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:

Global miRNA dosage control of embryonic germ layer specification

Nature volume 593, pages 602–606 (2021)Cite this article

Subjects

Abstract

MicroRNAs (miRNAs) have essential functions during embryonic development, and their dysregulation causes cancer1,2. Altered global miRNA abundance is found in different tissues and tumours, which implies that precise control of miRNA dosage is important1,3,4, but the underlying mechanism(s) of this control remain unknown. The protein complex Microprocessor, which comprises one DROSHA and two DGCR8 proteins, is essential for miRNA biogenesis5,6,7. Here we identify a developmentally regulated miRNA dosage control mechanism that involves alternative transcription initiation (ATI) of DGCR8. ATI occurs downstream of a stem-loop in DGCR8 mRNA to bypass an autoregulatory feedback loop during mouse embryonic stem (mES) cell differentiation. Deletion of the stem-loop causes imbalanced DGCR8:DROSHA protein stoichiometry that drives irreversible Microprocessor aggregation, reduced primary miRNA processing, decreased mature miRNA abundance, and widespread de-repression of lipid metabolic mRNA targets. Although global miRNA dosage control is not essential for mES cells to exit from pluripotency, its dysregulation alters lipid metabolic pathways and interferes with embryonic development by disrupting germ layer specification in vitro and in vivo. This miRNA dosage control mechanism is conserved in humans. Our results identify a promoter switch that balances Microprocessor autoregulation and aggregation to precisely control global miRNA dosage and govern stem cell fate decisions during early embryonic 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

Fig. 1: ATI of DGCR8 links dynamic Microprocessor autoregulation to altered DGCR8: DROSHA stoichiometry and aggregation.
Fig. 2: Microprocessor aggregation reduces the efficiency of miRNA processing and global miRNA dosage, leading to de-repression of lipid metabolic genes.
Fig. 3: ATI-mediated miRNA dosage control determines germ layer specification during mES cell differentiation.
Fig. 4: ATI-mediated miRNA dosage control mechanism is conserved in human cells and tissues.

Similar content being viewed by others

Data availability

The RNA-seq and small RNA-seq data that support the findings of this study have been deposited in GEO with accession number GSE165017. Published polyA(+) RNA-seq data for mES cell differentiation is from the GEO database under accession number GSE11233414. The published polyA(+) RNA-seq for EpiS cells and cells of the three germ layers reported in this paper is available on ArrayExpress (https://www.ebi.ac.uk/arrayexpress/) with accession E-MTAB-490427. Source data are provided with this paper.

Code availability

Perl scripts are available from https://github.com/lyuxuehui/ATI-of-DGCR8.

References

  1. Lin, S. & Gregory, R. I. MicroRNA biogenesis pathways in cancer. Nat. Rev. Cancer 15, 321–333 (2015).

    Article  CAS  Google Scholar 

  2. Bartel, D. P. Metazoan MicroRNAs. Cell 173, 20–51 (2018).

    Article  CAS  Google Scholar 

  3. Lu, J. et al. MicroRNA expression profiles classify human cancers. Nature 435, 834–838 (2005).

    Article  ADS  CAS  Google Scholar 

  4. Landgraf, P. et al. A mammalian microRNA expression atlas based on small RNA library sequencing. Cell 129, 1401–1414 (2007).

    Article  CAS  Google Scholar 

  5. Lee, Y. et al. The nuclear RNase III Drosha initiates microRNA processing. Nature 425, 415–419 (2003).

    Article  ADS  CAS  Google Scholar 

  6. Denli, A. M., Tops, B. B., Plasterk, R. H., Ketting, R. F. & Hannon, G. J. Processing of primary microRNAs by the Microprocessor complex. Nature 432, 231–235 (2004).

