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m6A modulates haematopoietic stem and progenitor cell specification

Nature volume 549, pages 273–276 (2017)Cite this article

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Abstract

N6-methyladenosine (m6A) has been identified as the most abundant modification on eukaryote messenger RNA (mRNA)1. Although the rapid development of high-throughput sequencing technologies has enabled insight into the biological functions of m6A modification2,3,4,5,6,7,8,9,10,11,12,13, the function of m6A during vertebrate embryogenesis remains poorly understood. Here we show that m6A determines cell fate during the endothelial-to-haematopoietic transition (EHT) to specify the earliest haematopoietic stem/progenitor cells (HSPCs) during zebrafish embryogenesis. m6A-specific methylated RNA immunoprecipitation combined with high-throughput sequencing (MeRIP–seq) and m6A individual-nucleotide-resolution cross-linking and immunoprecipitation with sequencing (miCLIP–seq) analyses reveal conserved features on zebrafish m6A methylome and preferential distribution of m6A peaks near the stop codon with a consensus RRACH motif. In mettl3-deficient embryos, levels of m6A are significantly decreased and emergence of HSPCs is blocked. Mechanistically, we identify that the delayed YTHDF2-mediated mRNA decay of the arterial endothelial genes notch1a and rhoca contributes to this deleterious effect. The continuous activation of Notch signalling in arterial endothelial cells of mettl3-deficient embryos blocks EHT, thereby repressing the generation of the earliest HSPCs. Furthermore, knockdown of Mettl3 in mice confers a similar phenotype. Collectively, our findings demonstrate the critical function of m6A modification in the fate determination of HSPCs during vertebrate embryogenesis.

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Figure 1: m6A methylome in zebrafish embryos.
Figure 2: HSPC generation is impaired in mettl3 morphants.
Figure 3: m6A inhibits notch1a expression to control HSPC specification.
Figure 4: YTHDF2-mediated mRNA decay contributes to the m6A repression of Notch pathway.

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References

  1. Jia, G., Fu, Y. & He, C. Reversible RNA adenosine methylation in biological regulation. Trends Genet. 29, 108–115 (2013)

    CAS  PubMed  Google Scholar 

  2. Zheng, G. et al. ALKBH5 is a mammalian RNA demethylase that impacts RNA metabolism and mouse fertility. Mol. Cell 49, 18–29 (2013)

    CAS  PubMed  Google Scholar 

  3. Xiao, W. et al. Nuclear m6A reader YTHDC1 regulates mRNA splicing. Mol. Cell 61, 507–519 (2016)

    CAS  PubMed  Google Scholar 

  4. Wang, X. et al. N6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

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

    ADS  PubMed  Google Scholar 

  6. Zhou, J. et al. Dynamic m6A mRNA methylation directs translational control of heat shock response. Nature 526, 591–594 (2015)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

  8. Alarcón, C. R., Lee, H., Goodarzi, H., Halberg, N. & Tavazoie, S. F. N6-methyladenosine marks primary microRNAs for processing. Nature 519, 482–485 (2015)

    ADS  PubMed  PubMed Central  Google Scholar 

  9. Zhao, B. S. et al. m6A-dependent maternal mRNA clearance facilitates zebrafish maternal-to-zygotic transition. Nature 542, 475–478 (2017)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  10. Xiang, Y. et al. RNA m6A methylation regulates the ultraviolet-induced DNA damage response. Nature 543, 573–576 (2017)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lence, T. et al. m6A modulates neuronal functions and sex determination in Drosophila. Nature 540, 242–247 (2016)

    ADS  CAS  PubMed  Google Scholar 

  12. Haussmann, I. U. et al. m6A potentiates Sxl alternative pre-mRNA splicing for robust Drosophila sex determination. Nature 540, 301–304 (2016)

    ADS  CAS  PubMed  Google Scholar 

  13. Fustin, J. M. et al. RNA-methylation-dependent RNA processing controls the speed of the circadian clock. Cell 155, 793–806 (2013)

