Skip to main content
SpringerLink
Log in

RNA m6A regulates transcription via DNA demethylation and chromatin accessibility

  • Article
  • Published:

From Nature Genetics

View current issue Submit your manuscript

Abstract

Transcriptional regulation, which integrates chromatin accessibility, transcription factors and epigenetic modifications, is crucial for establishing and maintaining cell identity. The interplay between different epigenetic modifications and its contribution to transcriptional regulation remains elusive. Here, we show that METTL3-mediated RNA N6-methyladenosine (m6A) formation leads to DNA demethylation in nearby genomic loci in normal and cancer cells, which is mediated by the interaction between m6A reader FXR1 and DNA 5-methylcytosine dioxygenase TET1. Upon recognizing RNA m6A, FXR1 recruits TET1 to genomic loci to demethylate DNA, leading to reprogrammed chromatin accessibility and gene transcription. Therefore, we have characterized a regulatory mechanism of chromatin accessibility and gene transcription mediated by RNA m6A formation coupled with DNA demethylation, highlighting the importance of the crosstalk between RNA m6A and DNA modification in physiologic and pathogenic process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1: RNA m6A modification levels are anti-correlated with DNA 5mC levels.
Fig. 2: DNA demethylase TET1 activities are correlated with genome-wide RNA m6A levels in KYSE30 cells.
Fig. 3: RNA m6A co-transcriptionally directs TET1 to demethylate DNA cytosines.
Fig. 4: FXR1 recruits TET1 to demethylate DNA.
Fig. 5: RNA m6A formation coupled with DNA demethylation plays a role in chromatin states and gene transcription.
Fig. 6: Validation of transcription regulation by RNA m6A formation coupled with DNA demethylation in ESCC.

Similar content being viewed by others

Data availability

The ChIP sequencing data for METTL3, TET1, TET2 and TET3 and the m6A sequencing data were downloaded from the NCBI Gene Expression Omnibus (GEO) database with the accession numbers GSE92257, GSE100955, GSE115964, GSE94688 and GSE52662, respectively. For integrated analysis of DNA methylation and m6A data, reduced representation bisulfite sequencing for mESCs was obtained from GEO dataset GSE57576. Human Methylation 450 K (HM450K) array data for HepG2, HeLa and A549 cell lines were obtained from ENCODE (GSE40699). m6A sequencing data in HepG2 (GSE102620), HeLa (GSE46705) and A549 (GSE54365) were from GEO datasets. WGBS, m6A sequencing and CUT&Tag raw data have been deposited in the GEO under accession code GSE168344 and the Genome Sequence Archive in BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences under accession number HRA000682. The following datasets were used to generate the data: ESCC Human Methylation 450K (HM450K) array data of TCGA (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga), hg19 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001405.13/_) and mm10 (https://www.ncbi.nlm.nih.gov/assembly/GCF_000001635.20/). Source data are provided with this paper.

Code availability

Only publicly available tools were used in data analysis and the parameters have been described wherever relevant in Methods and Reporting Summary.

References

  1. Baker, M. Making sense of chromatin states. Nat. Methods 8, 717–722 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Lee, T. I. & Young, R. A. Transcriptional regulation and its misregulation in disease. Cell 152, 1237–1251 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Klemm, S. L., Shipony, Z. & Greenleaf, W. J. Chromatin accessibility and the regulatory epigenome. Nat. Rev. Genet. 20, 207–220 (2019).

    Article  CAS  PubMed  Google Scholar 

  4. Bannister, A. J. & Kouzarides, T. Regulation of chromatin by histone modifications. Cell Res. 21, 381–395 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  5. Berger, S. L. Histone modifications in transcriptional regulation. Curr. Opin. Genet Dev. 12, 142–148 (2002).

    Article  CAS  PubMed  Google Scholar 

  6. Robertson, K. D. DNA methylation and chromatin: unraveling the tangled web. Oncogene 21, 5361–5379 (2002).

    Article  CAS  PubMed  Google Scholar 

  7. Poetsch, A. R. & Plass, C. Transcriptional regulation by DNA methylation. Cancer Treat. Rev. 37, S8–S12 (2011).

    Article  CAS  PubMed  Google Scholar 

  8. Liu, J. et al. N (6)-methyladenosine of chromosome-associated regulatory RNA regulates chromatin state and transcription. Science 367, 580–586 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Kumar, S., Chinnusamy, V. & Mohapatra, T. Epigenetics of modified DNA bases: 5-methylcytosine and beyond. Front Genet 9, 640 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Zhong, Z. et al. DNA methylation-linked chromatin accessibility affects genomic architecture in Arabidopsis. Proc. Natl Acad. Sci. USA 118, e2023347118 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Sheaffer, K. L., Elliott, E. N. & Kaestner, K. H. DNA hypomethylation contributes to genomic instability and intestinal cancer initiation. Cancer Prev. Res. (Philos.) 9, 534–546 (2016).

