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Stella safeguards the oocyte methylome by preventing de novo methylation mediated by DNMT1

Nature volume 564, pages 136–140 (2018)Cite this article

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Abstract

Postnatal growth of mammalian oocytes is accompanied by a progressive gain of DNA methylation, which is predominantly mediated by DNMT3A, a de novo DNA methyltransferase1,2. Unlike the genome of sperm and most somatic cells, the oocyte genome is hypomethylated in transcriptionally inert regions2,3,4. However, how such a unique feature of the oocyte methylome is determined and its contribution to the developmental competence of the early embryo remains largely unknown. Here we demonstrate the importance of Stella, a factor essential for female fertility5,6,7, in shaping the oocyte methylome in mice. Oocytes that lack Stella acquire excessive DNA methylation at the genome-wide level, including in the promoters of inactive genes. Such aberrant hypermethylation is partially inherited by two-cell-stage embryos and impairs zygotic genome activation. Mechanistically, the loss of Stella leads to ectopic nuclear accumulation of the DNA methylation regulator UHRF18,9, which results in the mislocalization of maintenance DNA methyltransferase DNMT1 in the nucleus. Genetic analysis confirmed the primary role of UHRF1 and DNMT1 in generating the aberrant DNA methylome in Stella-deficient oocytes. Stella therefore safeguards the unique oocyte epigenome by preventing aberrant de novo DNA methylation mediated by DNMT1 and UHRF1.

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Fig. 1: Stella disrupts the nuclear localization of UHRF1 through nuclear export.
Fig. 2: UHRF1 contributes to a hypermethylated genome and developmental deficiency in Stella−/− oocytes.
Fig. 3: An aberrant maternal genome from a Stella−/− oocyte impairs zygotic genome activation and preimplantation embryo development.
Fig. 4: DNMT1 contributes to the establishment of aberrant methylated sites.

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Data availability

Sequencing data reported in this paper are summarized in Supplementary Tables 2, 3 and have been deposited in the Gene Expression Omnibus database under accession code GSE78149. Uncropped film scans for Extended Data Figs. 1a, 2a–d, g–i, 8b are presented in Supplementary Fig. 1. Source data for Figs. 1c, 2d, 3c, i and Extended Data Figs. 1c–e, 2e, f, k, 1, 6c, 7j, 8d are provided. All other data are available from the corresponding author on reasonable request.

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Acknowledgements

The Stella−/− mice were provided by the RIKEN BRC through the National Bio-Resource Project of the MEXT, Japan, and they were initially deposited by T. Nakamura and T. Nakano. We thank staff from the Laboratory Animal Research Center for technical assistance. We thank X. Wang, Z. Shen and H. Huang for discussion. This work was primarily supported by the China National Science Foundation (31521002), and was also supported by the Chinese Ministry of Science and Technology (2015CB856200, 2016YFA0100400 and 2017YFA0504200), the Chinese Academy of Sciences (XDB08010103, XDBP10 and QYZDY-SSW-SMC031), the China National Science Foundation (31425013, 31530037, 31730047, 31721003, 31871446 and31871448), the Shanghai Chenguang Program (16CG17) and the Shanghai Municipal Medical and Health Discipline Construction Projects (2017ZZ02015). Z.Z. is sponsored by the Youth Innovation Promotion Association (2017133) of the Chinese Academy of Sciences. G.F. is supported by the National Institutes of Health (R01DE025474).

Author contributions

Y.L., Z.Z. and B.Z. designed this study. Y.L. performed the majority of the experiments. Z.Z. performed the bioinformatics analysis. J.C. and W. Liu isolated pronuclei and performed the mouse embryo experiments. W. Lai and B.L. performed the UHPLC–MS/MS experiments. X.L., L.L., S.X., Q.D., M.W., X.D., J.T., Y.Z. and Z.W. assisted with the experiments. P.Z. and J.W. provided Uhrf1fl/fl mice, G.F. and G.-L.X. provided Dnmt3afl/fl and Dnmt1fl/fl mice, G.-L.X. provided antibodies against mouse DNMT3A and DNMT3B. H.W. supervised the UHPLC–MS/MS experiments. S.G. supervised the mouse embryo manipulations. Y.L., Z.Z. and B.Z. wrote the manuscript, and all authors read and commented on the manuscript.