    Article  ADS  CAS  Google Scholar 

  7. Gregory, R. I. et al. The Microprocessor complex mediates the genesis of microRNAs. Nature 432, 235–240 (2004).

    Article  ADS  CAS  Google Scholar 

  8. Kwon, S. C. et al. Structure of human DROSHA. Cell 164, 81–90 (2016).

    Article  CAS  Google Scholar 

  9. Partin, A. C. et al. Cryo-EM structures of human Drosha and DGCR8 in complex with primary MicroRNA. Mol. Cell 78, 411–422.e4 (2020).

    Article  CAS  Google Scholar 

  10. Ha, M. & Kim, V. N. Regulation of microRNA biogenesis. Nat. Rev. Mol. Cell Biol. 15, 509–524 (2014).

    Article  CAS  Google Scholar 

  11. Han, J. et al. Posttranscriptional crossregulation between Drosha and DGCR8. Cell 136, 75–84 (2009).

    Article  CAS  Google Scholar 

  12. Triboulet, R., Chang, H. M., Lapierre, R. J. & Gregory, R. I. Post-transcriptional control of DGCR8 expression by the Microprocessor. RNA 15, 1005–1011 (2009).

    Article  CAS  Google Scholar 

  13. Du, P., Wang, L., Sliz, P. & Gregory, R. I. A biogenesis step upstream of Microprocessor controls miR-17∼92 expression. Cell 162, 885–899 (2015).

    Article  CAS  Google Scholar 

  14. Du, P. et al. An intermediate pluripotent state controlled by microRNAs is required for the naive-to-primed stem cell transition. Cell Stem Cell 22, 851–864.e5 (2018).

    Article  CAS  Google Scholar 

  15. Bernstein, E. et al. Dicer is essential for mouse development. Nat. Genet. 35, 215–217 (2003).

    Article  CAS  Google Scholar 

  16. Wang, Y., Medvid, R., Melton, C., Jaenisch, R. & Blelloch, R. DGCR8 is essential for microRNA biogenesis and silencing of embryonic stem cell self-renewal. Nat. Genet. 39, 380–385 (2007).

    Article  CAS  Google Scholar 

  17. Zhang, B. et al. A dosage-dependent pleiotropic role of Dicer in prostate cancer growth and metastasis. Oncogene 33, 3099–3108 (2014).

    Article  CAS  Google Scholar 

  18. Lambo, S. et al. The molecular landscape of ETMR at diagnosis and relapse. Nature 576, 274–280 (2019).

    Article  Google Scholar 

  19. Alberti, S., Gladfelter, A. & Mittag, T. Considerations and challenges in studying liquid-liquid phase separation and biomolecular condensates. Cell 176, 419–434 (2019).

    Article  CAS  Google Scholar 

  20. Maharana, S. et al. RNA buffers the phase separation behavior of prion-like RNA binding proteins. Science 360, 918–921 (2018).

    Article  ADS  CAS  Google Scholar 

  21. Case, L. B., Zhang, X., Ditlev, J. A. & Rosen, M. K. Stoichiometry controls activity of phase-separated clusters of actin signaling proteins. Science 363, 1093–1097 (2019).

    Article  ADS  CAS  Google Scholar 

  22. Patel, A. et al. A liquid-to-solid phase transition of the ALS protein FUS accelerated by disease mutation. Cell 162, 1066–1077 (2015).

    Article  CAS  Google Scholar 

  23. Lin, Y., Protter, D. S., Rosen, M. K. & Parker, R. Formation and maturation of phase-separated liquid droplets by RNA-binding proteins. Mol. Cell 60, 208–219 (2015).

    Article  CAS  Google Scholar 

  24. Hayashi, K., Ohta, H., Kurimoto, K., Aramaki, S. & Saitou, M. Reconstitution of the mouse germ cell specification pathway in culture by pluripotent stem cells. Cell 146, 519–532 (2011).

    Article  CAS  Google Scholar 

  25. Sladitschek, H. L. & Neveu, P. A. A gene regulatory network controls the balance between mesendoderm and ectoderm at pluripotency exit. Mol. Syst. Biol. 15, e9043 (2019).

