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:

Motif-driven interactions between RNA and PRC2 are rheostats that regulate transcription elongation

Nature Structural & Molecular Biology volume 28, pages 103–117 (2021)Cite this article

Subjects

Abstract

Although polycomb repressive complex 2 (PRC2) is now recognized as an RNA-binding complex, the full range of binding motifs and why PRC2–RNA complexes often associate with active genes have not been elucidated. Here, we identify high-affinity RNA motifs whose mutations weaken PRC2 binding and attenuate its repressive function in mouse embryonic stem cells. Interactions occur at promoter-proximal regions and frequently coincide with pausing of RNA polymerase II (POL-II). Surprisingly, while PRC2-associated nascent transcripts are highly expressed, ablating PRC2 further upregulates expression via loss of pausing and enhanced transcription elongation. Thus, PRC2-nascent RNA complexes operate as rheostats to fine-tune transcription by regulating transitions between pausing and elongation, explaining why PRC2–RNA complexes frequently occur within active genes. Nascent RNA also targets PRC2 in cis and downregulates neighboring genes. We propose a unifying model in which RNA specifically recruits PRC2 to repress genes through POL-II pausing and, more classically, trimethylation of histone H3 at Lys27.

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: Denaturing CLIP of EZH2 and SUZ12 in differentiating 16.7 mES cells.
Fig. 2: Integrative dCLIP–seq and ChIP–seq analyses of PRC2 subunits and H3K27me3 reveal a new class of PRC2-interacting nascent transcripts.
Fig. 3: PRC2’s interaction with nascent RNA fine-tunes gene-expression states and regulates POL-II pausing.
Fig. 4: SUZ12 ablation abolishes POL-II pausing at promoter-proximal regions of CLIP genes.
Fig. 5: Identification of RNA-binding motifs for PRC2.
Fig. 6: PRC2 motifs are linked to promoter-proximal POL-II pausing.
Fig. 7: G-quadruplex motifs are enriched at POL-II pause sites, and their ablation results in transcription upregulation.
Fig. 8: Unified model: RNA recruits PRC2 for rheostat control of transcription elongation in cis and also at neighboring genes.

Similar content being viewed by others

Data and software availability

All sequencing data have been deposited in the GEO under the accession number GSE141700. Source data are provided with this paper.

References

  1. Simon, J. A. & Kingston, R. E. Occupying chromatin: Polycomb mechanisms for getting to genomic targets, stopping transcriptional traffic, and staying put. Mol. Cell 49, 808–824 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  2. Margueron, R. & Reinberg, D. The polycomb complex PRC2 and its mark in life. Nature 469, 343–349 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Laugesen, A., Hojfeldt, J. W. & Helin, K. Molecular mechanisms directing PRC2 recruitment and H3K27 methylation. Mol. Cell 74, 8–18 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  4. Lee, J. T. Epigenetic regulation by long noncoding RNAs. Science 338, 1435–1439 (2012).

    Article  CAS  PubMed  Google Scholar 

  5. Zhao, J. et al. Genome-wide identification of polycomb-associated RNAs by RIP-seq. Mol. Cell 40, 939–953 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Zhao, J., Sun, B. K., Erwin, J. A., Song, J.-J. & Lee, J. T. Polycomb proteins targeted by a short repeat RNA to the mouse X chromosome. Science 322, 750–756 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Beltran, M. et al. The interaction of PRC2 with RNA or chromatin is mutually antagonistic. Genome Res. 26, 896–907 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Kaneko, S., Son, J., Shen, S. S., Reinberg, D. & Bonasio, R. PRC2 binds active promoters and contacts nascent RNAs in embryonic stem cells. Nat. Struct. Mol. Biol. 20, 1258–1264 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Guil, S. et al. Intronic RNAs mediate EZH2 regulation of epigenetic targets. Nat. Struct. Mol. Biol. 19, 664–670 (2012).