Author information

Author notes

  1. These authors contributed equally: Yingfeng Li, Zhuqiang Zhang, Jiayu Chen

Authors and Affiliations

  1. College of Life Sciences, Beijing Normal University, Beijing, China

    Yingfeng Li

  2. National Laboratory of Biomacromolecules, CAS Center for Excellence in Biomacromolecules, Institute of Biophysics, Chinese Academy of Sciences, Beijing, China

    Yingfeng Li, Zhuqiang Zhang, Xiang Li, Qiang Dong, Jiajun Tan, Yong Zheng & Bing Zhu

  3. National Institute of Biological Sciences, Beijing, China

    Yingfeng Li, Xiang Li, Shaohua Xu & Qiang Dong

  4. Clinical and Translational Research Center of Shanghai First Maternity and Infant Hospital, Shanghai Key Laboratory of Signaling and Disease Research, School of Life Sciences and Technology, Tongji University, Shanghai, China

    Jiayu Chen, Wenqiang Liu, Mingzhu Wang & Shaorong Gao

  5. State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Weiyi Lai, Baodong Liu & Hailin Wang

  6. Department of Molecular Biology, University of Texas Southwestern Medical Center, Dallas, TX, USA

    Liping Liu & Zhigao Wang

  7. Shanghai Key Laboratory of Regulatory Biology, Fengxian District Central Hospital-ECNU Joint Center of Translational Medicine, Institute of Biomedical Sciences and School of Life Sciences, East China Normal University, Shanghai, China

    Xiaoya Duan & Jiemin Wong

  8. College of Life Sciences, University of Chinese Academy of Sciences, Beijing, China

    Jiajun Tan & Bing Zhu

  9. Department of Molecular Physiology and Biophysics, Baylor College of Medicine, Houston, TX, USA

    Pumin Zhang

  10. Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, USA

    Guoping Fan

  11. State Key Laboratory of Molecular Biology, CAS Center for Excellence in Molecular Cell Science, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China

    Guo-Liang Xu

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  1. Yingfeng Li
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Correspondence to Bing Zhu.

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Extended data figures and tables

Extended Data Fig. 1 Analysis of regulation between Stella and UHRF1.

a, Stella associated with endogenous UHRF1 in HEK293 cells (left) and NIH3T3 cells (right) under stringent co-immunoprecipitation conditions (extensive wash with 500 mM NaCl). For gel source data, see Supplementary Fig. 1. b, Expression levels (fragments per kilobase of transcript per million, FPKM) of the most highly expressed genes and Uhrf1 in Stella+/− D5, D10, D20 and MII oocytes. c, Analysis of UHRF1 distribution in wild-type D20 growing oocytes; PFA/Triton co-fixation was performed before PFA fixation (see Methods). Left, representative images of the stained oocytes. Right, quantitative results of the UHRF1 signal intensities in the nuclear regions (N), cytoplasmic regions (C) and background (B). n indicates the number of oocytes analysed from one representative experiment. d, e, Left, representative images of zygotes (d) and early two-cell-stage embryos (e) stained with anti-Stella (red) and anti-UHRF1 (H-8, green). Right, scatter plots of the quantitative results of the UHRF1 nuclear/cytoplasmic intensity ratio. Het denotes heterozygous Stella+/−; KO denotes knockout Stella−/−. n indicates the number of cells analysed from two independent experiments. In c–e, data are mean ± s.d. and are analysed using an unpaired two-tailed Student’s t-test. ns, not significant (P > 0.05). In a, c, data shown are representatives of three independent experiments. Scale bars, 10 μm.

Source data

Extended Data Fig. 2 Stella regulates the cellular distribution and protein abundance of UHRF1 in oocytes.

