Histone H3 trimethylation at lysine 36 guides m6A RNA modification co-transcriptionally (original) (raw)

Code availability

Code used for data analysis including cutadapt v.1.13 (adapter remove), Bowtie v.1.1.2 (ChIP–seq, CLIP sequencing and miCLIP sequencing alignment), TopHat version v.2.1.1 (m6A-seq and ribosome-profiling alignment), featureCounts v.1.6.0 (reads count), HTSeq v.0.6.1p1 (reads count), RSEM v.1.2.31 (gene expression quantification), DEGseq v.1.28.0 (differential gene expression analysis), MACS v.1.4.2 (peak calling of H3K36me3 ChIP–seq), SICER v.1.1 (peak calling of METTL14 ChIP–seq), exomePeak v.2.8.0 (peak calling of m6A-seq), HOMER v.3.12 (motif analysis) and RiboDiff v.0.2.1 (differential translation efficiency analysis) are publicly available from the indicated references.

Data availability

All sequencing data that support the findings of this study have been deposited in the NCBI Gene Expression Omnibus (GEO) under accession number GSE110323. Previous published ChIP–seq data and m6A-seq data of HepG2 cells were re-analysed and are available under GEO accession codes GSE51334 and GSE37003. Previously published ENCODE data that were re-analysed here are available under accession codes ENCFF533JQH, ENCSR000ATD (H3K9me3) and ENCFF042EDV, ENCSR000DUE (H3K27me3) for heterochromatin and window correlation analysis. The expression data of SETD2 and MTC genes were derived from the TCGA Research Network (http://cancergenome.nih.gov/), GTEx program (https://www.gtexportal.org/), and CCLE project (https://portals.broadinstitute.org/ccle). The dataset derived from this resource that supports the findings of this study is available in the ChIPbase (http://rna.sysu.edu.cn/chipbase/)29,30 and CCLE (https://portals.broadinstitute.org/ccle) websites.

References

1. Egger, G., Liang, G., Aparicio, A. & Jones, P. A. Epigenetics in human disease and prospects for epigenetic therapy. Nature 429, 457–463 (2004).
   Article ADS CAS Google Scholar
2. Wang, X. et al. N 6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).
   Article ADS Google Scholar
3. Wang, X. et al. N 6-methyladenosine modulates messenger RNA translation efficiency. Cell 161, 1388–1399 (2015).
   Article CAS PubMed Google Scholar
4. Huang, H. et al. Recognition of RNA N 6-methyladenosine by IGF2BP proteins enhances mRNA stability and translation. Nat. Cell Biol. 20, 285–295 (2018).
   Article CAS PubMed Google Scholar
5. Batista, P. J. et al. m6A RNA modification controls cell fate transition in mammalian embryonic stem cells. Cell Stem Cell 15, 707–719 (2014).
   Article CAS PubMed Google Scholar
6. Wang, Y. et al. N 6-methyladenosine modification destabilizes developmental regulators in embryonic stem cells. Nat. Cell Biol. 16, 191–198 (2014).
   Article CAS PubMed Google Scholar
7. Geula, S. et al. m6A mRNA methylation facilitates resolution of naïve pluripotency toward differentiation. Science 347, 1002–1006 (2015).
   Article ADS CAS Google Scholar
8. Xiang, Y. et al. RNA m6A methylation regulates the ultraviolet-induced DNA damage response. Nature 543, 573–576 (2017).
   Article ADS CAS PubMed Google Scholar
9. Li, Z. et al. FTO plays an oncogenic role in acute myeloid leukemia as a N 6-methyladenosine RNA demethylase. Cancer Cell 31, 127–141 (2017).
   Article Google Scholar
10. Weng, H. et al. METTL14 inhibits hematopoietic stem/progenitor differentiation and promotes leukemogenesis via mRNA m6A modification. Cell Stem Cell 22, 191–205 (2018).
    Article CAS Google Scholar
11. Su, R. et al. R-2HG exhibits anti-tumor activity by targeting FTO/m6A/MYC/CEBPA signaling. Cell 172, 90–105 (2018).
    Article CAS Google Scholar
12. Deng, X., Su, R., Feng, X., Wei, M. & Chen, J. Role of N 6-methyladenosine modification in cancer. Curr. Opin. Genet. Dev. 48, 1–7 (2018).
    Article CAS Google Scholar
13. Meyer, K. D. et al. Comprehensive analysis of mRNA methylation reveals enrichment in 3′ UTRs and near stop codons. Cell 149, 1635–1646 (2012).
    Article CAS PubMed Google Scholar
14. Dominissini, D. et al. Topology of the human and mouse m6A RNA methylomes revealed by m6A-seq. Nature 485, 201–206 (2012).
    Article ADS CAS Google Scholar
15. Linder, B. et al. Single-nucleotide-resolution mapping of m6A and m6Am throughout the transcriptome. Nat. Methods 12, 767–772 (2015).
    Article CAS PubMed Google Scholar
16. Liu, J. et al. A METTL3–METTL14 complex mediates mammalian nuclear RNA N 6-adenosine methylation. Nat. Chem. Biol. 10, 93–95 (2014).
    Article CAS Google Scholar
17. Barski, A. et al. High-resolution profiling of histone methylations in the human genome. Cell 129, 823–837 (2007).
    Article CAS Google Scholar
18. Pokholok, D. K. et al. Genome-wide map of nucleosome acetylation and methylation in yeast. Cell 122, 517–527 (2005).
    Article CAS Google Scholar
19. Sun, X. J. et al. Identification and characterization of a novel human histone H3 lysine 36-specific methyltransferase. J. Biol. Chem. 280, 35261–35271 (2005).
    Article CAS Google Scholar
20. Whetstine, J. R. et al. Reversal of histone lysine trimethylation by the JMJD2 family of histone demethylases. Cell 125, 467–481 (2006).
    Article CAS Google Scholar
21. Carrozza, M. J. et al. Histone H3 methylation by Set2 directs deacetylation of coding regions by Rpd3S to suppress spurious intragenic transcription. Cell 123, 581–592 (2005).
    Article CAS Google Scholar
22. Joshi, A. A. & Struhl, K. Eaf3 chromodomain interaction with methylated H3-K36 links histone deacetylation to Pol II elongation. Mol. Cell 20, 971–978 (2005).
    Article CAS Google Scholar
23. Luco, R. F. et al. Regulation of alternative splicing by histone modifications. Science 327, 996–1000 (2010).
    Article ADS CAS PubMed Google Scholar
24. Dominguez, A. A., Lim, W. A. & Qi, L. S. Beyond editing: repurposing CRISPR–Cas9 for precision genome regulation and interrogation. Nat. Rev. Mol. Cell Biol. 17, 5–15 (2016).
    Article CAS Google Scholar
25. Kizer, K. O. et al. A novel domain in Set2 mediates RNA polymerase II interaction and couples histone H3 K36 methylation with transcript elongation. Mol. Cell. Biol. 25, 3305–3316 (2005).
    Article CAS PubMed Google Scholar
26. Olmos, Y., Hodgson, L., Mantell, J., Verkade, P. & Carlton, J. G. ESCRT-III controls nuclear envelope reformation. Nature 522, 236–239 (2015).
    Article ADS CAS PubMed Google Scholar
27. Wienken, C. J., Baaske, P., Rothbauer, U., Braun, D. & Duhr, S. Protein-binding assays in biological liquids using microscale thermophoresis. Nat. Commun. 1, 100 (2010).
    Article ADS Google Scholar
28. Hacker, K. E. et al. Structure/function analysis of recurrent mutations in setd2 protein reveals a critical and conserved role for a SET domain residue in maintaining protein stability and histone H3 Lys-36 trimethylation. J. Biol. Chem. 291, 21283–21295 (2016).
    Article CAS PubMed Google Scholar
29. Yang, J. H. et al. ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data. Nucleic Acids Res. 41, D177–D187 (2013).
    Article CAS Google Scholar
30. Zhou, K. R. et al. ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data. Nucleic Acids Res. 45, D43–D50 (2013).
    Article Google Scholar
31. Subramanian, A. et al. Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles. Proc. Natl Acad. Sci. USA 102, 15545–15550 (2005).
    Article ADS CAS PubMed Google Scholar