    Article  CAS  Google Scholar 

  26. Greve, T. S., Judson, R. L. & Blelloch, R. microRNA control of mouse and human pluripotent stem cell behavior. Annu. Rev. Cell Dev. Biol. 29, 213–239 (2013).

    Article  CAS  Google Scholar 

  27. Xie, D. et al. Phase separation of SERRATE drives dicing body assembly and promotes miRNA processing in Arabidopsis. Nat. Cell Biol. 23, 32–39 (2021).

    Article  CAS  Google Scholar 

  28. Yao, X. et al. CRISPR/Cas9-mediated targeted integration in vivo using a homology-mediated end joining-based strategy. J. Vis. Exp. 133, 56844 (2018).

    Google Scholar 

  29. Mori, M. et al. Hippo signaling regulates microprocessor and links cell-density-dependent miRNA biogenesis to cancer. Cell 156, 893–906 (2014).

    Article  CAS  Google Scholar 

  30. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  31. Griffiths-Jones, S., Saini, H. K., van Dongen, S. & Enright, A. J. miRBase: tools for microRNA genomics. Nucleic Acids Res. 36, D154–D158 (2008).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  33. Kim, D., Paggi, J. M., Park, C., Bennett, C. & Salzberg, S. L. Graph-based genome alignment and genotyping with HISAT2 and HISAT-genotype. Nat. Biotechnol. 37, 907–915 (2019).

    Article  CAS  Google Scholar 

  34. Liao, Y., Smyth, G. K. & Shi, W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics 30, 923–930 (2014).

    Article  CAS  Google Scholar 

  35. Chen, J., Bardes, E. E., Aronow, B. J. & Jegga, A. G. ToppGene Suite for gene list enrichment analysis and candidate gene prioritization. Nucleic Acids Res. 37, W305–W311 (2009).

    Article  CAS  Google Scholar 

  36. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).

    Article  ADS  CAS  Google Scholar 

  37. Jiang, S. et al. An expanded landscape of human long noncoding RNA. Nucleic Acids Res. 47, 7842–7856 (2019).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

We thank N. Kim, P. Li, Y. Qi, and X. Fu for discussions; the Biopolymer Facility at Harvard Medical School for RNA-seq and small RNA-seq Illumina high-throughput sequencing; the Core Facilities of the School of Life Sciences at Peking University, particularly S. Qin, C. Shan, L. Fu and S. Huang, for technical help with confocal imaging and radiolabelling assays; and the flow cytometry Core at National Center for Protein Sciences at Peking University, particularly H. Lyu and H. Yang, for technical help. Some work on protein purification was performed in N. Gao’s laboratory (Peking University); we thank them for assistance. We thank J. Xiao’s laboratory for providing the pFastBac-Dual vector, Sf21 cells and their assistance; H.-Y. Lee’s laboratory and Y. Wang’s laboratory for providing the K562 cells and DGCR8−/− cells, respectively; and F. Guo for providing the pFastBac-HTb-His6-DROSHA390–1374 vector. This work was supported by grants to P.D. from the Natural Science Foundation of China (32050214 and 32090012) and the National Key Research and Development Program of China (2019YFA0110000), M.P. was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) (K01DK121861), and a grant to R.I.G. from the US National Institute of General Medical Sciences (NIGMS) (R01GM086386).

Author information

Author notes

  1. These authors contributed equally: Yingzi Cui, Xuehui Lyu

Authors and Affiliations

  1. MOE Key Laboratory of Cell Proliferation and Differentiation, School of Life Sciences, Peking University, Beijing, China

    Yingzi Cui, Li Ding, Jennie Ong & Peng Du

  2. Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China

    Xuehui Lyu, Ye Qi, Jennie Ong & Peng Du

  3. Biomedical Pioneering Innovation Center, Beijing Advanced Innovation Center for Genomics, Peking University, Beijing, China