    Article  CAS  PubMed  Google Scholar 

  10. Montero, J. J. et al. TERRA recruitment of polycomb to telomeres is essential for histone trymethylation marks at telomeric heterochromatin. Nat. Commun. 9, 1548 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  11. Pandey, R. R. et al. Kcnq1ot1 antisense noncoding RNA mediates lineage-specific transcriptional silencing through chromatin-level regulation. Mol. Cell 32, 232–246 (2008).

    Article  CAS  PubMed  Google Scholar 

  12. Kotzin, J. J. et al. The long non-coding RNA Morrbid regulates Bim and short-lived myeloid cell lifespan. Nature 537, 239–243 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Klattenhoff, C. A. et al. Braveheart, a long noncoding RNA required for cardiovascular lineage commitment. Cell 152, 570–583 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Cifuentes-Rojas, C., Hernandez, A. J., Sarma, K. & Lee, J. T. Regulatory interactions between RNA and polycomb repressive complex 2. Mol. Cell 55, 171–185 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Wang, X. et al. Molecular analysis of PRC2 recruitment to DNA in chromatin and its inhibition by RNA. Nat. Struct. Mol. Biol. 24, 1028–1038 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Zhang, Q. et al. RNA exploits an exposed regulatory site to inhibit the enzymatic activity of PRC2. Nat. Struct. Mol. Biol. 26, 237–247 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Kaneko, S., Son, J., Bonasio, R., Shen, S. S. & Reinberg, D. Nascent RNA interaction keeps PRC2 activity poised and in check. Genes Dev. 28, 1983–1988 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  18. Davidovich, C., Zheng, L., Goodrich, K. J. & Cech, T. R. Promiscuous RNA binding by polycomb repressive complex 2. Nat. Struct. Mol. Biol. 20, 1250–1257 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Beltran, M. et al. G-tract RNA removes polycomb repressive complex 2 from genes. Nat. Struct. Mol. Biol. 26, 899–909 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Davidovich, C. & Cech, T. R. The recruitment of chromatin modifiers by long noncoding RNAs: lessons from PRC2. RNA 21, 2007–2022 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Davidovich, C. et al. Toward a consensus on the binding specificity and promiscuity of PRC2 for RNA. Mol. Cell 57, 552–558 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  22. Wang, X. et al. Targeting of polycomb repressive complex 2 to RNA by short repeats of consecutive guanines. Mol. Cell 65, 1056–1067 (2017).

    Article  CAS  PubMed  Google Scholar 

  23. Long, Y. et al. Conserved RNA-binding specificity of polycomb repressive complex 2 is achieved by dispersed amino acid patches in EZH2. Elife 6, e31558 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  24. Khalil, A. M. et al. Many human large intergenic noncoding RNAs associate with chromatin-modifying complexes and affect gene expression. Proc. Natl Acad. Sci. USA 106, 11667–11672 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Rosenberg, M. et al. Denaturing CLIP, dCLIP, pipeline identifies discrete RNA footprints on chromatin-associated proteins and reveals that CBX7 targets 3′ UTRs to regulate mRNA expression. Cell Syst. 5, 368–385.e15 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Garant, J. M., Perreault, J. P. & Scott, M. S. Motif independent identification of potential RNA G-quadruplexes by G4RNA screener. Bioinformatics 33, 3532–3537 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Kung, J. T. et al. Locus-specific targeting to the X chromosome revealed by the RNA interactome of CTCF. Mol. Cell 57, 361–375 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Heger, A., Webber, C., Goodson, M., Ponting, C. P. & Lunter, G. GAT: a simulation framework for testing the association of genomic intervals. Bioinformatics 29, 2046–2048 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Pinter, S. F. et al. Spreading of X chromosome inactivation via a hierarchy of defined Polycomb stations. Genome Res. 22, 1864–1876 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Ardehali, M. B. et al. Polycomb Repressive Complex 2 Methylates Elongin A to Regulate Transcription. Mol. Cell 68, 872–884.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Mousavi, K., Zare, H., Wang, A. H. & Sartorelli, V. Polycomb protein Ezh1 promotes RNA polymerase II elongation. Mol. Cell 45, 255–262 (2012).

