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Dppa2 and Dppa4 counteract de novo methylation to establish a permissive epigenome for development

Abstract

Early mammalian development entails genome-wide epigenome remodeling, including DNA methylation erasure and reacquisition, which facilitates developmental competence. To uncover the mechanisms that orchestrate DNA methylation dynamics, we coupled a single-cell ratiometric DNA methylation reporter with unbiased CRISPR screening in murine embryonic stem cells (ESCs). We identify key genes and regulatory pathways that drive global DNA hypomethylation, and characterize roles for Cop1 and Dusp6. We also identify Dppa2 and Dppa4 as essential safeguards of focal epigenetic states. In their absence, developmental genes and evolutionarily young LINE1 elements, which are specifically bound by DPPA2, lose H3K4me3 and gain ectopic de novo DNA methylation in pluripotent cells. Consequently, lineage-associated genes and LINE1 acquire a repressive epigenetic memory, which renders them incompetent for activation during future lineage specification. Dppa2/4 thereby sculpt the pluripotent epigenome by facilitating H3K4me3 and bivalency to counteract de novo methylation, a function co-opted by evolutionarily young LINE1 to evade epigenetic decommissioning.

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Fig. 1: Developmental model and ratiometric reporter for DNA demethylation CRISPR screening.
Fig. 2: Validation of candidate DNAme reprogramming factors reveals a role for Dusp6 and Cop1.
Fig. 3: Dppa2/4 occupancy promotes DNA hypomethylation in pluripotent cells.
Fig. 4: Dppa2 and Dppa4 establish chromatin states to safeguard against de novo methylation.
Fig. 5: Developmental genes and LINE1 acquire transcriptional silencing memory in Dppa2/4 mutants.
Fig. 6: Functional interaction between DNAme, H3K4me3 and gene silencing.

Data availability

All sequencing data derived from RNA-seq, EM-seq, Cut&Run-seq and CRISPR screening have been deposited in the Gene Expression Omnibus database under accession code GSE146863. Source data for Figs. 2c, 3e, 4a,e, 5d–h and 6a,b are available online.

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Acknowledgements

We thank C. Policarpi for advice on CUT&RUN and all members of the Hackett laboratory for experimental support. We thank M. Boulard and P. Avner for critical reading of the manuscript and E. Magnusdottir and A. Aulehla for their contributions to thesis advisory committee discussions. We thank H. Koseki and J. Sharif for kindly sharing floxed Dnmt1 ESCs. We thank European Molecular Biology Laboratory (EMBL) core facilities and, in particular, those of genetic and viral engineering (J. Sawitzke) and flow cytometry (C. Chaddick), for key experimental assistance. This study was funded by an EMBL program grant to J.A.H.

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K.H.G. performed experiments and bioinformatics analysis and contributed to the manuscript. J.A.H. designed and supervised the study, performed bioinformatics analyses and wrote the manuscript.

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Correspondence to Jamie A. Hackett.

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Peer review information Peer reviewer reports are available. Anke Sparmann was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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Extended data

Extended Data Fig. 1 Model and enhanced ratiometric reporter for developmental DNA demethylation.

