relative to respective control condition. g-h. Gene expression changes (log2 fold-changes) of gene sets selected from the ‘GO biological processes’ (g) and ‘hallmarks’ (h) collections, showing incomplete downregulation in paused Mettl3−/− ESCs. i. Western blot of total and phosphorylated mTOR, and of the downstream targets of mTORC1 (Ulk1, 4Ebp1 and S6K1) (left, representative of 4 biological replicates). Quantification of phosphorylated levels, normalized to total levels, show no significant change in paused ESCs between Mettl3+/+ and Mettl3−/− (right). Data are mean ± SD (b, d, g-i). P-values (as indicated on figure) by two-way ANOVA with Dunnett’s multiple comparison tests (a, i), one-way ANOVA with Dunnett’s multiple comparison tests (b), two-tailed unpaired Student’s t-tests (d, g-h).

Source data

Extended Data Fig. 3 Mapping m6A distribution in the transcriptome of paused ESCs.

a. Strategy for MeRIP-seq in ESCs with cell number-normalization (CNN) using human cell spiking. b. Validation of the CNN strategy for the MeRIP-seq. By mixing different ratios of human cells to ESCs (1, 2 or 4%), we simulated global changes in methylation. Spiking normalization allows capture of these differences, as shown here by MeRIP-qPCR for 3 methylated mRNAs (NeuroD1, Nr5a2, Sox1). Data are mean ± SD, n = 5 biological replicates with levels relative to 2% spike in each replicate. c. The specificity of the m6A capture was tested by spiking poly(A) RNA from ESCs with exogenous RNAs before performing MeRIP-qPCR. Data are mean ± SD, n = 3 biological replicates. P-values (as indicated on figure) by two-tailed unpaired Student’s t-tests. d. Examples of gene track views of MeRIP-seq, for mRNAs of pluripotency factors previously shown to be methylated in ESCs. e. Motif analysis performed with DREME in a 100 bp window surrounding MeRIP peak summits identifies several motifs corresponding to the consensus ‘DRACH’ m6A motif (where D = A, G or U; H = A, C or U). f. Distribution of differential m6A peaks, according to the type of structural element within the transcript. g. Examples of gene track views of MeRIP-seq, for mRNAs with significant hypermethylation (Tial1, Ptrf) or hypomethylation (Cenpt) in pausing of ESCs. h. GSEA of m6A changes in paused ESCs relative to control ESCs, using the ‘hallmarks’ collection. No single pathway is significantly enriched based on m6A changes (representative pathways are shown). P-values (as indicated on figure) by two-sided pre-ranked gene set enrichment analysis with Benjamini-Hochberg FDR correction.

Source data

Extended Data Fig. 4 Mapping the chromatin distribution of Mettl3 in paused ESCs.

a. m6A machinery (writers Mettl3, Mettl14 and Wtap; and erasers Fto and Alkbh5) in control (Ctrl) and paused ESCs by western blot in whole cell extracts (representative of 3 biological replicates). b. Increase of Mettl3 levels in chromatin extracts upon induction of paused pluripotency, measured by cell number-normalized (CNN) western blot (representative of 3 biological replicates). c. Strategy for Mettl3 ChIP-seq in ESCs with CNN approach using human cell spiking. d. Heatmap of the top 5000 most variable Mettl3 peaks by ChIP-seq across all samples, showing higher levels in paused ESCs (n = 2 biological replicates per group). e. Density plot of the average levels of Mettl3 binding in the TSS of all genes by ChIP-seq, separated by expression and methylation status, in control and paused ESCs. Mettl3 binding is highest in the TSS of expressed genes with a methylated transcript, and in paused ESCs. Number of genes (n) as indicated. Data as mean normalized Mettl3 level (n = 2 biological replicates per group). f. Examples of gene track views showing increased average levels (fold-change > 1.5) of m6A and Mettl3, by MeRIP-seq and Mettl3 ChIP-seq, respectively.

Source data

Extended Data Fig. 5 Capturing Mettl3-dependent changes in RNA stability in paused ESCs.

a. Strategy for RNA stability analysis based on intronic and exonic reads. b-c. Examples of genes with different (Slc16a1, Six4) and similar (Mtor, Gapdh) intronic and exonic mRNA fold-changes between Mettl3−/− and Mettl3+/+ ESCs based on RNA-seq data (b) and validation of stability changes by actinomycin D stability assay (c). N = 3 biological replicates, t1/2: half-life. d. Linear regression of log2 total conversion counts (relative to time 0 h), as measured by SLAM-seq, showing an increase in transcriptome stability in paused Mettl3−/− ESCs. e. Changes in RNA expression in paused Mettl3−/− ESCs based on RNA-seq data (fold-change > 1.5) are associated with changes in RNA half-life in paused Mettl3−/− ESCs. Data are mean ± SD (b) or mean ± SEM (c). P-values (as indicated on figure) by two-tailed paired Student’s t-tests (b), linear regression test with interaction (c) and one-way ANOVA (e). Boxes in the box plots define the interquartile range (IQR) split by the median, with whiskers extending to the most extreme values within 1.5 × IQR beyond the box.

