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Metabolic control of DNA methylation in naive pluripotent cells

Nature Genetics volume 53, pages 215–229 (2021)Cite this article

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Abstract

Naive epiblast and embryonic stem cells (ESCs) give rise to all cells of adults. Such developmental plasticity is associated with genome hypomethylation. Here, we show that LIF–Stat3 signaling induces genomic hypomethylation via metabolic reconfiguration. Stat3−/− ESCs show decreased α-ketoglutarate production from glutamine, leading to increased Dnmt3a and Dnmt3b expression and DNA methylation. Notably, genome methylation is dynamically controlled through modulation of α-ketoglutarate availability or Stat3 activation in mitochondria. Alpha-ketoglutarate links metabolism to the epigenome by reducing the expression of Otx2 and its targets Dnmt3a and Dnmt3b. Genetic inactivation of Otx2 or Dnmt3a and Dnmt3b results in genomic hypomethylation even in the absence of active LIF–Stat3. Stat3−/− ESCs show increased methylation at imprinting control regions and altered expression of cognate transcripts. Single-cell analyses of Stat3−/− embryos confirmed the dysregulated expression of Otx2, Dnmt3a and Dnmt3b as well as imprinted genes. Several cancers display Stat3 overactivation and abnormal DNA methylation; therefore, the molecular module that we describe might be exploited under pathological conditions.

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Fig. 1: LIF–Stat3 induces hypomethylation in ESCs via Dnmt3a/b regulation.
Fig. 2: Impact of Stat3 on DNA methylation and transcription.
Fig. 3: Nuclear Stat3 does not regulate Dnmt3a/b and 5mC.
Fig. 4: Stat3 controls DNA methylation via metabolic regulation.
Fig. 5: Alpha-ketoglutarate regulates 5mC mainly by controlling the levels of Dnmt3a/b.
Fig. 6: Otx2 links αKG to Dnmt3a/b expression.
Fig. 7: Mitochondrial Stat3 regulates ESC differentiation.
Fig. 8: Stat3 regulates Dnmts and imprinted transcripts in early mouse blastocysts.

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Data availability

Bulk and single-cell RNA-seq and RRBS data generated during the current study are available at the Gene Expression Omnibus (GEO) repository under the accession numbers GSE133926 and GSE134450. All RNA-seq and RRBS process data used in Figs. 1d,i, 2a–e,h, 4c, 5d, 6b,d, 7a–e, 8a–g and Extended Data Figs. 1a,b,e, 2b–k, 3b,c, 5c, 7a–d, 8a–f are reported in Supplementary Tables 1, 2 and 4. The RNA-seq data of Rex1-GFPd2 cells can be found at GEO under the accession number GSE111694. Mass spectrometry proteomic data of the following samples: S3+/+ cells in 2i; S3+/+, S3−/−, MitoS3.A and MitoS3.B cells in 2iLIF used in Figs. 1f, 5g, and Extended Data Fig. 1c are reported in Supplementary Table 3 and on ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD020385. The primers and oligonucleotide sequences are in Supplementary Tables 6–8. Additional data that support the findings of this study, such as mass spectrometry measurements, analysis pipelines and reagents, are available from the corresponding authors on reasonable request. Source data are provided with this paper.

Code availability

All software and bioinformatic tools used in the present study are publicly available.

References

  1. Ito, S. et al. Role of Tet proteins in 5mC to 5hmC conversion, ES-cell self-renewal and inner cell mass specification. Nature 466, 1129–1133 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Ito, S. et al. Tet proteins can convert 5-methylcytosine to 5-formylcytosine and 5-carboxylcytosine. Science 333, 1300–1303 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Messerschmidt, D. M., Knowles, B. B. & Solter, D. DNA methylation dynamics during epigenetic reprogramming in the germline and preimplantation embryos. Genes Dev. 28, 812–828 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Monk, M., Boubelik, M. & Lehnert, S. Temporal and regional changes in DNA methylation in the embryonic, extraembryonic and germ cell lineages during mouse embryo development. Development 99, 371–382 (1987).