    Lan Ke, Dechang Yang & Ge Gao

  4. Center for Bioinformatics, School of Life Sciences, Peking University, Beijing, China

    Lan Ke, Dechang Yang & Ge Gao

  5. State Key Laboratory of Protein and Plant Gene Research, School of Life Sciences, Peking University, Beijing, China

    Lan Ke, Dechang Yang & Ge Gao

  6. Stem Cell Program, Division of Hematology/Oncology, Boston Children’s Hospital, Boston, MA, USA

    Mehdi Pirouz & Richard I. Gregory

  7. Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA

    Mehdi Pirouz & Richard I. Gregory

  8. Harvard Stem Cell Institute, Cambridge, MA, USA

    Richard I. Gregory

  9. Department of Pediatrics, Harvard Medical School, Boston, MA, USA

    Richard I. Gregory

  10. Harvard Initiative for RNA Medicine, Boston, MA, USA

    Richard I. Gregory

Authors
  1. Yingzi Cui
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Xuehui Lyu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Li Ding
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lan Ke
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Dechang Yang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Mehdi Pirouz
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Ye Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Jennie Ong
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ge Gao
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Peng Du
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Richard I. Gregory
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.C. performed all phase-separation-related experiments (with some help from Y.Q. and X.L.), 5′ RACE experiments, and the teratoma assay. Y.C. and P.D. performed Microprocessor cleavage assays. Y.C. performed EB and neural differentiation with help from X.L. and M.P.  P.D. performed the miRNA reporter assay and constructed the ΔSL1 mES cell line; other cell lines were constructed by Y.C. and X.L. X.L. and P.D. carried out qRT–PCR, western blot, and RNA-seq bioinformatic analysis. L.D. performed small RNA-seq bioinformatic analysis. L.K. and D.Y. performed bioinformatic analysis on GTEX datasets under the supervision of G.G. P.D. and R.I.G. designed all experiments, analysed data, and wrote the manuscript with input from J.O., Y.C., and X.L.

Corresponding authors

Correspondence to Peng Du or Richard I. Gregory.

Ethics declarations

Competing interests

The authors declare no competing interests. R.I.G is a co-founder and scientific advisory board member of 28/7 Therapeutics and Theon Therapeutics.

Additional information

Peer review information Nature thanks Hannele Ruohola-Baker 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 figures and tables

Extended Data Fig. 1 ATI of DGCR8 links dynamic Microprocessor autoregulation to altered DGCR8:DROSHA stoichiometry.

a, Ratio of splicing events covering different exons based on RNA-seq data during mES cell-to-EB differentiation over a 13-day time course (Fig. 1a). b, Agarose gel analysis of 5′ RACE products (left) and summary of Sanger sequencing of 5′ RACE colonies (right). PCR products outlined by red arrows were purified and further processed for cloning and Sanger sequencing. The numbers indicate the proportion of all sequenced clones that map to a particular nucleotide. Stem-loop 1 (SL1) sequence in 5′ UTR is highlighted in red (Fig. 1b). c, Stem-loop structures (SL1 and SL2) in the 5′ UTR and CDS region of DGCR8 mRNA. Green arrowheads indicate CRISPR–Cas9 design for SL1 deletion. d, PCR analysis of genomic DNA for SL1 knockout (ΔSL1) in mES cells. e, qRT–PCR analysis of DGCR8 and DROSHA mRNA expression in wild-type and ΔSL1 mES cells. Data were normalized to GAPDH, and error bars indicate s.d. (n = 3, technical replicates). f, Western blots of DGCR8 and DROSHA proteins in wild-type and ΔSL1 mES cells transfected with corresponding siRNAs. Experiments were repeated three times with similar results (Methods).