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Extended Data Fig. 6 Screening for candidate anti-pausing factors.

a. Quantification of the number of expressed genes in Mettl3+/+ and Mettl3−/− ESCs based on intronic RNA-seq, in control and paused conditions. Expressed genes are further defined as having high expression (log2 normalized reads > 5, n = 3 biological replicates). b. Heatmap of gene expression based on intronic reads for all genes expressed in Mettl3+/+ or Mettl3−/− ESCs (left) with average expression per sample (right, scored as median z-scores of all genes), showing defective hypotranscription in paused Mettl3−/− ESCs. c. Identification of putative anti-pausing factors kept in check by m6A methylation and thereby destabilization of their transcript in paused pluripotency, based on RNA-seq, MeRIP-seq and SLAM-seq data in ESCs (see Methods for details). d. Expression levels (log2 cpm) of the Myc factors in diapaused embryos (left, data from Boroviak et al.) and paused ESCs (right). Horizontal bars represent the mean, with 3 biological replicates per group, except for diapaused embryos which has 2 replicates. e. mTOR inhibition by dual knockdown of Rptor and Rictor reduces Mycn expression measured by RT-qPCR in ESCs in FBS/LIF/2i medium (n = 4 biological replicates). Data are mean ± SD (a, b, e). P-values (as indicated on figure) by two-way ANOVA with Tukey’s multiple comparisons test (a), two-tailed Student’s t-tests (b), and one-way ANOVA with Dunnett’s multiple comparison tests (e).

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Extended Data Fig. 7 Regulation of Myc family members and downstream targets by Mettl3 in paused pluripotency.

a. Quantification of N-Myc protein levels, showing increased expression in Mettl3−/− ESCs, as shown in Fig. 5c. N = 4 biological replicates. b. Representative western blot of N-Myc protein levels, showing increased expression in Mettl3−/− #2–4 ESCs. N = 2 biological replicates. c. Increased expression of N-Myc in Mettl3−/− ESCs grown in FBS/LIF/2i (compared to Mettl3+/+ ESCs) measured by RT-qPCR (n = 3 biological replicates). Levels are normalized to control Mettl3+/+ ESCs grown in FBS/LIF, as shown in Fig. 5b. d. Mycn expression in Mettl3−/− ESCs is restored to levels comparable to Mettl3+/+ ESCs by transfecting a catalytically active form of Mettl3, and not its inactive mutant form (RT-qPCR, n = 4 biological replicates). e. Validation of Mycn knockdown by RT-qPCR in paused ESCs grown in FBS/LIF (left) or FBS/LIF/2i (right). f-g. Blocking of Myc signaling by Mycn knockdown (e, in FBS/LIF/2i) or chemical inhibitor 10058-F4 (f, in FBS/LIF) partially restores the pausing phenotype in paused Mettl3−/− ESCs in terms of cell proliferation (left, n = 3 biological replicates) and total RNA levels per cell (right, n = 4 and 5 biological replicates). h. Treatment with 10058-F4 partially restores the expression of Myc target genes in paused Mettl3−/− ESCs (RT-qPCR, n = 5 biological replicates). Data are mean ± SD (a, c-h), or mean ± SEM (g left). P-values (as indicated on figure) by two-tailed unpaired Student’s t-tests (a, d, f), by two-way ANOVA with Tukey’s multiple comparisons test (c) or Dunnett’s multiple comparison tests (d-e, h), one-way ANOVA with Dunnett’s multiple comparison tests (f-g).

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Extended Data Fig. 8 m6A-dependent regulation of Mycn mRNA stability.

a. Knockout of Ythdf2 and triple knockout of Ythdf1-3 (TKO) phenocopy the knockout of Mettl3 in paused ESCs, with increased total RNA levels per cell (left, n = 4 biological replicates) and proliferation (right, n = 4 biological replicates) compared to wildtype (WT) ESCs. b. Increased expression of N-Myc in paused Ythdf2−/− and TKO ESCs measured by RT-qPCR (n = 4 biological replicates, relative to paused WT). c. Validation of m6A changes in Mettl3+/+ and Mettl3−/− ESCs by m6A-qPCR (n = 6 biological replicates). d. Mettl3 and Ythdf2 binding of the Mycn transcript, measured by RIP-qPCR in 3 biological replicates. NeuroD1 and Sox2 were used as positive controls, and Actb and Gapdh were used as negative controls. e-f. Mycn is the only Myc family member regulated at the RNA stability level by Mettl3, as evidenced by analysis of exonic and intronic mRNA fold-changes (left, n = 3 biological replicates per group) and SLAM-seq analysis of RNA half-life (right, with half-lives derived from 2 independent time courses). g. Nascent RNA capture by EU incorporation shows minimal changes in nascent transcription for Myc factors in Mettl3−/− ESCs (n = 5 biological replicates). h. Increased Mycn mRNA stability in paused Ythdf2−/− ESCs compared to paused Mettl3+/+ ESCs, as measured by an actinomycin D stability assay (n = 3 biological replicates). t1/2: half-life. Data are mean ± SD (a-e, g) or mean ± SEM (h). P-values (as indicated on figure) by one-way ANOVA with Dunnett’s multiple comparison tests (a-c), two-tailed Student’s t-tests (d-e), two-way ANOVA with Dunnett’s multiple comparison tests (g), and linear regression test with interaction (h).