a, Confocal imaging (left) and western blotting (right) of the clonal HEK293 cell with a GFP–UHRF1 transgene (GU293) shown in Fig. 1d. b, UHRF1 translocated from the nuclear fraction to the cytosolic fraction in T-REx-293 cells expressing a doxycycline-inducible Stella. c, Pull-down analysis of the interaction between GFP–UHRF1 and mCherry-tagged Stella(NESmut). d, Pull-down analysis of the interaction between GFP–UHRF1 and mCherry-tagged Stella mutants. Experiments were performed under mild conditions (wash with 150 mM NaCl, top) or stringent conditions (wash with 500 mM NaCl, bottom). KRR, K85E/R86E/R87E mutant; KR, K113E/R114E mutant. e, Stella−/− growing oocytes were electroporated with the indicated mRNA, with immunostaining performed 16 h after electroporation. Left, representative images immunostained for Stella (red) and UHRF1 (green). Right, a summary of the subcellular distribution of UHRF1, n indicates the number of oocytes analysed from two independent experiments. f, Stella+/− growing oocytes were cultured in vitro in M2 medium supplemented with 20 nM leptomycin B and 240 μM dibutyryl-cAMP for 3 h before immunostaining. Left, representative images immunostained for Stella (red) and UHRF1 (green). Right, a summary of the subcellular distribution of UHRF1, n indicates the number of oocytes analysed from two independent experiments. g–i, Levels of UHRF1 in germinal vesicle oocytes (g), D20 growing oocytes (h) and D15 growing oocytes (i). In i, growing oocytes were treated with 20 μM MG-132 or DMSO for 12 h before analysis. j, RNA-seq analysis of Uhrf1 expression in oocytes. k, l, Quantification of UHRF1 immunostaining intensity. Data are mean ± s.d. and were analysed using an unpaired two-tailed Student’s t-test. n denotes the number of oocytes analysed from one representative experiment. For gel source data relating to a–d, g–i, see Supplementary Fig. 1. Data shown are representatives of three (b) or two (a, c, d, g–i, k, l) independent experiments. Scale bars, 10 μm.

Source data

Extended Data Fig. 3 Stella prevents UHRF1-dependent aberrant DNA methylation during oogenesis.

a, DNA methylation level in sperm from Stella+/− and Stella−/− mice. The cumulative bar plot shows the proportions of 100-bp tiles in four methylation level ranges. b, c, Average DNA methylation levels of different genomic contexts (b) and repeat elements (c) in MII oocytes. d, Genome browser views of DNA methylation in the HoxA, HoxB, HoxC and HoxD cluster regions of the genome. e, DNA methylation of CpG islands in MII oocytes and sperm from Stella+/− and Stella−/− mice. The cumulative bar plot shows the proportions of 100-bp tiles in five methylation level ranges. f, Genome browser views of DNA methylation in the DMRs of the maternally imprinted genes Zac1 (also known as Plagl1) and Impact. g, Bisulfite sequencing analysis of the DMRs of the paternally imprinted genes H19 and Rasgrf1 in D20 growing oocytes. The data shown are representative of two independent experiments.

Extended Data Fig. 4 Generation and analysis of the Uhrf1 conditional knockout mouse.

a, An illustration of the targeted Uhrf1 allele: Uhrf1tm1a(EUCOMM)Wtsi and the strategy to generate the Uhrf1 conditional knockout mouse. b, Immunostaining of wild-type and Uhrf1−/− fully grown oocytes with anti-UHRF1 antibodies (H-8 or M-132). The experiment was performed once with four independent anti-UHRF1 antibodies. Scale bars, 10 µm. c, Representative images of preimplantation embryos after 96 h of in vitro culture. Stella+/−, n = 3; Stella−/−, n = 2; Stella+/−Uhrf1−/−, n = 2; Stella−/−Uhrf1−/−, n = 2 independent experiments. Scale bar, 100 μm.

Extended Data Fig. 5 Relationship between transcription and DNA methylation in oocytes upon Stella knockout.

a, Scatter plots showing gene transcriptional changes in Stella+/− and Stella−/− D5, D10, D20 growing oocytes and MII oocytes. Genes that were upregulated or downregulated more than twofold in Stella−/− oocytes are indicated in red or green, respectively. b, Expression levels and fold changes of gene expression with aberrantly methylated promoters (TSS ± 2 kb) (left) and CGIs (right) in Stella−/− MII oocytes. FPKMs are derived from two biological replicates of RNA-seq experiments. c, Plots of the changes of promoter methylation against the average gene expression between Stella+/− and Stella−/− D5, D10 and D20 growing oocytes.