    Article  CAS  PubMed  Google Scholar 

  32. Wang, A. H. et al. The elongation factor Spt6 maintains ESC pluripotency by controlling super-enhancers and counteracting polycomb proteins. Mol. Cell 68, 398–413.e6 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Mi, H. et al. Protocol Update for large-scale genome and gene function analysis with the PANTHER classification system (v.14.0). Nat. Protoc. 14, 703–721 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Jonkers, I. & Lis, J. T. Getting up to speed with transcription elongation by RNA polymerase II. Nat. Rev. Mol. Cell Biol. 16, 167–177 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  35. Min, I. M. et al. Regulating RNA polymerase pausing and transcription elongation in embryonic stem cells. Genes Dev. 25, 742–754 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Adelman, K. & Lis, J. T. Promoter-proximal pausing of RNA polymerase II: emerging roles in metazoans. Nat. Rev. Genet. 13, 720–731 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kanhere, A. et al. Short RNAs are transcribed from repressed polycomb target genes and interact with polycomb repressive complex-2. Mol. Cell 38, 675–688 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Mahat, D. B. et al. Base-pair-resolution genome-wide mapping of active RNA polymerases using precision nuclear run-on (PRO-seq). Nat. Protoc. 11, 1455–1476 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Danko, C. G. et al. Signaling pathways differentially affect RNA polymerase II initiation, pausing, and elongation rate in cells. Mol. Cell 50, 212–222 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Rahl, P. B. et al. c-Myc regulates transcriptional pause release. Cell 141, 432–445 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Nabet, B. et al. The dTAG system for immediate and target-specific protein degradation. Nat. Chem. Biol. 14, 431–441 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Pasini, D., Bracken, A. P., Hansen, J. B., Capillo, M. & Helin, K. The polycomb group protein Suz12 is required for embryonic stem cell differentiation. Mol. Cell. Biol. 27, 3769–3779 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Sarma, K. et al. ATRX directs binding of PRC2 to Xist RNA and polycomb targets. Cell 159, 1228 (2014).

    Article  CAS  PubMed  Google Scholar 

  44. Fay, M. M., Lyons, S. M. & Ivanov, P. RNA G-quadruplexes in biology: principles and molecular mechanisms. J. Mol. Biol. 429, 2127–2147 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Kwok, C. K. & Merrick, C. J. G-quadruplexes: prediction, characterization, and biological application. Trends Biotechnol. 35, 997–1013 (2017).

    Article  CAS  PubMed  Google Scholar 

  46. Hon, J., Martinek, T., Zendulka, J. & Lexa, M. pqsfinder: an exhaustive and imperfection-tolerant search tool for potential quadruplex-forming sequences in R. Bioinformatics 33, 3373–3379 (2017).

    Article  CAS  PubMed  Google Scholar 

  47. Biffi, G., Di Antonio, M., Tannahill, D. & Balasubramanian, S. Visualization and selective chemical targeting of RNA G-quadruplex structures in the cytoplasm of human cells. Nat. Chem. 6, 75–80 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Rocca, R. et al. Molecular recognition of a carboxy pyridostatin toward G-quadruplex structures: why does it prefer RNA? Chem. Biol. Drug Des. 90, 919–925 (2017).

    Article  CAS  PubMed  Google Scholar 

  49. Sun, S. et al. Jpx RNA activates Xist by evicting CTCF. Cell 153, 1537–1551 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Lee, J. T. & Bartolomei, M. S. X-inactivation, imprinting, and long noncoding RNAs in health and disease. Cell 152, 1308–1323 (2013).

