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 profiling of RNA–chromatin interactions reveals co-regulatory gene expression networks in Arabidopsis

Nature Plants volume 7, pages 1364–1378 (2021)Cite this article

Subjects

Abstract

It is increasingly evident that various RNAs can bind chromatin to regulate gene expression and genome organization. Here we adapted a sequencing-based technique to profile RNA–chromatin interactions at a genome-wide scale in Arabidopsis seedlings. We identified more than 10,000 RNA–chromatin interactions mediated by protein-coding RNAs and non-coding RNAs. Cis and intra-chromosomal interactions are mainly mediated by protein-coding RNAs, whereas inter-chromosomal interactions are primarily mediated by non-coding RNAs. Many RNA–chromatin interactions tend to positively correlate with DNA–DNA interactions, suggesting their mutual influence and reinforcement. We further show that some RNA–chromatin interactions undergo alterations in response to biotic and abiotic stresses and that altered RNA–chromatin interactions form co-regulatory networks. Our study provides a global view on RNA–chromatin interactions in Arabidopsis and a rich resource for future investigations of regulatory roles of RNAs in gene expression and genome organization.

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: Characterization of RNA–chromatin interactions.
Fig. 2: Genome view of representative RNA–chromatin interactions.
Fig. 3: Comparison of RNA–chromatin interactions detected by GRID-seq with DNA–DNA interactions detected by BL-Hi-C.
Fig. 4: Alterations of RNA–chromatin interactions in response to Pst DC3000.
Fig. 5: Functional relevance of the alterations of RNA–chromatin interactions in response to Pst DC3000.
Fig. 6: Alterations of RNA–chromatin interactions in response to heat.
Fig. 7: Functional relevance of the alterations of RNA–chromatin interactions in response to heat.

Similar content being viewed by others

Data availability

The GRID-seq, BL-Hi-C and GRO-seq datasets generated in this study have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/) under accession numbers GSE163845 and GSE181598. Source data are provided with this paper.

Code availability

All related scripts have been uploaded to GitHub (https://github.com/Qirnalab/GRID-seq).

References

  1. Fu, X. D. Non-coding RNA: a new frontier in regulatory biology. Natl Sci. Rev. 1, 190–204 (2014).

    Article  CAS  PubMed  Google Scholar 

  2. Engreitz, J. M., Ollikainen, N. & Guttman, M. Long non-coding RNAs: spatial amplifiers that control nuclear structure and gene expression. Nat. Rev. Mol. Cell Biol. 17, 756–770 (2016).

    Article  CAS  PubMed  Google Scholar 

  3. Rinn, J. L. & Chang, H. Y. Genome regulation by long noncoding RNAs. Annu. Rev. Biochem. 81, 145–166 (2012).

    Article  CAS  PubMed  Google Scholar 

  4. Chen, L. et al. R-ChIP using inactive RNase H reveals dynamic coupling of R-loops with transcriptional pausing at gene promoters. Mol. Cell 68, 745–757.e5 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Xiao, R. et al. Pervasive chromatin–RNA binding protein interactions enable RNA-based regulation of transcription. Cell 178, 107–121.e18 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Bose, D. A. et al. RNA binding to CBP stimulates histone acetylation and transcription. Cell 168, 135–149.e22 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  7. Koziol, M. J. & Rinn, J. L. RNA traffic control of chromatin complexes. Curr. Opin. Genet. Dev. 20, 142–148 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Nozawa, R. S. et al. SAF-A regulates interphase chromosome structure through oligomerization with chromatin-associated RNAs. Cell 169, 1214–1227.e18 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Nozawa, R. S. & Gilbert, N. RNA: nuclear glue for folding the genome. Trends Cell Biol. 29, 201–211 (2019).