    CAS  PubMed  Google Scholar 

  14. Kissa, K. & Herbomel, P. Blood stem cells emerge from aortic endothelium by a novel type of cell transition. Nature 464, 112–115 (2010)

    ADS  CAS  PubMed  Google Scholar 

  15. Boisset, J. C. et al. In vivo imaging of haematopoietic cells emerging from the mouse aortic endothelium. Nature 464, 116–120 (2010)

    ADS  CAS  PubMed  Google Scholar 

  16. Bertrand, J. Y. et al. Haematopoietic stem cells derive directly from aortic endothelium during development. Nature 464, 108–111 (2010)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  17. Zhao, B. S. & He, C. Fate by RNA methylation: m6A steers stem cell pluripotency. Genome Biol. 16, 43 (2015)

    PubMed  PubMed Central  Google Scholar 

  18. Wang, Y. et al. N6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

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

    ADS  CAS  PubMed  Google Scholar 

  20. Ping, X. L. et al. Mammalian WTAP is a regulatory subunit of the RNA N6-methyladenosine methyltransferase. Cell Res. 24, 177–189 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  PubMed Central  Google Scholar 

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

    ADS  CAS  PubMed  Google Scholar 

  23. Zhang, P. et al. G protein-coupled receptor 183 facilitates endothelial-to-hematopoietic transition via Notch1 inhibition. Cell Res. 25, 1093–1107 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Lizama, C. O. et al. Repression of arterial genes in hemogenic endothelium is sufficient for haematopoietic fate acquisition. Nat. Commun. 6, 7739 (2015)

    ADS  PubMed  Google Scholar 

  25. Gama-Norton, L. et al. Notch signal strength controls cell fate in the haemogenic endothelium. Nat. Commun. 6, 8510 (2015)

    ADS  CAS  PubMed  Google Scholar 

  26. Zhu, T. et al. Crystal structure of the YTH domain of YTHDF2 reveals mechanism for recognition of N6-methyladenosine. Cell Res. 24, 1493–1496 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  27. Linder, B. et al. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat. Methods 12, 767–772 (2015)

    CAS  PubMed  PubMed Central  Google Scholar 

  28. Lawson, N. D. & Weinstein, B. M. In vivo imaging of embryonic vascular development using transgenic zebrafish. Dev. Biol. 248, 307–318 (2002)

    CAS  PubMed  Google Scholar 

  29. He, Q. et al. Inflammatory signaling regulates hematopoietic stem and progenitor cell emergence in vertebrates. Blood 125, 1098–1106 (2015)

    CAS  PubMed  Google Scholar 

  30. Wei, Y. et al. Ncor2 is required for hematopoietic stem cell emergence by inhibiting Fos signaling in zebrafish. Blood 124, 1578–1585 (2014)

    CAS  PubMed  Google Scholar 

  31. Zhang, C. et al. Inhibition of endothelial ERK signalling by Smad1/5 is essential for haematopoietic stem cell emergence. Nat. Commun. 5, 3431 (2014)

    ADS  PubMed  Google Scholar 

  32. Lv, J ., Wang, L ., Gao, Y ., Ding, Y. Q . & Liu, F. 5-hydroxytryptamine synthesized in the aorta-gonad-mesonephros regulates hematopoietic stem and progenitor cell survival. J. Exp. Med. 214, 529–545 (2017)

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Chen, T . et al. m6A RNA methylation is regulated by microRNAs and promotes reprogramming to pluripotency. Cell Stem Cell 16, 289–301 (2015)

    CAS  PubMed  Google Scholar 

  34. Tang, F. et al. RNA-Seq analysis to capture the transcriptome landscape of a single cell. Nat. Protocols 5, 516–535 (2010)

    CAS  PubMed  Google Scholar 

  35. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17, 10–12 (2011)

    Google Scholar 

  36. Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014)

    CAS  PubMed  PubMed Central  Google Scholar 

  37. Trapnell, C., Pachter, L. & Salzberg, S. L. TopHat: discovering splice junctions with RNA-seq. Bioinformatics 25, 1105–1111 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

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

    PubMed  PubMed Central  Google Scholar 

  39. Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010)

