Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Article
  • Published:

The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition

Nature Cell Biology volume 17, pages 1523–1535 (2015)Cite this article

Subjects

Abstract

For nearly a century developmental biologists have recognized that cells from embryos can differ in their potential to differentiate into distinct cell types. Recently, it has been recognized that embryonic stem cells derived from both mice and humans exhibit two stable yet epigenetically distinct states of pluripotency: naive and primed. We now show that nicotinamide N-methyltransferase (NNMT) and the metabolic state regulate pluripotency in human embryonic stem cells (hESCs). Specifically, in naive hESCs, NNMT and its enzymatic product 1-methylnicotinamide are highly upregulated, and NNMT is required for low S-adenosyl methionine (SAM) levels and the H3K27me3 repressive state. NNMT consumes SAM in naive cells, making it unavailable for histone methylation that represses Wnt and activates the HIF pathway in primed hESCs. These data support the hypothesis that the metabolome regulates the epigenetic landscape of the earliest steps in human development.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Naive and primed ESCs are metabolically different.
Figure 2: Metabolomic analysis of naive and primed ESCs.
Figure 3: Primed ESCs accumulate lipids whereas naive ESCs use fatty acids as a source of energy.
Figure 4: Amino acids methionine and tryptophan are differentially regulated in naive and primed hESCs.
Figure 5: High NNMT expression in naive hESCs regulates histone methylation status.
Figure 6: The Wnt pathway is active in naive hESCs.
Figure 7: HIF1α is required for the naive-to-primed hESC transition.
Figure 8: NNMT affects the naive-to-primed hESC transition by repressing the Wnt pathway and activating the HIF pathway.

Similar content being viewed by others

References

  1. 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 

  2. Factor, D. C. et al. Epigenomic comparison reveals activation of “Seed” enhancers during transition from naive to primed pluripotency. Cell Stem Cell 14, 854–863 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Tesar, P. J. et al. New cell lines from mouse epiblast share defining features with human embryonic stem cells. Nature 448, 196–199 (2007).

    Article  CAS  PubMed  Google Scholar 

  4. Wu, J. et al. An alternative pluripotent state confers interspecies chimaeric competency. Nature 521, 316–321 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Brons, I. G. et al. Derivation of pluripotent epiblast stem cells from mammalian embryos. Nature 448, 191–195 (2007).

    Article  CAS  PubMed  Google Scholar 

  6. Chan, Y. S. et al. Induction of a human pluripotent state with distinct regulatory circuitry that resembles preimplantation epiblast. Cell Stem Cell 13, 663–675 (2013).

    Article  CAS  PubMed  Google Scholar 

  7. Duggal, G. et al. Alternative routes to induce naive pluripotency in human embryonic stem cells. Stem Cells 33, 2686–2698 (2015).

    Article  CAS  PubMed  Google Scholar 

  8. Gafni, O. et al. Derivation of novel human ground state naive pluripotent stem cells. Nature 504, 282–286 (2013).

    Article  CAS  PubMed  Google Scholar 

  9. Takashima, Y. et al. Resetting transcription factor control circuitry toward ground-state pluripotency in human. Cell 158, 1254–1269 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Theunissen, T. W. et al. Systematic identification of culture conditions for induction and maintenance of naive human pluripotency. Cell Stem Cell 15, 524–526 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Valamehr, B. et al. Platform for induction and maintenance of transgene-free hiPSCs resembling ground state pluripotent stem cells. Stem Cell Rep. 2, 366–381 (2014).

    Article  CAS  Google Scholar 

  12. Ware, C. B. et al. Derivation of naive human embryonic stem cells. Proc. Natl Acad. Sci. USA 111, 4484–4489 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Bracha, A. L., Ramanathan, A., Huang, S., Ingber, D. E. & Schreiber, S. L. Carbon metabolism-mediated myogenic differentiation. Nat. Chem. Biol. 6, 202–204 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Folmes, C. D. et al. Somatic oxidative bioenergetics transitions into pluripotency-dependent glycolysis to facilitate nuclear reprogramming. Cell Metab. 14, 264–271 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Greer, S. N., Metcalf, J. L., Wang, Y. & Ohh, M. The updated biology of hypoxia-inducible factor. EMBO J. 31, 2448–2460 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Mathieu, J. et al. Hypoxia-inducible factors have distinct and stage-specific roles during reprogramming of human cells to pluripotency. Cell Stem Cell 14, 592–605 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Panopoulos, A. D. et al. The metabolome of induced pluripotent stem cells reveals metabolic changes occurring in somatic cell reprogramming. Cell Res. 22, 168–177 (2012).