a, Schematic for design and optimisation of the ratiometric enhanced eRGM cellular DNAme reporter. The system consists of a methylation-sensitive imprinted promoter, which controls expression of GFP according to its level of DNA methylation. The DNA methylation level is set by an antisense upstream genomic region (DNAme sensor), which acquires a level of DNAme that tracks the global DNA methylation state in the cell. The sensor subsequently adjusts DNAme at the imprinted promoter to equivalence via proximity spreading. This two-stage system generates a robust read-out of global DNAme status (upper panels). Changing the DNAme sensor to a region that is resistant to DNA demethylation (for example IAP) prevents eRGM activation in hypomethylated conditions (left FACS plot), confirming that the sensor DNAme status controls activity. Moreover switching the imprinted promoter from Snrpn to Kcnq1ot1 enables a greater degree of expression upon DNA hypomethylation, thereby increasing the dynamic range of the reporter (right FACS plot). Shown in grey is activity in the ‘off’ hypermethyalted state. Finally, by coupling eRGM with a second methylation-insensitive reporter (Ef1a-mCherry), a single-cell ratiometric score can be generated that normalises for confounding factors. b, Transcriptomics from ESC maintained in Serum/Lif (hypermethylated), titrated t2i/L (hypermethylated) or 2i/L (hypomethylated). The t2i/L and 2i/L transcriptomes are highly comparable despite distinct global methylation states (hyper−and hypo-methylation, respectively), implying transition between these conditions isolates epigenetic resetting without confounding changes in cell identity. c, Screen shot of genomic methylation pattern from naïve E3.5 epiblast and naïve ESC demonstrating in vitro resetting from t2i/L to 2i/L establishes a highly comparable methylome as in vivo resetting. d, Representative FACS plots of progressive eRGM (GFP) activation by DNA demethylation during 12 day transition from t2i/L to 2i/L in independent eRGM cell lines (Line #1 and #2). Normalising to mCherry (lower panel) enables a ratiometric single-cell readout (each datapoint is a single cell) (lower right panel), which closely tracks global methylation levels (upper right). e, CRISPR screen for gene KO that enable eRGM activation even under hypermethylated conditions (t2i/L) identifies key known DNA methylation and chromatin regulators, confirming eRGM specificity and sensitivity to modulation of epigenetic systems.

Extended Data Fig. 2 Validation of CRISPR screen candidates for epigenetic reprogramming.

a, Scatter plot showing significance (RRA) values for candidate reprogramming factors from screens of independent eRGM cell lines are highly correlated. A FDR < 0.05 was used as a threshold to identify final candidates. b, Gene ontology (GO) analysis of final candidate factors from the screens shows enrichment for nuclear activity, and involvement in chromatin and/or nucleic acid processes consistent with being epigenetic regulators. c, Percentage of cells that remain eRGM-negative in hypomethylation-inducing 2i/L upon knockout of the indicated candidate reprogramming factor. Shown is data from KO generated in eRGM line#2, analogous to independent KO in eRGM line#1 (Fig. 2a) d, Flow cytometry histograms (GFP on x-axis) demonstrating knockout of most candidates leads to a significant block of eRGM activation amongst single cells, implying altered epigenetic resetting. Shown is percentage single-cells with eRGM-negative ‘off’ (marked in grey) after 12 days in DNA hypomethylation-inducing 2i/L culture.

Extended Data Fig. 3 The DNA methylation landscape in Dppa2/4 knockout.

a, Western blot confirming loss of DPPA2, DPPA4, or both (DKO) protein(s) upon generation of clonal knockout ESC lines. Note loss of DPPA2 protein leads to depletion of DPPA4, and reciprocally, potentially due to disruption of the heterodimeric complex that stabilises each protein. Uncropped image of the blot available as source data. Shown above are schamtics of DPPA2 (red) and DPPA4 (green) b, Heatmap showing methylation status of significant differentially methylated regions (from sliding 50 CpG windows) (DMR) in Dppa2 and Dppa4 knockout ESC or EpiLC. Note Dppa4 DMRs are highly correlated with Dppa2. c, Gene ontology (GO) of genes associated with differentially methylated promoters (DMP) in Dppa2 KO ESC or EpiLC, determined by direct analysis of +1 kb to -1kb of TSS, reveals enrichment for developmental-associated gene classes. d, Genome view showing DNA methylation patterns in WT and Dppa2/4 KO ESC and EpiLC. Each datapoint represents the windowed average methylation of 15-20 CpG sites. e, Top three enriched DPPA2 binding motifs from DREME analysis of DPPA2 binding peaks at non-repetitive elements, reveal preference for GC. f, Scatter plot of DPPA2 enrichment at all DPPA2 binding peaks in ESC and EpiLC demonstrating a similar binding pattern of DPPA2 in ESC and EpiLC. g, Genome view of DPPA2 CUT&RUN-seq tracks showing that DPPA2 binds at the genomic sensor region used in eRGM (upstream of Dazl) in ESC and EpiLC and protects it from de novo methylation.