    CAS  PubMed  PubMed Central  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  41. Heinz, S. 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)

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Chen, K. et al. High-resolution N6-methyladenosine (m6A) map using photo-crosslinking-assisted m6A sequencing. Angew. Chem. Int. Ed. Engl. 54, 1587–1590 (2015)

    CAS  PubMed  Google Scholar 

  43. König, J. et al. iCLIP reveals the function of hnRNP particles in splicing at individual nucleotide resolution. Nat. Struct. Mol. Biol. 17, 909–915 (2010)

    PubMed  PubMed Central  Google Scholar 

  44. Patil, D. P. et al. m6A RNA methylation promotes XIST-mediated transcriptional repression. Nature 537, 369–373 (2016)

    ADS  CAS  PubMed  PubMed Central  Google Scholar 

  45. Moore, M. J. et al. Mapping Argonaute and conventional RNA-binding protein interactions with RNA at single-nucleotide resolution using HITS-CLIP and CIMS analysis. Nat. Protocols 9, 263–293 (2014)

    CAS  PubMed  Google Scholar 

  46. Shah, A., Qian, Y., Weyn-Vanhentenryck, S. M. & Zhang, C. CLIP Tool Kit (CTK): a flexible and robust pipeline to analyze CLIP sequencing data. Bioinformatics 33, 566–567 (2017)

    CAS  PubMed  Google Scholar 

  47. Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows–Wheeler transform. Bioinformatics 25, 1754–1760 (2009)

    CAS  PubMed  PubMed Central  Google Scholar 

  48. Weyn-Vanhentenryck, S. M. et al. HITS-CLIP and integrative modeling define the Rbfox splicing-regulatory network linked to brain development and autism. Cell Reports 6, 1139–1152 (2014)

    CAS  PubMed  Google Scholar 

  49. Crooks, G. E., Hon, G., Chandonia, J. M. & Brenner, S. E. WebLogo: a sequence logo generator. Genome Res. 14, 1188–1190 (2004)

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Wang, Y. et al. GSA: Genome Sequence Archive(). Genomics Proteomics Bioinformatics 15, 14–18 (2017)

    PubMed  PubMed Central  Google Scholar 

  51. BIG Data Center Members. The BIG Data Center: from deposition to integration to translation. Nucleic Acids Res. 45 D18–D24 (2017)

  52. Parson, M. J. et al. Notch-responsive cells initiate the secondary transition in larval zebrafish pancreas. Mech. Dev. 126 898–912 (2009)

    Google Scholar 

Download references

Acknowledgements

We thank F. C. Tang and L. Yang for cell sorting and bioinformatics analysis, D. Liu and J.W. Xiong for reagents, H.L. Wang and W.Y. Lai for the UHPLC-MRM-MS/MS analysis. This work was supported by the National Natural Science Foundation of China (31425016 and 81530004 to F.L., and 31625016 to Y.Y.), the Ministry of Science and Technology of China (2016YFA0100500 to F. L. and 2016YFC0900300 to Y.Y.) and the Strategic Priority Research Program of the Chinese Academy of Sciences, China (XDA01010110 to F.L.; QYZDY-SSW-SMC027 and XDB14030300 to Y.Y.).

Author information

Author notes

  1. Chunxia Zhang, Yusheng Chen, Baofa Sun, Lu Wang and Ying Yang: These authors contributed equally to this work.

Authors and Affiliations

  1. State Key Laboratory of Membrane Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing, 100101, China

    Chunxia Zhang, Lu Wang, Dongyuan Ma, Junhua Lv, Jian Heng, Yanyan Ding, Yuanyuan Xue, Xinyan Lu & Feng Liu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Chunxia Zhang, Yusheng Chen, Junhua Lv, Jian Heng, Yanyan Ding, Yuanyuan Xue, Xinyan Lu, Yun-Gui Yang & Feng Liu

  3. CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, CAS Center for Excellence in Molecular Cell Science, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, 100101, China

    Yusheng Chen, Baofa Sun, Ying Yang, Wen Xiao & Yun-Gui Yang

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  1. Chunxia Zhang
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Contributions