    Article  CAS  PubMed  Google Scholar 

  18. Rafalski, V. A., Mancini, E. & Brunet, A. Energy metabolism and energy-sensing pathways in mammalian embryonic and adult stem cell fate. J. Cell Sci. 125, 5597–5608 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Yanes, O. et al. Metabolic oxidation regulates embryonic stem cell differentiation. Nat. Chem. Biol. 6, 411–417 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. 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 

  21. Shyh-Chang, N. et al. Influence of threonine metabolism on S-adenosylmethionine and histone methylation. Science 339, 222–226 (2013).

    Article  PubMed  Google Scholar 

  22. Shiraki, N. et al. Methionine metabolism regulates maintenance and differentiation of human pluripotent stem cells. Cell Metab. 19, 780–794 (2014).

    Article  CAS  PubMed  Google Scholar 

  23. Yan, L. et al. Single-cell RNA-Seq profiling of human preimplantation embryos and embryonic stem cells. Nat. Struct. Mol. Biol. 20, 1131–1139 (2013).

    Article  CAS  PubMed  Google Scholar 

  24. Berra, E. et al. HIF prolyl-hydroxylase 2 is the key oxygen sensor setting low steady-state levels of HIF-1α in normoxia. EMBO J. 22, 4082–4090 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Simonson, T. S. et al. Genetic evidence for high-altitude adaptation in Tibet. Science 329, 72–75 (2010).

    Article  CAS  PubMed  Google Scholar 

  26. Nguyen-Tran, D. H. et al. Molecular mechanism of sphingosine-1-phosphate action in Duchenne muscular dystrophy. Dis. Model. Mech. 7, 41–54 (2014).

    Article  CAS  PubMed  Google Scholar 

  27. Opitz, C. A. et al. An endogenous tumour-promoting ligand of the human aryl hydrocarbon receptor. Nature 478, 197–203 (2011).

    Article  CAS  PubMed  Google Scholar 

  28. Ulanovskaya, O. A., Zuhl, A. M. & Cravatt, B. F. NNMT promotes epigenetic remodeling in cancer by creating a metabolic methylation sink. Nat. Chem. Biol. 9, 300–306 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Mathieu, J. et al. Hypoxia induces re-entry of committed cells into pluripotency. Stem Cells 31, 1737–1748 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kraus, D. et al. Nicotinamide N-methyltransferase knockdown protects against diet-induced obesity. Nature 508, 258–262 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Graf, U., Casanova, E. A. & Cinelli, P. The role of the Leukemia Inhibitory Factor (LIF)—pathway in derivation and maintenance of murine pluripotent stem cells. Genes (Basel) 2, 280–297 (2011).

    Article  CAS  Google Scholar 

  32. Tomida, M., Ohtake, H., Yokota, T., Kobayashi, Y. & Kurosumi, M. Stat3 up-regulates expression of nicotinamide N-methyltransferase in human cancer cells. J. Cancer Res. Clin. Oncol. 134, 551–559 (2008).

    Article  CAS  PubMed  Google Scholar 

  33. Gilles, C. et al. Transactivation of vimentin by β-catenin in human breast cancer cells. Cancer Res. 63, 2658–2664 (2003).

    CAS  PubMed  Google Scholar 

  34. Davidson, K. C. et al. Wnt/β-catenin signaling promotes differentiation, not self-renewal, of human embryonic stem cells and is repressed by Oct4. Proc. Natl Acad. Sci. USA 109, 4485–4490 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. ten Berge, D. et al. Embryonic stem cells require Wnt proteins to prevent differentiation to epiblast stem cells. Nat. Cell Biol. 13, 1070–1075 (2011).

    Article  CAS  PubMed  Google Scholar 

  36. Prigione, A. & Adjaye, J. Modulation of mitochondrial biogenesis and bioenergetic metabolism upon in vitro and in vivo differentiation of human ES and iPS cells. Int. J. Dev. Biol. 54, 1729–1741 (2010).