Extended Data Fig. 6 The post-fertilization fate of the DNA methylome in the Stella−/− oocyte.

a, A schematic illustration of the experimental procedure. PN4–PN5 stage pronuclei were isolated from zygotes at 11–12 h after intracytoplasmic sperm injection (ICSI). Maternal pronuclei and paternal pronuclei were subjected to RRBS analysis separately. b, A histogram of the RDLs in paternal pronuclei. The RDLs of CpG sites with a methylation level ≥75% in sperm are shown. c, Left, representative images of zygotes stained with anti-5hmC antibody. MII oocytes from Stella+/− and Stella−/− female mice were fertilized with wild-type sperm. The male and female pronuclei are indicated. PB, polar body. Scale bar, 10 µm. Right, analysis of the relative 5hmC intensity in the parental pronuclei. Data are mean ± s.d., an unpaired two-tailed Student’s t-test was used. n indicates the number of zygotes analysed from two independent experiments. d, A schematic illustration of the experimental procedure. Two-cell stage embryos were collected 28 h after IVF and their polar bodies were removed. e, Box plots of the methylation levels of normal ooAMRs and extra ooAMRs in MII oocytes, maternal pronuclei and two-cell-stage embryos. The allele-specific tiles are not discriminated in two-cell-stage embryos. The midline of the box indicates the median, the outer edges represent the first and third quartiles, and values outside 1.5× the interquartile range (IQR) were designated outliers and are not shown. The upper lower whiskers show the maximum and minimum values that are not outliers, respectively. Data are from two (MII, mPN) or three (2C) biological replicates.

Source data

Extended Data Fig. 7 Analysis of defects in the Stella−/− oocyte.

a, A scatter plot showing gene transcriptional changes in StellaΔm/+ and control two-cell-stage embryos. Genes that are upregulated or downregulated more than twofold in StellaΔm/+ embryos are indicated in red and green, respectively. b, ZGA genes are divided into three groups on the bases of their expression changes in StellaΔm/+ two-cell-stage embryos. c, A Venn diagram showing how many of the 167 ZGA-defective genes can also be defined as ZGA-defective (≥twofold decrease) in K/W or W/K spindle-transferred two-cell-stage embryos when compared with W/W two-cell-stage embryos. d, Methylation levels in the promoter regions of ZGA-defective genes defined in the indicated spindle-transferred two-cell-stage embryos. The corresponding methylation level in StellaΔm/+ and control two-cell-stage embryos is shown. e, A Venn diagram showing how many of the 412 upregulation-defective genes can also be defined as upregulation-defective (≥2-fold decrease) in K/W or W/K spindle-transferred two-cell-stage embryos when compared with W/W two-cell-stage embryos. f, Methylation levels in the promoter regions of upregulation-defective genes defined in the indicated spindle-transferred two-cell-stage embryos. The corresponding methylation level in StellaΔm/+ and control two-cell-stage embryos is shown. g, A Venn diagram showing how many of the 102 downregulation-defective genes can also be defined as downregulation-defective (≥2-fold increase) in K/W or W/K spindle transferred two-cell-stage embryos compared with W/W two-cell-stage embryos. h, Methylation levels in the promoter regions of downregulation-defective genes defined in the indicated spindle-transferred two-cell-stage embryos. The corresponding methylation level in StellaΔm/+ and control two-cell-stage embryos is shown. In d, f, h, the midline of the box indicates the median, the outer edges represent the first and third quartiles, and values outside 1.5× IQR are designated outliers and are not shown. The upper and lower whiskers show the maximum and minimum values that are not outliers, respectively. Data were analysed using an unpaired two-tailed Student’s t-test, with three biological replicates for each sample. i, Representative images of the reconstituted embryos analysed in Fig. 3i after 96 h of in vitro culture. W/W, n = 2; K/W, n = 3; W/K, n = 3; K/K, n = 2 independent experiments. Scale bar, 50 μm. j, The preimplantation development rate of the indicated microinjected embryos during in vitro culture. Each dot represents one biological replicate, and the mean values of biological replicates are connected by lines.

Source data

Extended Data Fig. 8 Analysis of DNMTs in the Stella−/− oocyte.