    Article  CAS  Google Scholar 

  12. Batista, P. J. et al. m(6)A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Yang, Y., Hsu, P. J., Chen, Y. S. & Yang, Y. G. Dynamic transcriptomic m(6)A decoration: writers, erasers, readers and functions in RNA metabolism. Cell Res. 28, 616–624 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Huang, H., Weng, H. & Chen, J. m(6)A modification in coding and non-coding RNAs: roles and therapeutic implications in cancer. Cancer Cell 37, 270–288 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Xu, W. et al. METTL3 regulates heterochromatin in mouse embryonic stem cells. Nature 591, 317–321 (2021).

    Article  CAS  PubMed  Google Scholar 

  16. Chelmicki, T. et al. m6A RNA methylation regulates the fate of endogenous retroviruses. Nature 591, 312–316 (2021).

    Article  CAS  PubMed  Google Scholar 

  17. Liu, J. et al. The RNA m6A reader YTHDC1 silences retrotransposons and guards ES cell identity. Nature 591, 322–326 (2021).

    Article  CAS  PubMed  Google Scholar 

  18. Huang, H. et al. Histone H3 trimethylation at lysine 36 guides m(6)A RNA modification co-transcriptionally. Nature 567, 414–419 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  19. Li, Y. et al. N(6)-Methyladenosine co-transcriptionally directs the demethylation of histone H3K9me2. Nat. Genet. 52, 870–877 (2020).

    Article  CAS  PubMed  Google Scholar 

  20. Wu, X. & Zhang, Y. TET-mediated active DNA demethylation: mechanism, function and beyond. Nat. Rev. Genet. 18, 517–534 (2017).

    Article  CAS  PubMed  Google Scholar 

  21. Cedar, H. & Bergman, Y. Linking DNA methylation and histone modification: patterns and paradigms. Nat. Rev. Genet. 10, 295–304 (2009).

    Article  CAS  PubMed  Google Scholar 

  22. Ooi, S. K. et al. DNMT3L connects unmethylated lysine 4 of histone H3 to de novo methylation of DNA. Nature 448, 714–717 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  23. Matsumura, Y. et al. H3K4/H3K9me3 bivalent chromatin domains targeted by lineage-specific DNA methylation pauses adipocyte differentiation. Mol. Cell 60, 584–596 (2015).

    Article  CAS  PubMed  Google Scholar 

  24. Nakamura, T. et al. PGC7 binds histone H3K9me2 to protect against conversion of 5mC to 5hmC in early embryos. Nature 486, 415–419 (2012).

    Article  CAS  PubMed  Google Scholar 

  25. Liu, X. et al. UHRF1 targets DNMT1 for DNA methylation through cooperative binding of hemi-methylated DNA and methylated H3K9. Nat. Commun. 4, 1563 (2013).

    Article  PubMed  CAS  Google Scholar 

  26. 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 

  27. Edupuganti, R. R. et al. N(6)-methyladenosine (m(6)A) recruits and repels proteins to regulate mRNA homeostasis. Nat. Struct. Mol. Biol. 24, 870–878 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  28. Tahiliani, M. et al. Conversion of 5-methylcytosine to 5-hydroxymethylcytosine in mammalian DNA by MLL partner TET1. Science 324, 930–935 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Rudenko, A. et al. Tet1 is critical for neuronal activity-regulated gene expression and memory extinction. Neuron 79, 1109–1122 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Smith, J. A. et al. FXR1 splicing is important for muscle development and biomolecular condensates in muscle cells. J. Cell Biol. 219, e201911129 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  31. Cheng, Y. et al. N(6)-Methyladenosine on mRNA facilitates a phase-separated nuclear body that suppresses myeloid leukemic differentiation. Cancer Cell 39, 958–972 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  32. Lee, J. H. et al. Enhancer RNA m6A methylation facilitates transcriptional condensate formation and gene activation. Mol. Cell 81, 3368–3385.e9 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. Ries, R. J. et al. m(6)A enhances the phase separation potential of mRNA. Nature 571, 424–428 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  34. Martin, E. W. & Holehouse, A. S. Intrinsically disordered protein regions and phase separation: sequence determinants of assembly or lack thereof. Emerg. Top. Life Sci. 4, 307–329 (2020).

    Article  CAS  PubMed  Google Scholar 

  35. Harmon, T. S., Holehouse, A. S., Rosen, M. K. & Pappu, R. V. Intrinsically disordered linkers determine the interplay between phase separation and gelation in multivalent proteins. Elife 6, e30294 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  36. Ren, W. et al. Direct readout of heterochromatic H3K9me3 regulates DNMT1-mediated maintenance DNA methylation. Proc. Natl Acad. Sci. USA 117, 18439–18447 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  37. Kuppers, D. A. et al. N(6)-methyladenosine mRNA marking promotes selective translation of regulons required for human erythropoiesis. Nat. Commun. 10, 4596 (2019).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  38. Balasubramanian, D. et al. H3K4me3 inversely correlates with DNA methylation at a large class of non-CpG-island-containing start sites. Genome Med 4, 47 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Ren, W. et al. DNMT1 reads heterochromatic H4K20me3 to reinforce LINE-1 DNA methylation. Nat. Commun. 12, 2490 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Abakir, A. et al. N(6)-methyladenosine regulates the stability of RNA:DNA hybrids in human cells. Nat. Genet. 52, 48–55 (2020).