    Article  CAS  PubMed  Google Scholar 

  51. Schertzer, M. D. et al. lncRNA-induced spread of polycomb controlled by genome architecture, RNA abundance, and CpG island DNA. Mol. Cell. 75, 523–537 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lee, J. T. Lessons from X-chromosome inactivation: long ncRNA as guides and tethers to the epigenome. Genes Dev. 23, 1831–1842 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Bernstein, E. et al. Mouse polycomb proteins bind differentially to methylated histone H3 and RNA and are enriched in facultative heterochromatin. Mol. Cell. Biol. 26, 2560–2569 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  54. Stock, J. K. et al. Ring1-mediated ubiquitination of H2A restrains poised RNA polymerase II at bivalent genes in mouse ES cells. Nat. Cell Biol. 9, 1428–1435 (2007).

    Article  CAS  PubMed  Google Scholar 

  55. Brookes, E. et al. Polycomb associates genome-wide with a specific RNA polymerase II variant, and regulates metabolic genes in ESCs. Cell Stem Cell 10, 157–170 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Kaneko, S. et al. Interactions between JARID2 and noncoding RNAs regulate PRC2 recruitment to chromatin. Mol. Cell 53, 290–300 (2014).

    Article  CAS  PubMed  Google Scholar 

  57. Zovoilis, A., Cifuentes-Rojas, C., Chu, H. P., Hernandez, A. J. & Lee, J. T. Destabilization of B2 RNA by EZH2 activates the stress response. Cell 167, 1788–1802.e13 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Dobenecker, M. W. et al. Coupling of T cell receptor specificity to natural killer T cell development by bivalent histone H3 methylation. J. Exp. Med. 212, 297–306 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Hernandez, A. J. et al. B2 and ALU retrotransposons are self-cleaving ribozymes whose activity is enhanced by EZH2. Proc. Natl Acad. Sci. USA 117, 415–425 (2020).

    Article  CAS  PubMed  Google Scholar 

  60. Vierstra, J. et al. Mouse regulatory DNA landscapes reveal global principles of cis-regulatory evolution. Science 346, 1007–1012 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Lee, J. T. & Lu, N. Targeted mutagenesis of Tsix leads to nonrandom X inactivation. Cell 99, 47–57 (1999).

    Article  CAS  PubMed  Google Scholar 

  62. Kim, J., Cantor, A. B., Orkin, S. H. & Wang, J. Use of in vivo biotinylation to study protein–protein and protein–DNA interactions in mouse embryonic stem cells. Nat. Protoc. 4, 506–517 (2009).

    Article  CAS  PubMed  Google Scholar 

  63. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Ramirez, F., Dundar, F., Diehl, S., Gruning, B. A. & Manke, T. deepTools: a flexible platform for exploring deep-sequencing data. Nucleic Acids Res. 42, W187–W191 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Ye, T. et al. seqMINER: an integrated ChIP–seq data interpretation platform. Nucleic Acids Res. 39, e35 (2011).

    Article  CAS  PubMed  Google Scholar 

  66. Warden, C. D., Yuan, Y. & Wu, X. Optimal calculation of RNA-seq fold-change values. Int. J. Computational Bioinforma. Silico Modeling 2, 285–292 (2013).

    Google Scholar 

  67. Garant, J. M., Perreault, J. P. & Scott, M. S. G4RNA screener web server: user focused interface for RNA G-quadruplex prediction. Biochimie 151, 115–118 (2018).

    Article  CAS  PubMed  Google Scholar 

  68. Ran, F. A. et al. Genome engineering using the CRISPR–Cas9 system. Nat. Protoc. 8, 2281–2308 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Flemr, M. & Buhler, M. Single-step generation of conditional knockout mouse embryonic stem cells. Cell Rep. 12, 709–716 (2015).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

We thank R. Aguilar, H. Lee and H. Sunwoo for sharing reagents and providing support on initial stages of the project. We also thank T. Jégu, A. Kriz and all the Lee lab members for helpful advice and numerous constructive discussions. We give special thanks to the MGH Next Generation Sequencing Core and Harvard University Nascent Transcriptomics Core for excellent technical assistance. This work was funded by grants from the NIH (R01-HD097665) and HHMI to J.T.L.