Extended Data Fig. 2 SL1 depletion drives irreversible Microprocessor aggregation in mouse ES cells.

a, Immunofluorescence (IF) followed by confocal imaging of DGCR8 and DROSHA proteins in wild-type and ΔSL1 mES cells. Fluorescence signals of DGCR8 and DROSHA are shown in red and green, respectively, and DAPI stain is shown in blue. The merged signals are also shown. b, Western blot of DGCR8 and DROSHA proteins in wild-type and reporter mES cells expressing endogenous mCherry–DGCR8 fusion protein. c, Images of endogenous mCherry–DGCR8 fusion protein in the wild-type and ΔSL1 reporter ES cells. d, Representative time-lapse images of two proximate assemblies of Microprocessor in living ΔSL1 mES cells transfected with plasmids expressing tagged mCherry–DGCR8 and eGFP–DROSHA. e, Images of Microprocessor aggregates before and after treatment with 10% 1,6-hexanediol for 3 min in living ΔSL1 mES cells. f, Representative images of FRAP analysis of Microprocessor aggregates in living ΔSL1 mES cells transfected with plasmids expressing tagged mCherry–DGCR8 and eGFP–DROSHA. Targeted region is highlighted in a white box, and DGCR8 (red), DROSHA (green) and merged (yellow) signals are shown. Normalized fluorescence intensity of DGCR8 and DROSHA are shown. Data are represented as mean ± s.d. (n = 6, independent observations in six separated aggregates). g, Images of Microprocessor aggregates in ΔSL1 mES cells transfected with plasmids expressing tagged mCherry–DGCR8 and eGFP–DROSHA before and after microinjection with RNase. All experiments were repeated at least three times with similar results (Methods).

Extended Data Fig. 3 Imbalanced DGCR8:DROSHA stoichiometry drives irreversible Microprocessor aggregation in vitro.

a, Top, prediction of disordered regions in DGCR8 protein by PONDR (http://pondr.com/). Bottom, schematic diagram showing the domains of DGCR8. b, Coomassie blue staining of purified rDGCR8 and rDROSHA proteins at different concentrations, as well as two mutant versions of rDGCR8 proteins with deletion of ΔCTT and ΔRhed domains. c, Representative images of phase separation of rDGCR8, rDGCR8-ΔCTT, rDGCR8-ΔRhed and rDROSHA at different concentrations in physiological buffer. d, Representative images of the aggregates of labelled rDGCR8 (30 μM) before and after treatment with 10% 1,6-hexanediol for 5 min. e, Representative images of FRAP analysis of rDGCR8 puncta. Targeted region is highlighted in a white box. Normalized fluorescence intensity of rDGCR8 in FRAP analysis is represented as mean ± s.d. (n = 6, independent observations in six separated aggerates). f, g, Representative confocal images of pre-formed Microprocessor aggregates (32 μM rDGCR8:8 μM rDROSHA) under conditions of dilution and high salt (1 M NaCl). rDGCR8 (red), rDROSHA (green) and merged (yellow) signals are shown. h, i, Images of pre-formed Microprocessor aggregates (32 μM rDGCR8:8 μM rDROSHA) followed by the addition of extra rDROSHA to achieve a 2:1 ratio or treatment with 10% 1,6-hexanediol for 10 min. j, Representative time-lapse images of two proximate Microprocessor aggregates for the times indicated. rDGCR8 (red), rDROSHA (green) and merged (yellow) signals are shown. k, FRAP analysis of Microprocessor aggregates in vitro. Targeted droplet region is highlighted in a white box, and rDGCR8 (red), rDROSHA (green) and merged (yellow) signals are shown. Right, normalized fluorescence intensities. Data are represented as mean ± s.d. (n = 7, independent observations in seven separated aggregates). All experiments were repeated at least three times with similar results (Methods).

Extended Data Fig. 4 Microprocessor aggregation reduces the efficiency of pri-miRNA processing and global miRNA dosage, which leads to the de-repression of lipid metabolic genes.

