Abstract
Endogenous retroviruses (ERVs) are abundant and heterogenous groups of integrated retroviral sequences that affect genome regulation and cell physiology throughout their RNA-centred life cycle1. Failure to repress ERVs is associated with cancer, infertility, senescence and neurodegenerative diseases2,3. Here, using an unbiased genome-scale CRISPR knockout screen in mouse embryonic stem cells, we identify m6A RNA methylation as a way to restrict ERVs. Methylation of ERV mRNAs is catalysed by the complex of methyltransferase-like METTL3–METTL144 proteins, and we found that depletion of METTL3–METTL14, along with their accessory subunits WTAP and ZC3H13, led to increased mRNA abundance of intracisternal A-particles (IAPs) and related ERVK elements specifically, by targeting their 5′ untranslated region. Using controlled auxin-dependent degradation of the METTL3–METTL14 enzymatic complex, we showed that IAP mRNA and protein abundance is dynamically and inversely correlated with m6A catalysis. By monitoring chromatin states and mRNA stability upon METTL3–METTL14 double depletion, we found that m6A methylation mainly acts by reducing the half-life of IAP mRNA, and this occurs by the recruitment of the YTHDF family of m6A reader proteins5. Together, our results indicate that RNA methylation provides a protective effect in maintaining cellular integrity by clearing reactive ERV-derived RNA species, which may be especially important when transcriptional silencing is less stringent.
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Data availability
The raw imaging data that support the findings of this study are available from the corresponding authors on request owing to size considerations. All sequencing data derived from CRISPR–Cas9 screens I and II, RNA-seq, MeRIP-seq and CUT&RUN have been deposited in the Gene Expression Omnibus (GEO) under accession number GSE145616. Previously published data were downloaded from GEO: Mettl3 control and Mettl3 knockout nuclear RNA-seq: (GSE133585)19. Source data are provided with this paper.
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Acknowledgements
We thank the members of the Bourc’his laboratory for their support; M. Greenberg and A. Shkumatava for critical reading of the manuscript; G. Cristofari and J. J. Zylicz for suggestions; I. Pinheiro for help with FACS analysis; E. Nora for targeting plasmids; and M. Schultz and M. Borensztein for assistance with cell culture and immunofluorescence experiments. We acknowledge the ICGex NGS platform of the Institut Curie -supported by grants ANR-10-EQPX-03 (Equipex) and ANR-10-INBS-09-08 (France Génomique) from the Agence Nationale de la Recherche- and the Cell and Tissue Imaging Platform (PICT-IBiSA) of Institut Curie -member of the French National Research Infrastructure France-BioImaging (ANR-10-INBS-04). The laboratory of D.B. is part of the LABEX DEEP (ANR-11-LABX-0044, ANR-10-IDEX-0001-02). This work was supported by the Fondation Bettencourt Schueller, the Association Robert Debré pour la Recherche Médicale (ARDRM), the Fondation pour la Recherche Médicale (FRM) and the Association de Recherche contre le Cancer (ARC-PJA-20191209637). T.C. was a recipient of an EMBO postdoctoral fellowship; E.R. and M.D were supported by PhD fellowships from la Ligue contre le Cancer and from Région Ile-de-France, respectively.
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Contributions
D.B. and T.C. conceived and designed the study. T.C. performed the genetic screen, genetic engineering of the different ES cell lines (reporter, genetic knockouts and auxin-degron), RNA-seq, MeRIP-seq, degron experiments, actinomycin D assays, m6A quantification by ELISA and immunoblots. E.R. contributed to generating and characterizing ES cell knockouts of Mettl3, Mettl14, Wtap, Zc3h13, Ythdf1, Ythdf2 and Ythdf3 and aided degron experiments. T.C., M.D. and L.B. performed CUT&RUN experiments. S.R. and C.F. aided the genetic screen, and M.D. the MeRIP-seq. F.D. provided degron-targeting vectors and helped with degron design. A.T. performed the bioinformatic analyses. S.L. aided sequencing library preparations. D.B. and T.C. interpreted the data and wrote the manuscript. All authors read and approved the final manuscript.