Author information

Author notes

  1. These authors contributed equally: Michael Rosenberg, Roy Blum.

Authors and Affiliations

  1. Department of Molecular Biology, Massachusetts General Hospital, Boston, MA, USA

    Michael Rosenberg, Roy Blum, Barry Kesner, Eric Aeby, Attila Szanto & Jeannie T. Lee

  2. Department of Genetics, Harvard Medical School, Boston, MA, USA

    Michael Rosenberg, Roy Blum, Barry Kesner, Eric Aeby, Attila Szanto & Jeannie T. Lee

  3. Canada’s Michael Smith Genome Sciences Centre, Vancouver, British Columbia, Canada

    Jean-Michel Garant

  4. RNA Group/Groupe ARN, Département de Biochimie, Faculté de Médecine et des Sciences de la Santé, Pavillon de Recherche Appliquée au Cancer, Université de Sherbrooke, Sherbrooke, Québec, Canada

    Jean-Michel Garant

Authors
  1. Michael Rosenberg
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Roy Blum
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Barry Kesner
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Eric Aeby
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jean-Michel Garant
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Attila Szanto
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Jeannie T. Lee
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

M.R. and J.T.L. conceived of the PRC2-RNA interactome project. M.R. designed the dCLIP method and conducted the dCLIP, EMSA, western blot and cellular functional assays. R.B. devised the bioinformatics pipeline and performed the computational analysis for identification of consensus motifs. B.K. aligned sequencing data and called dCLIP–seq peaks. E.A. performed PRO-seq experiments and generated the data. J.-M.G contributed the machine-learning algorithm for rG4 analysis and for comparison between EZH2 and SUZ12 dCLIP replicates. A.S. generated RNA-seq data for 16.7 cells and performed the initial alignment. M.R., R.B. and J.T.L. analyzed data, determined project direction, drafted figures and wrote the manuscript.

Corresponding author

Correspondence to Jeannie T. Lee.

Ethics declarations

Competing interests

J.T.L. is a cofounder of Translate Bio and Fulcrum Therapeutics and is also a scientific advisor to Skyhawk Therapeutics.

Additional information

Peer review information Beth Moorefield was the primary editor on this article and managed its editorial process and peer review in collaboration with the rest of the editorial team.

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

Extended data

Extended Data Fig. 1 Denaturing CLIP of EED, EZH2, and SUZ12 in 16.7 mES cells.

a, Representative dCLIP experiment with N-terminally – Flag-Biotagged EED protein. Left panel, autoradiography of dCLIP experiment. Right panel, Western blot with anti-FLAG antibody. CBX7-Flag was used as control for FLAG Western. Red arrow, FLAG-Biotagged-EED signal. b, Representative dCLIP experiment with C-terminally – HA-Biotagged EED protein. Left panel, autoradiography of dCLIP experiment. Right panel, Western blot with the anti-HA antibody. Red arrow, HAG-Biotagged-EED signal. mEed-4H and mEed-2G are two clonal cell lines expressing physiological levels of FLAG-Biotagged-EED. mEed-11B and mEed-12B are two clonal cell lines expressing physiological levels of HA-Biotagged-EED. Note the lack of EED-specific dCLIP signal in both panels. c, Representative dCLIP experiments for EZH2. Left panel, autoradiography of dCLIP experiment. Right panel, Western blot with the anti-GFP antibody. Red arrows, GFP-Biotagged-EZH2 / SUZ12 signal. Ezh2-4A and Ezh2-5A are two clonal 16.7 mES cell lines expressing physiological levels of GFP-Biotagged-EZH2. d, Representative dCLIP experiments for SUZ12. Suz12-8E and Suz12-8H are two clonal cell lines expressing physiological levels of GFP-Biotagged-SUZ12. Red asterisk in (b) depicts short N-terminal truncated fragment characteristic for N-terminally-tagged SUZ12 protein 76. n = 3 for all the representative experiments. Unprocessed blot images are provided as source data.