    Article  PubMed  Google Scholar 

  37. Varum, S. et al. Energy metabolism in human pluripotent stem cells and their differentiated counterparts. PLoS ONE 6, e20914 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Zhang, J. et al. UCP2 regulates energy metabolism and differentiation potential of human pluripotent stem cells. EMBO J. 30, 4860–4873 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Zhou, W. et al. Assessment of hypoxia inducible factor levels in cancer cell lines upon hypoxic induction using a novel reporter construct. PLoS ONE 6, e27460 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Trojer, P. & Reinberg, D. Histone lysine demethylases and their impact on epigenetics. Cell 125, 213–217 (2006).

    Article  CAS  PubMed  Google Scholar 

  41. Escobar, T. M. et al. miR-155 activates cytokine gene expression in Th17 cells by regulating the DNA-binding protein Jarid2 to relieve polycomb-mediated repression. Immunity 40, 865–879 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Landeira, D. & Fisher, A. G. Inactive yet indispensable: the tale of Jarid2. Trends Cell Biol. 21, 74–80 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Blauwkamp, T. A., Nigam, S., Ardehali, R., Weissman, I. L. & Nusse, R. Endogenous Wnt signalling in human embryonic stem cells generates an equilibrium of distinct lineage-specified progenitors. Nat. Commun. 3, 1070 (2012).

    Article  PubMed  Google Scholar 

  44. Clevers, H., Loh, K. M. & Nusse, R. Stem cell signaling. An integral program for tissue renewal and regeneration: Wnt signaling and stem cell control. Science 346, 1248012 (2014).

    Article  PubMed  Google Scholar 

  45. Mazumdar, J. et al. O2 regulates stem cells through Wnt/β-catenin signalling. Nat. Cell Biol. 12, 1007–1013 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Grow, E. J. et al. Intrinsic retroviral reactivation in human preimplantation embryos and pluripotent cells. Nature 522, 221–225 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. ENCODE Project Consortium An integrated encyclopedia of DNA elements in the human genome. Nature 489, 57–74 (2012).

    Article  Google Scholar 

  48. Bernstein, B. E. et al. The NIH roadmap epigenomics mapping consortium. Nat. Biotechnol. 28, 1045–1048 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Taylor, S. D. et al. Targeted enrichment and high-resolution digital profiling of mitochondrial DNA deletions in human brain. Aging Cell 13, 29–38 (2014).

    Article  CAS  PubMed  Google Scholar 

  50. Cox, J. & Mann, M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat. Biotechnol. 26, 1367–1372 (2008).

    Article  CAS  PubMed  Google Scholar 

  51. Liesenfeld, D. B. et al. Metabolomics and transcriptomics identify pathway differences between visceral and subcutaneous adipose tissue in colorectal cancer patients: the ColoCare study. Am. J. Clin. Nutr. 102, 433–443 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Kind, T. et al. FiehnLib: mass spectral and retention index libraries for metabolomics based on quadrupole and time-of-flight gas chromatography/mass spectrometry. Anal. Chem. 81, 10038–10048 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Meissen, J. K. et al. Induced pluripotent stem cells show metabolomic differences to embryonic stem cells in polyunsaturated phosphatidylcholines and primary metabolism. PLoS ONE 7, e46770 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Kind, T. et al. LipidBlast in silico tandem mass spectrometry database for lipid identification. Nat. Methods 10, 755–758 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  56. Anders, S., Pyl, P. T. & Huber, W. HTSeq-a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Carvalho, B. S. & Irizarry, R. A. A framework for oligonucleotide microarray preprocessing. Bioinformatics 26, 2363–2367 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Irizarry, R. A. et al. Exploration, normalization, and summaries of high density oligonucleotide array probe level data. Biostatistics 4, 249–264 (2003).

    Article  PubMed  Google Scholar 

  60. Gautier, L., Cope, L., Bolstad, B. M. & Irizarry, R. A. affy–analysis of Affymetrix GeneChip data at the probe level. Bioinformatics 20, 307–315 (2004).

    Article  CAS  PubMed  Google Scholar 

  61. Johnson, W. E., Li, C. & Rabinovic, A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics 8, 118–127 (2007).

    Article  PubMed  Google Scholar 

  62. Shen, L. et al. diffReps: detecting differential chromatin modification sites from ChIP-seq data with biological replicates. PLoS ONE 8, e65598 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Denisenko, O. N. & Bomsztyk, K. The product of the murine homolog of the Drosophila extra sex combs gene displays transcriptional repressor activity. Mol. Cell Biol. 17, 4707–4717 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Sperber, H. et al. miRNA sensitivity to Drosha levels correlates with pre-miRNA secondary structure. RNA 20, 621–631 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Biechele, T. L., Adams, A. M. & Moon, R. T. Transcription-based reporters of Wnt/β-catenin signaling. Cold Spring Harb. Protoc. 2009, pdb prot5223 (2009).