C.Z. performed most of the experiments; Y.C. and B.S. performed bioinformatics analysis; L.W. validated the HSPC phenotypes and helped to perform the reporter assay; Y.Y. helped to performed high-throughput sequencing; D.M. generated the mettl3 mutants; J.L. validated the mechanism in mouse embryos; J.H. helped to generate the plasmid constructs; Y.D., Y.X., and X.L. helped to collect embryos for FACS; W.X. performed the EMSA assay; C.Z., Y.-G.Y., and F.L. conceived the project, analysed the data, and wrote the paper. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yun-Gui Yang or Feng Liu.

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The authors declare no competing financial interests.

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Reviewer Information Nature thanks A. Bigas, G. Rechavi and the other anonymous reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Figure 1 Quantification and characterization of m6A mRNA methylation during zebrafish embryonic development.

a, UHPLC-MRM-MS/MS quantification of m6A level at different stages during zebrafish development. b, Venn diagram showing the overlap of m6A peaks detected from two independent MeRIP–seq experiments using control embryos. c, Percentage of mRNAs and non-coding RNAs containing m6A peaks. d, Bar plots showing the number of representative non-coding RNAs containing m6A peaks. e, Gene ontology biological processes enriched in m6A-containing mRNAs. f, Percentage of m6A-methylated mRNAs with different numbers of m6A peaks.

Source data

Extended Data Figure 2 Germ layer formation and primitive haematopoiesis are unaltered in mettl3 morphants.

a, Protein levels of METTL3 were decreased in mettl3 morphants. b, UHPLC-MRM-MS/MS analysis showing a significant decrease of m6A levels in mRNAs of mettl3 morphants. c, WISH analysis showing that expression of ectoderm marker gsc, mesoderm marker eve1, and endoderm marker sox17 was not affected in mettl3 morphants. d, Double fluorescence in situ hybridization analysis showing the specific expression of mettl3 in endothelial cells. The white arrowheads indicate the co-expression of mettl3 and endothelial cell marker fli1a. e, qPCR analysis showing that mettl3 was highly expressed in endothelial cells and haemogenic endothelium. Endothelial cells, haemogenic endothelium, and HSPCs were sorted from 28 hpf Tg(kdrl:mCherry;runx1:en-GFP) embryos. f, WISH analysis showing the normal expression of primitive erythroid marker gata1 and myeloid marker pu1 in mettl3 morphants at 24 hpf. g, qPCR analysis showing that the expression of runx1 and cmyb was significantly decreased in mettl3 morphants at 32 hpf. h, Protein level of Runx1 was reduced in mettl3 morphants at 32 hpf. i, WISH analysis showing that the expression of arterial genes deltaC, tbx20, hey2, and ephrinB2a was increased in mettl3 morphants. j, k, TUNEL assay and l, m, BrdU assay showing that the number of apoptotic cells and proliferating cells in AGM was not notably changed in mettl3 morphant Tg(fli1a:EGFP) embryos at 28 hpf. k, m, Quantification of the apoptotic or proliferative cells with GFP+ signals in AGM region. White dashed squares indicate the AGM. Error bars, mean ± s.d., n = 3 technical replicates (b, e, g) and n = 5 embryos (k, m), *P < 0.05, **P < 0.01, ***P < 0.001, NS, not significant, Student’s t-test. Scale bars, 100 μm.

Source data

Extended Data Figure 3 Generation and validation of mettl3 mutants.

a, The left panel showing the target site in the third exon of mettl3 designed for the CRISPR–Cas9 mutants. The right panel revealing the 17 bp insertion in the mettl3 target site. b, Graphic representation of the truncated protein sequence with the MT-A70 domain missing. c, d, WISH (c) and qPCR (d) analyses showing that the expression of mettl3 was absent in the homozygous mettl3 mutants. e, Protein level of METTL3 was not detectable in the homozygous mutants. Error bars, mean ± s.d., n = 3 technical replicates (d), ***P < 0.001, n.d., not detectable, Student’s t-test. Scale bars, 100 μm.