    Article  CAS  PubMed  Google Scholar 

  41. Yang, X. et al. m(6)A promotes R-loop formation to facilitate transcription termination. Cell Res. 29, 1035–1038 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Kang, H. J. et al. TonEBP recognizes R-loops and initiates m6A RNA methylation for R-loop resolution. Nucleic Acids Res. 49, 269–284 (2021).

    Article  CAS  PubMed  Google Scholar 

  43. Arab, K. et al. GADD45A binds R-loops and recruits TET1 to CpG island promoters. Nat. Genet. 51, 217–223 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Suzuki, M. M. & Bird, A. DNA methylation landscapes: provocative insights from epigenomics. Nat. Rev. Genet. 9, 465–476 (2008).

    Article  CAS  PubMed  Google Scholar 

  45. Xi, Y. et al. Multi-omic characterization of genome-wide abnormal DNA methylation reveals diagnostic and prognostic markers for esophageal squamous-cell carcinoma. Signal Transduct. Target. Ther. 7, 53 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  46. Jeziorska, D. M. et al. DNA methylation of intragenic CpG islands depends on their transcriptional activity during differentiation and disease. Proc. Natl Acad. Sci. USA 114, E7526–e7535 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. Fu, Y., Dominissini, D., Rechavi, G. & He, C. Gene expression regulation mediated through reversible m6A RNA methylation. Nat. Rev. Genet. 15, 293–306 (2014).

    Article  CAS  PubMed  Google Scholar 

  48. Arnold, M., Soerjomataram, I., Ferlay, J. & Forman, D. Global incidence of oesophageal cancer by histological subtype in 2012. Gut 64, 381–387 (2015).

    Article  PubMed  Google Scholar 

  49. Lin, D. C., Wang, M. R. & Koeffler, H. P. Genomic and epigenomic aberrations in esophageal squamous cell carcinoma and implications for patients. Gastroenterology 154, 374–389 (2018).

    Article  PubMed  Google Scholar 

  50. Cui, X. L. et al. A human tissue map of 5-hydroxymethylcytosines exhibits tissue specificity through gene and enhancer modulation. Nat. Commun. 11, 6161 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  51. Chen, S., Zhou, Y., Chen, Y. & Gu, J. fastp: an ultra-fast all-in-one FASTQ preprocessor. Bioinformatics 34, i884–i890 (2018).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  52. Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  54. Cui, X., Meng, J., Zhang, S., Chen, Y. & Huang, Y. A novel algorithm for calling mRNA m6A peaks by modeling biological variances in MeRIP-seq data. Bioinformatics 32, i378–i385 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. 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 

  56. Schwartz, S. et al. Perturbation of m6A writers reveals two distinct classes of mRNA methylation at internal and 5’ sites. Cell Rep. 8, 284–296 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  57. Liu, L., Zhang, S. W., Huang, Y. & Meng, J. QNB: differential RNA methylation analysis for count-based small-sample sequencing data with a quad-negative binomial model. BMC Bioinf. 18, 387 (2017).

    Article  CAS  Google Scholar 

  58. 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 

  59. Krueger, F. & Andrews, S. R. Bismark: a flexible aligner and methylation caller for Bisulfite-Seq applications. Bioinformatics 27, 1571–1572 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  61. Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  62. Akalin, A. et al. methylKit: a comprehensive R package for the analysis of genome-wide DNA methylation profiles. Genome Biol. 13, R87 (2012).

    Article  PubMed  PubMed Central  Google Scholar 

  63. Park, Y. & Wu, H. Differential methylation analysis for BS-seq data under general experimental design. Bioinformatics 32, 1446–1453 (2016).

    Article  CAS  PubMed  Google Scholar 

  64. Thorvaldsdóttir, H., Robinson, J. T. & Mesirov, J. P. Integrative Genomics Viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform 14, 178–192 (2013).

    Article  PubMed  CAS  Google Scholar 

  65. Ramírez, F. et al. deepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, W160–W165 (2016).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  66. Uren, P. J. et al. Site identification in high-throughput RNA-protein interaction data. Bioinformatics 28, 3013–3020 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. 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 

  68. Tarasov, A., Vilella, A. J., Cuppen, E., Nijman, I. J. & Prins, P. Sambamba: fast processing of NGS alignment formats. Bioinformatics 31, 2032–2034 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Ross-Innes, C. S. et al. Differential oestrogen receptor binding is associated with clinical outcome in breast cancer. Nature 481, 389–393 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Love, M. I., Huber, W. & Anders, S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  71. Goldman, M. J. et al. Visualizing and interpreting cancer genomics data via the Xena platform. Nat. Biotechnol. 38, 675–678 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  72. Li, B. & Dewey, C. N. RSEM: accurate transcript quantification from RNA-seq data with or without a reference genome. BMC Bioinf. 12, 323 (2011).