    Article  CAS  PubMed  Google Scholar 

  10. Li, X. & Fu, X. D. Chromatin-associated RNAs as facilitators of functional genomic interactions. Nat. Rev. Genet. 20, 503–519 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Li, X. et al. GRID-seq reveals the global RNA–chromatin interactome. Nat. Biotechnol. 35, 940–950 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Xiang, J. F. et al. Human colorectal cancer-specific CCAT1-L lncRNA regulates long-range chromatin interactions at the MYC locus. Cell Res. 24, 513–531 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Jain, A. & Vale, R. D. RNA phase transitions in repeat expansion disorders. Nature 546, 243–247 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Langdon, E. M. et al. mRNA structure determines specificity of a polyQ-driven phase separation. Science 360, 922–927 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Bell, J. C. et al. Chromatin-associated RNA sequencing (ChAR-seq) maps genome-wide RNA-to-DNA contacts. eLife 7, e27024 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  16. Sridhar, B. et al. Systematic mapping of RNA–chromatin interactions in vivo. Curr. Biol. 27, 602–609 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  17. Chekanova, J. A. Long non-coding RNAs and their functions in plants. Curr. Opin. Plant Biol. 27, 207–216 (2015).

    Article  CAS  PubMed  Google Scholar 

  18. Datta, R. & Paul, S. Long non-coding RNAs: fine-tuning the developmental responses in plants. J. Biosci. 44, 77 (2019).

    Article  PubMed  Google Scholar 

  19. Liu, F., Marquardt, S., Lister, C., Swiezewski, S. & Dean, C. Targeted 3′ processing of antisense transcripts triggers Arabidopsis FLC chromatin silencing. Science 327, 94–97 (2010).

    Article  CAS  PubMed  Google Scholar 

  20. Tian, Y. et al. PRC2 recruitment and H3K27me3 deposition at FLC require FCA binding of COOLAIR. Sci. Adv. 5, eaau7246 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Swiezewski, S., Liu, F., Magusin, A. & Dean, C. Cold-induced silencing by long antisense transcripts of an Arabidopsis Polycomb target. Nature 462, 799–802 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Heo, J. B. & Sung, S. Vernalization-mediated epigenetic silencing by a long intronic noncoding RNA. Science 331, 76–79 (2011).

    Article  CAS  PubMed  Google Scholar 

  23. Kim, D. H. & Sung, S. Vernalization-triggered intragenic chromatin loop formation by long noncoding RNAs. Dev. Cell 40, 302–312.e4 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  24. Zhao, X. et al. Global identification of Arabidopsis lncRNAs reveals the regulation of MAF4 by a natural antisense RNA. Nat. Commun. 9, 5056 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  25. Seo, J. S. et al. ELF18-INDUCED LONG-NONCODING RNA associates with mediator to enhance expression of innate immune response genes in Arabidopsis. Plant Cell 29, 1024–1038 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Wierzbicki, A. T., Haag, J. R. & Pikaard, C. S. Noncoding transcription by RNA polymerase Pol IVb/Pol V mediates transcriptional silencing of overlapping and adjacent genes. Cell 135, 635–648 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  27. Ariel, F. et al. Noncoding transcription by alternative RNA polymerases dynamically regulates an auxin-driven chromatin loop. Mol. Cell 55, 383–396 (2014).

    Article  CAS  PubMed  Google Scholar 

  28. Zhou, B. et al. GRID-seq for comprehensive analysis of global RNA-chromatin interactions. Nat. Protoc. 14, 2036–2068 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Zhu, J., Liu, M., Liu, X. & Dong, Z. RNA polymerase II activity revealed by GRO-seq and pNET-seq in Arabidopsis. Nat. Plants 4, 1112–1123 (2018).

    Article  CAS  PubMed  Google Scholar 

  30. Liu, J. et al. Genome-wide analysis uncovers regulation of long intergenic noncoding RNAs in Arabidopsis. Plant Cell 24, 4333–4345 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Di, C. et al. Characterization of stress-responsive lncRNAs in Arabidopsis thaliana by integrating expression, epigenetic and structural features. Plant J. 80, 848–861 (2014).