Source data

Extended Data Figure 4 HSPC generation is impaired in mettl3 mutants.

a, WISH analysis showing that the expression of HSPC markers runx1 and cmyb, and the differentiated markers gata1 (erythroid), pu1 (myeloid), and rag1 (lymphoid) was decreased in mettl3 mutants. b, Confocal imaging showing that the numbers of haemogenic endothelium and emerging HSPCs in the AGM (white arrowheads), and T lymphocytes in the thymus (dashed circles) were significantly reduced in mettl3 mutants from a Tg(kdrl:mCherry;runx1:en-GFP) background. c, Quantification of haemogenic endothelium and emerging HSPCs in the AGM. d, PCR analysis showing that expression of runx1 and cmyb was significantly decreased in mettl3 mutants. e, WISH analysis showing that decreased expression of runx1 in mettl3 mutants was partially rescued by overexpression of mettl3 in endothelial cells driven by a fli1a-promoter (left panels), but not by overexpression of mettl3 in muscle cells driven by fmylz-promoter (right panels). Error bars, mean ± s.d., n = 10 embryos (c) and n = 3 technical replicates (d), ***P < 0.001, Student’s t-test. Scale bars, 100 μm (a, left panels in b, e). Scale bars, 25 μm (right panels in b).

Source data

Extended Data Figure 5 m6A directly targets notch1a to repress its expression.

a, A strong correlation of gene expression between the two mettl3 morphant replicates. Scatter plots comparing the fold changes (log2) in normalized gene expression from replicates of mettl3 knockdown. The Pearson correlation coefficient (R) and number of differentially-expressed genes (N) are shown in the bottom right corner. b, Heat map depicting the normalized gene expression of differentially-expressed genes in control and mettl3 morphants. c, Gene ontology biological processes enriched in genes upregulated and downregulated in mettl3 morphants, respectively. d, qPCR analysis showing the expression change of selected mettl3 target genes. e, The electrophoresis analysis after m6A-RIP showing that the m6A enrichment in notch1a mRNA was absent in mettl3 morphants. Amplicons obtained from input and flow-through samples were used as positive and negative controls, respectively. f, g, The m6A-RIP-qPCR analysis showing that the m6A enrichment in notch1a mRNA was high in endothelial cells and haemogenic endothelium at 28 hpf (f) and increased from 24 hpf to 28 hpf (g). h, i, qPCR analysis showing that the expression of notch1a was significantly increased in mettl3 mutants (h) and decreased in mettl3 overexpressed embryos (i). j, Protein levels of Notch1 were decreased in mettl3 overexpressed embryos. k, The increased NICD expression in mettl3 morphants was restored by the treatment with Notch inhibitor DBZ. l, WISH analysis showing that the decreased expression of HSPC markers runx1 and cmyb, and increased expression of arterial endothelial cell marker ephrinB2a in mettl3 morphants was restored by DBZ treatment or notch1a MO co-injection. Error bars, mean ± s.d., n = 3 technical replicates, *P < 0.05, **P < 0.01, ***P < 0.001, Student’s t-test. Scale bars, 100 μm.

Source data

Extended Data Figure 6 YTHDF2 functions as an m6A reader to mediate m6A regulation of HSPC development.

a, WISH analysis showing that the expression of HSPC markers runx1 and cmyb, and the differentiated markers gata1 (erythroid), pu1 (myeloid), and rag1 (lymphoid) was decreased in ythdf2 morphants. Scale bars, 100 μm. b, The number of haemogenic endothelium and emerging HSPCs in the AGM (white arrowheads) was significantly reduced in Tg(kdrl:mCherry;runx1:en-GFP) embryos injected with ythdf2 MO. c, Quantification of haemogenic endothelium cells and emerging HSPCs in the AGM is shown in the lower panel. Error bars, mean ± s.d., n = 10 embryos, ***P < 0.001, Student’s t-test. Scale bars, 100 μm. d, Protein level of Runx1 was decreased in ythdf2 morphants. e, The upper panel displaying the conserved region of YTHDF2 in humans, mice, and zebrafish. The lower panel showing the mutation sites of the m6A recognition site in zebrafish ythdf2. f, runx1 expression and the number of haemogenic endothelium cells and emerging HSPCs (arrowheads in c) in ythdf2 morphants with overexpression of wild-type or mutated ythdf2 mRNA. g, Venn diagram showing overlap of YTHDF2 binding targets and m6A-methylated mRNAs. h, A strong correlation of gene expression between two ythdf2 morphant replicates. Scatter plots comparing the fold changes (log2) in normalized gene expression from replicates of ythdf2 morphants. The Pearson correlation coefficient (R) and number of differentially-expressed genes (N) are shown in the bottom right corner. i, Scatter plots showing the fold changes (log2) in normalized gene expression of METTL3 and YTHDF2 common target genes.