    Article  CAS  Google Scholar 

Download references

Acknowledgements

This study was supported by the National Key R&D Program of China (2021YFA1302100 to J. Zheng), Program for Guangdong Introducing Innovative and Entrepreneurial Teams (2017ZT07S096 to D.L.), Natural Science Foundation of China (82072617 to J. Zheng) and Sun Yat-sen University Intramural Funds (to D.L. and to J. Zheng). J.C. is a Leukemia & Lymphoma Society Scholar and is supported by the Simms/Mann Family Foundation.

Author information

Author notes

  1. These authors contributed equally: Shuang Deng, Jialiang Zhang, Jiachun Su, Zhixiang Zuo.

Authors and Affiliations

  1. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China and Collaborative Innovation Center for Cancer Medicine, Guangzhou, China

    Shuang Deng, Jialiang Zhang, Jiachun Su, Zhixiang Zuo, Lingxing Zeng, Kaijing Liu, Yanfen Zheng, Xudong Huang, Ruihong Bai, Lisha Zhuang, Ying Ye, Ling Pan, Junge Deng, Guandi Wu, Rui Li, Shaoping Zhang, Dongxin Lin & Jian Zheng

  2. Department of Medical Oncology, Sun Yat-sen University Cancer Center, Guangzhou, China

    Kaijing Liu

  3. Department of Pathology, Sun Yat-sen University Cancer Center, Guangzhou, China

    Mei Li

  4. Department of Etiology and Carcinogenesis, National Cancer Center/National Clinical Research Center/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China

    Chen Wu & Dongxin Lin

  5. Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China

    Dongxin Lin & Jian Zheng

  6. Department of Systems Biology, Beckman Research Institute of City of Hope, Monrovia, CA, USA

    Jianjun Chen

Authors
  1. Shuang Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Jialiang Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Jiachun Su
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Zhixiang Zuo
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Lingxing Zeng
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kaijing Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Yanfen Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Xudong Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Ruihong Bai
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Lisha Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Ying Ye
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Mei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Ling Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Junge Deng
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Guandi Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  16. Rui Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  17. Shaoping Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  18. Chen Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  19. Dongxin Lin
    View author publications

    You can also search for this author in PubMed Google Scholar

  20. Jianjun Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  21. Jian Zheng
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

J. Zheng, J.C. and D.L. conceived and designed the entire project. D.S. and J. Zhang designed and supervised the research. J. Zhang and J.S. prepared all samples for high-throughput sequencing and LC-MS/MS. L. Zeng and K.L. performed WGBS, CUT&Tag, ATAC-seq and 5mC ELISA assays. Y.Z. and X.H. performed m6A-seq, PAR-CLIP sequencing, RNA sequencing and qPCR. R.B. and L. Zhuang performed immunofluorescence staining, co-immunoprecipitation, Western blot assays and RNA electrophoretic mobility shift assays. L.P. and J.D. performed RNA pulldown, chromatin immunoprecipitation assays and cell proliferation, migration and invasion assays. J. Zhang and J.S. designed sgRNAs and prepared dCas13 cell samples. D.S. performed statistical and bioinformatic analyses of high-throughput sequencing data. Y.Y. and R.L. were engaged in analysis of public data. Z.Z. supervised all bioinformatic analyses. M.L., G.W., S.Z. and C.W. were responsible for tissue sample preparation. D.S., J. Zheng, D.L. and J.C. prepared manuscript, and all authors proved the manuscript.

Corresponding authors

Correspondence to Dongxin Lin, Jianjun Chen or Jian Zheng.

Ethics declarations

Competing interests

J.C. is a is a scientific advisor for Race Oncology.

Peer review

Peer review information

Nature Genetics thanks the anonymous reviewers for their contribution to the peer review of this work.

Additional information

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

Extended data

Extended Data Fig. 1 The relationship between RNA m6A and DNA 5mC.

a, Venn plots show overlap of m6A peaks and CpGs in A549, HeLa, HepG2 and mouse embryonic stem cells (mESC). b, The distribution of DNA methylation levels of CpGs close to m6A peak center in indicated cell lines. c, Box plots show DNA methylation levels of the CpGs near or far from m6A peak center in indicated cells with high or low gene expression. Sample size of each group: A549: n = 3. HepG2: n = 2. Hela: n = 2. mESC: n = 2. The difference was examined by two-sided Wilcoxon rank-sum test.