Source data

Extended Data Fig. 2 Correlation between biological dCLIP replicates and EZH2 versus SUZ12 dCLIP.

a, A genome-wide pairwise comparisons of enriched dCLIP peaks over 1 kb bins per two biological replicates of EZH2 and SUZ12 dCLIP-seq samples (see Methods for details). Pearson’s correlation coefficients (R2) are shown. b, A genome-wide pairwise comparisons of enriched dCLIP peaks over 1 kb bins between EZH2 and SUZ12 dCLIP-seq. Pearson’s correlation coefficients (R2) are shown. c, Gene-based pairwise comparisons of enriched dCLIP peaks between EZH2 and SUZ12 dCLIP-seq. Pearson’s correlation coefficients (R2) are shown. d, Gene-based pairwise comparisons of enriched dCLIP peaks between EZH2 / SUZ12 dCLIP-seq and CBX7 dCLIP29. Pearson’s correlation coefficients (R2) are shown. Note a much lower correlation between EZH2/SUZ12 dCLIP enriched genes and CBX7 dCLIP enriched genes, despite the same method being applied in both studies.

Extended Data Fig. 3 Machine learning modeling.

Receiver Operating Characteristic (ROC) curves for artificial neural network of each of dCLIP-seq libraries, along with ROC curves for testing sets generated from a corresponding biological replicate of dCLIP-seq, and two control samples of CTCF CLIP. For each of PRC2 subunits we introduced the interactome sequences obtained from one biological replicate, trained an ANN model and performed accuracy testing (validation) using sequences obtained from the same interactome as well as the corresponding biological replicate. Two samples of CTCF interactome27 were employed as a control. The predictive power of each of the four ANNs generated based on the RNA sequences of dCLIP-seq libraries was determined by the average area under the ROC curve (AUC). See also Supplementary Table 2 and text for further details.

Extended Data Fig. 4 Correlation between transcriptomic datasets.

a, Comparison of enriched peaks signal per gene to EZH2-RIP data from Zhao et al5. b, Comparison of enriched peaks signal per gene to EZH2 PAR-CLIP data from Kaneko et al8. c, Comparison of enriched peaks signal per gene to SUZ12 iCLIP data from Beltran et al7. (enrichment values per-gene obtained from GSE120696 were used). d, Wildtype 16.7 cells used in this study and TsixTST/+ 16.7 cells29 have a similar transcriptomic profile on d7, as the TsixTST allele affects only the choice of which X chromosome will be inactivated, but does not affect the general transcriptome. Shown is a comparison of RPKM values per gene for the two cell lines. e, Concordance between homo-pairs of dCLIP-seq libraries and hetero-pairs of dCLIP-seq libraries and ChIP-seq libraries. Spearman correlation was calculated within each library pair for each of the regions (rows) depicted in the heatmap by comparing between the two matrices of normalized gene body density signal. The distribution of Spearman correlation coefficients per gene (n = 2038) of each of the library pairs was presented as a boxplots group. Box boundaries represent 25th and 75th percentiles; the center line represents the median; whiskers indicate ±1.5 times the interquartile range (IQR). EZH2 and H3K27Me3 ChIP-seq datasets were from Pinter et al29 and originated from 16.7 cells at Day 7 of differentiation. f,g, Comparison of RNA-seq signal per gene in 16.7 WT Day 7 mES cells (f) or TsixTST/+ 16.7 mES cells29 (g) versus Day 5 WT CJ7 mouse ES cells from Ardehali et al30 (RPKM values per-gene were obtained from GSE104657 – sub-datasets GSM2805147 and GSM2805148). 16.7 and TsixTST/+ transcriptomic profiles showed good correlation with profile from Ardehali et al30. Note high Pearson correlation values in (d), (e) and (f).