    Article  CAS  PubMed  Google Scholar 

  32. Liang, Z. et al. BL-Hi-C is an efficient and sensitive approach for capturing structural and regulatory chromatin interactions. Nat. Commun. 8, 1622 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  33. Wang, C. et al. Genome-wide analysis of local chromatin packing in Arabidopsis thaliana. Genome Res. 25, 246–256 (2015).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Grob, S., Schmid, M. W. & Grossniklaus, U. Hi-C analysis in Arabidopsis identifies the KNOT, a structure with similarities to the flamenco locus of Drosophila. Mol. Cell 55, 678–693 (2014).

    Article  CAS  PubMed  Google Scholar 

  35. Feng, S. et al. Genome-wide Hi-C analyses in wild-type and mutants reveal high-resolution chromatin interactions in Arabidopsis. Mol. Cell 55, 694–707 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Fransz, P., De Jong, J. H., Lysak, M., Castiglione, M. R. & Schubert, I. Interphase chromosomes in Arabidopsis are organized as well defined chromocenters from which euchromatin loops emanate. Proc. Natl Acad. Sci. USA 99, 14584–14589 (2002).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Moissiard, G. et al. MORC family ATPases required for heterochromatin condensation and gene silencing. Science 336, 1448–1451 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Katagiri, F., Thilmony, R. & He, S. Y. The Arabidopsis thaliana–Pseudomonas syringae interaction. Arabidopsis Book 1, e0039 (2002).

    Article  PubMed  PubMed Central  Google Scholar 

  39. Fu, Z. Q. et al. A type III effector ADP-ribosylates RNA-binding proteins and quells plant immunity. Nature 447, 284–288 (2007).

    Article  CAS  PubMed  Google Scholar 

  40. Lee, H. J. et al. Different roles of glycine-rich RNA-binding protein7 in plant defense against Pectobacterium carotovorum, Botrytis cinerea, and tobacco mosaic viruses. Plant Physiol. Biochem. 60, 46–52 (2012).

    Article  CAS  PubMed  Google Scholar 

  41. Morita-Yamamuro, C. et al. The Arabidopsis gene CAD1 controls programmed cell death in the plant immune system and encodes a protein containing a MACPF domain. Plant Cell Physiol. 46, 902–912 (2005).

    Article  CAS  PubMed  Google Scholar 

  42. Zhang, R. et al. Evolution of PHAS loci in the young spike of allohexaploid wheat. BMC Genomics 21, 200 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Kuhn, H. et al. Key components of different plant defense pathways are dispensable for powdery mildew resistance of the Arabidopsis mlo2 mlo6 mlo12 triple mutant. Front Plant Sci. 8, 1006 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  44. Zhao, Y., Wu, G., Shi, H. & Tang, D. RECEPTOR-LIKE KINASE 902 associates with and phosphorylates BRASSINOSTEROID-SIGNALING KINASE1 to regulate plant immunity. Mol. Plant 12, 59–70 (2019).

    Article  CAS  PubMed  Google Scholar 

  45. Yu, A. et al. Dynamics and biological relevance of DNA demethylation in Arabidopsis antibacterial defense. Proc. Natl Acad. Sci. USA 110, 2389–2394 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Yi, H. & Richards, E. J. A cluster of disease resistance genes in Arabidopsis is coordinately regulated by transcriptional activation and RNA silencing. Plant Cell 19, 2929–2939 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Kotak, S. et al. Complexity of the heat stress response in plants. Curr. Opin. Plant Biol. 10, 310–316 (2007).

    Article  CAS  PubMed  Google Scholar 

  48. Pecinka, A. et al. Epigenetic regulation of repetitive elements is attenuated by prolonged heat stress in Arabidopsis. Plant Cell 22, 3118–3129 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Nishizawa-Yokoi, A., Yoshida, E., Yabuta, Y. & Shigeoka, S. Analysis of the regulation of target genes by an Arabidopsis heat shock transcription factor, HsfA2. Biosci. Biotechnol. Biochem. 73, 890–895 (2009).