    Article  PubMed  Google Scholar 

  66. Gonzalez, F. et al. An iCRISPR platform for rapid, multiplexable, and inducible genome editing in human pluripotent stem cells. Cell Stem Cell 15, 215–226 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

We thank members of the Ruohola-Baker laboratory for helpful discussions throughout this work. We thank D. Djukovic and J. K. Meissen for help with mass spectrometry, D. Jones for help with RNA-seq analysis, W. Zhou, A. Nelson, S. Shannon, S. Sidhu, C. Cavanaugh, Y. Zhang, W. Heath, K. Sankary, E. Engelhart and K. Toor for technical help, K. Au for performing karyotype analysis, A. Madan for performing RNA-seq, P. Treuting for teratoma analysis, K. Bomsztyk (University of Washington, USA) for providing EED antibody, B. Cravatt (Scripps Research Institute, USA) for providing NNMT overexpression constructs and R. Jaenisch (Whitehead Institute for Biomedical Research, USA) and J. Hanna (Weizmann Institute of Science, Israel) for providing WIN1 cells and LIS1 cells, respectively. This work is supported in part by the University of Washington’s Proteomics Resource (UWPR95794). R.T.M. is an Investigator, and A.M.R. and Z.X. are Associates, of the HHMI. This work was supported by fellowship from the American Heart Association to J.M., AG-NS-0577-09 from the Ellison Medical Foundation for J.H.B., Schultz Fellowship for H.S., grants from the National Institutes of Health 1U24DK097154 for O.F., RO1ES019319 for J.H.B., R01GM097372, R01GM97372-03S1 and R01GM083867 for H.R.-B., 1P01GM081619 for C.A.B., R.T.M., C.B.W., A.M. and H.R.-B., and the NHLBI Progenitor Cell Biology Consortium (U01HL099997; UO1HL099993) for H.R.-B.

Author information

Author notes

  1. Henrik Sperber, Julie Mathieu and Yuliang Wang: These authors contributed equally to this work.

Authors and Affiliations

  1. Department of Biochemistry, University of Washington, Seattle, Washington 98195, USA

    Henrik Sperber, Julie Mathieu, Amy Ferreccio, Karin A. Fischer, Arikketh Devi, Damien Detraux & Hannele Ruohola-Baker

  2. Institute for Stem Cell and Regenerative Medicine, University of Washington, Seattle, Washington 98109, USA

    Henrik Sperber, Julie Mathieu, Amy Ferreccio, Jennifer Hesson, Zhuojin Xu, Karin A. Fischer, Arikketh Devi, Damien Detraux, Haiwei Gu, Stephanie L. Battle, Cristina Valensisi, Lilyana Margaretha, Aaron M. Robitaille, C. Anthony Blau, Daniel Raftery, R. David Hawkins, Randall T. Moon, Carol B. Ware & Hannele Ruohola-Baker

  3. Department of Chemistry, University of Washington, Seattle, Washington 98195, USA

    Henrik Sperber

  4. Department of Biomedical Engineering, Portland, Oregon 97239, USA

    Yuliang Wang & Adam A. Margolin

  5. Computational Biology Program, School of Medicine, Oregon Health & Science University, Portland, Oregon 97239, USA

    Yuliang Wang & Adam A. Margolin

  6. Department of Comparative Medicine, University ofWashington, Seattle, Washington 98195, USA

    Jennifer Hesson & Carol B. Ware

  7. Department of Genetic Engineering, SRM University, Kattankulathur 603203, India

    Arikketh Devi

  8. Department of Anesthesiology and Pain Medicine, Northwest Metabolomics Research Center, University of Washington, Seattle, Washington 98195, USA

    Haiwei Gu & Daniel Raftery

  9. Department of Medicine, Division of Medical Genetics and Department of Genome Sciences, University of Washington, Seattle, Washington 98195, USA