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Extended data figures and tables
Extended Data Fig. 1 Validation of the IAPEz-reporter ES cell line.
a, Immunoblot showing Cas9 protein levels in parental (E14) and doxycycline (dox)-inducible IAPEz-reporter ES cells. Lamin B1 served as loading control. b, Flow cytometry analysis of GFP expression in IAPEz-reporter ES cells before and upon 48 h of dox (1 μM) induction and blasticidin (blast, 5 μg ml−1) selection. Left, overlaid dot blots (last 130,000 events) and histograms normalized to 100% scale (right). Analysis was performed using NovoExpress software (Acea Biosciences). For pseudocolour plots and gating strategy, see Supplementary Fig. 2. Note that blasticidin selection further increases the fraction of GFP-positive cells, and not all blasticidin-resistant cells accumulate detectable GFP levels. Therefore, blasticidin selection is more sensitive than the GFP signal to subtle changes in the expression of IAPEz reporter. c, Immunofluorescence detection of GFP in dox-induced IAPEz-reporter ES cells. Scale bar, 10 μm. d, Immunoblot showing KAP1 protein levels in IAPEz-reporter ES cells after KAP1-specific sgRNA introduction and blasticidin selection. Scrambled sgRNA served as control. Blasticidin treatment shows that only cells with successful KAP1 depletion become antibiotic-resistant. Lamin B1 served as loading control. e, Flow cytometry dot blot analysis (last 50,000 events) of GFP expression in cells from d. Discontinued lines denote GFP-positive threshold. For pseudocolour plots and gating strategy, see Supplementary Fig. 2. KAP1 depletion combined with blasticidin selection leads to IAPEz-reporter reactivation and GFP expression. Experiments in a–e were replicated at least twice with similar results.
Extended Data Fig. 2 Early versus late genome-wide CRISPR–Cas9 screen for IAPEz suppressors.
a, Schematic of the screening process. b, Ranked P values (permutation test) for enriched genes in early (left) and late (right) blasticidin treatment, as in a. Discontinued lines indicate genes ranked in top 100 according to P value (top) and P = 0.05 (bottom). Known IAP regulators (black, when P < 0.05) and Mettl3, Mettl14, Wtap and Zc3h13 (red) are reported. Presented data were derived from biological duplicates.
Extended Data Fig. 3 Generation and validation of Mettl3-, Mettl14-, Wtap- and Zc3h13-knockout mouse ES cells.
a–d, Mettl3-knockout (a), Mettl14-knockout (b), Wtap-knockout (c), and Zc3h13-knockout (d), mediated by CRISPR–Cas9. Schematic representation showing sgRNAs targeting of indicated loci (top) and immunoblots (bottom) confirming depletion of indicated proteins. PCNA or lamin B1 served as loading controls. In red, clones selected for downstream analyses presented in Figs. 2, 3, Extended Data Figs. 3e–g, 4–7. Western blots for clones indicated in red were repeated at least twice with similar results. e, RNA-seq heat maps showing expression of selected pluripotency and post-implantation markers in wild-type, Mettl3-knockout, Mettl14-knockout, Wtap-knockout and Zc3h13-knockout ES cells. Asterisks indicate genes with log2FC > 0.75 and FDR < 0.05. Data represent averages of two independent RNA-seq. f, Representative images of wild-type, Mettl3-, Mettl14-, Wtap- and Zc3h13-knockout ES cells grown in serum-free 2i + LIF medium, or after 24 h conversion to serum + LIF medium. Scale bar, 0.5 mm. When replacing 2i + LIF with serum + LIF medium, marked morphological changes were observed in all knockout lines. Experiments were repeated twice with similar results. g, Growth curves of wild-type, Mettl3-, Mettl14-, Wtap- and Zc3h13-knockout ES cells cultured in serum-free 2i + LIF medium, or after conversion to serum + LIF. Data are mean ± s.d. from three independent experiments. When replacing 2i + LIF with serum + LIF medium, the self-renewal ability of Wtap-knockout cells was severely impaired and Zc3h13-knockout cells disappeared (highlighted by an asterisk). This could explain the lower sgRNA ranking for these two genes upon extended selection time in the screen, which was performed in serum + LIF medium (Extended Data Fig. 2b).