    Article  CAS  PubMed  Google Scholar 

  50. Fu, C. et al. Increased fes1a thermotolerance is induced by BAG6 knockout. Plant Mol. Biol. 100, 73–82 (2019).

    Article  CAS  PubMed  Google Scholar 

  51. Meiri, D. et al. Involvement of Arabidopsis ROF2 (FKBP65) in thermotolerance. Plant Mol. Biol. 72, 191–203 (2010).

    Article  CAS  PubMed  Google Scholar 

  52. Goulas, E. et al. The chloroplast lumen and stromal proteomes of Arabidopsis thaliana show differential sensitivity to short- and long-term exposure to low temperature. Plant J. 47, 720–734 (2006).

    Article  CAS  PubMed  Google Scholar 

  53. Kawamura, Y. & Uemura, M. Mass spectrometric approach for identifying putative plasma membrane proteins of Arabidopsis leaves associated with cold acclimation. Plant J. 36, 141–154 (2003).

    Article  CAS  PubMed  Google Scholar 

  54. Schubert, T. et al. Df31 protein and snoRNAs maintain accessible higher-order structures of chromatin. Mol. Cell 48, 434–444 (2012).

    Article  CAS  PubMed  Google Scholar 

  55. Wang, Q. et al. Cajal bodies are linked to genome conformation. Nat. Commun. 7, 10966 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  56. Dundr, M. et al. Actin-dependent intranuclear repositioning of an active gene locus in vivo. J. Cell Biol. 179, 1095–1103 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Calandrelli, R. et al. Stress-induced RNA–chromatin interactions promote endothelial dysfunction. Nat. Commun. 11, 5211 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  58. Pontvianne, F. et al. Identification of nucleolus-associated chromatin domains reveals a role for the nucleolus in 3D organization of the A. thaliana genome. Cell Rep. 16, 1574–1587 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  59. Boisvert, F. M., van Koningsbruggen, S., Navascues, J. & Lamond, A. I. The multifunctional nucleolus. Nat. Rev. Mol. Cell Biol. 8, 574–585 (2007).

    Article  CAS  PubMed  Google Scholar 

  60. Kalinina, N. O., Makarova, S., Makhotenko, A., Love, A. J. & Taliansky, M. The multiple functions of the nucleolus in plant development, disease and stress responses. Front. Plant Sci. 9, 132 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  61. Nemeth, A. & Langst, G. Genome organization in and around the nucleolus. Trends Genet. 27, 149–156 (2011).

    Article  CAS  PubMed  Google Scholar 

  62. Pontvianne, F. & Liu, C. Chromatin domains in space and their functional implications. Curr. Opin. Plant Biol. 54, 1–10 (2020).

    Article  CAS  PubMed  Google Scholar 

  63. Wang, L. C., Wu, J. R., Hsu, Y. J. & Wu, S. J. Arabidopsis HIT4, a regulator involved in heat-triggered reorganization of chromatin and release of transcriptional gene silencing, relocates from chromocenters to the nucleolus in response to heat stress. N. Phytol. 205, 544–554 (2015).

    Article  CAS  Google Scholar 

  64. Hayashi, K. & Matsunaga, S. Heat and chilling stress induce nucleolus morphological changes. J. Plant Res. 132, 395–403 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  65. Picart-Picolo, A., Picart, C., Picault, N. & Pontvianne, F. Nucleolus-associated chromatin domains are maintained under heat stress, despite nucleolar reorganization in Arabidopsis thaliana. J. Plant Res. 133, 463–470 (2020).

    Article  CAS  PubMed  Google Scholar 

  66. Khan, A. et al. The Arabidopsis STRESS RESPONSE SUPPRESSOR DEAD-box RNA helicases are nucleolar- and chromocenter-localized proteins that undergo stress-mediated relocalization and are involved in epigenetic gene silencing. Plant J. 79, 28–43 (2014).

