Abstract

The TGFβ pathway has essential roles in embryonic development, organ homeostasis, tissue repair and disease1,2. These diverse effects are mediated through the intracellular effectors SMAD2 and SMAD3 (hereafter SMAD2/3), whose canonical function is to control the activity of target genes by interacting with transcriptional regulators3. Therefore, a complete description of the factors that interact with SMAD2/3 in a given cell type would have broad implications for many areas of cell biology. Here we describe the interactome of SMAD2/3 in human pluripotent stem cells. This analysis reveals that SMAD2/3 is involved in multiple molecular processes in addition to its role in transcription. In particular, we identify a functional interaction with the METTL3–METTL14–WTAP complex, which mediates the conversion of adenosine to N6-methyladenosine (m6A) on RNA4. We show that SMAD2/3 promotes binding of the m6A methyltransferase complex to a subset of transcripts involved in early cell fate decisions. This mechanism destabilizes specific SMAD2/3 transcriptional targets, including the pluripotency factor gene NANOG, priming them for rapid downregulation upon differentiation to enable timely exit from pluripotency. Collectively, these findings reveal the mechanism by which extracellular signalling can induce rapid cellular responses through regulation of the epitranscriptome. These aspects of TGFβ signalling could have far-reaching implications in many other cell types and in diseases such as cancer5.

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References

  1. 1.

    & TGFβ superfamily signaling in embryonic development and homeostasis. Dev. Cell 16, 329–343 (2009)

  2. 2.

    . & The harmonies played by TGFβ in stem cell biology. Cell Stem Cell 11, 751–764 (2012)

  3. 3.

    & TGFβ signaling to chromatin: how SMADs regulate transcription during self-renewal and differentiation. Semin. Cell Dev. Biol. 32, 107–118 (2014)

  4. 4.

    & An adenine code for DNA: a second life for N6-methyladenine. Cell 161, 710–713 (2015)

  5. 5.

    , & The roles of TGFβ in the tumour microenvironment. Nat. Rev. Cancer 13, 788–799 (2013)

  6. 6.

    , & NODAL inhibits differentiation of human embryonic stem cells along the neuroectodermal default pathway. Dev. Biol. 275, 403–421 (2004)

  7. 7.

    , & Activin/NODAL and FGF pathways cooperate to maintain pluripotency of human embryonic stem cells. J. Cell Sci. 118, 4495–4509 (2005)

  8. 8.

    , , & TGFβ/activin/NODAL signaling is necessary for the maintenance of pluripotency in human embryonic stem cells. Development 132, 1273–1282 (2005)

  9. 9.

    et al. Inhibition of activin/NODAL signaling promotes specification of human embryonic stem cells into neuroectoderm. Dev. Biol. 313, 107–117 (2008)

  10. 10.

    et al. Activin/NODAL signaling and NANOG orchestrate human embryonic stem cell fate decisions by controlling the H3K4me3 chromatin mark. Genes Dev. 29, 702–717 (2015)

  11. 11.

    et al. Efficient differentiation of human embryonic stem cells to definitive endoderm. Nat. Biotechnol. 23, 1534–1541 (2005)

  12. 12.

    et al. Signaling pathways controlling pluripotency and early cell fate decisions of human induced pluripotent stem cells. Stem Cells 27, 2655–2666 (2009)

  13. 13.

    et al. Pluripotency factors regulate definitive endoderm specification through eomesodermin. Genes Dev. 25, 238–250 (2011)

  14. 14.

    et al. Development of definitive endoderm from embryonic stem cells in culture. Development 131, 1651–1662 (2004)

  15. 15.

    et al. A majority of m6A residues are in the last exons, allowing the potential for 3′ UTR regulation. Genes Dev. 29, 2037–2053 (2015)

  16. 16.

    et al. m6A mRNA modifications are deposited in nascent pre-mRNA and are not required for splicing but do specify cytoplasmic turnover. Genes Dev. 31, 990–1006 (2017)

  17. 17.

    et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012)

  18. 18.

    et al. 5′ UTR m6A promotes cap-independent translation. Cell 163, 999–1010 (2015)

  19. 19.

    et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012)

  20. 20.

    et al. Promoter-bound METTL3 maintains myeloid leukaemia by m6A-dependent translation control. Nature 552, 126–131 (2017)

  21. 21.

    et al. The N6-methyladenosine (m6A)-forming enzyme METTL3 controls myeloid differentiation of normal hematopoietic and leukemia cells. Nat. Med. 23, 1369–1376 (2017)

  22. 22.

    et al. Stem cells. m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation. Science 347, 1002–1006 (2015)

  23. 23.

    et al. m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014)

  24. 24.

    et al. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5′ sites. Cell Reports 8, 284–296 (2014)

  25. 25.

    et al. N6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014)

  26. 26.

    et al. N6-methyladenosine demethylase FTO targets pre-mRNAs and regulates alternative splicing and 3′-end processing. Nucleic Acids Res. 45, 11356–11370 (2017)

  27. 27.

    et al. Optimized inducible shRNA and CRISPR/Cas9 platforms for in vitro studies of human development using hPSCs. Development 143, 4405–4418 (2016)

  28. 28.

    et al. Activin/NODAL signalling maintains pluripotency by controlling NANOG expression. Development 136, 1339–1349 (2009)

  29. 29.

    et al. NANOG and CDX2 pattern distinct subtypes of human mesoderm during exit from pluripotency. Cell Stem Cell 15, 310–325 (2014)

  30. 30.

    et al. Activin/NODAL signaling controls divergent transcriptional networks in human embryonic stem cells and in endoderm progenitors. Stem Cells 29, 1176–1185 (2011)

  31. 31.

    et al. Targeted gene correction of α1-antitrypsin deficiency in induced pluripotent stem cells. Nature 478, 391–394 (2011)

  32. 32.