    Article  CAS  PubMed  Google Scholar 

  5. Smith, Z. D. et al. A unique regulatory phase of DNA methylation in the early mammalian embryo. Nature 484, 339–344 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Ishida, M. & Moore, G. E. The role of imprinted genes in humans. Mol. Aspects Med. 34, 826–840 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Boroviak, T. et al. Lineage-specific profiling delineates the emergence and progression of naive pluripotency in mammalian embryogenesis. Dev. Cell 35, 366–382 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Do, D. V. et al. A genetic and developmental pathway from STAT3 to the OCT4–NANOG circuit is essential for maintenance of ICM lineages in vivo. Genes Dev. 27, 1378–1390 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Martello, G., Bertone, P. & Smith, A. Identification of the missing pluripotency mediator downstream of leukaemia inhibitory factor. EMBO J. 32, 2561–2574 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Mohammed, H. et al. Single-cell landscape of transcriptional heterogeneity and cell fate decisions during mouse early gastrulation. Cell Rep. 20, 1215–1228 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Ye, S., Li, P., Tong, C. & Ying, Q. L. Embryonic stem cell self-renewal pathways converge on the transcription factor Tfcp2l1. EMBO J. 32, 2548–2560 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Boroviak, T. & Nichols, J. Primate embryogenesis predicts the hallmarks of human naive pluripotency. Development 144, 175–186 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Ying, Q.-L. et al. The ground state of embryonic stem cell self-renewal. Nature 453, 519–523 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Ficz, G. et al. FGF signaling inhibition in ESCs drives rapid genome-wide demethylation to the epigenetic ground state of pluripotency. Cell Stem Cell 13, 351–359 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Habibi, E. et al. Whole-genome bisulfite sequencing of two distinct interconvertible DNA methylomes of mouse embryonic stem cells. Cell Stem Cell 13, 360–369 (2013).

    Article  CAS  PubMed  Google Scholar 

  16. Hackett, J. A. et al. Synergistic mechanisms of DNA demethylation during transition to ground-state pluripotency. Stem Cell Rep. 1, 518–531 (2013).

    Article  CAS  Google Scholar 

  17. Leitch, H. G. et al. Naive pluripotency is associated with global DNA hypomethylation. Nat. Struct. Mol. Biol. 20, 311–316 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Smith, A. G. et al. Inhibition of pluripotential embryonic stem cell differentiation by purified polypeptides. Nature 336, 688–690 (1988).

    Article  CAS  PubMed  Google Scholar 

  19. Carbognin, E., Betto, R. M., Soriano, M. E., Smith, A. G. & Martello, G. Stat3 promotes mitochondrial transcription and oxidative respiration during maintenance and induction of naive pluripotency. EMBO J. 35, 618–634 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Gough, D. J. et al. Mitochondrial STAT3 supports Ras-dependent oncogenic transformation. Science 324, 1713–1716 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Wegrzyn, J. et al. Function of mitochondrial Stat3 in cellular respiration. Science 323, 793–797 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Lu, C. & Thompson, C. B. Metabolic regulation of epigenetics. Cell Metab. 16, 9–17 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Dawlaty, M. M. et al. Loss of Tet enzymes compromises proper differentiation of embryonic stem cells. Dev. Cell 29, 102–111 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. von Meyenn, F. et al. Impairment of DNA methylation maintenance is the main cause of global demethylation in naive embryonic stem cells. Mol. Cell 62, 848–861 (2016).

    Article  Google Scholar 

  25. Martello, G. et al. Esrrb is a pivotal target of the Gsk3/Tcf3 axis regulating embryonic stem cell self-renewal. Cell Stem Cell 11, 491–504 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Yamane, M., Ohtsuka, S., Matsuura, K., Nakamura, A. & Niwa, H. Overlapping functions of Krüppel-like factor family members: targeting multiple transcription factors to maintain the naive pluripotency of mouse embryonic stem cells. Development 145, dev162404 (2018).

  27. Elhamamsy, A. R. Role of DNA methylation in imprinting disorders: an updated review. J. Assist. Reprod. Genet. 34, 549–562 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Ferguson-Smith, A. C. Genomic imprinting: the emergence of an epigenetic paradigm. Nat. Rev. Genet. 12, 565–575 (2011).

    Article  CAS  PubMed  Google Scholar 

  29. Hackett, J. A., Kobayashi, T., Dietmann, S. & Surani, M. A. Activation of lineage regulators and transposable elements across a pluripotent spectrum. Stem Cell Rep. 8, 1645–1658 (2017).

    Article  CAS  Google Scholar 

  30. Sánchez-Castillo, M. et al. CODEX: a next-generation sequencing experiment database for the haematopoietic and embryonic stem cell communities. Nucleic Acids Res. 43, D1117–D1123 (2015).

    Article  PubMed  Google Scholar 

  31. Matsuda, T. et al. STAT3 activation is sufficient to maintain an undifferentiated state of mouse embryonic stem cells. EMBO J. 18, 4261–4269 (1999).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Tang, Y. et al. Jak/Stat3 signaling promotes somatic cell reprogramming by epigenetic regulation. Stem Cells 30, 2645–2656 (2012).

    Article  CAS  PubMed  Google Scholar 

  33. Peron, M. et al. Mitochondrial STAT3 regulates proliferation of tissue stem cells. Preprint at bioRxiv https://doi.org/10.1101/2020.07.17.208264 (2020).

  34. Carey, B. W., Finley, L. W. S., Cross, J. R., Allis, C. D. & Thompson, C. B. Intracellular α-ketoglutarate maintains the pluripotency of embryonic stem cells. Nature 518, 413–416 (2015).