Download references

Acknowledgements

This work was supported in part by the National Institutes of Health (NIH) grants R01 CA214965 (J.C.), R01 CA211614 (J.C.), R01 CA178454 (J.C.), R01 CA182528 (J.C.), R01 CA236399 (J.C.), RM1 HG008935 (C.H.), R21 CA187276 (G.H.), R01 CA163493 (J.G.), R35 CA197628 (M.M.), U10 CA180827 (M.M.), R01 CA137060 (M.M.), R01 CA157644 (M.M.), R01 CA172558 (M.M.) and R01 CA213138 (M.M.), and grants 2017YFA0504400 (J.Y.), 91440110 (J.Y.) and 31671349 (L.Q.) from National Nature Science Foundation of China, and Cancer Center Support Grant (P30CA33572) from City of Hope National Medical Center. J.C. is a Leukemia & Lymphoma Society (LLS) Scholar. C.H. is an investigator of the Howard Hughes Medical Institute (HHMI). M.M. is an HHMI Faculty Scholar. B.S.Z. is an HHMI International Student Research Fellow. F.A. was supported by a Deutsche Forschungsgemeinschaft (DFG) fellowship (AU 525/1-1).

Reviewer information

Nature thanks Abid Khan, Brian Strahl and the other anonymous reviewer(s) for their contribution to the peer review of this work.