    Article  CAS  PubMed  Google Scholar 

  67. Huang, K. C., Lin, W. C. & Cheng, W. H. Salt hypersensitive mutant 9, a nucleolar APUM23 protein, is essential for salt sensitivity in association with the ABA signaling pathway in Arabidopsis. BMC Plant Biol. 18, 40 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  68. Durrant, W. E., Wang, S. & Dong, X. Arabidopsis SNI1 and RAD51D regulate both gene transcription and DNA recombination during the defense response. Proc. Natl Acad. Sci. USA 104, 4223–4227 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Calikowski, T. T. & Meier, I. in Arabidopsis Protocols (eds Salinas, J. & Sanchez-Serrano, J. J.) 393–402 (Humana, 2006).

  70. Lamesch, P. et al. The Arabidopsis Information Resource (TAIR): improved gene annotation and new tools. Nucleic Acids Res. 40, D1202–D1210 (2012).

    Article  CAS  PubMed  Google Scholar 

  71. 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  PubMed  PubMed Central  CAS  Google Scholar 

  72. Li, H. et al. The sequence alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  73. Yamamoto, Y. Y. & Obokata, J. ppdb: a plant promoter database. Nucleic Acids Res. 36, D977–D981 (2008).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. Wang, D. et al. Reprogramming transcription by distinct classes of enhancers functionally defined by eRNA. Nature 474, 390–394 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  76. Langmead, B., Wilks, C., Antonescu, V. & Charles, R. Scaling read aligners to hundreds of threads on general-purpose processors. Bioinformatics 35, 421–432 (2019).

    Article  CAS  PubMed  Google Scholar 

  77. McCarthy, D. J., Chen, Y. & Smyth, G. K. Differential expression analysis of multifactor RNA-seq experiments with respect to biological variation. Nucleic Acids Res. 40, 4288–4297 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  79. Shen, L., Shao, N., Liu, X. & Nestler, E. ngs.plot: quick mining and visualization of next-generation sequencing data by integrating genomic databases. BMC Genomics 15, 284 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  80. Rao, S. S. et al. A 3D map of the human genome at kilobase resolution reveals principles of chromatin looping. Cell 159, 1665–1680 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  81. Lun, A. T. & Smyth, G. K. diffHic: a Bioconductor package to detect differential genomic interactions in Hi-C data. BMC Bioinform. 16, 258 (2015).

    Article  CAS  Google Scholar 

  82. Schmid, M. W., Grob, S. & Grossniklaus, U. HiCdat: a fast and easy-to-use Hi-C data analysis tool. BMC Bioinform. 16, 277 (2015).

    Article  CAS  Google Scholar 

  83. Cao, X., Yan, Z., Wu, Q., Zheng, A. & Zhong, S. GIVE: portable genome browsers for personal websites. Genome Biol. 19, 92 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  84. Gu, Z., Gu, L., Eils, R., Schlesner, M. & Brors, B. circlize implements and enhances circular visualization in R. Bioinformatics 30, 2811–2812 (2014).

    Article  CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by grants from the National Key R&D Program of China (grant no. 2016YFA0500800) and the National Natural Science Foundation of China (grant nos 31788103 and 31620103908) to Y.Q. We thank Y. Xue and Z. Cai for their technical support in the GRO-seq experiments.