    Article  CAS  PubMed  Google Scholar 

  35. Hou, P. et al. Intermediary metabolite precursor dimethyl-2-ketoglutarate stabilizes hypoxia-inducible factor-1α by inhibiting prolyl-4-hydroxylase PHD2. PLoS ONE 9, e113865 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Xu, Y. et al. Genome-wide regulation of 5hmC, 5mC, and gene expression by Tet1 hydroxylase in mouse embryonic stem cells. Mol. Cell 42, 451–464 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Chen, T., Ueda, Y., Dodge, J. E., Wang, Z. & Li, E. Establishment and maintenance of genomic methylation patterns in mouse embryonic stem cells by Dnmt3a and Dnmt3b. Mol. Cell. Biol. 23, 5594–5605 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Tischler, J. et al. Metabolic regulation of pluripotency and germ cell fate through α-ketoglutarate. EMBO J. 38, e99518 (2018).

    PubMed  PubMed Central  Google Scholar 

  39. Laukka, T. et al. Fumarate and succinate regulate expression of hypoxia-inducible genes via TET enzymes. J. Biol. Chem. 291, 4256–4265 (2016).

    Article  CAS  PubMed  Google Scholar 

  40. Xiao, M. et al. Inhibition of α-KG-dependent histone and DNA demethylases by fumarate and succinate that are accumulated in mutations of FH and SDH tumor suppressors. Genes Dev. 26, 1326–1338 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Teslaa, T. & Teitell, M. A. Pluripotent stem cell energy metabolism: an update. EMBO J. 34, 138–153 (2015).

    Article  CAS  PubMed  Google Scholar 

  42. Nishiyama, A. et al. Systematic repression of transcription factors reveals limited patterns of gene expression changes in ES cells. Sci. Rep. 3, 5–10 (2013).

    Article  Google Scholar 

  43. Correa-Cerro, L. S. et al. Generation of mouse ES cell lines engineered for the forced induction of transcription factors. Sci. Rep. 1, 167 (2011).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Nishiyama, A. et al. Uncovering early response of gene regulatory networks in ESCs by systematic induction of transcription factors. Cell Stem Cell 5, 420–433 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Buecker, C. et al. Reorganization of enhancer patterns in transition from naive to primed pluripotency. Cell Stem Cell 14, 838–853 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Yang, S.-H. et al. Otx2 and Oct4 drive early enhancer activation during embryonic stem cell transition from naive pluripotency. Cell Rep. 7, 1968–1981 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Pawlak, M. & Jaenisch, R. De novo DNA methylation by Dnmt3a and Dnmt3b is dispensable for nuclear reprogramming of somatic cells to a pluripotent state. Genes Dev. 25, 1035–1040 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  48. Grabole, N. et al. Prdm14 promotes germline fate and naive pluripotency by repressing FGF signalling and DNA methylation. EMBO Rep. 14, 629–637 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Yamaji, M. et al. PRDM14 ensures naive pluripotency through dual regulation of signaling and epigenetic pathways in mouse embryonic stem cells. Cell Stem Cell 12, 368–382 (2013).

    Article  CAS  PubMed  Google Scholar 

  50. Dan, J. et al. Roles for Tbx3 in regulation of two-cell state and telomere elongation in mouse ES cells. Sci. Rep. 3, 3492 (2013).

    Article  PubMed  PubMed Central  Google Scholar 

  51. Palamarchuk, A. et al. Tcl1 protein functions as an inhibitor of de novo DNA methylation in B-cell chronic lymphocytic leukemia (CLL). Proc. Natl Acad. Sci. USA 109, 2555–2560 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Acampora, D., Giovannantonio, L. G. D. & Simeone, A. Otx2 is an intrinsic determinant of the embryonic stem cell state and is required for transition to a stable epiblast stem cell condition. Development 140, 43–55 (2013).

    Article  CAS  PubMed  Google Scholar 

  53. Kalkan, T. et al. Tracking the embryonic stem cell transition from ground state pluripotency. Development 144, 1221–1234 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  54. Koh, K. P. et al. Tet1 and Tet2 regulate 5-hydroxymethylcytosine production and cell lineage specification in mouse embryonic stem cells. Cell Stem Cell 8, 200–213 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Meissner, A. et al. Genome-scale DNA methylation maps of pluripotent and differentiated cells. Nature 454, 766–770 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Tahiliani, M. et al. Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science 324, 930–935 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Tsumura, A. et al. Maintenance of self-renewal ability of mouse embryonic stem cells in the absence of DNA methyltransferases Dnmt1, Dnmt3a and Dnmt3b. Genes Cells 11, 805–814 (2006).