    Serum-free and feeder-free culture conditions for human embryonic stem cells. Methods Mol. Biol. 690, 57–66 (2011)

  33. 33.

    et al. Generation of functional hepatocytes from human embryonic stem cells under chemically defined conditions that recapitulate liver development. Hepatology 51, 1754–1765 (2010)

  34. 34.

    et al. Early cell fate decisions of human embryonic stem cells and mouse epiblast stem cells are controlled by the same signalling pathways. PLoS One 4, e6082 (2009)

  35. 35.

    et al. A lentiviral RNAi library for human and mouse genes applied to an arrayed viral high-content screen. Cell 124, 1283–1298 (2006)

  36. 36.

    et al. Inducible and deterministic forward programming of human pluripotent stem cells into neurons, skeletal myocytes, and oligodendrocytes. Stem Cell Reports 8, 803–812 (2017)

  37. 37.

    & Extracting gene function from protein-protein interactions using quantitative BAC interactomics (QUBIC). Methods 53, 453–459 (2011)

  38. 38.

    , & Protocol for micro-purification, enrichment, pre-fractionation and storage of peptides for proteomics using StageTips. Nat. Protoc. 2, 1896–1906 (2007)

  39. 39.

    , , , & Multiplex peptide stable isotope dimethyl labeling for quantitative proteomics. Nat. Protoc. 4, 484–494 (2009)

  40. 40.

    , , & A quantitative proteomics tool to identify DNA–protein interactions in primary cells or blood. J. Proteome Res. 14, 1315–1329 (2015)

  41. 41.

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

  42. 42.

    et al. Quantitative proteomics combined with BAC transgeneomics reveals in vivo protein interactions. J. Cell Biol. 189, 739–754 (2010)

  43. 43.

    et al. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 13, 2498–2504 (2003)

  44. 44.

    et al. Enrichr: interactive and collaborative HTML5 gene list enrichment analysis tool. BMC Bioinformatics 14, 128 (2013)

  45. 45.

    , , , & Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing. Nat. Protoc. 8, 176–189 (2013)

  46. 46.

    , & Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014)

  47. 47.

    et al. TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions. Genome Biol. 14, R36 (2013)

  48. 48.

    et al. MeTDiff: a novel differential RNA methylation analysis for MeRIP–seq data. IEEE/ACM Trans. Comput. Biol. Bioinforma. PP, 1 (2015)

  49. 49.

    et al. The birth of the epitranscriptome: deciphering the function of RNA modifications. Genome Biol. 13, 175 (2012)

  50. 50.

    , & Epitranscriptome sequencing technologies: decoding RNA modifications. Nat. Methods 14, 23–31 (2017)

  51. 51.

    et al. Model-based analysis of ChIP–seq (MACS). Genome Biol. 9, R137 (2008)

  52. 52.

    fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets. Bioinformatics 33, 746–748 (2017)

  53. 53.

    , & RSeQC: quality control of RNA-seq experiments. Bioinformatics 28, 2184–2185 (2012)

  54. 54.

    et al. RCAS: an RNA centric annotation system for transcriptome-wide regions of interest. Nucleic Acids Res. 45, e91 (2017)

  55. 55.

    DREME: motif discovery in transcription factor ChIP–seq data. Bioinformatics 27, 1653–1659 (2011)

  56. 56.

    , & Dalliance: interactive genome viewing on the web. Bioinformatics 27, 889–890 (2011)

  57. 57.

    , & featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014)

  58. 58.

    , & Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014)

  59. 59.

    et al. A survey of best practices for RNA-seq data analysis. Genome Biol. 17, 13 (2016)

  60. 60.

    , , , & GAT: a simulation framework for testing the association of genomic intervals. Bioinformatics 29, 2046–2048 (2013)

  61. 61.

    et al. Simple combinations of lineage-determining transcription factors prime cis-regulatory elements required for macrophage and B cell identities. Mol. Cell 38, 576–589 (2010)

  62. 62.

    , , , & Exome-based analysis for RNA epigenome sequencing data. Bioinformatics 29, 1565–1567 (2013)

  63. 63.

    et al. Transcript assembly and quantification by RNA-seq reveals unannotated transcripts and isoform switching during cell differentiation. Nat. Biotechnol. 28, 511–515 (2010)

  64. 64.

    & Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files v.1.33 (2011)

  65. 65.

    & Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012)

  66. 66.

    et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009)

  67. 67.