Extended Data Fig. 5 Randomized controls and Distribution of Pausing Indices for the data presented in Fig. 3e.

a, To rule out the possibility that the strong right shift observed in CLIP genes (Fig. 3a) was due to higher gene expression levels, we generated randomized models for CLIP and No CLIP categories matched for expression levels of CLIP and No CLIP gene groups, respectively (see Methods for more details). Boxplots for each of the two gene groups (No CLIP-matched (n = 467), and CLIP-matched (n = 414)) showing the distribution of gene expression measured by RNA-seq (RPKM) in two WT samples and two EED-KO samples. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ± 1.5× IQR). b, A comparison of gene expression changes following Eed knockout (EED-KO) in the randomized model: CLIP-matched control group compared to No CLIP-matched control group. We plotted CDP curves for CLIP-matched controls (n = 414), and No CLIP-matched controls (n = 467), depicting fold-change alterations in expression levels (RNA-seq) upon EED-KO. Statistical significance of differential gene expression response to Eed ablation was calculated by Wilcoxon test (unpaired, one-sided) between CLIP-matched gene group (red) versus No CLIP-mathced gene group (blue). c, Heatmap depicting PRO-seq (two biological replicates) and RNA-seq signal distribution in the vicinity of empirically defined POL-II pause sites. These pause sites were utilized for the analysis in Fig. 3f. d, Localization distribution of empirically defined POL-II pause sites relative to Refseq-annotated TSS. See Methods for further details.

Extended Data Fig. 6 Effect of SUZ12-degradation on pluripotency factors and differentiation markers.

a, Relative expression of pluripotency factors in undifferentiated 16.7 cells vs 16.7 SUZ12-degron cells, normalized to b-actin. Four biological replicates of 16.7 WT cells and 3 biological replicates of 16.7 SUZ12-degron cells were analyzed. One replicate of 16.7 WT cells was set as a reference (Expression = 1) and other replicates of WT and all values for SUZ12-degron cells were normalized to this replicate. b, Relative expression of pluripotency factors (Nanog, Oct4, Rex1 and Sox2), X chromosome inactivation marker (Xist) and differentiation markers (Gata4, Gata6, Nestin, Sox1 and Sox17) in Day 7 differentiated WT cells vs SUZ12-degron cells. Data normalized to β-actin. 4 biological replicates of 16.7 WT cells and 3 biological replicates of 16.7 SUZ12-degron cells were analyzed. One replicate of 16.7 WT cells was set as a reference (Expression = 1). The significance was determined by unpaired Student t-test. c, Fold-change in the expression of pluripotency factors (Nanog, Oct4, Rex1 and Sox2) and X chromosome inactivation marker (Xist) in Day 7 WT and SUZ12-degron cells. d, Additional examples for No CLIP genes shown in Fig. 4f. Statistical data for panels a,b,c is provided as Source Data.

Source data

Extended Data Fig. 7 Additional examples of representative genes shown in Fig. 6b.

Additional examples of genes that manifest PRC2 dCLIP signals at their 5’ regions. Note proximity of G-rich RNA-binding motif (P14) within PRC2 dCLIP peaks. G4RNA Screener tool indicated potential rG4-forming structures (orange bars). Pausing sites were defined empirically by PRO-seq signals (summits) for active POL-II. DNAse-hypersensitivity sites were from Vierstra et al60. See also Fig. 6b.