    Stephanie L. Battle, Cristina Valensisi & R. David Hawkins

  10. University of California Davis Genome Center, California 95616, USA

    Megan Showalter & Oliver Fiehn

  11. Fred Hutchinson Cancer Research Center, Seattle, Washington 98109, USA

    Jason H. Bielas, Nolan G. Ericson, Daciana Margineantu, David Hockenbery & Daniel Raftery

  12. Department of Pathology, University of Washington School of Medicine, Seattle, Washington 98195, USA

    Jason H. Bielas

  13. Biochemistry Department, King Abdulaziz University, Faculty of Science, Jeddah21589, Saudi Arabia

    Oliver Fiehn

  14. Department of Medicine, Division of Hematology, University of Washington, Seattle, Washington 98195, USA

    C. Anthony Blau

  15. Howard Hughes Medical Institute, Chevy Chase, Maryland 20815, USA

    Randall T. Moon

  16. Department of Pharmacology, University of Washington, Seattle, Washington 98109, USA

    Randall T. Moon

Authors
  1. Henrik Sperber
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Julie Mathieu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Yuliang Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Amy Ferreccio
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jennifer Hesson
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhuojin Xu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Karin A. Fischer
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Arikketh Devi
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Damien Detraux
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Haiwei Gu
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Stephanie L. Battle
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Megan Showalter
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Cristina Valensisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Jason H. Bielas
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Nolan G. Ericson
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Lilyana Margaretha
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Aaron M. Robitaille
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Daciana Margineantu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Oliver Fiehn
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. David Hockenbery
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. C. Anthony Blau
    View author publications

    You can also search for this author in PubMed Google Scholar

  22. Daniel Raftery
    View author publications

    You can also search for this author in PubMed Google Scholar

  23. Adam A. Margolin
    View author publications

    You can also search for this author in PubMed Google Scholar

  24. R. David Hawkins
    View author publications

    You can also search for this author in PubMed Google Scholar

  25. Randall T. Moon
    View author publications

    You can also search for this author in PubMed Google Scholar

  26. Carol B. Ware
    View author publications

    You can also search for this author in PubMed Google Scholar

  27. Hannele Ruohola-Baker
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

H.S., J.M., Y.W. and H.R.-B. designed the study. H.S., J.M., Y.W., A.F., J.H., Z.X., K.A.F., A.D., D.D., H.G., S.L.B., M.S., C.V., N.G.E., L.M., A.M.R. and D.M. performed all experiments in the study under the guidance of J.H.B., O.F., D.H., C.A.B., D.R., A.M., R.D.H., R.T.M., C.B.W. and H.R.-B. H.S., J.M., Y.W. and H.R.-B. wrote the manuscript.

Corresponding author

Correspondence to Hannele Ruohola-Baker.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Integrated supplementary information

Supplementary Figure 1 Differences between naive and primed human and mouse ESCs.