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Extended Data Fig. 9 Targeted m6A demethylation controls expression of Mycn in paused ESCs.

a. Model of lentiviral dCasRx epitranscriptomic editor with the m6A demethylase ALKBH5 and single guide RNA. b. Validation of the expression of the dCasRx-ALKBH5 fusion by western blot in parental (non-infected) ESCs, infected ESCs, and 2 infected clones. Clone 5 was used for all experiments (representative of 2 biological replicates). c. Guide RNAs (gRNAs) transfected for non-targeting control and Mycn-targeting conditions. d. Changes in m6A using the dCasRx-ALKBH5 editor in paused ESCs. Guides #2 and #3 significantly reduce m6A in Mycn transcripts, as measured by m6A-qPCR, and were selected for all subsequent experiments. N = 7 biological replicates. NT: non-targeting. e. Dot blot showing that the global increase of m6A in paused ESCs is not affected by the dCasRx-ALKBH5 editor, with Mettl3−/− ESCs as negative control. Representative of 3 biological replicates. MB: methylene blue. f. Quantification of N-Myc protein levels, showing increased expression with the dCasRx-ALKBH5 editor targeting Mycn in paused ESCs, with representative blot shown in Fig. 6e. N = 3 biological replicates. g. Demethylation of Mycn increases the total RNA levels per cell in paused ESCs (n = 6 biological replicates). Data are mean ± SD (d, f-g) and P-values (as indicated on figure) by one-way ANOVA with Dunnett’s multiple comparison tests (d, f-g).

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Extended Data Fig. 10 Transcriptional changes by RNA-seq upon m6A demethylation of Mycn in paused ESCs.

a. Mycn expression is increased following targeting with the m6A demethylase ALKBH5 based on exonic reads, but not intronic reads, which is consistent with post-transcriptional regulation (n = 4 biological replicates per condition). b. A global increase in transcripts levels is measured following Mycn mRNA demethylation using both exonic and intronic reads, which is consistent with globally elevated nascent transcription. c. Representative pathways of the GSEA of gene expression changes upon demethylation of Mycn mRNA in paused ESCs using the ‘hallmarks’ collection. d-e. Genes upregulated upon demethylation of Mycn mRNA are enriched for N-Myc targets, as identified in ESCs by ChIP by Chen et al.36. GSEA using a random set of 100 N-Myc targets (c). Venn diagram showing a significant overlap between genes upregulated in paused Mettl3−/− ESCs, genes upregulated following Mycn demethylation, and N-Myc ChIP targets. Data are mean ± SD (a). P-values (as indicated on figure) by two-way ANOVA with Dunnett’s multiple comparison tests (a), two-sided pre-ranked gene set enrichment analysis with Benjamini-Hochberg FDR correction (c, d), and one-sided simulation using hypergeometric distributions (e).

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Supplementary information

Reporting Summary

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Supplementary Tables 1–7

Supplementary Table 1: Differential expression in paused ES cells with Mettl3 KO from RNA-seq data. N = 3 biological replicates per group. Supplementary Table 2: GSEA using RNA-seq data. Expression data presented in Supplementary Table 1 (n = 3 biological replicates per group). Supplementary Table 3: Differential methylation analysis from MeRIP–seq data. N = 3 biological replicates per group. Supplementary Table 4: RNA decay analysis from SLAM–seq data. Half-lives were measured using four time points, with samples collected over two independent experiments. Supplementary Table 5: Top 100 ranked m6A anti-pausing candidates in paused pluripotency. Supplementary Table 6: Differential expression with Mycn mRNA demethylation in paused ES cells from RNA-seq data. N = 4 biological replicates per group. Supplementary Table 7: List of primers and antibodies.

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Collignon, E., Cho, B., Furlan, G. et al. m6A RNA methylation orchestrates transcriptional dormancy during paused pluripotency. Nat Cell Biol 25, 1279–1289 (2023). https://doi.org/10.1038/s41556-023-01212-x

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