Extended Data Fig. 2 METTL3 regulates both RNA m6A and DNA 5mC levels.

a, Western blots show knockout (KO) efficiency of FTO or METTL14. b, Effects of FTO or METTL14 KO on global DNA 5mC levels. c, d, Effects of METTL3 knockdown on DNA methyltransferase and demethylase levels determined by qRT-PCR (c) and Western blotting (d). e, Western blots show the KO efficiency of METTL3 and ectopic expression of wild-type METTL3 or its mutant. f, Consensus sequence motif of m6A peaks. P-value was from HOMER based on binomial distribution. g, Metagene analysis of m6A peaks. h, RNA m6A levels in cells with METTL3 expression alteration. i, Count of significant differentially methylated regions (DMRs) in METTL3-depleted cells with the ectopic restore of METTL3 or its mutant. j, Metagene analysis of the DNA hypermethylated regions in cells with METTL3 KO. k, Heatmap shows differential DNA methylation levels of the hypermethylated DMRs close to m6A peaks in cells with or without METTL3 KO. DMRs are sorted in ascending order based on the beta value per region. l, Violin plots show differential DNA methylation levels of CpGs between the regions near and far from m6A peaks. Data were computed with methylKit with 2 biologically independent samples. m, Violin plots show methylation levels of CpGs in the non-m6A regions in cells with or without METTL3 KO. P-values in l and m were for two-sided Wilcoxon test. n, Venn plot shows overlap between hypomethylated m6A peaks (52.7%) with hypermethylated DMRs in cells with METTL3 KO versus control. o, p, Effects of METTL3 knockdown on m6A levels (o) and DNA 5mC levels (p) of the representative genes. q, r, Effects of the ectopic expression of METTL3 or its mutant on m6A levels (q) and DNA 5mC-modified levels (r) of the indicated genes with METTL3 KO. Data are mean ± s.e.m. of three independent experiments in b, c and o − r. CNGA4 was a non-m6A control. P values by unpaired two-sided t-test.

Source data

Extended Data Fig. 3 Genome-wide colocalization of RNA m6A and TET1.

a, Metagene analysis of m6A, Mettl3 and Tet1/2/3 ChIP peaks in mESCs. b, Density of distance from Tet1 to m6A center in mESCs. c, d METTL3 (left) and TET1 (right) binding (c) and the peak numbers (d) in cells with or without METTL3 or TET1 knockout. e, Reads per kilobase per million (RPKM) of METTL3 (left) or TET1 (right) CUT&Tag-signals relative to METTL3 CUT&Tag peak center. f, Correlation of TET1 and METTL3 CUT&Tag-signals. g, Density of distance from METTL3 to TET1 CUT&Tag-peaks. h, Colocalization of TET1 with METTL3 and m6A levels of exampled genes. Number represents the range of CUT&Tag-signal. i, Correlation of Tet1 and Mettl3 ChIP- signals in mESCs. j, Density of distance from Tet1 to Mettl3 ChIP-peaks in mESCs. k, Density of distance from METTL3 KO-decreased TET1 CUT&Tag-peaks to m6A peak center. l, Effects of METTL3 knockdown on TET1 enrichment in indicated genes. m, Effects of ectopic expression of METTL3 and its mutant on TET1 enrichment in indicated genes in cells with METTL3 KO. n, Western blot shows METTL3 KO efficiency and ectopic expression of TET1. o, METTL3 KO increased DNA 5mC levels of indicated genes, which could be rescued by ectopic expression of TET1. p, Effects of ectopic expression of METTL3 and its mutant on DNA 5hmC, 5fC or 5caC levels. q, Effects of ectopic expression of wild-type METTL3 and its mutant on counts of hypomethylated or hypermethylated DNA 5hmC peak upon METTL3 KO. r, Western blots show the effect of histone enzyme knockdown on histone modification level. s, Western blots show the effect of histone methylation enzyme knockdown on histone methylation level upon METTL3 KO. t, Effect of histone methyltransferase KO on global DNA 5mC level in cells with or without METTL3 KO. Data in l, m, o, p and t are mean ± s.e.m. of 3 independent experiments. CNGA4 was a non-m6A control. P values by unpaired two-sided t-test. Peaks in c, e are sorted in descending order based on the mean value per region.

Source data

Extended Data Fig. 4 RNA m6A co-transcriptionally directs TET1 to target chromatin.

a, The proportion (pie chart) and enrichment (bar chart) of the Pol II CUT&Tag peaks in the different gene regions. b, Percentage of the annotated Pol II CUT&Tag peaks in protein-coding RNAs, non-coding RNAs, pseudogene RNAs and other RNAs. c, Venn plots show overlap between the Tet1-binding sites and the Pol II-binding sites identified by chromatin immunoprecipitation (ChIP) sequencing in mouse embryo stem cells (mESC). d, Density of the distance from Tet1-binding sites to Pol II-binding sites identified by ChIP sequencing in mESC. e, Effect of METTL3 knockdown on TET1 enrichment on pol II-bound regions in KYSE510 and HEK293T cells. f, Effects of the ectopic expression of wild-type METTL3 (WT) and its mutant (Mut) on TET1 enrichment on pol II-bound regions in KYSE510 and HEK293T cells with endogenous METTL3 knockout (KO). Data in e, f are mean ± s.e.m. of 3 independent experiments. CNGA4 was a non-m6A control. P values by unpaired two-sided t-test.