A: PCA of RNA-seq and microarray data from this study, Margaretha et al. (in preparation) and other studies6,8,9,10,23, normalized to primed hESC in each study. Analysis was performed as described previously9. B: PCA of naïve and primed microarray data8,9,10. ComBat was applied on the combined dataset. PC1 is associated with naïve versus primed difference and explained majority of variation (65.9%) and recapitulated the trend in Fig. 1a. C: DNMT3L expression (qPCR) across naïve (n = 6) and primed hESCs (n = 3). SEM ∗∗∗p < 0.001, 2-tailed t-test. D: Histopathology of a mature cystic teratoma generated from H1 4iLIF, composed of primitive to well-differentiated tissues from ecto- (Ec, neuroectodermal rossettes), meso- (Me, bone), and endodermal (En, hepatoid exocrine glands) embryonic layers. Bar in top panel = 100 μm (100 ×). Boxed regions (200 ×). Teratoma were obtained from 3/3 mice. E, F: A trace of OCR changes during naïve (Elf1, E and WIN1, F) to primed (cultured in AF 1–3 days) hESC transition. Elf1: n = 33, Elf1AF 1D: n = 29, Elf1AF 2D: n = 20, Elf1AF 3D: n = 28; WIN1: n = 4, WIN1AF: n = 6; s.e.m. G: ECAR changes after oligomycin injection in naïve (Elf1: n = 39, H1 4iLIF: n = 6) and primed (H1: n = 12, H7: n = 27) hESC transition. S.e.m.; ∗∗∗p < 0.001, 2-tailed t-test. H: ECAR changes after oligomycin injection during naïve (Elf1: n = 33) to primed (Elf1AF overnight: n = 29, 2 days: n = 20, 3 days: n = 28) hESC transition. S.e.m.; ∗∗∗p < 0.0001, p = 0.59 (Elf1AF ON versus Elf1), p = 0.11 (Elf1AF 2D versus Elf1), 2-tailed t-test. I: A trace of OCR changes in H7 and Elf1 hESCs, n = 6, s.e.m. J, K: Metabolic profile of H1 (primed, n = 10), H1 2iF (naïve toggled, n = 11) hESCs, and R1 (naïve, n = 10) mouse ESCs. A trace of OCR changes (J, s.d.). OCR changes after FCCP treatment for primed (H1) hESCs, and naïve human (H1 2iF) and mouse (R1) ESCs (K, s.e.m.; p = 0.225 for R1 versus H12iF and p < 0.0001 for H1 versus H12iF). L, M: Mitochondrial DNA copy numbers in Elf1, H1 and H7 hESCS. n = 3; s.e.m.; p = 0.3327 (L), p = 0.13 (M), 2-tailed t-test. N: Mitochondrial DNA mutation frequency in Elf1 and H1 hESCs. n = 3; s.e.m.; NS.:non significant p = 0.34 for 12S rRNA and p = 0.062 for COXII, 2-tailed t-test. O: Mitochondrial DNA deletion frequency in Elf1 and H1 hESCs. n = 3; s.e.m.; p = 0.44 for ND1/ND2 and p = 0.46 for Common; 2-tailed t-test. P: Label-free quantification of protein expression is reproducible. Tryptic digestions of Elf1 2iLIF and ElfAF hESCs were analysed in triplicate by nano-LC MS/MS (average Pearson correlation: 0.86). For raw data and p-values, see Supplementary Table 4. n = number of biological replicates.

Supplementary Figure 2 Mass spectrometry profiling of naive and primed human and mouse ESCs

A: Heatmap generated through hierarchical Pearson clustering of metabolite expression from GC-TOF shows that naïve ESCs cluster away from primed ESCs, regardless of origin (mouse or human). B: volcano plot of differentially abundant metabolites between primed hESCs (Elf1 AF) and naïve hESCs (Elf1) detected by GC-TOF. C, D: PCA plot of water-soluble untargeted GC-MS (C) and LC-MS (D) metabolomics data showing a separation of naïve ESCs (mouse:R1 and human: Elf1) and primed ESCs (mouse: Epi, and human: Elf1 AF, H1, H7).

Supplementary Figure 3 Lipid metabolism in naive and primed human and mouse ESCs

A: Lipid droplets are known to be increased in human pluripotent and embryonic stem cells67,68. BODIPY 493/503 staining shows an increase of lipid droplet accumulation in primed hESCs (H7, H1, Elf1 AF) compared to naïve hESCs (Elf1, H1 2iF, H1 4iLIF). Images were taken at 5X magnification. Scale bar represents 100μm. B: Oil Red O staining shows an increase of lipid droplet accumulation in primed hESCs (H7) compared to naïve hESCs (Elf1). Scale bar represents 20μm. C: H3K27me3 reads mapped 5kb around transcription start site (TSS) of CPT1A were plotted for Chan et al ChIP-seq data sets. Primed cells have more H3K27me3 repressive marks around TSS of CPT1A. D: Expression of key genes involved in fatty acid synthesis from RNA-seq analysis in naïve (Elf1, n=2) and primed (H1, n=1) hESCs. Negative binomial test p-values are shown. E: A trace of OCR changes under Seahorse palmitate assay with addition of 2 doses of palmitate or BSA vehicle followed by 2 doses of ETO in hESCs H1 pushed toward a more naïve state using 4iLIF (H1 4iLIF) and primed hESCs H1. n=6 for H1BSA, n=5 for H1PALM, H1 4iLIF BSA and H1 4iLIF PALM. F: More abundant lipids in primed human cells (Elf1AF) are more unsaturated than more abundant lipids in naïve human cells (Elf1), n=6. G: More abundant lipids in primed mouse cells (EpiSC) have more carbon atoms than more abundant lipids in naïve mouse cells (R1), n=6. Boxes represent median, 25th and 75th quantiles. Whiskers extend 1.5 IQR above 75th quantile and below 25th quantile. Dots represent values beyond whiskers. H-I: Relative fold change of expression of genes involved in transport of fatty acids into mitochondria and genes involved in 4 steps of fatty acid beta-oxidation from RNASeq analysis in human ESCs (H1 vs. lf1, H) and mouse ESCs (Epiblasts vs.ICM, I). Three isoforms have been described: CPT1A, CPT1B and CPT1C; however, CPT1C is not involved in fatty acid beta-oxidation48. For raw data, see Supplementary Table 4. n = number of biological replicates.