Author information

Author notes

1. These authors contributed equally: Huilin Huang, Hengyou Weng, Keren Zhou, Tong Wu, Boxuan Simen Zhao

Authors and Affiliations

1. Department of Systems Biology, Beckman Research Institute of City of Hope, Monrovia, CA, USA
   Huilin Huang, Hengyou Weng, Mingli Sun, Zhenhua Chen, Xiaolan Deng, Gang Xiao, Franziska Auer, Lars Klemm, Huizhe Wu, Xi Qin, Rui Su, Lei Dong, Chao Shen, Chenying Li, Ying Qing, Xi Jiang, Markus Müschen & Jianjun Chen
2. Department of Cancer Biology, University of Cincinnati College of Medicine, Cincinnati, OH, USA
   Huilin Huang, Hengyou Weng, Xiaolan Deng, Huizhe Wu, Zhixiang Zuo, Xi Qin, Ana Mesquita, Rui Su, Lei Dong, Chao Shen, Chenying Li, Ying Qing, Xi Jiang, Jun-Lin Guan & Jianjun Chen
3. Key Laboratory of Gene Engineering of the Ministry of Education, Sun Yat-sen University, Guangzhou, China
   Keren Zhou, Wenju Sun, Lianghu Qu & Jianhua Yang
4. State Key Laboratory for Biocontrol, Sun Yat-sen University, Guangzhou, China
   Keren Zhou, Wenju Sun, Lianghu Qu & Jianhua Yang
5. Department of Chemistry, University of Chicago, Chicago, IL, USA
   Tong Wu, Boxuan Simen Zhao, Jun Liu, Zhike Lu, Hang Yin & Chuan He
6. Department of Biochemistry and Molecular Biology, University of Chicago, Chicago, IL, USA
   Tong Wu, Boxuan Simen Zhao, Jun Liu, Zhike Lu, Hang Yin & Chuan He
7. Institute for Biophysical Dynamics, University of Chicago, Chicago, IL, USA
   Tong Wu, Boxuan Simen Zhao, Jun Liu, Zhike Lu, Hang Yin & Chuan He
8. Howard Hughes Medical Institute, University of Chicago, Chicago, IL, USA
   Tong Wu, Boxuan Simen Zhao, Jun Liu, Zhike Lu, Hang Yin & Chuan He
9. Department of Pharmacology, School of Pharmacy, China Medical University, Shenyang, China
   Mingli Sun, Xiaolan Deng, Huizhe Wu & Minjie Wei
10. Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangzhou, China
    Zhixiang Zuo
11. Division of Pathology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
    Yunzhu Dong, Yile Zhou & Gang Huang
12. Division of Experimental Hematology and Cancer Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
    Yunzhu Dong, Yile Zhou & Gang Huang
13. Intergrative Genomics Core, Beckman Research Institute of City of Hope, Monrovia, CA, USA
    Hanjun Qin, Shu Tao, Juan Du & Xiwei Wu
14. Division of Developmental Biology, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
    Celvie L. Yuan & Yueh-Chiang Hu
15. Department of Pharmacology, and Bone Marrow Transplantation Center of the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
    Xi Jiang
16. Institute of Hematology, Zhejiang University & Zhejiang Engineering Laboratory for Stem Cell and Immunotherapy, Hangzhou, China
    Xi Jiang
17. Department of Pediatrics, University of Cincinnati College of Medicine, Cincinnati, OH, USA
    Miao Sun
18. Division of Human Genetics, Cincinnati Children’s Hospital Medical Center, Cincinnati, OH, USA
    Miao Sun

Authors

1. Huilin Huang
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2. Hengyou Weng
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3. Keren Zhou
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4. Tong Wu
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5. Boxuan Simen Zhao
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6. Mingli Sun
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7. Zhenhua Chen
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8. Xiaolan Deng
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9. Gang Xiao
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10. Franziska Auer
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11. Lars Klemm
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12. Huizhe Wu
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13. Zhixiang Zuo
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14. Xi Qin
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15. Yunzhu Dong
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16. Yile Zhou
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17. Hanjun Qin
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18. Shu Tao
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19. Juan Du
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20. Jun Liu
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21. Zhike Lu
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22. Hang Yin
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23. Ana Mesquita
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24. Celvie L. Yuan
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25. Yueh-Chiang Hu
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26. Wenju Sun
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27. Rui Su
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28. Lei Dong
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29. Chao Shen
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30. Chenying Li
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31. Ying Qing
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32. Xi Jiang
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33. Xiwei Wu
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34. Miao Sun
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35. Jun-Lin Guan
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36. Lianghu Qu
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37. Minjie Wei
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38. Markus Müschen
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39. Gang Huang
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40. Chuan He
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41. Jianhua Yang
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42. Jianjun Chen
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Contributions

H.H., H. Weng and J.C. conceived and designed the entire project. H.H., H. Weng, G.H., C.H., J.Y. and J.C. designed and supervised the research. H.H. and H. Weng prepared all samples for high-throughput sequencing, T.W. and B.S.Z. performed H3K36me3 ChIP–seq and m6A-seq with input from H.Y. T.W. performed PAR-CLIP sequencing, miCLIP sequencing and ribosome profiling; H. Weng performed METTL14 ChIP–seq; H.H. performed mRNA stability profiling. H.H. and H. Weng performed dot blot and gene-specific ChIP and MeRIP assays. H.H. and H. Weng prepared samples for LC–MS/MS; and B.S.Z. and T.W. performed mass spectrometry. H.H. performed CLIP assays, gel shift, peptide pull-down, immunofluorescence, in vitro differentiation of mouse ES cells and gene expression correlation analyses. H. Weng isolated nascent RNA. H.H. and H. Weng performed microscale thermophoresis. H.H. and H. Weng performed co-immunoprecipitation with input from Z.C. H.H. performed western blots with help from H. Weng, Mingli Sun, Z.C., X.D., H. Wu and X.Q. H.H. performed qPCR with help from H. Weng, Mingli Sun, H. Wu and X.Q. G.X. designed sgRNAs and prepared dCas9 cell samples with help from L.K. and F.A., and H.H. and H. Weng detected m6A and H3K36me3 levels in specific loci. H. Weng constructed plasmids with input from H.H., Mingli Sun and X.Q. H.H. and H. Weng prepared all SETD2-knockdown and overexpression cell lines, A.M. established SETD2-knockout HeLa cells, F.A. established METTL14-knockout HepG2 cells. Y.D., Y.Z. and G.H. provided _Setd2_-knockout mice and SETD2-inducible knockout mouse ES cells. T.W. purified METTL3 and METTL14 recombinant protein with help from J.L. Z.Z. performed integrated analysis of ChIP–seq and m6A-seq data using public data. K.Z. analysed all genome-wide and transcriptome-wide data with input from W.S. and supervision from J.Y. and H.H. H.Q., S.T., J.D., Z.L., C.L.Y., Y.-C.H., R.S., L.D., C.S., C.L., Y.Q., X.J., X.W., Miao Sun, J.-L.G., L.Q., M.W., M.M., G.H., C.H., J.Y. and J.C. contributed reagents, analytic tools and/or grant support; H.H., H. Weng and J.C. wrote the manuscript and all authors commented on the manuscript.