a, Microprocessor in vitro cleavage assay of mouse pri-mir-125b using whole-cell lysate from wild-type and ΔSL1 mES cells. Microprocessor purified by immunoprecipitation from Flag–DROSHA-293T cells was used as a control. The pri-mir125b without lysate was the CK sample. b, Quantification of pri-miRNA cleavage activity calculated based on the density of pre-miRNA bonds in the assays shown in Fig. 2b and Extended Data Fig. 4a. c, Luciferase reporter in vivo cleavage assay of pri-mir-125b in wild-type, DGCR8−/−, and ΔSL1 mES cells. Data are represented as mean ± s.d. (n = 3, technical replicates). ****P < 0.0001, two-sided Student’s t-test. d, Scatter plot of global miRNA expression based small RNA-seq data in wild-type and ΔSL1 mES cells. The small RNA-seq data were normalized on the basis of spike-in RNAs. Differentially expressed miRNAs are represented by coloured circles, and the number of up- and downregulated miRNAs is shown. FC, fold change; two-sided Student’s t-test. e, Heat map of the expression of common up- or downregulated genes in ΔSL1 and DGCR8−/− mES cells compared to wild-type mES cells. The enrichment of Gene Ontology (GO) terms and the number of genes in each group are shown. Two-sided Student’s t-test. f, Venn diagram of mRNAs with expression changes in ΔSL1 and DGCR8−/− cells compared to wild-type mES cells. Number of genes in each group is shown. Two-sided Student’s t-test. g, GSEA analyses of lipid metabolic gene sets by comparing ΔSL1 and DGCR8−/− cells with wild-type mES cells. NES, normalized enrichment score. P values calculated by GSEA software. FDR, false discovery rate. h, Network of miRNAs and lipid metabolic genes. i, miRNA target sites on PDK4, LCLAT1 and GPCPD1 mRNAs, and luciferase miRNA target reporter assay in wild-type, ΔSL1 and DGCR8−/− mES cells. Data are represented as mean ± s.d. (n = 3, technical replicates). ****P < 0.0001, two-sided Student’s t-test. Mutations introduced into the miRNA target sites on PDK4 and LCLAT1, and the mutation sequences are shown in red font. Exact P values are provided in the Source Data (c, i) and Supplementary Table (d–f). Details of statistics replications are given in ‘Statistics and reproducibility’ in Methods.

Source data

Extended Data Fig. 5 Imbalanced DGCR8:DROSHA stoichiometry during EB differentiation drives Microprocessor aggregation.

a, Box plots of the relative expression of naive and primed genes during EpiLC differentiation of wild-type, ΔSL1 and DGCR8−/− mES cells. The number of naive and primed genes is shown and representative markers are listed. For box plots, centre line is median; box limits are 25th and 75th percentiles; whiskers extend to 1.5× IQR from the 25th and 75th percentiles. b, PCA of the wild-type and ΔSL1 cells by all expressed mRNAs during mES cell-to-EpiLC differentiation. c, d, The relative expression and calculated ratio of DGCR8 and DROSHA mRNA based on RNA-seq data during EB differentiation over a 13-day time course. e, Western blot of DGCR8 and DROSHA at different time points during wild-type mES cell-to-EB differentiation. The densities of the DROSHA and DGCR8 bands were normalized to the ACTINB band to calculate the relative protein ratios. f, Western blot of DGCR8 and DROSHA in differentiated EB cells (day 8) before and after treatment with a proteasome inhibitor MG132 (10 μM) for 2 h. The densities of the DROSHA and DGCR8 bands were normalized to the ACTINB band to calculate the relative protein ratios. g, Representative images of Microprocessor aggregates in differentiated EB cells (day 8) derived from dual-reporter ES cells endogenously expressing both mCherry–DGCR8 and eGFP–DROSHA fusion proteins. All experiments were repeated at least twice with similar results (Methods).