Source data

Extended Data Fig. 5 FXR1 acts as an m6A reader and binds with TET1.

a, Western blot shows no direct association between TET1 and METTL3 in KYSE510 and HEK293T cells. b, Immunofluorescence analysis shows colocalization of FXR1 and TET1 in KYSE30 cells. Scale bar, 20 μm. c, RNA pulldown assays using endogenous FXR1 or the ectopically expressed KH domain deletion mutant (FXR1-ΔKH) with RNA oligos show that only oligo-m6A (but not oligo-A) binds FXR1 but not FXR1-ΔKH. d, The distributions of FXR1 or FXR1-ΔKH in whole cell lysate (WCL), the cytoplasm, and the nuclei. GAPDH and H3 were loading controls. e, The distribution of FXR1 PAR-CLIP sequencing peaks in different regions of mRNAs. f, The consensus sequence motifs for FXR1 binding detected by PAR-CLIP sequencing and HOMER analysis. g, h, Effect of METTL3 knockdown on FXR1 RNA (g) and protein (h) levels determined by qRT-PCT or Western blotting. i, METTL3 knockdown significantly reduced FXR1 binding to the select mRNAs in KYSE510 and HEK293T cells. j, Effects of the ectopic expression of wild-type METTL3 (WT) or its mutant (Mut) on FXR1 binding to the select mRNAs in KYSE510 and HEK293T cells with endogenous METTL3 KO. k, Effects of METTL14 KO or FTO KO on FXR1 binding to the select mRNAs in KYSE510 and HEK293T cells. Data in g, i − k are mean ± s.e.m. of 3 independent experiments. CNGA4 was a non-m6A control. ns, not significant; P values by unpaired two-sided t-test.

Source data

Extended Data Fig. 6 FXR1 recruits TET1 to RNA m6A sites.

a, FXR1 binding (top) and peak numbers (bottom) upon FXR1 knockout. b, Overlap of FXR1, METTL3 and TET1 binding. c, Overlap of FXR1 transcriptome-wide and genome-wide binding (60.6%). d, DNA-binding of METTL3 to FXR1 in METTL3-depleted cells affected by wild-type METTL3 or its mutant. e, Density of distance from METTL3 knockout-decreased FXR1 CUT&Tag-peaks to m6A peak center. f, Western blot shows FXR1 knockout efficiency. g, FXR1 knockout-increased global DNA 5mC levels by ELISA (top) or LC-MS/MS (bottom). h, Effects of histone methyltransferases knockout on global 5mC level upon FXR1 depletion. i, Correlations between FXR1 and TET1 CUT&Tag-signals. j, TET1 binding (left) and the statistics by two-sided Wilcoxon test in 2 biologically independent samples (right) upon FXR1 knockout. k, Effects of FXR1 knockout on significant DMR counts affected by FXR1 or its KH domain deletion mutant (FXR1-ΔKH). l, Density of distance from FXR1 KO-induced hyper-DMR to m6A peak center. m, FXR1 KO-induced binding pattern alteration of TET1 to FXR1 CUT&Tag-peaks affected by FXR1 or FXR1-ΔKH mutant. n, TET1 CUT&Tag-signals in selected genes upon FXR1 knockout. Number represents the range of the CUT&Tag-signal. o, Effects of FXR1 knockout on TET1 enrichment. p, Effects of overexpressing FXR1 and FXR1-ΔKH mutant on DNA 5hmC, 5fC or 5caC levels. q, Counts of significantly differential DNA 5hmC peak upon overexpression of FXR1 or FXR1-ΔKH mutant. r, Density of distance from FXR1 knockout-induced DNA hypo-5hmC to the center of DNA hypermethylation region. s, Density of distance from FXR1 knockout-induced hypo-5hmC to m6A peak center. t, Overlap of genes with hyper-5mC and hypo-5hmC (93.2%) within genes having hypo-m6A-RNA upon FXR1 depletion. u, FXR1 TUD and the TET1 CXXC domain are responsible for their interaction. Top shows truncation forms of TET1 (left) and FXR1 (right) while bottom shows their interactions. v, Validation of dPspCas13b-FXR1 fusion protein expression. w, x, Effects of FXR1 (left) and TET1 (right) knockout on global RNA m6A levels by ELISA (w) and MeRIP-sequencing (x). Data in g, h, o, p and w are mean ± s.e.m. of 3 independent experiments. P values by unpaired two-sided t-test. CUT&Tag-peaks in heatmap are sorted in descending order based on the mean value per region.