    Article  CAS  PubMed  Google Scholar 

  58. Zhou, W. et al. HIF1α induced switch from bivalent to exclusively glycolytic metabolism during ESC-to-EpiSC/hESC transition. EMBO J. 31, 2103–2116 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Bourillot, P. Y. et al. Novel STAT3 target genes exert distinct roles in the inhibition of mesoderm and endoderm differentiation in cooperation with Nanog. Stem Cells 27, 1760–1771 (2009).

    Article  CAS  PubMed  Google Scholar 

  60. Niwa, H., Ogawa, K., Shimosato, D. & Adachi, K. A parallel circuit of LIF signalling pathways maintains pluripotency of mouse ES cells. Nature 460, 118–122 (2009).

    Article  CAS  PubMed  Google Scholar 

  61. Boroviak, T., Loos, R., Bertone, P., Smith, A. & Nichols, J. The ability of inner-cell-mass cells to self-renew as embryonic stem cells is acquired following epiblast specification. Nat. Cell Biol. 16, 516–528 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  62. McLaughlin, K. et al. DNA methylation directs polycomb-dependent 3D genome re-organization in naive pluripotency. Cell Rep. 29, 1974–1985 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Takeda, K. et al. Targeted disruption of the mouse Stat3 gene leads to early embryonic lethality. Proc. Natl Acad. Sci. USA 94, 3801–3804 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Leeb, M., Dietmann, S., Paramor, M., Niwa, H. & Smith, A. Genetic exploration of the exit from self-renewal using haploid embryonic stem cells. Cell Stem Cell 14, 385–393 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Avalle, L. et al. STAT3 localizes to the ER, acting as a gatekeeper for ER-mitochondrion Ca2+ fluxes and apoptotic responses. Cell Death Differ. 26, 932–942 (2019).

    Article  CAS  PubMed  Google Scholar 

  66. Wang, L. et al. JAK/STAT3 regulated global gene expression dynamics during late-stage reprogramming process. BMC Genomics 19, 183 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  67. Hwang, I.-Y. et al. Psat1-dependent fluctuations in α-ketoglutarate affect the timing of ESC differentiation. Cell Metab. 24, 494–501 (2016).

    Article  CAS  PubMed  Google Scholar 

  68. Zhang, J. et al. LIN28 regulates stem cell metabolism and conversion to primed pluripotency. Cell Stem Cell 19, 66–80 (2016).

    Article  CAS  PubMed  Google Scholar 

  69. Mullen, A. R. et al. Reductive carboxylation supports growth in tumour cells with defective mitochondria. Nature 481, 385–388 (2012).

    Article  CAS  Google Scholar 

  70. Choi, J. et al. Prolonged Mek1/2 suppression impairs the developmental potential of embryonic stem cells. Nature 548, 219–223 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Yagi, M. et al. Derivation of ground-state female ES cells maintaining gamete-derived DNA methylation. Nature 548, 224–227 (2017).

    Article  CAS  PubMed  Google Scholar 

  72. Gretarsson, K. J. & 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).

    Article  CAS  PubMed  Google Scholar 

  73. Chen, T., Ueda, Y., Xie, S. & Li, E. A novel Dnmt3a isoform produced from an alternative promoter localizes to euchromatin and its expression correlates with activede novo methylation. J. Biol. Chem. 277, 38746–38754 (2002).

    Article  CAS  PubMed  Google Scholar 

  74. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

  75. Neri, F., Incarnato, D., Krepelova, A., Parlato, C. & Oliviero, S. Methylation-assisted bisulfite sequencing to simultaneously map 5fC and 5caC on a genome-wide scale for DNA demethylation analysis. Nat. Protoc. 11, 1191–1205 (2016).

    Article  CAS  PubMed  Google Scholar 

  76. Xi, Y. & Li, W. BSMAP: whole genome bisulfite sequence MAPping program. BMC Bioinform. 10, 232 (2009).

    Article  Google Scholar 

  77. Akalin, A. et al. methylKit: A comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13, R87 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  78. Boroviak, T. et al. Single cell transcriptome analysis of human, marmoset and mouse embryos reveals common and divergent features of preimplantation development. Development 145, dev167833 (2018).

  79. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

  80. Anders, S., Pyl, P. T. & Huber, W. HTSeq—a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  PubMed  Google Scholar 

  81. Anders, S. & Huber, W. Differential expression analysis for sequence count data. Genome Biol. 11, R106 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Lê, S., Josse, J. & Husson, F. FactoMineR: an R package for multivariate analysis. J. Stat. Softw. 25, 1–18 (2008).

    Article  Google Scholar 

  83. Risso, D., Perraudeau, F., Gribkova, S., Dudoit, S. & Vert, J.-P. A general and flexible method for signal extraction from single-cell RNA-seq data. Nat. Commun. 9, 284 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Gong, T. & Szustakowski, J. D. DeconRNASeq: a statistical framework for deconvolution of heterogeneous tissue samples based on mRNA-Seq data. Bioinformatics 29, 1083–1085 (2013).