    SeqMonk: A tool to visualise and analyse high throughput mapped sequence data (2014)

  68. 68.

    Linear models and empirical bayes methods for assessing differential expression in microarray experiments. Stat. Appl. Genet. Mol. Biol. 3, e3 (2004)

  69. 69.

    & Analysis of relative gene expression data using real-time quantitative PCR and the 2−ΔΔCt method. Methods 25, 402–408 (2001)

  70. 70.

    , , , & A comparison of apparent mRNA half-life using kinetic labeling techniques vs decay following administration of transcriptional inhibitors. Anal. Biochem. 198, 19–29 (1991)

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Acknowledgements

We thank Cambridge Genomic Services for help with next-generation sequencing. This work was supported by the European Research Council starting grant ‘Relieve IMDs’ (L.V., S.B., A.B., P.M.); the Cambridge University Hospitals National Institute for Health Research Biomedical Research Center (L.V., J.K., A.-S.L.); the Wellcome Trust PhD program (A.O., L.Y.); a British Heart Foundation PhD studentship (FS/11/77/39327 to A.B.); a Grant-in-Aid for JSPS Fellows (16J08005 to S.N.); and a core support grant from the Wellcome Trust and Medical Research Council to the Wellcome Trust–Medical Research Council Cambridge Stem Cell Institute.

Author information

Author notes

    • Alessandro Bertero
    •  & Sasha Mendjan

    Present addresses: Department of Pathology, University of Washington, Seattle 98109, Washington, USA (A.B.); Institute of Molecular Biotechnology, Vienna 1030, Austria (S.M.)

    • Alessandro Bertero
    •  & Stephanie Brown

    These authors contributed equally to this work.

Affiliations

  1. Wellcome Trust–MRC Cambridge Stem Cell Institute, Anne McLaren Laboratory and Department of Surgery, University of Cambridge, Cambridge CB2 0SZ, UK

    • Alessandro Bertero
    • , Stephanie Brown
    • , Pedro Madrigal
    • , Anna Osnato
    • , Daniel Ortmann
    • , Loukia Yiangou
    • , Juned Kadiwala
    • , An-Sofie Lenaerts
    • , Shota Nakanoh
    • , Rodrigo Grandy
    • , Sasha Mendjan
    •  & Ludovic Vallier
  2. Wellcome Trust Sanger Institute, Hinxton CB10 1SA, UK

    • Pedro Madrigal
    •  & Ludovic Vallier
  3. Department of Molecular Biology, Radboud University, Nijmegen 6525GA, The Netherlands

    • Nina C. Hubner
    •  & Hendrik G. Stunnenberg
  4. Francis Crick Institute and Department of Molecular Neuroscience, University College London, London NW1 1AT, UK

    • Igor Ruiz de los Mozos
    • , Christoph Sadée
    •  & Jernej Ule
  5. Department of Pathology, University of Cambridge, Cambridge CB2 1QP, UK

    • Edward Farnell

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Contributions

A.B. conceived the study, performed or contributed to most of the experiments, analysed data and wrote the manuscript with input from the other authors. S.B. contributed to study conception, performed co-immunoprecipitation, NeMeRIP and RNA-IP experiments, and analysed data. P.M., I.R.d.l.M, and C.S. analysed NeMeRIP–seq. A.O. performed PLA and co-immunoprecipitations and analysed RNA-seq data. D.O., L.Y. and J.K. assisted with hPSC gene editing and differentiation; N.C.H. performed quantitative proteomics and data analysis. A.-S.L., S.N. and R.G. assisted with hPSC culture. E.F. optimized NeMeRIP–seq libraries. J.U. contributed to study conception and supervision. H.G.S. supervised quantitative proteomics. S.M. contributed to study conception and supervision, and assisted with SMAD2/3 co-immunoprecipitation. L.V. conceived, supervised and supported the study, and wrote and provided final approval of the manuscript.

Competing interests

The authors declare no competing financial interests.

Corresponding author

Correspondence to Ludovic Vallier.

Reviewer Information Nature thanks C. Mason and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Supplementary information

PDF files

  1. 1.

    Life Sciences Reporting Summary

  2. 2.

    Supplementary Information

    This file contains a Supplementary Discussion and Supplementary References.

  3. 3.

    Supplementary Figure 1

    This file contains uncropped western blot scans with size marker indications.

Excel files

  1. 1.

    Supplementary Table 1

    This file contains SMAD2/3 interacting proteins in hESCs and endoderm cells.

  2. 2.

    Supplementary Table 2

    This file contains m6A peaks in exons, introns, and spanning exon-intron junctions.

  3. 3.

    Supplementary Table 3

    This file contains gene ontology enrichment analysis for transcripts where m6A is downregulated after inhibition of Activin/Nodal signalling.

  4. 4.

    Supplementary Table 4

    This file contains genes differentially expressed during exit from pluripotency in WTAP inducible knockdown hESCs.

  5. 5.

    Supplementary Table 5

    This file contains a list of oligonucleotides used in the study.

  6. 6.

    Supplementary Table 6

    This file contains a list of antibodies used in the study.

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DOI

https://doi.org/10.1038/nature25784

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