Author information

Author notes

  1. These authors contributed equally: Lanxia Li, Haofei Luo.

Authors and Affiliations

  1. Center for Plant Biology, School of Life Sciences, Tsinghua University, Beijing, China

    Lanxia Li, Haofei Luo, Lu Han, Yan Li & Yijun Qi

  2. Tsinghua-Peking Center for Life Sciences, Beijing, China

    Lanxia Li, Haofei Luo, Lu Han, Yan Li & Yijun Qi

  3. Department of Cellular and Molecular Medicine, University of California, San Diego, La Jolla, CA, USA

    Do-Hwan Lim & Xiang-Dong Fu

  4. Institute of Genomic Medicine, University of California, San Diego, La Jolla, CA, USA

    Do-Hwan Lim & Xiang-Dong Fu

Authors
  1. Lanxia Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haofei Luo
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Do-Hwan Lim
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Lu Han
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Yan Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xiang-Dong Fu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yijun Qi
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

Y.Q. conceived this project. L.L., X.-D.F. and Y.Q. designed the experiments. L.L. performed all the experiments with assistance from D.-H.L., L.H. and Y.L. H.L. performed all bioinformatic analyses. H.L. and L.L. analysed the data. Y.L. and Y.Q. wrote the manuscript.

Corresponding authors

Correspondence to Xiang-Dong Fu or Yijun Qi.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Peer review information Nature Plants thanks Julia Chekanova, Chang Liu 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

Extended Data Fig. 1 Characterization of GRID-seq libraries.

a, Major steps of the GRID-seq technology. Seedlings are crosslinked with formaldehyde and nuclei are isolated. Genomic DNA is fragmented by AluI. The 3’ end of RNA is ligated to 5’-adenylated linker and the linker is extended by reverse transcription to generate first-strand cDNA complementary to the RNA. The other end of linker and AluI-cleaved DNA are then ligated, forming a RNA-linker-DNA chimera. After reverse crosslinking, DNA purification, biotin selection, second-strand cDNA synthesis and MmeI digestion, the desired RNA-DNA chimeras are purified and subjected to library construction and sequencing. b, Pipeline of GRID-seq bioinformatic analysis. PCR duplications and adapter sequence are removed. Filtered reads, which contain the linker sequence, are divided into RNA parts and DNA parts according to the orientation of linker sequence. The RNA/DNA parts are mapped to the Arabidopsis nuclear genome (TAIR10) and mate pairs are merged to identify unique RNA-DNA interactions. Finally, background interactions are deducted to identify specific RNA-DNA interactions. c, Scatter plot showing background-corrected RNA reads in two biological replicates of GRID-seq data. The Pearson correlation coefficient is shown. d, Nucleotide frequency of mapped RNA (upper panel) and DNA (lower panel) reads. e, Strand orientation of mapped RNA and DNA reads. f, Pie chart showing the percentages of different types of RNAs in GRID-seq data (upper). Pie chart showing the percentages of uniquely mapped DNA reads distributed in different genomic regions in GRID-seq data (lower), distal intergenic: > 1 Kb to the nearest gene, upstream: within 1 Kb upstream of the nearest TSS, downstream: within1 Kb downstream of the nearest TTS. g, Scatter plot showing background-corrected RNA reads in two biological replicates of GRO-seq data. The Pearson correlation coefficient is shown. h, Meta- analyses showing two biological replicates of GRO-seq signals across the gene region. TSS (transcription start site), TTS (transcription termination site). i, Scatter plot of background-corrected RNA reads in GRID-seq data versus RNA reads in GRO-seq data. Blue dots represent RNAs that have comparable reads in GRID-seq and GRO-seq. Pink dots represent RNAs that have higher reads in GRID-seq. Orange dots represent RNAs that have higher reads in GRO-seq. RPKM: reads per kilobase per million mapped reads. FPKM: fragments per kilobase per million mapped reads. j, Boxplot showing the transcription levels of target genes that are involved in cis, short-range intra-chromosomal, long-range intra-chromosomal and inter-chromosomal interactions as determined by two biological replicates of GRO-seq. On each box, the horizontal mark indicates the median, and the bottom and top edges of the box indicate the 25th and 75th percentiles, respectively. The whiskers extend to the most extreme data points not considered outliers (99.3 percentage coverage). Significance of difference between groups is determined by Kruskal-Wallis test.

Source data

Extended Data Fig. 2 Networks of RNA-chromatin interactions mediated by lncRNAs with more than 10 targets.