Corresponding authors

Correspondence toGang Huang, Chuan He, Jianhua Yang or Jianjun Chen.

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Competing interests

C.H. is a scientific founder and a scientific advisor board member of Accent Therapeutics, Inc.; J.C. is a scientific founder and the chief scientific officer of Genovel Biotech Corp. Both hold equities with their corresponding company.

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

Extended data figures and tables

Extended Data Fig. 1 Overlap of H3K36me3 with m6A and co-expression of their writer genes.

a, Overlaps of m6A sites with histone modification sites in the human genome. Histogram showed the observed and expected percentages of m6A sites that overlap with various histone modification sites; the ENCODE ChIP–seq data and m6A-seq data (Gene Expression Omnibus (GEO) accession GSE37003) obtained from HepG2 cells were used for the analyses. b, Distance of histone modifications to the nearest m6A peaks. Input and H3K27me3 were shown as negative controls. c–e, Correlation of SETD2 with individual m6A MTC genes (that is, METTL3, METTL14 and WTAP) in expression in normal tissues (c), cancer samples (d), and cell lines (e), based on the data from Genotype Tissue Expression (GTEx), The Cancer Genome Atlas (TCGA), and Cancer Cell Line Encyclopedia (CCLE) databases, respectively. Note that every dot represents one tissue type (c) or one cancer type (d). f, Co-expression analysis of SETD2 and individual m6A MTC genes in 33 cancer cell lines based on our qRT-PCR data. The value of each gene represents relative expression as normalized to HEK293T cells. Correlation coefficient (r) and P values were calculated by Pearson’s correlation analysis.

Extended Data Fig. 2 Cellular m6A level is regulated by the H3K36me3 methyltransferase SETD2 and the demethylase KDM4A.

a, Western blot showing knockdown efficiency of SETD2 using shSETD2#1, and the decrease of H3K36me3 in HepG2 and HeLa cells. GAPDH and H3 were used as loading controls. b, LC–MS/MS quantification of m6A abundance in poly(A) RNA from HeLa cells after shRNA knockdown of SETD2 (shSETD2#1) or of individual m6A MTC genes (METTL3, METTL14, WTAP) in comparison to control cells. Data are mean ± s.d. of two independent experiments. c, Knockdown efficiency of shRNAs was verified by qRT–PCR. Data are mean ± s.d. of three independent experiments. d, Dot blot (left) and quantification (right, data are mean ± s.d.) of m6A in total RNA from control cells or cells with knockdown of SETD2 or individual m6A MTC genes. Methylene blue (MB) was used as a loading control. e, shRNA knockdown of SETD2 (shSETD2#1 and shSETD2#2) was verified by western blot in HepG2 cells. f, Dot blot (right) and quantification (left, data are mean ± s.d.) of m6A in total RNA after shRNA knockdown of SETD2 in HepG2 cells. g, Western blot showing depletion of SETD2 and H3K36me3 in wild-type (WT) or SETD2-knockout (KO) HeLa cells. h, LC–MS/MS quantification of m6A in poly(A) RNA from wild-type or SETD2-knockout HeLa cells. Data are mean ± s.d. of two independent experiments. i, Dot blot (right) and quantification (left, data are mean ± s.d.) of m6A in total RNA from wild-type or SETD2-knockout HeLa cells. j, Western blot confirmed the overexpression of HA-tagged KDM4A and a decrease in H3K36me3 in HEK293T cells. k, Quantification (left, data are mean ± s.d.) and dot blot (right) showing hypomethylation of m6A in poly(A) RNA from HEK293T cells with KDM4A overexpression compared to control cells. l, Genotyping of wild-type (+/+) and SETD2 heterozygous knockout (+/−) mice. c-kit+ bone marrow (BM) cells were used. m, Western blot showing H3K36me3 level in c-kit+ bone marrow cells from wilde-type or SETD2 heterozygous knockout mice. n, o, LC–MS/MS quantification (n) and dot blot analysis (o, data are mean ± s.d. on left) of m6A in poly(A) RNA from c-kit+ bone marrow cells of wild-type and SETD2 heterozygous knockout mice. Data are mean ± s.d. of two technical replications and three mice were used for each group in n. p, Western blot showing expression of SUV39H1 and H3K9me3 in HeLa cells transfected with short interfering RNA (siRNA) against SUV39H1 (siSUV39H1) or negative control (siNC). q, r, Dot blot (right) and quantification (left, data are mean ± s.d.) of m6A in total RNA (q) or poly(A) RNA (r) from HeLa cells transfected with SUV39H1 siRNA or control. s, A498 cells were transfected with GFP-fused wild-type or mutated SETD2, and were selected for GFP-positive cells after 48 h by cell sorting. The increase in SETD2 mRNA expression in SETD2-overexpressed cells was confirmed by qRT–PCR. Data are mean ± s.d. of three independent experiments. t, Western blot showing H3K36me3 level in A498 cells after overexpression of wild-type (WT) or mutated (that is, R1625C and ΔSRI) SETD2 compared to empty pEGFP vector (vector). HEK293T served as a positive control. u, Dot blot detecting m6A in poly(A) RNA from A498 cells after overexpression of wild-type or mutated SETD2 compared to empty pEGFP vector. v, Western blot showing that silencing of individual m6A MTC genes (METTL3, METTL4 or WTAP) did not affect cellular H3K36me3 levels in HepG2 cells. *P < 0.05, **P < 0.01, ***P < 0.001, two-tailed student’s _t_-test. N.S., not significant. Images are representative of three (a, d, g and i) or two (e, f, j, k, m, o–r, t–v) independent experiments.