Supplementary Figure 4 Tryptophan and SAM metabolic pathways in naive and primed hESCs.

A: RNA expression of IDO1 in human 8-cell embryo and primed hESCs at passage 0 (hESCp0) and 10 (hESCp10) analysed by single cell RNA Seq data23. B: RNA expression of IDO1, IDO2, TDO2 and AADAT in naïve hESCs Elf1, primed hESCs H1 and H1 differentiated during 4 days12, 49 detected by microarray (n = 2). C: IDO expression in H1 cells and H1 differentiated toward various lineages47, n = 2, error bar represents 2 SEM above and below the mean. D: NNMT expression in various tissues analysed by RNASeq50. E: NNMT expression in various organs of rats over time (from 2 to 104 weeks) analysed by RNASeq50. F: NNMT expression in H1 cells and H1 differentiated toward various lineages47. G-H: Primed hESCs (H1 n = 4, ELF AF n = 6, WIN1 TESR n = 6) have higher amounts of nicotinamide (G) and SAM (H), than naïve hESCs (WIN1 n = 4, Elf1 n = 4, H1 2iF n = 4, H1 4iLIF n = 4). S.e.m.; p = 0.0019 for nicotinamide, p = 0.0026 for SAM; 2-tailed t-test. I: Overexpression of NNMT decreases H3K27me3 marks in primed Elf AF cells 3 days after transient transfection. Antibody against NNMT detected a single band around 50kDA. Unprocessed original scans of blots are shown in Supplementary Fig. 9. For raw data, see Supplementary Table 4. n = number of biological replicates.

Supplementary Figure 5 Histone methylation, NNMT and miRNA expression changes.

A, B: Reads for H3K4me3 (A) and H3K27ac (B) mapped 5 kb around TSS were plotted for Chan et al. ChIP-seq data set. C–F: Reads for H3K4me1 (C), H3K4me3 (D), H3K9me3 (E) and H3K27ac (F) mapped 5 kb around transcription start sites (TSS) were plotted for Gafni et al. ChIP-seq data set. G: Reads for H3K4me3 mapped 5 kb around TSS were plotted for Theunissen et al. ChIP-seq data set. H: Western blot analysis of H3K27me3 mark in naïve hESCs (WIN1) and cells pushed toward the primed stage (WIN1 TeSR). I, J: RNA-seq data of histone methyltransferases (I) and histone demethylases (J) in naïve (Elf1) and primed (H1) hESCs. K: qPCR analysis of miR-10a, miR-518b and miR-520f in Elf1 cells after transfection with siRNA against NNMT or luciferase (50 nM, 72 h). n = 3, S.e.m., N.S. = non significant, p = 0.16 (miR-518b)p = 0.36 (miR-520f) and p = 0.058 (miR-10a), 2-tailed t-test L: Western blot analysis of HIF1α and H3K27me3 in WIN1 cells after transfection with siRNA against NNMT or luciferase (50 nM, 72 h). M: STAT3 is phosphorylated in H1 cells pushed toward a more naïve stage (H1 2iF), even without LIF addition to the media. N: qPCR analysis of NNMT expression after treatment of Elf1 cells with 100 μM of STAT3 inhibitor. n = 3, s.e.m., N.S. = non significant, p = 0.139 (Stat3i 6 h versus Elf1) and p = 0.267 (Stat3i 24 h), 2-tailed t-test. Unprocessed original scans of blots are shown in Supplementary Fig. 9. For raw data, see Supplementary Table 4. n = number of biological replicates.

Supplementary Figure 6 Wnt pathway gene expression and effects by Wnt inhibitors.