Extended Data Figure 7 Characterization of specific m6A modification site in notch1a mRNA.

a, Pie chart displaying the distribution of miCLIP-called m6A sites in five transcript segments: TSS, 5′ UTR, CDS, stop codon, and 3′ UTR regions of mRNAs. b, Metagene profiles of miCLIP-called m6A site distribution along a normalized transcript composed of three rescaled non-overlapping segments: 5′ UTR, CDS, and 3′ UTR in control embryos. The inner panel showing the sequence motif of miCLIP-called m6A sites. c, Integrative Genomics Viewer (IGV) tracks displaying the distribution of a specific miCLIP-called m6A site in notch1a mRNA. The yellow shading indicates the single m6A site. d, Schematic depiction of mutation of the m6A site in notch1a mRNA. e, f, Fluorescence microscopy and qPCR analyses showing that overexpression of mettl3 and ythdf2 suppressed the expression of the notch1a-egfp reporter with wild-type m6A modification site, but not the expression of the notch1a-egfp reporter with mutation site. Error bars, mean ± s.d., n = 10 embryos, ***P < 0.001, n.s., not significant, Student’s t-test. Scale bars, 500 μm. g, WISH analysis showing that the expression of runx1 in HSPCs was decreased by overexpression of notch1a with wild-type or mutated m6A modification site from 24 hpf, but only the phenotype caused by overexpression of rhoca with a wild-type site was rescued by overexpression of mettl3 and ythdf2. Scale bars, 100 μm.

Source data

Extended Data Figure 8 m6A directly targets rhoca to repress its expression.

a, IGV tracks displaying MeRIP–seq (upper panels) and RNA-seq (lower panels) read distribution in rhoca mRNA of control and mettl3 morphants. The green dots at the bottom of the tracks depicting the positions of m6A peaks. b, The m6A-RIP-qPCR analysis showing that the m6A enrichment in rhoca mRNA was significantly decreased in mettl3 morphants. c, qPCR analysis showing that the expression of rhoca was significantly increased in endothelial cells of mettl3 morphants. d, e, qPCR analysis showing that the expression of rhoca was significantly increased in mettl3 mutants (d) and decreased in mettl3-overexpressed embryos (e). f, Protein level of phosphorylated ERK was increased in rhoca-overexpressed embryos. The right panel showing the quantification of pERK level. g, The hyperphosphorylation of ERK was restored by co-injection with rhoca MO. The right panel showing the quantification of pERK level. h, qPCR analysis showing that the decreased expression of runx1 was restored by co-injection with rhoca MO or U0126 treatment. i, WISH analysis showing that the decreased expression of HSC markers runx1 and cmyb, and the increased expression of arterial endothelial cell marker ephrinB2a in mettl3 morphants were restored by co-injection with rhoca MO or U0126 treatment. Scale bars, 100 μm. j, IGV tracks displaying YTHDF2-RIP-seq (upper panels) and RNA-seq (lower panels) read distribution in control and ythdf2 morphants within rhoca mRNA. The green dots at the bottom of the tracks depicting the positions of m6A peaks and the grey shading indicating the YTHDF2 binding regions. k, qPCR analysis showing the delayed rhoca mRNA degradation in both mettl3 morphants (with a 34.5% decrease) and ythdf2 morphants (with a 37.5% decrease) compared with control (with a 26.1% decrease) after treatment with actinomycin D for 4 h and 8 h, respectively. Error bars, mean ± s.d., n = 3 technical replicates, *P < 0.05, **P < 0.01, ***P < 0.001 by Student’s t-test.