Source data

Extended Data Fig. 3 Silencing of SETD2 reprograms H3K36me3 and m6A landscapes.

a, Histogram showing H3K36me3 peak numbers in control (shCtrl) and SETD2 knockdown (shSETD2#1) HepG2 cells. b, Cumulative curves and box plot showing reduction of H3K36me3 in SETD2-knockdown HepG2 cells relative to control cells. P values were calculated using two-sided Wilcoxon and Mann–Whitney test. For the box plot, the top whisker denotes the ninety-fifth percentile (shCtrl = 3.436, shSETD2#1 = 2.073), the top of the box is the seventy-fifth percentile (shCtrl = 1.898, shSETD2#1 = 1.035), horizontal line denotes the median (shCtrl = 1.358, shSETD2#1 = 0.645), the bottom of the box denotes the twenty-fifth percentile (shCtrl = 0.868, shSETD2#1 = 0.339), and the bottom whisker is the fifth percentile (shCtrl = 0.001, shSETD2#1 = 0.001). c, Circos plot showing genome-wide decrease in H3K36me3 after SETD2 shRNA knockdown in HepG2 cells compared to control cells. d, The proportion (pie chart, top) and enrichment (histogram, bottom) of hypomethylated H3K36me3 peak distribution in the promoter, 5′ UTR, CDS, stop codon, or 3′ UTR regions in SETD2-knockdown HepG2 cells. The enrichment was determined by the proportion of H3K36me3 peaks normalized to the length of the region. e, Metagene profiles of H3K36me3 in SETD2-knockdown and control HepG2 cells. f, Cumulative curves of m6A abundance (that is, log2(m6A EF + 1)) in control (shCtrl) and SETD2-knockdown (shSETD2#1) HepG2 cells. Abundance of m6A immunoprecipitation was normalized to input when calculating the enrichment fold. P value was calculated using two-sided Wilcoxon and Mann–Whitney test. g, Scatter plot of m6A peaks in control (shCtrl) and SETD2 knockdown (shSETD2#2) HepG2 cells. The abundance of m6A peaks was calculated as enrichment fold (EF; IP/input) from three independent m6A-seq replications. Hypermethylated peaks are in red; hypomethylated peaks are in green. h, Cumulative curves of m6A abundance (log2(m6A EF +1)) in control and SETD2-knockdown HepG2 cells. P value was calculated using two-sided Wilcoxon and Mann–Whitney test. i, Circos plot showing the distribution of hypermethylated (hyper) and hypomethylated (hypo) m6A peaks in human transcriptome (left) and chromosome 5 (chr5; right) after shRNA knockdown of SETD2 or individual m6A MTC genes. j, Pie charts showing the percentages of hypomethylated m6A peaks distribution in various RNA species by SETD2 knockdown in HepG2 cells. k, The proportion (pie chart, top) and enrichment (histogram, bottom) of hypomethylated m6A peak distribution in the 5′ UTR, CDS, stop codon, or 3′ UTR regions in SETD2-knockdown HepG2 cells. Enrichment was determined by the proportion of m6A peaks normalized to the length of the region. l, HOMER motif analysis revealed conserved motifs enriched in hypomethylated peaks. P values were calculated by HOMER algorithm. m, Numbers of m6A sites in control and SETD2 knockdown (sh#1 or sh#2) HepG2 cells identified by miCLIP. n, HOMER motif analysis revealed conserved motifs of C-to-T mutations or truncations identified by miCLIP. P values were calculated by HOMER algorithm. o, The proportion (pie chart, top) and enrichment (histogram, bottom) of miCLIP-identified m6A distribution in gene body regions. p, Percentages of various RNA species containing miCLIP-identified m6A residues. q, Metagene profiling of miCLIP-identified m6A residues in control and SETD2-knockdown cells.