    Article  CAS  PubMed  Google Scholar 

  85. Rappsilber, J., Ishihama, Y. & Mann, M. Stop and go extraction tips for matrix-assisted laser desorption/ionization, nanoelectrospray, and LC/MS sample pretreatment in proteomics. Anal. Chem. 75, 663–670 (2003).

    Article  CAS  PubMed  Google Scholar 

  86. Michalski, A. et al. Mass spectrometry-based proteomics using Q Exactive, a high-performance benchtop quadrupole Orbitrap mass spectrometer. Mol. Cell. Proteomics 10, M111.011015 (2011).

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Acknowledgements

We thank H. Leitch for critical reading of the manuscript, and M. Montagner, S. Dupont and the Martello laboratory for their discussions and suggestions. We thank P. Martini for help with next-generation sequencing data analyses and D. De Stefani for technical help with the mitochondrial assays. We thank A. Simeone (Institute of Genetics and Biophysics, CNR, Naples, Italy) for the Otx2-null ESCs. We thank F. Caicci and A. Dinarello for electron microscopy acquisition and technical support. The J.N. laboratory is supported by BBSRC grant no. RG77233. The G.M. laboratory is supported by grants from the Giovanni Armenise–Harvard Foundation, the Telethon Foundation (grant no. TCP13013) and an ERC Starting Grant (MetEpiStem). The S.O. laboratory is supported by grants from Associazione Italiana Ricerca sul Cancro (grant no. AIRC -IG 2017 Id. 20240), the Telethon Foundation (grant no. GGP19201A) and by PRIN 2017. The N.M. laboratory is supported by grants from the Giovanni Armenise–Harvard Foundation, Intramural Transition grant from Università degli Studi di Milano, European Foundation for the Study of Diabetes/Lilly Programme 2015, Associazione Italiana Ricerca sul Cancro (grant no. AIRC -IG 2019 Id. 23127), MIUR Progetto Eccellenza to Department of Pharmacological and Biomolecular Sciences (DiSFeB), University of Milan, Italy.

Author information

Author notes

  1. Valentina Perrera

    Present address: Neuroscience Sector, International School for Advanced Studies (SISSA), Trieste, Italy

  2. Danny Incarnato

    Present address: Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, the Netherlands

  3. Giovanni Rigoni

    Present address: Department of Medical Biochemistry and Biophysics, Karolinska Institute, Stockholm, Sweden

Authors and Affiliations

  1. Department of Molecular Medicine, Medical School, University of Padua, Padua, Italy

    Riccardo M. Betto, Linda Diamante, Valentina Perrera, Mattia Arboit & Graziano Martello

  2. Department of Pharmacological and Biomolecular Sciences (DiSFeB), University of Milan, Milan, Italy

    Matteo Audano, Silvia Pedretti & Nico Mitro

  3. Department of Life Sciences and Systems Biology, University of Turin, Turin, Italy

    Stefania Rapelli, Andrea Lauria, Danny Incarnato & Salvatore Oliviero

  4. Italian Institute for Genomic Medicine (IIGM), Candiolo, Italy

    Stefania Rapelli, Andrea Lauria & Salvatore Oliviero

  5. Department of Molecular Genetics, Groningen Biomolecular Sciences and Biotechnology Institute (GBB), University of Groningen, Groningen, the Netherlands

    Danny Incarnato

  6. Department of Biology, University of Padua, Padua, Italy

    Giovanni Rigoni, Maria E. Soriano & Graziano Martello

  7. Université Paris-Saclay, Institut de Chimie des Substances Naturelles, CNRS, Gif-sur-Yvette, France

    Vincent Guerineau & David Touboul

  8. Wellcome–MRC Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK

    Giuliano Giuseppe Stirparo, Tim Lohoff & Jennifer Nichols

  9. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK

    Thorsten Boroviak & Jennifer Nichols

  10. Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK

    Thorsten Boroviak

  11. Centre for Trophoblast Research, University of Cambridge, Cambridge, UK

    Thorsten Boroviak

  12. Wellcome Trust–Medical Research Council Stem Cell Institute, Jeffrey Cheah Biomedical Centre, University of Cambridge, Cambridge, UK

    Thorsten Boroviak

  13. Telethon Institute of Genetics and Medicine (TIGEM), Pozzuoli, Italy

    Paolo Grumati

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G.M., N.M. and S.O. designed the study. R.M.B. and L.D. performed the ESC culture, molecular characterization and functional assays and visualization. V.P. and L.D. performed the MeDIP–qPCR. R.M.B., V.P. and S.R. performed the RRBS. R.M.B. and S.R. performed the western blots. A.L. and D.I. performed the RRBS integrated analysis. M. Arboit and L.D. performed the RNA-seq analyses. R.M.B., S.P. and M. Audano performed the metabolomic analyses. R.M.B., V.P., V.G. and D.T. performed the nucleotide mass spectrometry. P.G. performed the proteomics. M.E.S. and G.R. performed the mitochondrial and nuclear fractionations. L.D. and G.G.S. performed the single-cell RNA-seq analyses. T.L., T.B. and J.N. performed the embryo dissections and single-cell RNA-seq library preparations. G.M. wrote the manuscript with input from all authors. G.M., N.M. and S.O. supervised the study.