Source data

Extended Data Figure 9 Characterization of specific m6A modification site in rhoca mRNA.

a, IGV tracks displaying the distribution of specific miCLIP-called m6A site in rhoca mRNA. The yellow shading indicating the single m6A site. b, Schematic depiction of the mutation of m6A site in rhoca mRNA. c, d, Fluorescence microscopy and qPCR analyses showing that overexpression of mettl3 and ythdf2 suppressed the expression of rhoca-egfp reporter with wild-type m6A modification site, but not the expression of rhoca-egfp reporter with mutation site. Error bars, mean ± s.d., n = 3 technical replicates, *P < 0.05, n.s., not significant, Student’s t-test. Scale bars, 500 μm. e, WISH analysis showing that the expression of runx1 was decreased by overexpression of rhoca with wild-type or mutated m6A modification site from 24 hpf, but only the phenotype caused by overexpression of rhoca with wild-type site was rescued by overexpression of mettl3 and ythdf2. Scale bars, 100 μm.

Source data

Extended Data Figure 10 Conserved function of m6A regulation in mouse haematopoietic development.

a, qPCR analysis showing the expression of Mettl3, Mettl14, Wtap, and Ythdf2 in mouse CD31+CD45−Ter119− endothelial cells, CD31+cKit+ haemogenic endothelium and CD34+cKit+ HSPCs, respectively. b, Immunofluorescence staining showing the expression pattern of METTL3 in the E10.5 AGM. Scale bars, 100 μm. c, qPCR analysis showing that the expression of Mettl3 was significantly decreased by the siMettl3 transfection. d, CFU-C assay of E10.5 AGM showing that the number of CFU-mix, CFU-granulocyte-macrophage (CFU-GM), and CFU-erythroid (CFU-E) was decreased upon knockdown of Mettl3. e, qPCR analysis showing that the expression of Notch1, ephrinB2, Hes1, and Hey1 was increased after knockdown of Mettl3. f, The m6A-RIP-qPCR analysis showing the m6A enrichment in Notch1 mRNA in the E10.5 AGM. g, Schematic representation of the role of m6A methylation in HSPC fate determination during EHT via METTL3-notch1a. Error bars, mean ± s.d., n = 3 technical replicates (a, c, e, f), n = 4 embryos (d), *P < 0.05, **P < 0.01, ***P < 0.001, n.s., not significant, Student’s t-test.

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Supplementary information

Supplementary Information

This file contains a discussion regarding m6A regulation of RhoCA/ERK pathway during HSPC development. (PDF 67 kb)

Supplementary Figure

This file contains uncropped scans with size marker indications. (PDF 2291 kb)

Reporting Summary (PDF 48 kb)

Supplementary Data 1

This file contains processed sequencing data. (XLS 13515 kb)

Supplementary Data 2

This file contains processed sequencing data. (XLSX 5404 kb)

Supplementary Data 3

This file contains processed sequencing data. (XLSX 2748 kb)

Supplementary Data 4

This file contains processed sequencing data. (XLSX 4055 kb)

Supplementary Data 5

This file contains the sequences of morpholinos, siRNA, and PCR primers used in the study. (XLSX 14 kb)

Time-lapse lineage tracing of ETH in control and mettl3 morphant embryos.

The fate transition via EHT was observed in control, but not in mettl3 morphant Tg(kdrl:mCherry;cmyb:EGFP) embryo from 33 hpf to 40 hpf by Nikon A1 confocal microscopy. The yellow arrowheads indicate the HE and emerging HSPCs. (MP4 2969 kb)

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Zhang, C., Chen, Y., Sun, B. et al. m6A modulates haematopoietic stem and progenitor cell specification. Nature 549, 273–276 (2017). https://doi.org/10.1038/nature23883

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