Extended Data Fig. 4 Genome/transcriptome-wide and locus-specific co-regulation of H3K36me3 and m6A.

a, Circos plot showing global distribution of H3K36me3, m6A and H3K9me3, in human genome (left) and chromosome 5 (right) of HepG2 cells. b, Distribution of H3K9me3 relative to nearest m6A sites in HepG2 cells. c, Percentages and peak numbers of H3K36me3 and m6A in H3K9me3-negative, H3K9me3-positive, H3K27me3-negative or H3K27me3-positive regions in HepG2 cells. d, Metagene profiles of m6A at H3K36me3-positive or -negative sites. e, Distribution of H3K36me3 peaks with or without m6A modifications in gene body and intergenic regions. f, g, Quantification of H3K36me3 (f) and m6A (g) level in representative genes as determined by ChIP–qPCR and gene-specific m6A-qPCR assays in SETD2-knockdown (shSETD2#1) and control (shCtrl) HepG2 cells. h, i, Quantification of H3K36me3 (h) and m6A (i) level in representative genes as determined by ChIP–qPCR and gene specific m6A-qPCR assays in SETD2-knockdown (shSETD2#2) and control (shCtrl) HepG2 cells. j, k, Quantification of H3K36me3 (j) and m6A (k) level in representative genes as determined by ChIP–qPCR and gene-specific m6A qRT–PCR assays in KDM4A-overexpressed and control (vector) HEK293T cells. Data in f–k are mean ± s.d. of three independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001; two-tailed Student’s _t_-test.

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Extended Data Fig. 5 Locus-specific regulation of m6A by H3K36me3.

a, b, Schematic showing the epigenetic editing of H3K36me3 by dCas9–KDM4A on the MYC CRD (a) or by dCas–SETD2 on GNG4 (b). c, Distribution of H3K36me3 and m6A peaks across the GNG4 mRNA transcript. d, Schematic showing endogenous GNG4 and artificial _MYC_-GNG4 fusion gene. The primers used to distinguish endogenous GNG4 (GNG4 5′UTR), _MYC_-GNG4 fusion or _GNG4_-vector fusion (GNG4 pcDNA) sites were indicated by black, orange or blue arrows, respectively. e, Quantification of H3K36me3 (top) and m6A (bottom) in endogenous GNG4 or MYC-GNG4 fusion genes by ChIP–qPCR or gene specific m6A assay in control (shCtrl) or SETD2-knockdown (shSETD2#1) HEK293T cells. Data are mean ± s.d. of five independent experiments. ***P < 0.001; two-tailed Student’s _t_-test.

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Extended Data Fig. 6 Effect of SETD2 knockdown on gene expression, mRNA stability and translation.

a, Distribution of H3K36me3 and m6A peaks across the MYC mRNA transcript. b, Histogram showing significantly upregulated (fold change > 0.585, P < 0.05) and downregulated (fold change < −0.585, _P_ < 0.05) genes in HepG2 cells after shRNA knockdown of SETD2 or individual MTC genes (_METTL3_, _METTL14_, _WTAP_). **c**, Correlation of fold change in gene expression (measured as log2(expression in shRNA knockdown/expression in shRNA control)) between cells with shRNA knockdown of SETD2 and individual MTC genes. **d**, Cumulative curves of mRNA half-life in HepG2 cells after shRNA knockdown of _SETD2_ (shSETD2#1) or _METTL14_ (shMTL14) or shRNA control (shCtrl). _P_ values were calculated using two-sided Wilcoxon and Mann–Whitney test. **e**, Decay curves and half-life (_t_1/2) of _MYC_ mRNA in control, SETD2- or METTL14-knockdown cells, derived from transcriptome-wide mRNA stability profiling. Data are mean ± s.d. of two independent experiments. **f**, Correlation of changes in mRNA half-life between SETD2-knockdown and METTL14-knockdown HepG2 cells. **g**, Histograms showing genes with significantly increased (fold change > 0.585, P < 0.05) or decreased (fold change < −0.585, P < 0.05) translation efficiency in SETD2- or METTL14-knockdown HepG2 cells. h, Correlation of changes in translation efficiency between SETD2-knockdown and METTL14-knockdown HepG2 cells. Correlation coefficient (r) and P values were calculated by Pearson’s correlation analysis.

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Extended Data Fig. 7 Mechanism of H3K36me3-dependent m6A modification.

a, Binding of METTL3 (top) and METTL14 (bottom) to target mRNAs was determined by CLIP–qPCR assays. b, qRT–PCR data showing that knockdown of SETD2 in HepG2 and HeLa cells did not downregulate the expression of m6A MTC genes. c, qRT–PCR data showing that overexpression of SETD2 in A498 cells did not significantly change the expression of m6A MTC genes. d, qRT–PCR showing that overexpression of KDM4A in HEK293T cells did not affect the expression of individual m6A MTC genes. Gene expression in b–d was normalized to GAPDH mRNA. e, Expression of individual MTC proteins in cells after shRNA silencing (shSETD2 sh#1 and sh#2), SETD2 overexpression (OE; wild type or mutants R1625C and ΔSRI), or KDM4A overexpression was determined by western blot analysis. f, Interaction of METTL3 and METTL14 in SETD2-knockdown or control cells with forced expression of HA–METTL3. g, Western blot showing forced expression of METTL3, METTL14 and WTAP in HeLa cells. h, Co-immunoprecipitation of HA-tagged individual m6A MTC proteins in HeLa cell lysates with or without pretreatment with DNase (DN; 100 U ml−1) or RNase (RN; 2 μg ml−1) for 1 h at room temperature. i, Co- immunoprecipitation of HA-tagged METTL14 showed no interaction between METTL14 and mono- or di-methylation of H3K36 (H3K36me1 and H3K36me2) in HeLa cells. j, Co-immunoprecipitation of endogenous METTL3 or METTL14 in HeLa cells. The interaction between METTL3 or METTL14 with H3K36me3 was detected by western blot analysis. k, Co-immunoprecipitation of endogenous H3K36me3 in HepG2 cells without or with shRNA knockdown of individual m6A MTC genes. l, The m6A/A ratio was quantified by LC–MS/MS in HepG2 cells after knockdown of SETD2 and/or METTL14. Data are mean ± s.d. of two (b, l) or three (a, c, d) independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001; two-tailed Student’s _t_-test. Images in e–k are representative of three independent experiments.