Each line represents an RNA-chromatin interaction, with the arrowhead pointing to target gene. Blue lines represent cis interactions; green lines represent intra-chromosomal interactions; and orange lines represent inter-chromosomal interactions. Each box contains a gene. Purple boxes indicate ncRNA genes, and grey boxes indicate protein-coding genes. All genes are labeled with their names except unannotated genes in TAIR10.

Source data

Extended Data Fig. 3 Correlation between RNA-chromatin interactions and DNA-DNA interactions.

a-b, GIVE plots showing RNA-chromatin interactions and DNA-DNA interactions around At3g56825 (U2.4) and At4g13495 (lncRNA). The RNA-chromatin interaction pairs are plotted from RNA ends (top bar) to DNA ends (bottom bar). Each line represents one pair of RNA-chromatin interaction, with deeper color depicting higher RPM. DNA-DNA interactions are displayed similarly. Black boxes and lines indicate genes on the chromosomes. The interaction RPM are sum of two biological replicates.

Source data

Extended Data Fig. 4 Comparison of GRID-seq and GRO-seq data of mock- and Pst DC3000-treated seedlings.

a, Heatmap showing the correlation between GRO-seq libraries. The Pearson correlation coefficients are shown. b, Scatter plot of nascent transcript levels in mock- and DC3000-treated seedlings as determined by GRO-seq. Red dots represent nascent RNAs whose interactions with chromatin are up-regulated by DC3000 (left panel). Blue dots represent nascent RNAs whose interactions with chromatin are down-regulated by DC3000 (right panel). c, Genome browser views of GRO-seq signals at ABCG36, FAB1C and At3g56360 in mock- and DC3000-treated seedlings. Results from two biological replicates are shown.

Source data

Extended Data Fig. 5 Comparison of GRID-seq and GRO-seq data of mock- and heat-treated seedlings.

a, Heatmap showing the correlation between GRO-seq libraries. The Pearson correlation coefficients are shown. b, Scatter plot of nascent transcript levels in mock- and heat-treated seedlings as determined by GRO-seq. Red dots represent nascent RNAs whose interactions with chromatin are up-regulated by heat (left panel). Blue dots represent nascent RNAs whose interactions with chromatin are down-regulated by heat (right panel). The red dots representing RNAs of HSP101 and BAG6 are pointed out with arrows. c, Genome browser views of GRO-seq signals at HSP101, BAG6 and ERD6 in mock- and heat-treated seedlings. Results from two biological replicates are shown.

Source data

Supplementary information

Supplementary Information

Supplementary Table 1.

Reporting Summary

Supplementary Data 1

Summary of the datasets generated in this study.

Supplementary Data 2

List of RNA–chromatin interactions.

Supplementary Data 3

Number of interactions mediated by individual RNAs or DNAs.

Supplementary Data 4

List of chromatin–chromatin interactions.

Supplementary Data 5

List of RNA–chromatin interactions regulated by DC3000 treatment.

Supplementary Data 6

List of genes differentially transcribed in response to DC3000 treatment.

Supplementary Data 7

List of RNA–chromatin interactions regulated by heat treatment.

Supplementary Data 8

List of genes differentially transcribed in response to heat treatment.

Source data

Source Data Fig. 1

Statistical source data.

Source Data Fig. 2

Statistical source data.

Source Data Fig. 3

Statistical source data.

Source Data Fig. 4

Statistical source data.

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

Statistical source data.

Source Data Extended Data Fig. 2

Statistical source data.

Source Data Extended Data Fig. 3

Statistical source data.

Source Data Extended Data Fig. 4

Statistical source data.

Source Data Extended Data Fig. 5

Statistical source data.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Li, L., Luo, H., Lim, DH. et al. Global profiling of RNA–chromatin interactions reveals co-regulatory gene expression networks in Arabidopsis. Nat. Plants 7, 1364–1378 (2021). https://doi.org/10.1038/s41477-021-01004-x

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41477-021-01004-x

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