Corresponding authors

Correspondence to Nico Mitro, Salvatore Oliviero or Graziano Martello.

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Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Genetics thanks Heather Christofk, Christian Frezza, 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 Dnmt3a/b control 5mC levels downstream of LIF–Stat3.

a, Distribution of DNA methylation levels at CpG islands in S3+/+ cells cultured in Serum LIF, 2i or 2iLIF and S3-/- cells in 2iLIF. b, Gene expression analysis in RGd2 cells in 2i with or without LIF. Socs3 was used as a control of LIF–Stat3 activation. Bars: mean of n = 2 biological replicates.c, Proteomics data from S3+/+ and S3-/- cells in 2iLIF. Yellow and blue dots indicate respectively proteins that are more or less abundant (difference > 1 or < -1, p-value < 0.05) in S3+/+ relative to S3-/-. n = 5 biological replicates. d, Western blot of E14 mES cells and of Dnmt3a KO, Dnmt3b KO, and Dnmt3a/b double KO cells (two clones for each mutant genotype) in Serum LIF. Two E14 samples are loaded on the right and left for each KO cell line. B-ACTIN used as a loading control. Representative images of n = 2 independent experiments. e, Distribution of DNA methylation levels at CpG islands in E14 mES cells in 2i or 2iLIF and two clones of Dnmt3a/b double KO mES cells in 2i.f, Gene expression analysis of S3+/+ cells cultured in 2iLIF transiently expressing an Empty Vector, Dnmt3a (two isoforms, Dnmt3a1 and Dnmt3a2 – as previously identified in73), Dnmt3b, or the three genes simultaneously (Dnmt3a1/a2/b OE). Bars: mean ± s.e.m. of n = 3 independent experiments, shown as dots. g, Anti-5mC immunofluorescence on S3+/+ in 2i and 2iLIF, and Dnmt3a1 OE, Dnmt3a2 OE, Dnmt3b OE, and Dnmt3a1/a2/b triple OE cells in 2iLIF. Violin plots of an average of 89 nuclei per sample. n = 4 experiments. h, Gene expression analysis of S3+/+ cells in 2iLIF stably expressing shRNA to knock-down Tet1 and Tet2 simultaneously or a scrambled control shRNA. Bars: mean ± s.e.m. of n = 4 experiments. i, Anti-5mC immunofluorescence on S3+/+ in 2iLIF transiently expressing a scrambled control shRNA and shRNA against Tet1/Tet2. Violin plots of an average of 78 nuclei per sample. n = 4 experiments.All violin and boxplots indicate the 1st, 2nd and 3rd quartiles, with whiskers indicating minimum and maximum value. All p-values calculated by two-tailed unpaired T-test. Scale bars: 20μm.

Source data

Extended Data Fig. 2 Impact of LIF–Stat3 and Dnmt3a/b on genome methylation.

a, MeDIP-qPCR of repetitive elements in S3+/+ and S3-/- cells. Mock immunoprecipitations with a non-specific IgG antibody served as negative controls. Mean of 2 experiments, shown as dots. b, Volcano plot showing the significant differentially methylated CpG sites (q-value < 0.01, difference > 10% or < 10%) between S3+/+ 2i and S3+/+ 2iLIF cells, out of 1,327,475 detected sites. c, d Scatter plot showing the mutual changes in expression and DNA methylation at active promoters (c) and enhancers (d) between S3+/+ 2i and S3+/+ 2iLIF cells. Red dots: genes for which both changes were statistically significant (q-value < 0.01). e, Scatter plot comparing effects on transcription of the absence of LIF (S3+/+ 2i) and the absence of Stat3 (S3-/- 2iLIF). Pearson’s correlation coefficient (R) and corresponding p-value are indicated in the panel. f, Volcano plot showing the significant differentially methylated CpG sites (q-value < 0.01, difference > 10% or < -10%) between E14 2i and E14 2iLIF cells, out of 1,084,350 detected sites. g, CpG methylation changes caused by LIF addition (y axis) or by Dnmt3a/b deletion (x axis, Dnmt3a/b dKO.1). Dots indicate all CpG sites covered (sequencing depth >10x) in at least one technical replicate of each sample; blue dots: hypomethylated sites (q-value < 0.01, difference < -10 %). h, Volcano plot showing the significant differentially methylated CpG sites between Dnmt3a/b dKO.1 and E14 cells cultivated in 2i.i, CpG methylation changes caused by LIF (y axis) or by Dnmt3a/b deletion (x axis, Dnmt3a/b dKO.2), as described in panel g. j, Volcano plot showing the significant differentially methylated CpG sites between Dnmt3a/b dKO.2 and E14 cells cultivated in 2i. k, Venn diagram of CpG sites whose methylation status is dependent on either LIF (light blue) or Dnmt3a/b (red) or on both (grey intersection), for an independent mutant Dnmt3a/b dKO clone (Dnmt3a/b dKO.2).