a, Stella−/− growing oocytes stained with anti-UHRF1 and anti-H3K9me3 (left) or anti-H3K9me2 (right) antibodies. b, Validation of the specificity of two anti-DNMT1 antibodies, M377 and H-300. Wild-type E14 and Dnmt1−/− mouse embryonic stem cells were used for immunostaining (top) and western blotting (bottom) analysis. For gel source data, see Supplementary Fig. 1. c, Stella+/− and Stella−/− growing oocytes stained with anti-UHRF1 (H-8) (top) and anti-DNMT1 (H-300) (middle) antibodies. d, Summary of the DNMT1 nuclear staining pattern in Fig. 4a, b. Two categories were defined: diffused and pericentric heterochromatin (PCH)-enriched. n indicates the number of oocytes analysed from at least two independent experiments. e, Stella+/− and Stella−/− growing oocytes stained with anti-UHRF1 (M-132) (left) and anti-DNMT3B (middle). f, Validation of the specificity of anti-DNMT3B antibody. Wild-type E14 and Dnmt3b−/− mouse embryonic stem cells were used for immunostaining. g, Stella+/− and Stella−/− growing oocytes stained with anti-UHRF1 (M-132) (left) and anti-DNMT3A (right). Images shown are representative of three (a, c, e, g) or two (b, f) independent experiments. Scale bars, 10 μm.

Source data

Extended Data Fig. 9 De novo activity of DNMT1 independent of DNMT3A in the Stella−/− oocyte.

a, Stella−/−Dnmt3a−/− and Stella−/−Dnmt1−/− germinal vesicle oocytes stained with anti-DNMT3A (left) and anti-DNMT1 (M377) (middle). Images shown are representative of two independent experiments. Scale bars, 10 μm. b, c, Venn diagrams show the Uhrf1, Dnmt1 and Dnmt3a-dependent ooAMRs in Stella+/− oocytes (b, ooAMRs that methylated in Stella+/− oocytes but unmethylated in Stella+/−Uhrf1−/−, Stella+/−Dnmt1−/− and Stella+/−Dnmt3a−/− oocytes) and in Stella−/− oocytes (c, ooAMRs that methylated in Stella−/− oocytes but unmethylated in Stella−/−Uhrf1−/−, Stella−/−Dnmt1−/− and Stella−/−Dnmt3a−/− oocytes). ooAMRs that have methylation level >75% or <25% are defined as methylated or unmethylated, respectively. d, Hierarchical clustering heat map shows the methylation status of Dnmt1-dependent ooAMRs in Stella−/− oocytes (definition as in c) from D5 to MII and in all types of MII oocytes. e, Violin plot of the methylation levels of 100-bp CG tiles for Stella+/− oocytes and Stella−/−Dnmt3a−/− oocytes. The violin plot is shown as the combination of a box plot (midline, median; outer edges, first and third quartiles) and a kernel density. Data analysed from two (Stella−/−) or three (Stella−/−Dnmt3a−/−) biological replicates. f, Distribution of ooAMRs in active or inactive genomic regions. g, DNA methylation of normal ooAMRs and extra ooAMRs at different stages of oocyte development. The median methylation levels of 100-bp tiles are connected by lines, and black bars represent the third and first quartiles. For both Stella+/− and Stella−/− oocytes: D5, n = 3; D10, D20, MII, n = 2 independent experiments. h, Box plots showing RPKM values from RNA-seq of normal ooAMRs that are categorized by their methylation level in Stella−/− Dnmt3a−/− MII oocytes. Gene expression levels in Stella+/− Dnmt3a−/− and Stella−/−Dnmt3a−/− MII oocytes are shown. The midline of the box indicates the median, the outer edges represent the first and third quartiles, and values outside 1.5× IQR are designated outliers and are not shown. The upper and lower whiskers show the maximum and minimum values that are not outliers, respectively. Two-way ANOVA (RPKM ~ methylation × genotype) was performed, methylation level has a statistically significant effect on the RPKM level (degrees of freedom (Df) = 2, F = 74, P < 2 × 10−16), but not the genotype (Df = 1, F = 1.5, P = 0.22) or its interaction with the methylation level (Df = 2, F = 0.13, P = 0.88). Data analysed from two biological replicates.

Extended Data Fig. 10 Reproducibility of RRBS experiments.

a–c, Heat maps constructed from all 100-bp CG tiles that are commonly covered by MII oocyte (a), pronuclei (b) and two-cell embryo (c) samples. The genetic background and number of replicates are indicated. All samples are hierarchically clustered by similarity.

Supplementary information

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This file contains Supplementary Text (discussion on the regulation mechanism between Stella and UHRF1) and Supplementary Figure 1, the uncropped blots shown in the manuscript.

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Li, Y., Zhang, Z., Chen, J. et al. Stella safeguards the oocyte methylome by preventing de novo methylation mediated by DNMT1. Nature 564, 136–140 (2018). https://doi.org/10.1038/s41586-018-0751-5

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