Extended Data Fig. 6 ATI-mediated miRNA dosage control determines germ layer specification during mES cell differentiation in vitro.

a, Ratios of splicing events covering different exons based on RNA-seq data from various germ layer cells. Ep, EpiS cell; Ec, ectoderm; Me, mesoderm; En, endoderm. b, The relative expression of pluripotent genes in wild-type and ΔSL1 cells during neural and EB differentiation. NP, neural progenitor. c, Heat maps of the relative expression of genes that are up- or downregulated in ΔSL1 cells compared with wild-type cells during EB differentiation. The enrichment of Gene Ontology (GO) terms and the number of genes in each group are shown. d, Heat maps of gene expression showing up- or downregulation in ΔSL1 mES cells compared with wild-type mES cells during neural differentiation. The enrichment of GO terms and the number of genes in each group are shown. e, Heat map of the relative expression of marker genes for three germ layers during neural differentiation. Representative markers are listed on the right. f, Scatter plot of global miRNA expression based small RNA-seq data in mES cells and neuronal progenitor cells (day 5). All experiments were repeated at least twice with similar results (Methods).

Extended Data Fig. 7 miRNA dosage control affects germ layer specification during teratoma formation in mouse.

a, Histology of teratomas stained with haematoxylin and eosin (HE). Left, low-power view of teratoma; differentiated areas are shown with different colours (black, ectoderm; red, endoderm). Right, representative images of ectoderm (neural tube) and endoderm (glandular structure) tissues. b, Comparison of the relative areas of ectoderm and endoderm tissues between wild-type and ΔSL1 teratomas, showing the area of each tissue divided by the total area according to HE staining. Data are represented as mean ± s.e.m. (WT ectoderm, n = 68; ΔSL1 ectoderm, n = 58; WT endoderm, n = 65; ΔSL1 endoderm, n = 17 independent observations). Sixteen (wild-type: 8, ΔSL1: 8) sections of eight (wild-type: 4, ΔSL1: 4) teratomas from four mice were used for analysis. **P < 0.01, ****P < 0.0001, two-sided Student’s t-test. c, Immunofluorescence of GATA4 protein (endoderm marker) in sections of teratomas derived from wild-type and ΔSL1 mES cells. Fluorescence signals of GATA4 are shown in pink, and DAPI staining is shown in blue. d, Comparison of the relative area of GATA4+ cells in teratomas derived from wild-type and ΔSL1 mES cells. Data are represented as mean ± s.e.m. (n = 439 independent observations), according to the immunofluorescence of 34 (wild-type: 17, ΔSL1: 17) sections of 18 (wild-type: 9, ΔSL1: 9) teratomas from nine mice. e, GATA4+ cell numbers relative to the total area in teratomas derived from wild-type and ΔSL1 mES cells according to the staining in c. Exact P values are provided in the Source Data. Details of statistical replications are given in the Methods.

Source data

Extended Data Fig. 8 Inhibition of lipid metabolism affects germ layer specification during EB differentiation in vitro.

a, Heat map of the relative expression of lipid metabolic genes, which is controlled by miRNA dosage in mES cells, during mES cell-to-EB differentiation over a 13-day time course. b, Protocol of mES cell-to-EB differentiation including treatment with lipid metabolic inhibitor GW9662. c, Heat map of the relative expression of lipid metabolic genes suppressed by GW9662 in differentiated EB cells. Gene number and representative genes are listed. The lipid metabolic genes are from the GO term ‘lipid metabolic process’. d, qRT–PCR analysis of the relative expression of germ layer marker genes during EB differentiation with GW9662 treatment. Data are normalized to GAPDH and represented as mean ± s.d. (n = 3 technical replicates). e, Heat map of the relative expression of germ layer marker genes during mES cell-to-EB differentiation under DMSO or GW9662 treatment. All experiments were repeated at least twice with similar results (Methods).

Extended Data Fig. 9 ATI-mediated miRNA dosage control mechanism is conserved in human cells and tissues.