Source data

Extended Data Fig. 7 FXR1 and m6A-RNA form liquid-like condensates to facilitate FXR1 and TET1 interaction.

a, Western blot shows no direct association between FXR1 and METTL3. b, Western blot shows impaired interaction between FXR1 and TET1 upon METTL3 depletion. IP, immunoprecipitation. c, Amino acid number in FXR1 KH domain and the predicted intrinsic disordered region (IDR) flanking it (left). Right shows binding of FXR1 to m6A. d, Effects of FXR1 on liquid-like condensate formation. Representative imaging (left) of condensate formation in cells expressing mNeonGreen-fused FXR1 or two FXR1 mutants with the deletion of KH (FXR1-ΔKH) or IDR (FXR1-ΔIDR) domain. Scale bar, 10 μm. Right is the quantitative statistics. ND, not detected. e, Representative fluorescence recovery after photobleaching (FRAP) of mNeonGreen-FXR1 condensate at different times (top). Pre, pre-bleaching, and quantification of FRAP (bottom). Scale bar, 10 μm. f, Time-lapse images of FXR1-m6A RNA droplets showing a droplet fusion event at different times. Scale bars, 2 μm. g, Representative fluorescence recovery images of FXR1-m6A RNA droplets (left) and quantification of FRAP data (right) at different times. The bleaching event occurred at 0 s. Scale bars, 2 μm. h, Effects of METTL3 KO on FXR1-m6A RNA liquid-like condensate formation in cells transfected with mNeonGreen-FXR1. Top is representative images and lower is quantitative statistics. Scale bar, 10 μm. i, Effects of FXR1 on FRX1-m6A phase separation in vitro. Left shows representative images of phase separation using 1.0 μM of mNeonGreen-FXR1, mNeonGreen-FXR1-ΔKH, or mNeonGreen-FXR1-ΔIDR and 5 nM RNA oligo with (RR[m6A]C) or without (RR[A]C) m6A. Assay without the RNA oligo served as Control. Scale bar, 10 μm. Right is quantification statistics of particle areas. Data in d, h and i are mean ± s.e.m. and in c, g are mean ± S.D. P values by two-sided Student’s t test. j, Western blot analysis of GST pulldown product shows the direct interaction between TET1 and FXR1 but not indicated mutant types. k, Differential effects of Cy3-labeled RNA oligo without m6A (RR[A]C) and with m6A (RR[m6A]C) on phase separation caused by mNeonGreen-FXR1 (1 μM, green) and Alexa-Fluor-647-labeled TET1 (1 μM, purple) in vitro. Scale bars: 5 μm.

Source data

Extended Data Fig. 8 Chromatin accessibility and gene transcription in cells with METTL3, FXR1 or TET1 knockout.

a, Bar plot shows differential accessible regions (DARs) of chromatin in cells with METTL3, FXR1 or TET1 knockout (KO). b, Enrichment of DARs in the annotated gene regions in cells with METTL3, FXR1 or TET1 KO. c, Venn plots show overlaps between the regions with loss (left) or gain (right) of accessibility in cells with FXR1, METTL3 or TET1 KO compared with that in cells without KO of these 3 genes. d, Overlap between genes in DARs and genes with differential expression levels in cells with METTL3, FXR1 or TET1 KO compared with that in cells without KO of these 3 genes. DA, genes in DARs; DE, genes having differential expression levels in cells with KO compared with that in cells without KO. e, Venn plots show overlaps between the down-regulated genes in the ATAC loss regions (left panel) and up-regulated genes in the ATAC gain regions (right panel) in KYSE30 cells with or without FXR1, METTL3 or TET1 KO. f, g, FXR1, METTL3 and TET1 binding signals detected by CUT&Tag sequencing and chromatin accessibility signals detected by ATAC-sequencing in select 6 genes as indicated in figure in KYSE30 cells with or without FXR1, METTL3 and TET1 KO. h, Venn plots show overlaps between the down-regulated genes in the intersected hyper-DMR and ATAC loss regions (left panel) and up-regulated genes in the intersected hyper-DMR and ATAC gain regions (right panel) in KYSE30 cells with or without FXR1, METTL3 or TET1 KO. i, Proportions of differential Pol II occupancy (calculated by R package DiffBind) in the intersect ATAC loss and intersect ATAC gain regions in KYSE30 cells with or without METTL3 KO. j, Effects of gRNA-guided dPspCas13b-FXR1 fusion protein on mRNA levels of select genes in cells with or without METTL3 KO. NT, untargeted gRNA. Data are mean ± s.e.m. of 3 independent experiments. P values by unpaired two-sided t-test.

Source data

Extended Data Fig. 9 The correlation of DNA 5mC and RNA m6A in clinical ESCC samples.