Source data

Extended Data Fig. 4 Chromatin state at DPPA2 binding sites and upon knockout.

a, Aligned probe plot showing enrichment of DPPA2, H3K4me3, H3K27me3, and H3K9me3 centered on DPPA2 binding sites + /-4kb in WT ESC and EpiLC. Plots are ordered equivalently, by DPPA2 binding enrichment, which is shown in red. H3K4me3 shows strong enrichment over nearly all DPPA2 sites, whilst H3K27me3 shows modest enrichment. b, As in (a), but chromatin enrichment is cantered on differentially methylated regions (DMR). H3K4me3 is enriched in WT ESC at sites prone to hypermethylation upon Dppa2/4 knockout c, Genome view showing H3K4me3 and H3K27me3 in in WT and Dppa2/4 KO ESC and EpiLC, over representative developmental genes. d, Trend plot showing the enrichment of chromatin marks (H3K4me3 and H3K27me3 or H3K9me3) over gene promoters (upper panels) or over full-length LINE1 promoters (lower panels) that acquire DNAme in Dppa2 KO. H3K4me3 exhibits a dramatic depletion in Dppa2 or Dppa4 KO, whilst H3K27me3 and H3K9me3 exhibit no significant changes at these loci. e, Left: Boxplot showing H3K27me3 enrichment over all, DPPA2-bound and non-DPPA2 bound promoters binned for expression quintile. Box indicates the 25th, median and 75th percentiles, whiskers the 10th to the 90th percentiles. Right: Scatter plot of H3K27me3 enrichment at DPPA2-binding sites in WT and Dppa2−/− ESC and EpiLC. Significant differentially H3K27me3 enriched sites, all (blue) and overlapping promoters (red) are highlighted. Significance by LIMMA < 0.01. Showing that unlike H3K4me3, very few DPPA2 binding sites exhibit a change in H3K27me3 upon Dppa2 knockout.

Extended Data Fig. 5 Transcriptional and developmental competence upon Dppa2/4 deletion.

a, Volcano plot showing all differentially expressed genes (DEG) in Dppa2−/− and Dppa4−/− ESC and EpiLC compared to WT. Note many more genes are repressed than activated. b, Gene ontology (GO) of DEGs in ESC and EpiLC reveals a strong enrichment for developmental-associated terms, which is driven by silenced developmental genes in Dppa2/4 KO c, Representative examples of developmental genes that fail to activate in Dppa2/4 KO EpiLC. d, Heatmap showing log fold-change of expression normalised to WT of all transposable elements (TE) in WT, Dppa2−/−, Dppa4−/− and DKO ESC and EpiLC. e, qRT-PCR quantification of expression of L1Md_T and L1Md_A during ESC to endoderm differentiation in WT, Dppa2−/− and Dppa4−/− normalised to WT ESC. Data represent mean ± s.d. (n = 2 biologically independent experiments). Confirming that evolutionary young L1Md_T and L1Md_A exhibit impaired expression in Dppa2/4 KO, implying Dppa2/4 are required to maintain competence for TE activity.

Extended Data Fig. 6 Functional interaction between DNAme, H3K4me3, and gene silencing.

a, Scatter plot showing inter-relationships between changes in H3K4me3 and DNA methylation versus gene expression upon Dppa2 KO in EpiLC b, Trend plots showing the enrichment of H3K4me3 over gene promoters (upper panels) and over full-length LINE1 promoters (lower panels) that acquire DNAme in Dppa2 KO. Shown for WT, Dppa2 KO (or Dppa4 KO), Dnmt1 KO and Dnmt1/Dppa2 KO (or Dppa4 KO) in ESC and EpiLC. c, Proposed model of the interplay between H3K4me3 and DNA methylation at Dppa2/4 targets in regulating gene expression competence.

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Supplementary Table 1

Table containing a list of candidate reprogramming factors identified by the CRISPR screen, a list of DMPs following Dppa2/4 knockout and a list of gRNA sequences used to generate knockouts in this study.

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Unprocessed immunoblots.

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Gretarsson, K.H., Hackett, J.A. Dppa2 and Dppa4 counteract de novo methylation to establish a permissive epigenome for development. Nat Struct Mol Biol 27, 706–716 (2020). https://doi.org/10.1038/s41594-020-0445-1

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