Extended Data Fig. 4 Depletion of m6A methyltransferase complex results in deregulation of different retrotransposon families.
a, ELISA showing normalized m6A levels in total RNA in WT and KO ES cells. Data are mean ± s.d. of three independent RNA samples. P values were determined by two-sided Student’s t-test. b, Volcano plot showing log2FC in retrotransposon expression in Mettl14, Wtap and Zc3h13-knockout versus wild-type cells using a random assignment of multi-mapped reads. In red, blue, orange and green are significantly deregulated RepeatMasker annotations belonging to ERVK, ERV1, ERVL and L1 families, respectively. In grey, non-differentially expressed (non-DE) retrotransposons. P values were computed using limma and adjusted with the Benjamini–Hochberg correction (Methods). c–f, Volcano plots showing log2FC of retrotransposon expression in Mettl3-, Mettl14-, Wtap- and Zc3h13-knockout versus wild-type ES cells using a random assignment of multi-mapped reads. In red, blue, orange and green are significantly deregulated RepeatMasker annotations belonging to ERVK (c), ERV1 (d), ERVL (e) and L1 (f) families, respectively (log2FC > 0.75 and FDR < 0.05). In grey, non-differentially expressed retrotransposons. The P values were computed using limma and adjusted with the Benjamini–Hochberg correction.
Extended Data Fig. 5 Family-based analysis of retrotransposon expression using random versus unique mapping.
a, RNA-seq heat maps showing log2FC in expression of indicated retrotransposon families in Mettl3-, Mettl14-, Wtap- and Zc3h13-knockout versus wild-type ES cells allowing either multiple mapping with random allocation (left) or unique mapping only (right). In case of unique mappers with intragenic position, only elements transcribed in reverse orientation to the gene were included in the analysis. Note that although ERVK annotations show general upregulation in all knockout ES cells, L1s tended to be downregulated, with the exception of oldest L1MdA_IV, L1ME3Cz and L1MdV_I subfamilies. b, Heat maps of published nuclear RNA-seq data19 showing log2FC in expression for indicated retrotransposon families in two Mettl3-knockout versus control ES cells, allowing multiple mapping with random allocation. The two knockout lines show divergent levels of retrotransposon reactivation but consistently show greater reactivation of ERVK compared to ERVL and LINE1 families. Asterisks indicate retrotransposon families with log2FC > 0.75 and FDR < 0.05. Data were taken from accession GSE133585.
Extended Data Fig. 6 Analysis of gene expression in mutant ES cells of the m6A methyltransferase complex.
a, Venn diagram showing overlap of the upregulated genes (FDR < 0.05, log2FC > 0.75) in Mettl3-, Mettl14-, Wtap- and Zc3h13-knockout ES cells as identified by RNA-seq. In blue, genes (n = 941) commonly upregulated in all four knockout ES cells. b, Correlation between gene upregulation and proximity to retrotransposon annotations (−5 kb to +1 kb from the TSS). In blue, genes (n = 941) commonly upregulated in all four knockout ES cell lines. In grey, random genes plotted as mean + s.d. of n = 1,000 randomizations. P values were determined by permutation test. c, Dot plot showing splicing events (normalized counts per million) occurring between exons and RepeatMasker-annotated IAPs in wild-type, Mettl3-, Mettl14-, Wtap- and Zc3h13-knockout ES cells. Horizontal lines denote mean (n = 2 independent RNA-seq). d, Heat map showing average expression of selected retrotransposon regulators in wild-type, Mettl3-, Mettl14-, Wtap- and Zc3h13-knockout cells. Genes with log2FC > 0.75 and FDR < 0.05 are marked by an asterisk.