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Extended Data Fig. 8 METTL14 binds to H3K36me3 in vitro and in vivo.

a, Schematic of the indirect or direct models of H3K36me3 recruiting MTC. Left, the ‘adaptor’ model refers to an indirect interaction of m6A MTC with H3K36me3 through known H3K36me3-binding proteins (adaptors). Right, the ‘reading and writing’ (R/W) model proposes that the m6A writer complex functions as reader of H3K36me3 (green dot) and is recruited to chromatin to catalyse m6A methylations (yellow dots) in newly synthesized RNAs. b, Potential association of METTL3 or METTL14 with known H3K36me3 readers was examined by co-immunoprecipitation in HeLa cells with forced expression of HA-tagged METTL3 or METTL14. c, Western blot showing knockdown efficiency of MSH2 siRNA (si-MSH2). si-NC denotes non-targeting control siRNA. d, Western blot showing that MSH2 siRNA did not affect the interaction between METTL14 and H3K36me3. e, The direct binding of Flag-tagged recombinant human METTL3 or METTL14 proteins with histone H3 peptides with (me3+) or without (me3−) K36me3 modifications was examined by in vitro pull-down assays. f, Gel-shift assay of H3K9me3 or unmethylated histone H3 with recombinant human METTL3 or METTL14 in native SDS–PAGE gel. g, Top, schematic of full-length (FL) METTL14 and its truncations. Bottom, co-immunoprecipitation coupled with western blot showing the interaction of ectopically expressed full-length or truncated METTL14 with H3K36me3 in HeLa cells. h, Co-immunoprecipitation coupled with western blot showing the interaction of ectopically expressed truncated (Δ186–456 or Δ117–456) METTL14 with H3K36me3 in HepG2 cells. i, Co-immunoprecipitation coupled with western blot showing the interaction of ectopically expressed full-length or truncated (Δ138–143 or Δ153–161) METTL14 with H3K36me3 in HepG2 cells. j, Dot blot (right) and quantification (left, data are mean± s.d.) of m6A abundance in METTL14-inducible knockout cells (sgMETTL14) transduced with different METTL14 variants. Data are mean ± s.d. k, Pearson correlation coefficients of METTL14 ChIP–seq peaks with genomic H3K27me3 features in non-overlapping, non-repetitive windows of different sizes along the genome. l, Percentages of various RNA species containing METTL14-binding sites detected by PAR-CLIP sequencing analysis. m, The proportion (pie chart, top) and enrichment (histogram, bottom) of METTL14 RNA-binding sites distribution in gene body regions, identified by PAR-CLIP sequencing. n, HOMER motif analysis of T-to-C mutations or truncations identified by METTL14 PAR-CLIP sequencing. P value was calculated by HOMER algorithm. o, Co-immunoprecipitation showing that METTL14 bound to Ser2-phosphorylated (pSer2) Pol II in HeLa cells. p, Co-localization of METTL14 with H3K36me3 (top) or Pol II (pSer2) (bottom) in the nuclei of HepG2 cells. Scale bars, 10 μm. q, Dot blot (right) and quantification (left; data are mean ± s.d.) showing enrichment of m6A in chromatin-bound RNAs compared to that in RNAs from other cell fractions. r, Western blot showing treatment with the Pol II inhibitor DRB (100 µM for 3 h) did not affect H3K36me3 levels in HeLa cells. Dimethylsulfoxide (DMSO) was used as a vehicle control. s, t, Dot blot (right) and quantification (left, data are mean ± s.d.) showing the m6A abundance in total RNA (s) or poly(A) RNA (t) was reduced after DRB treatment (100 µM for 3 h) in HeLa cells. u, Co-immunoprecipitation showing association of METTL14 with H3K36me3, METTL3 or Pol II CTD, with or without DRB treatment. v, Distribution of METTL14-binding sites on chromatin in DRB-treated cells. Images in b–j, o and q–u are representative of three independent experiments. *P < 0.05, **P < 0.01, ***P < 0.001; two-tailed student’s _t_-test.