a, Ratios of splicing events covering different exons based on RNA-seq data in different human cell lines (Fig. 4a). b, ChIP–seq signals for various transcription factors (TFs) at DGCR8 5′ UTR are shown in GM12878, MCF-7 and HepG2 cells, and representative transcription factors are listed in red. Histograms show the number of transcription factors that bind to the two distinct promoters (Fig. 4a). c, 5′ RACE experiment for DGCR8 mRNA in H1299 and K562 cells. Agarose gel analysis of 5′ RACE products (left) and summary of Sanger sequencing of 5′ RACE colonies (right). Red arrows indicate PCR products that were purified and further processed for cloning and Sanger sequencing. The numbers indicate the proportion of all sequenced clones that map to a particular nucleotide. SL1 sequence in 5′ UTR is shown in red (Fig. 4b). d, Imaging of tagged mCherry–DGCR8 and eGFP–DROSHA in living RPE1, HepG2, and K562 cells transfected with corresponding plasmids. e, FRAP analysis of Microprocessor aggregates in HepG2 cells transfected with plasmids expressing tagged mCherry–DGCR8 and eGFP–DROSHA. Targeted region is highlighted in a white box. DGCR8 (red), DROSHA (green) and merged (yellow) signals are shown. Normalized fluorescence intensity of DGCR8 and DROSHA in FRAP analysis. Data are presented as mean ± s.d. (n = 6, independent observations in six separated aggregates). f, Representative images of Microprocessor aggregates in HepG2 cells transfected with plasmids expressing tagged mCherry–DGCR8 and eGFP–DROSHA before and after treatment with 10% 1,6-hexanediol (1,6-Hex) for 3 min. g, qRT–PCR analysis of DGCR8 and DROSHA mRNA expression in wild-type and ΔSL1 H1299 cells. Data are normalized to GAPDH and represented as mean ± s.d. (n = 3 technical replicates). h, qRT–PCR analysis of mature miRNA expression. Data were normalized to U2 snoRNA. Data are represented as mean ± s.d. (n = 3 technical replicates). i, Formula used to calculate the ATI ratio (representing ATI appearance) and exon ratio based on sequencing coverage of RNA-seq data. j, Box plot shows the ATI of DGCR8 mRNA appearance in different human tissues based on RNA-seq from GTEx dataset. Number of samples analysed is shown in purple. Tissues mainly derived from embryonic endoderm are highlighted in red. For box plots, centre line is median; box limits are 25th and 75th percentiles; whiskers extend to 1.5× IQR from the 25th and 75th percentiles. All experiments were repeated at least twice with similar results (Methods).

Supplementary information

Supplementary Figure 1

This file contains the original source images of Western blot, Microprocessor assay.

Reporting Summary

Supplementary Table 1

Global miRNA expression and P-value calculated by two-sided Student’s t-test in WT mESCs and ΔSL1 mESCs, corresponding to Fig. 2c and Extended Data Fig. 4d.

Supplementary Table 2

Genes with expressional changes and P-value calculated by two-sided Student’s t-test in ΔSL1 and DGCR8-/- ESCs compared with WT ESCs, corresponding to Figs. 2d, Extended Data Fig. 4e, 4f.

Supplementary Table 3

Naive and primed genes expression in WT, ΔSL1 and DGCR8-/- mESCs during EpiLC differentiation, corresponding to Extended Data Fig. 5a.

Supplementary Table 4

Genes with expressional changes in ΔSL1 compared with WT mESCs during EB and neural differentiation, corresponding to Extended Data Fig. 6b-6d.

Supplementary Table 5

The expression of three germ layers marker genes in WT and ΔSL1 mESCs during neural and EB differentiation, corresponding to Fig. 3b, Extended Data Fig. 6e.

Supplementary Table 6

Global miRNA expression during neural differentiation, corresponding to Extended Data Fig. 6f.

Supplementary Table 7

The expression of lipid metabolic genes and three germ layer genes during EB differentiation treatment with lipid metabolic inhibitor GW9662, corresponding to Extended Data Figs. 8a, 8c, 8e.

Supplementary Table 8

Global miRNA expression in human cell lines, corresponding to Fig.4c.

Supplementary Table 9

List of primers and siRNA sequences used in the study.

Source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Cui, Y., Lyu, X., Ding, L. et al. Global miRNA dosage control of embryonic germ layer specification. Nature 593, 602–606 (2021). https://doi.org/10.1038/s41586-021-03524-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41586-021-03524-0

This article is cited by

Comments

By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.

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