Extended Data Fig. 8 G-quadruplex motifs are enriched in interacting transcripts and POL-II pause sites.

a, Positive correlation between RNA stability measured following treatment with rG4 stabilizer cPDS (2μM) [relative to DMSO controls] and putative rG4 gene content screened by artificial enrual network algorithm, PQSfinder. RNA stability data presented is Mean±SD of 4 biological replicates. b, PQSfinder demonstrates that rG4 is highly enriched in CLIP targets relative to No CLIP transcripts. rG4 ratio defined as % of summit regions with putative rG4. rG4-ratio of PRC2 dCLIP summit-regions (100 nt around the most prominent binding site ±50 nt) harboring putative rG4 element was plotted for two EZH2 and two SUZ12 replicates, with CTCF and CBX7 as controls. Black boxplots: rG4-ratios of 2,000 random sets, each simulating an equal number of summit regions as in the tested library. c, Left: rG4 abundance per gene (‘Empirical rG4 content’) is higher in CLIP versus No CLIP transcripts. rG4 elements (per PQSfinder) in CLIP summit regions and in simulated No CLIP summit regions. Density plots of rG4 content per gene are presented for CLIP and No CLIP genes, respectively. Right: Nascent RNAs of CLIP genes have a higher potential to generate rG4 structures compared to nascent transcripts of No CLIP genes. p values by Wilcoxon test (unpaired, one-sided). Statistical data for panel a is provided as Source Data.

Source data

Extended Data Fig. 9 RNA also targets PRC2 to neighboring genes to control POL-II pausing.

a, Boxplots depicting the distribution of distances between Canonical-NG (n = 279), CLIP-NG (n = 218), or No CLIP-NG (n = 238) and the linked TSS of nearest corresponding neighboring gene. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ± 1.5× IQR). P values were determined using One-sided Wilcoxon test. Note the insignificant differences in distances between CLIP genes and No CLIP genes, whereas the distances of Canonical genes were significantly longer. b, Comparison of RNA stability between selected CLIP genes (n = 13) and No CLIP genes (n = 11) in Day 7 16.7 cells. Data presented is a Mean±S.D of 3 biological replicates. Significance determined by unpaired student t-test. c, Boxplots depicting H3K27me3 ChIP-seq enrichment levels (−1 kb to TTS) in the WT mES cells for CLIP-NG (n = 218), No CLIP-NG (n = 238) and Canonical-NG (n = 279) and corresponding CLIP (n = 414), No CLIP (n = 467) and Canonical (n = 603) genes. ChIP-seq datasets for Day 7 16.7 cells were from Pinter et al29. Box boundaries represent 25th and 75th percentiles; center line represents the median; whiskers indicate ± 1.5× IQR). P values were determined using One-sided Wilcoxon test. Statistical data for panel b is provided as Source Data.

Source data

Supplementary information

Supplementary Information

Supplementary Notes 1 and 2.

Reporting Summary

Supplementary Table 1

dCLIP peaks for biological replicates of EZH2 and SUZ12.

Supplementary Table 2

AUC scores for biological replicates of EZH2 and SUZ12.

Supplementary Table 3

A list of PRC2 dCLIP-positive genes.

Supplementary Table 4

Gene lists for Canonical, CLIP and No CLIP groups.

Supplementary Table 5

Gene lists for gene ontology categories presented in Fig. 3c.

Supplementary Table 6

Lists for oligonucleotides and antibodies used in this study.

Source data

Source Data Fig. 4

Unprocessed blots

Source Data Fig. 5

Uncropped images

Source Data Fig. 5

Statistical source data

Source Data Fig. 6

Statistical source data

Source Data Fig. 7

Statistical source data

Source Data Extended Data Fig. 1

Unprocessed blots

Source Data Extended Data Fig. 6

Statistical source data

Source Data Extended Data Fig. 8

Statistical source data

Source Data Extended Data Fig. 9

Statistical source data

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Rosenberg, M., Blum, R., Kesner, B. et al. Motif-driven interactions between RNA and PRC2 are rheostats that regulate transcription elongation. Nat Struct Mol Biol 28, 103–117 (2021). https://doi.org/10.1038/s41594-020-00535-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41594-020-00535-9

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