a, m6A sequencing and HOMER analysis show the consensus sequence motifs of RNA m6A peaks in both ESCC and normal tissues. P value was from HOMER based on binomial distribution. b, Metagene analysis of m6A peaks in different regions of mRNA in tissue samples. c, Pie plot shows percentage of the annotated m6A peaks in protein coding RNAs, nonprotein-coding RNAs, pseudogene RNAs, and other type RNAs. d, Correlation of the CpG methylation levels in our ESCC samples detected by whole-genome bisulfite sequencing (WGBS) and TCGA-ESCC samples detected by the Human Methylation 450 K (HM450K) array. The two-sided Pearson correlation was calculated using R function cor.test(), in which P value was based on t test. e, Ridge plots of RNA m6A and DNA 5mC levels in the annotated genomic locations, that is, the 3′untranslated region (UTR), exon, promoter, intron, 5′UTR, and intergenic region, in tumor and normal samples. X axis represents RNA m6A and DNA 5mC levels in the m6A regions. The m6A peaks with a level 1.5 times the interquartile range above the upper quartile were considered as outliers and removed in the ridge plots. f, Volcano plot shows differentially methylated CpGs in ESCC samples minus the adjacent normal samples. P value was calculated using R package MethylKit based on logistic regression. g, Density plot shows the distribution of correlations between RNA m6A levels and DNA 5mC levels in the annotated genomic locations. h, Profiles of m6A and FXR1, TET1 or METTL3 CUT&Tag regions in KYSE30 cells for the indicated genes in control cells, cells with METTL3 knockout (KO) or MTTL3 KO cells with the ectopic expression of wild-type (WT) METTL3 or its mutant (Mut). Note: as m6A can also be deposited by other m6A methyltransferase such as METTL16, METTL3 KO may not reduce m6A abundance dramatically in all loci.

Extended Data Fig. 10 RNA m6A formation coupled DNA 5mC demethylation plays a role in the development of ESCC.

a, Box plots show the RNA levels of METTL3, TET1 and FXR1 determined by qRT-PCR in ESCC compared with the paired adjacent normal samples (N = 215). The lines in the middle of the box are medians and the upper and lower lines indicate 25th and 75th percentiles. The lines in the upper and lower of the whiskers indicate the maxima and minima. The whiskers represent 1.5× the interquartile range. P was calculated by two-sided Wilcoxon rank-sum test. b, Kaplan-Meier estimate of survival time in ESCC patients by the METTL3, TET1 or FXR1 RNA level alone or the combination of three genes. c, d, Effects of FXR1 KO on proliferation (c), migration and invasion (d) of ESCC cells. Top panels of d are the representative transwell pictures and bottom panels are the quantitative statistics. e, f, Effects of METTL3 KO on proliferation (e), migration and invasion (f) of ESCC cells with or without the ectopic FXR1 expression. Top panels of f are the representative transwell pictures and bottom panels are the quantitative statistics. g, h, Effects of FXR1 KO on proliferation (g), migration and invasion (h) of ESCC cells with or without the ectopic expression of FXR1 or its mutants lacking KH domain (FXR1-ΔKH) or TUD domain (FXR1-ΔTUD). Upper panels of h are representative transwell pictures and lower panels are the quantitative statistics. i, j, Effects of TET1 KO on proliferation, migration and invasion of ESCC cells with or without the ectopic FXR1 expression. Top panels of j are the representative transwell pictures and bottom panels are the quantitative statistics. Cell proliferation ability was determined by the CCK8 assay and results are means ± s.e.m. from 3 independent experiments while the migration or invasion ability was examined by transwell assay, and the results are mean ± s.e.m. of cells from 3 random fields. Scale bars in transwell images, 200 μm. P values by unpaired two-sided t-test.

Source data

Supplementary information

Source data

Source Data Fig. 1

Statistical Source Data for Fig. 1.

Source Data Fig. 3

Unprocessed Western Blots for Fig. 3.

Source Data Fig. 4

Unprocessed Western Blots for Fig. 4.

Source Data Fig. 4

Statistical Source Data for Fig. 4.

Source Data Extended Data Fig. 2

Unprocessed Western Blots for Extended Data Fig. 2.

Source Data Extended Data Fig. 2

Statistical Source Data for Extended Data Fig. 2.

Source Data Extended Data Fig. 3

Unprocessed Western Blots for Extended Data Fig. 3.

Source Data Extended Data Fig. 3

Statistical Source Data for Extended Data Fig. 3.

Source Data Extended Data Fig. 4

Statistical Source Data for Extended Data Fig. 4.

Source Data Extended Data Fig. 5

Unprocessed Western Blots for Extended Data Fig. 5.

Source Data Extended Data Fig. 5

Statistical Source Data for Extended Data Fig. 5.

Source Data Extended Data Fig. 6

Unprocessed Western Blots for Extended Data Fig. 6.

Source Data Extended Data Fig. 6

Statistical Source Data for Extended Data Fig. 6.

Source Data Extended Data Fig. 7

Unprocessed Western Blots for Extended Data Figure 7.

Source Data Extended Data Fig. 7

Statistical Source Data for Extended Data Figure 7.

Source Data Extended Data Fig. 8

Statistical Source Data for Extended Data Figure 8.

Source Data Extended Data Fig. 10

Statistical Source Data for Extended Data Figure 10.

Rights and permissions

Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Deng, S., Zhang, J., Su, J. et al. RNA m6A regulates transcription via DNA demethylation and chromatin accessibility. Nat Genet 54, 1427–1437 (2022). https://doi.org/10.1038/s41588-022-01173-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41588-022-01173-1

  • Springer Nature America, Inc.

This article is cited by

Search

Navigation