Extended Data Fig. 7 IAP mRNAs undergo METTL3-dependent m6A methylation.
a, Left, Venn diagram showing overlap between m6A peaks identified in wild-type and Mettl3-knockout MeRIP-seq. Right, input-normalized cumulative distribution of signal intensity for m6A peaks in wild-type (top), overlapping (middle) and Mettl3-knockout specific (bottom) cells. A set of novel m6A peaks was detected in Mettl3-knockout cells; however, they are probably false positives (weak signal intensity compared to canonical wild-type peaks). b, Normalized MeRIP-seq read density in wild-type (red) and Mettl3-knockout (blue) cells across the 5′ UTR, coding sequence and 3′ UTR of mRNA for the genes with at least one m6A peak. Discontinued lines represent respective inputs. c, UCSC genome browser tracks showing m6A distribution (red, normalized read density, RPM) at indicated genes in wild-type and Mettl3-knockout ES cells. In black, input RNA. Results represent average signal of three independent MeRIP-seq experiments. d, Inputs (top) and background-normalized m6A signal distributions (bottom) across the IAPEz consensus sequence in wild-type (red) and Mettl3-knockout (blue) cells. Vertical black lines denote positions of RRACH motifs. Discontinued vertical black lines denote regions of m6A enrichment present in wild-type and lost in Mettl3-knockout cells. e, Average of input-normalized m6A signal intensities along the MMERVK10C consensus sequence in wild-type (red) and Mettl3-knockout (blue) cells. Vertical black lines denote RRACH motif positions. Discontinued vertical black lines denote region of m6A enrichment present in wild-type and lost in Mettl3-knockout cells. f, Average of m6A signal intensities for indicated MMERVK10C sequence segments in wild-type (red) and Mettl3-knockout (red) ES cells. Data are mean ± s.d. of three independent MeRIP-seq experiments. g, h, Background-normalized m6A signal distribution (g) and average of input-normalized m6A signal intensities across the L1MdA_I consensus sequence in wild-type (red) and Mettl3-knockout (blue) cells (h). Vertical black lines denote RRACH motif positions. i, Alignment between 5′ UTRs of IAPEz and MMERVK10C consensus sequences. IAP-specific, MMERVK10C-specific and common RRACH motifs are indicated in blue, yellow and green, respectively. j, Box plot showing log2FC in expression of uniquely mapped IAPEz copies grouped according to number of 5′ UTR-associated RRACH motifs in Mettl14-, Wtap- and Zc3h13-knockout versus wild-type cells. Box plots are as in Fig. 2d. Only copies with a minimum of 10 reads in at least one sample were conserved.
Extended Data Fig. 8 Auxin-inducible degron of endogenous METTL3, METTL14 and ZC3H13.
a, Schematic of TIR1 and SCF1 complex-dependent degradation of endogenously AID-tagged proteins in presence of auxin. b, Immunoblot showing protein levels of endogenously 3×Flag-AID-tagged METTL3 and METTL14 in single and double degron ES cell lines. TIR1-only ES cells were used as control for protein levels. PCNA served as loading control. c, Immunoblot showing efficiency and reversibility of METTL3 and METTL14 depletion after 0–96 h auxin treatment followed by 48 h auxin wash-off. PCNA served as loading control (related to Fig. 4b). d, ELISA showing normalized m6A levels in total RNA after 0, 6 and 96 h of auxin-induced degradation of METTL3, METTL14 and METTL3;METTL14 double degron. Data are mean ± s.d. of three technical replicates. TIR1-only ES cells served as control. Experiment was repeated twice with similar results. e, RT–qPCR showing normalized IAP mRNA levels relative to 0 h using Δ1- or gag-specific primers after auxin-induced degradation of METTL14 (light blue), METTL3 (blue) and METTL3;METTL14 double degron (dark blue). TIR1-only (black) ES cells served as control. Data are mean ± s.d. of three independent auxin inductions). f, Growth curves of TIR1-only and AID-METTL3;METTL14 double degron ES cells treated with either DMSO (grey) or auxin (black) for 0–96 h. Data are mean ± s.d. of three independent auxin inductions. Note that contrary to constitutive m6A-knockout ES cells, proliferation rate is not altered by acute m6A loss (similar rate between AID-METTL3;-METTL14 double degron and TIR1-only ES cells). However, prolonged auxin treatment may have negative effect on the proliferation rate. g–i, ZC3H13 auxin-dependent degron. Schematic of ZC3H13 degron engineering in mouse ES cells (g), immunoblot showing protein levels of endogenously 3×Flag-AID-tagged ZC3H13 (h) and degron efficiency in presence of auxin (i). TIR1-only ES cells were used as control for protein levels. KAP1 served as loading control. j, RT–qPCR showing normalized IAP mRNA levels relative to 0 h using LTR-specific primers after auxin-induced degradation of ZC3H13. Data are mean ± s.d. from three independent auxin inductions. Immunoblots presented in b, c, h, i were repeated at least twice with similar results.