A–D: Wnt ligands and Wnt targets are up-regulated in naïve hESCs compared to primed hESCs, as detected by RNA seq in Takashima et al. (A), Chan et al. (B) and microarray in Theunissen et al. (C) and Gafni et al. (D). E: siRNA against β-catenin inhibits the reporter activity in naïve Elf1 cells after 72 h. Scale bars represent 200 μm. F: treatment of primed hESCs (Elf1 AF) with Wnt3a CM or GSK3 inhibitor (CHIR99021, 10 μM) for 3 days induces differentiation and re-activation of the BAR reporter. G: A trace of OCR following mitostress protocol in naïve hESCs (WIN1) with or without treatment with Wnt inhibitor IWP2 (2 μM, 48 h). n = 5 for Win1 and n = 3 for WIN1 + IWP2. For raw data, see Supplementary Table 4. n = number of biological replicates.

Supplementary Figure 7 Responses to HIF1 over expression and knockout experiments.

A: Expression of IDO1 in naïve hESCs (Elf1) after infection with an empty vector (EV) or HIF1 overexpressing (HIF1 OE) virus, analysed by qPCR (n = 2 independent replicates). B, C: HIF1α OE pushes in Elf1 cells toward primed-like cells. When cultured in a ‘permissive media’ (with LIF but without GSK3 and MEK inhibitors) naïve hESCs Elf1α overexpressing HIF1α for 3 days show higher percentage of primed hESC-like colonies than cells infected with an empty vector control (EV). Morphology is shown in B and quantification of the percentage of primed hESC-like colonies in C. Scale bar represents 200 μm. n = 261 (EV) and n = 327 (HIF1 OE) colonies were counted from 3 independent experiments, s.e.m., ∗p = 0.0183, 2-tailed t-test. D: Analysis of mutation in the CRISPR-Cas9 KO of HIF1α (Elf1 gHIF1 6.2.1) by RNA seq: the variant site carries the AG- > AGG insertion, as noted by the purple insertion symbol. E: A trace of OCR following mitostress protocol in CRISPR HIF1α KO cells (gHIF1 6.2.1) in cultured in naïve conditions or pushed to primed for 3 days (AF D3). N = 6 ; s.e.m. For raw data, see Supplementary Table 4. n = number of biological replicates.

Supplementary Figure 8 NNMT CRISPR knockout experiments.

A, B: schematic representation of the CRISPR-Cas9 KO experimental procedure using plasmid transfection (A) or gRNA transfection (B). In vitro transcribed gRNAs targeting NNMT were transfected in doxycycline-induced iCas9 hESCs Elf1 or electroporated as plasmids. Single clones were picked and amplified prior analysis by DNA sequencing. C, D: Sequencing trace files of heterozygous CRISPR-Cas9 mutant clones (NNMT 6.2.2, C; gNNMT 7.3.5, D). The guide RNA is represented in green. E: Relative fold change of expression of genes involved in transport of fatty acids into mitochondria and genes involved in 4 steps of fatty acid beta-oxidation from RNASeq analysis in human ESCs (Elf1 CRISPR-Cas9 KO gNNMT 7.2.1 versus Elf1) as detected by RNA seq. F: Expression of key genes involved in fatty acid synthesis from RNA-seq analysis in naïve (Elf1) and naïve NNMT CRISPR-Cas9 KO (Elf1 gNNMT 7.2.1) hESCs. In E and F, n = 1 for gNNMT 7.2.1 and n = 2 for Elf1. G: Detection of mutation in the CRISPR-Cas9 KO of NNMT (Elf1 gNNMT 7.2.1) by RNA seq. The variant site carries the TC- > TCC insertion, as noted by the purple insertion symbol. H: Number of genes for the overlap between genes expressed higher (lower) in gNNMT 7.2.1 compared to Elf1 and genes expressed higher (lower) in primed lines compared to naïve lines from multiple studies. Color shade is proportional to the number of overlapping genes. Significance is shown in Fig. 8i. n = number of biological replicates.

Supplementary information

Supplementary Information

Supplementary Information (PDF 1687 kb)

Supplementary Table 1

Supplementary Information (XLSX 18773 kb)

Supplementary Table 2

Supplementary Information (XLSX 18 kb)

Supplementary Table 3

Supplementary Information (XLSX 246 kb)

Supplementary Table 4

Supplementary Information (XLSX 220 kb)

Supplementary Table 5

Supplementary Information (XLSX 207 kb)

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Sperber, H., Mathieu, J., Wang, Y. et al. The metabolome regulates the epigenetic landscape during naive-to-primed human embryonic stem cell transition. Nat Cell Biol 17, 1523–1535 (2015). https://doi.org/10.1038/ncb3264

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/ncb3264

This article is cited by

Search

Advanced search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing