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

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.

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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.

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).

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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

Affiliations

Authors

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.

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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

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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

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