Extended Data Fig. 9 Retrotransposon chromatin profiling in AID-METTL3;METTL14 double degron ES cells using random assignment of multi-mapped reads on full-length elements.
a, Composite profiles (top) and density plots (bottom) showing enrichment of H3K4me3, H3K27ac and H3K9me3 as determined by CUT&RUN on m6A-bound and non-m6A bound genes (±2 kb) (gene lists were obtained based on MeRIP-seq results) in AID-METTL3;METTL14 double degron ES cells treated with DMSO or auxin for 96 h. b, Heat map showing average H3K27ac, H3K4me3 and H3K9me3 levels (log2(immunoprecipitate/IgG)) for 56 retrotransposon families in AID-METTL3;METTL14 double degron ES cells treated with DMSO or auxin for 96 h. c, Violin plots and box plots (inside) showing H3K4me3, H3K27ac and H3K9me3 levels at L1MdA_I-IV copies (n = 436 copies) in AID-METTL3;METTL14 double degron ES cells treated with DMSO (grey) or auxin (red) for 96 h. Only copies with a minimum of 10 reads in at least one sample were conserved. Box plots are as in Fig. 2d. P values were determined by two-sided Student’s t-test. d, Composite profiles showing H3K4me3 (top), H3K27ac (middle) and H3K9me3 (bottom) coverage along full-length (>5 kb) L1MdA_I copies (n = 1,400) in AID-METTL3;METTL14 double degron ES cells treated with DMSO (control, grey) or auxin (red) for 96 h. Results in a–d represent averages of two independent CUT&RUN.
Extended Data Fig. 10 YTHDF2 binds to ERV mRNAs.
a, Schematic representation showing sgRNA targeting of the Ythdf2 gene. b, Immunoblot showing YTHDF2 protein levels in wild-type and YTHDF2-depleted cells. PCNA served as loading control. Immunoblotting was repeated twice with similar results c, RIP–qPCR showing YTHDF2 enrichment on indicated ERVKs and Nanog in wild-type and Ythdf2-knockout ES cells (two independent clones). Rabbit IgG served as control. Data are mean ± s.d. from three independent experiments. d, Generation of mutant mutYthdf2-3 and mutYthdf1-2-3 ES cells using CRISPR–Cas9. Schematic representation showing sgRNA sequences; mutation/deletion information based on Sanger sequencing is provided.
Supplementary information
Supplementary Figures
This file contains Supplementary Figure 1 | Uncropped images of Western blot gels. Supplementary Figure 2 | Pseudocolor plots and gating strategies for FACS analyses presented in Extended Data Fig. 1.
Supplementary Table
Supplementary Table 1: IAPEz nucleotide sequence used in IAPEz reporter cassette; primer sequences used for CRISPR-Cas9 library amplification and sequencing for screens I and II; primers used in RT-qPCR experiments.
Supplementary Table
Supplementary Table 2: List of sgRNA sequences used for generation of mutant and degron mouse ESCs.
Supplementary Table
Supplementary Table 3: List of antibodies.
Supplementary Table
Supplementary Table 4: CRISPR-Cas9 knockout screen I and II gene ranking obtained using MAGeCK.
Supplementary Table
Supplementary Table 5: Sequencing statistics for the CRISPR-Cas9 Screens I and II, RNA seq, MeRIP-seq and CUT&RUN.
Supplementary Table
Supplementary Table 6: Mettl3-, Mettl14-, Wtap- and Zc3h13-KO RNA-seq results for differentially-expressed retrotransposons.
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Chelmicki, T., Roger, E., Teissandier, A. et al. m6A RNA methylation regulates the fate of endogenous retroviruses. Nature 591, 312–316 (2021). https://doi.org/10.1038/s41586-020-03135-1
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DOI: https://doi.org/10.1038/s41586-020-03135-1
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