Source data

Extended Data Fig. 3 Effect of Stat3 inactivation on imprinted transcripts.

a, MeDIP-qPCR of two control regions for DNA methylation change (Gapdh as negative and H19 as positive control). Mock immunoprecipitations with a non-specific IgG antibody served as negative controls. Mean ± s..e.m of 4 experiments for Gapdh and mean of 2 experiments for H19, shown as dots. b, Differentially Methylated Regions and associated imprinted genes. Table reports Differentially Methylated Regions (DMRs) analyzed by RRBS with associated coordinates (reference genome: mm10); information about these DMRs in mouse genome was collected from three different databases (WAMIDEX https://atlas.genetics.kcl.ac.uk/; MouseBook - Imprinting Loci https://www.mousebook.org/; Geneimprint http://geneimprint.com/site/genes-by-species.Imprinted genes whose expression is controlled by the same DMR are grouped accordingly and indicated in the second column of the table. For each imprinted gene, fourth column reports the expected effect on gene expression - either upregulation or downregulation - caused by methylation deposition at the associated DMR (data from literature). The last column of the table shows observed expression levels (RNAseq data) of each imprinted gene in S3-/- cells, were hypermethylation was detected at the corresponding DMR (see Fig. 2f). c, Pie charts showing the number of up- and down-regulated genes (q-value < 0.01, Benjamini-Hochberg adjustment and log2 FC > 1 or < -1) in S3-/- cells with respect to S3+/+ cells among all expressed genes (left), or among all expressed imprinted genes (right).

Source data

Extended Data Fig. 4 Modulating mitochondrial activity affects 5mC levels.

a, Anti-5mC immunofluorescence on S3+/+ cells in 2i or 2iLIF and S3-/- cells in 2iLIF treated with EdU for 4 h. Violin plots show the distribution of an average of 67 nuclei per sample; one representative experiment is shown for each condition. b, Anti-5mC immunofluorescence on S3+/+ cells treated with Rotenone or Antimycin A. Violin plots show the distribution of an average of 74 nuclei per sample. 3 independent experiments shown as individual violins. c, Anti-5mC immunofluorescence on E14 in 2iLIF and 2i, and on Dnmt3a/b double KO cells in 2iLIF, treated with Vehicle, Rotenone or Antimycin A. Violin plots show the distribution of an average of 183 nuclei per sample. 3 experiments shown as individual violins. d, Confocal images of S3+/+, S3-/- cells and MitoS3.A/B clones stained with anti-Stat3 and anti-Atad3 antibodies. Representative images of 3 independent experiments. e, Electron Microscopy images of STAT3 protein stained by Diaminobenzidine photooxidation method, in S3-/- and MitoS3.A cells. Representative images of 2 experiments. M, mitochondria; N, nucleus. f, Expression analysis of Socs3 in S3+/+, S3-/- cells and MitoS3.A/B clones in 2iLIF. Bars: mean of n = 2 experiments, shown as dots. g, (Left) Western blot of S3+/+, S3-/- cells and MitoS3.A and MitoS3.B clones in 2iLIF. LAMIN B: loading control. (Right) Western blot of total lysates or mitochondrial and nuclear fractions. The nuclear protein LAMIN B and mitochondrial marker TIM23 confirmed successful nuclear and mitochondrial isolation. Representative images of 2 independent experiment. h, Oxygen consumption rate measured by Seahorse extracellular flux assay of S3+/+, S3-/- and MitoS3.A/B clones cultured in 2iLIF. Mean and S.D. of n = 5 biological replicates is shown. All violin and boxplots indicate the 1st, 2nd and 3rd quartiles, with whiskers indicating minimum and maximum value. All p-values calculated by two-tailed unpaired T-test. Scale bars: 20μm.