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Extended Data Fig. 9 Silencing of SETD2 demethylates H3K36me3 and m6A of pluripotency factors and inhibits the in vitro differentiation of mouse ES cells.

a, qRT-PCR showing downregulation of Setd2, but not m6A MTC genes (Mettl3, Mettl14 or Wtap), in Dox-induced SETD2-knockdown mouse ES cells. b, Western blot showing reduction of SETD2 and decrease of H3K36me3 in Dox-induced SETD2-knockdown mouse ES cells after 48 h of Dox treatment. c, Immunofluorescence showing expression of stage-specific embryonic antigen-1 (SSEA1; left) and OCT4 (right) in control (−Dox) and SETD2-knockdown (+Dox) mouse ES cells after 3 days of LIF withdrawal. Scale bars, 10 μm. d, Changes in mRNA expression of endoderm differentiation markers during in vitro differentiation of mouse ES cells. e, shRNA knockdown of Mettl14 by mouse ES cells as determined by qRT–PCR. f, shRNA silencing of METTL14 promotes the expression of pluripotency markers in mouse ES cells. HSP90 serves as a loading control. g, shRNA silencing of METTL14 delayed emerging of endoderm markers in mouse ES cells as determined by qRT-PCR. h, Numbers of H3K36me3 peaks identified in mouse ES cells with or without Dox-induced SETD2 knockdown by ChIP–seq. i, ChIP–seq revealed global hypomethylation of H3K36me3 in mouse ES cells with doxycycline treatment as shown by histogram distribution and box plots. For the box plot, top whisker denotes the ninety-fifth percentile (shCtrl = 2.519, shSETD2 = 1.081), top of the box denotes the seventy-fifth percentile (shCtrl = 1.281, shSETD2 = 0.527), horizontal lines denotes the median (shCtrl = 0.886, shSETD2 = 0.312), bottom of the box is the twenty-fifth percentile (shCtrl = 0.454, shSETD2 = 0.155), and bottom whisker is the fifth percentile (shCtrl = 0.000, shSETD2 = 0.000). P value was calculated using two-sided Wilcoxon and Mann–Whitney test. j, Dot blot (left) and quantification (right, data are mean ± s.d.) revealed reduction of cellular m6A in mouse ES cells by doxycycline-induced knockdown of SETD2. k, m6A-seq revealed global hypomethylation of m6A in mouse ES cells after doxycycline treatment as shown by histogram distribution and box plots. For the box plot, top whisker is the ninety-fifth percentile (shCtrl = 18.500, shSETD2 = 10.200), top of the box is the seventy-fifth percentile (shCtrl = 9.530, shSETD2 = 5.360), horizontal line is the median (shCtrl = 5.480, shSETD2 = 3.700), bottom of the box is the twenty-fifth percentile (shCtrl = 3.530, shSETD2 = 2.110), and bottom whisker is fifth percentile (shCtrl = 2.000, shSETD2 = 0.000). P value was calculated using two-sided Wilcoxon and Mann–Whitney test. l, HOMER motif analysis revealed conserved motifs enriched in hypomethylated peaks. P value was calculated by HOMER algorithm. m, The enrichment of hypomethylated m6A peak distribution. The enrichment fold was determined by the proportion of m6A peaks normalized by the length of the region. n, Gene set enrichment analysis (GSEA)31 of downregulated genes after differentiation (day (D) 0 to D6) and upregulated genes after SETD2 silencing (−Dox to +Dox). o, Determination of H3K36me3 abundance in pluripotency factors by ChIP–qPCR. p, mRNA levels of pluripotency factors in mouse ES cells as examined by qRT-PCR. Data are mean ± s.d. of two (a) or three (d, e, g, j, o and p) independent experiment. *P < 0.05, **P < 0.01, ***P < 0.001; two-tailed Student’s _t_-test. Images in b, f and j are representative of three independent experiments.

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Extended Data Fig. 10 Effect of SETD2 and METTL14 on mouse ES cell differentiation.

a, b, qRT-PCR analysis of Setd2 (a) and Mettl14 (b) mRNA expression in mouse ES cells. c, The fold changes (D6 versus D0) in gene expression of pluripotency factors and differentiation markers during in vitro differentiation of mouse ES cells after knockdown of SETD2 (+Dox) and/or METTL14 (shMettl14). d, e, qRT-PCR analysis of Setd2 (d) and Mettl14 (e) mRNA expression demonstrating the knockdown of Setd2 (+Dox) and overexpression of Mettl14 in mouse ES cells. f, The fold changes (D6 versus D0) in gene expression of pluripotency factors and differentiation markers during in vitro differentiation of mouse ES cells after knockdown of SETD2 (+Dox) and/or METTL14 overexpression. Data are mean ± s.d. of three independent experiments in a–f. *P < 0.05, **P < 0.01, ***P < 0.001; two-tailed Student’s _t_-test.

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Supplementary information

Supplementary Information

This file contains Methods and Supplementary references.

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Supplementary Figures

Supplementary Figure 1: Unprocessed gel blots. Of note, for some immunoblotting assays membranes were cut into several pieces to incubate with different antibodies, and therefore the raw images of these membranes are of small size.

Supplementary Table

Supplementary Table 1: List of TRC lentiviral shRNAs.

Supplementary Table

Supplementary Table 2: Primers used in qRT–PCR, ChIP-qPCR, CLIP-qPCR, and gene specific m6A assay.

Supplementary Table

Supplementary Table 3: The sequences of 20 nt spacer in gRNAs for dCAS9 experiments.

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Huang, H., Weng, H., Zhou, K. et al. Histone H3 trimethylation at lysine 36 guides m6A RNA modification co-transcriptionally.Nature 567, 414–419 (2019). https://doi.org/10.1038/s41586-019-1016-7

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• Received: 20 February 2018
• Accepted: 11 February 2019
• Published: 13 March 2019
• Issue Date: 21 March 2019
• DOI: https://doi.org/10.1038/s41586-019-1016-7