Source data

Extended Data Fig. 5 Metabolic reconfiguration following Stat3 deletion.

a, Diagram representing mass isotopomer distribution (MID) of OAA, Citrate and αKG in both oxidative and reductive Glutamine pathways; MID was analysed following 8 h of metabolic tracing with [U-13C5]-Glutamine. Orange box: mitochondrion. Full circles: 13C-labeled carbons. Color scale outlines the comparison between MID profile in S3-/- relative to S3+/+for n = 6 biological replicates; blue: isotopomers (or biochemical pathways) under-represented is S3-/- cells; red: isotopomers or pathways over-represented in S3-/- cells. Each isotopomer is corrected for natural isotope abundances. b, Metabolic tracing analysis of different isotopomers of TCA cycle intermediates (Succinate, Oxaloacetate, Citrate and αKG) using [U-13C5]-Glutamine. Barplot represents mass isotopomer distribution (MID %) at 3 different time points (2 h, 4 h, 8 h). Black circles: 13C‐labeled carbons. Bars: mean ± s.e.m of n = 6 biological replicates. * p-value<0.05, two-tailed unpaired T-test. Each isotopomer is corrected for natural isotope abundances. c, Expression of IDH1, measured by RT q-PCR and RNAseq in S3+/+ and S3-/- cells. Bars: mean of n = 2 biological replicates. d, Gene expression analysis of two Hif1a targets in S3+/+ cells cultured in 2iLIF in normoxia (high O2–21%), in hypoxia (low O2, 21%), or in normoxia with the addition of αKG or DM-αKG. Bars: indicate mean ± s.e.m. of n = 7 experiments for high O2 and low O2, and n = 4 for treatments with αKG and DM-αKG, shown as dots.

Source data

Extended Data Fig. 6 Mitochondrial Stat3 slows down ESC differentiation.

Gene expression analysis by RT–qPCR of S3+/+, S3-/- and MitoS3.A/B clones cultured with 2iLIF or without 2iLIF for 24 h or 48 h. Data show expression of Imprinted genes a, Mesoderm b, Ectoderm c, and PGCs d, markers that are more readily induced in S3-/- and MitoS3.A/B clones rescues this effect. Beta-actin served as an internal control. Bars indicate mean ± s.e.m. of n = 3 independent experiments, shown as dots. Two-tailed unpaired T-test relative to S3-/- for each time point.

Source data

Extended Data Fig. 7 Stat3 regulates transcripts associated to differentially methylated genomic features.

a, Left: boxplot reporting expression levels of genes down-regulated in S3-/- cells relative to S3+/+ cells (Fig. 7a, blue dots) and differentially methylated at promoter regions (Fig. 2b). Right: boxplot reporting expression levels of genes up-regulated in S3-/- cells with respect to S3+/+ cells (Fig. 7a, yellow dots) and with differential methylation at promoter regions (Fig. 2b). Each boxplot shows 1st, 2nd and 3rd quartile. Whiskers shows minimum and maximum values. Y axis represents mean-normalized TPM values for S3+/+, S3-/- and MitoS3.A and MitoS3.B) in two different conditions: following stable culturing of cells in 2iLIF (light color) and after 48 h of 2iLIF withdrawal from culture medium (dark color). b, Boxplot reporting expression levels of genes down-regulated (left) or up-regulated (right) in S3-/- cells relative to S3+/+ cells and differentially methylated at enhancer regions (Fig. 2c), as described above. c, Boxplot reporting expression levels of genes down-regulated (left) or up-regulated (right) in S3-/- cells relative to S3+/+ cells and differentially methylated at the associated DMR (Fig. 2f), as described above. d, Merge of genes contained in boxplots shown in Extended Data Fig. 7a–c.

Extended Data Fig. 8 Accelerated progression of Stat3 null embryos.

a, Volcano plot of genes differentially expressed between S3-/- and S3+/+ cells at E3.5. Red and blue dots indicate respectively transcripts that are upregulated or downregulated (log2 FC > 0.7 or FC < -0.7 respectively, q-value < 0.1) in S3-/- cells relative to S3+/+ cells. b, Diffusion pseudotime of E3.5 cells and the PrE gene signature computed with R package “destiny" (http://bioinformatics.oxfordjournals.org/content/32/8/1241) using all the expressed genes as input list. P-value calculated with two-tailed Mann-Whitney test. c, PCA plot computed with all the expressed genes. Colors represent different lineages/genotypes (left panel) or ratio between Gata6 and Nanog expression (right panel). d, Volcano plot of genes differentially expressed between S3-/- and S3+/+ cells at E3.75. Red and blue dots indicate respectively transcripts that are upregulated or downregulated (log2 FC > 0.7 or FC < - 0.7 respectively, q-value < 0.1) in S3-/- cells relative to S3+/+ cells. e, Fraction of identity between E3.75 EPI(left panel)/PrE(right panel) and E3.5 ICM, E4.5 EPI, E4.5 PrE, E5.5 EPI and E6.5 EPI stages computed with all the expressed genes. P-value calculated with two-tailed Mann-Whitney test.All boxplot shows 1st, 2nd and 3rd quartile. Whiskers show minimum and maximum values.

Extended Data Fig. 9

Diagram summarizing how mitochondrial Stat3 affects nuclear transcription.

Supplementary information

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Betto, R.M., Diamante, L., Perrera, V. et al. Metabolic control of DNA methylation in naive pluripotent cells. Nat Genet 53, 215–229 (2021). https://doi.org/10.1038/s41588-020-00770-2

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