Abstract
The biological functions of noncoding RNA N6-methyladenosine (m6A) modification remain poorly understood. In the present study, we depict the landscape of super-enhancer RNA (seRNA) m6A modification in pancreatic ductal adenocarcinoma (PDAC) and reveal a regulatory axis of m6A seRNA, H3K4me3 modification, chromatin accessibility and oncogene transcription. We demonstrate the cofilin family protein CFL1, overexpressed in PDAC, as a METTL3 cofactor that helps seRNA m6A methylation formation. The increased seRNA m6As are recognized by the reader YTHDC2, which recruits H3K4 methyltransferase MLL1 to promote H3K4me3 modification cotranscriptionally. Super-enhancers with a high level of H3K4me3 augment chromatin accessibility and facilitate oncogene transcription. Collectively, these results shed light on a CFL1–METTL3–seRNA m6A–YTHDC2/MLL1 axis that plays a role in the epigenetic regulation of local chromatin state and gene expression, which strengthens our knowledge about the functions of super-enhancers and their transcripts.
This is a preview of subscription content, access via your institution
Access options
Access Nature and 54 other Nature Portfolio journals
Get Nature+, our best-value online-access subscription
$29.99 / 30 days
cancel any time
Subscribe to this journal
Receive 12 print issues and online access
$209.00 per year
only $17.42 per issue
Buy this article
- Purchase on Springer Link
- Instant access to full article PDF
Prices may be subject to local taxes which are calculated during checkout
Similar content being viewed by others
Data availability
A panel of H3K27ac ChIP-seq datasets of normal pancreatic and PDAC tissues or cell lines were downloaded from the GEO and ENCODE databases, and the accessible IDs for these public datasets are provided in Supplementary Table 1. In addition, the ChIP-seq data of histone methylation (H3K4me3, H3K4me1 and H3K27me3) for PANC-1 cells were downloaded from the GEO database with accession nos. GSE31755 and GSE86666, respectively. For HEK293T, H3K27ac ChIP-seq dataset (accession nos. GSE51633, GSE156400 and GSE92879), m6A-seq dataset (accession no. GSE114543) and YTHDC1/2 iCLIP-seq datasets (accession no. GSE78030) were also downloaded from the GEO database. Single-cell RNA-seq data for PDAC were downloaded from the Genome Sequence Archive in BIG Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences (http://gsa.big.as.cn) with accession no. CRA001160. Clinical features and batch effect-normalized mRNA data were downloaded from TCGA data portal. The m6A-seq, PAR-CLIP-seq, CUT&Tag-seq, ATAC-seq and RNA-seq raw data have been deposited in the GEO under accession no. GSE211589 and the Genome Sequence Archive in BIG Data Center under accession no. HRA000095 (https://ngdc.cncb.ac.cn/gsa-human/browse/HRA000095). The following datasets were used to generate the data: PAAD clinical features and batch effect-normalized mRNA data were downloaded from TCGA data portal (https://xenabrowser.net/datapages), PDAC proteomic data53 tables were available at LinkedOmics (http://www.linkedomics.org/data_download/CPTAC-PDAC) and hg19 (https://www.ncbi.nIm.nih.gov/assembly/GCF_000001405.13) and all exon regions from the GRCH37 ENSEMBL archive (https://grch37.ensembl.org/index.html). 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 Nature Reporting Portfolio Summary. Analysis codes are available at Zenodo (https://doi.org/10.5281/zenodo.8394749)54.
References
Dixit, D. et al. The RNA m6A reader YTHDF2 maintains oncogene expression and is a targetable dependency in glioblastoma stem cells. Cancer Discov. 11, 480–499 (2021).
Zhang, J. et al. Excessive miR-25-3p maturation via N6-methyladenosine stimulated by cigarette smoke promotes pancreatic cancer progression. Nat. Commun. 10, 1858 (2019).
Deng, S. et al. RNA m6A regulates transcription via DNA demethylation and chromatin accessibility. Nat. Genet. 54, 1427–1437 (2022).
Li, Y. et al. N6-Methyladenosine co-transcriptionally directs the demethylation of histone H3K9me2. Nat. Genet. 52, 870–877 (2020).
Liu, J. et al. N6-Methyladenosine of chromosome-associated regulatory RNA regulates chromatin state and transcription. Science 367, 580–586 (2020).
Li, W., Notani, D. & Rosenfeld, M. G. Enhancers as non-coding RNA transcription units: recent insights and future perspectives. Nat. Rev. Genet. 17, 207–223 (2016).
Calo, E. & Wysocka, J. Modification of enhancer chromatin: what, how, and why? Mol. Cell 49, 825–837 (2013).
Pekowska, A. et al. H3K4 tri-methylation provides an epigenetic signature of active enhancers. EMBO J. 30, 4198–4210 (2011).
Zhao, Y. et al. MyoD induced enhancer RNA interacts with hnRNPL to activate target gene transcription during myogenic differentiation. Nat. Commun. 10, 5787 (2019).
Mao, R. et al. Enhancer RNAs: a missing regulatory layer in gene transcription. Sci. China Life Sci. 62, 905–912 (2019).
Heinz, S., Romanoski, C. E., Benner, C. & Glass, C. K. The selection and function of cell type-specific enhancers. Nat. Rev. Mol. Cell Biol. 16, 144–154 (2015).
Xiao, S. et al. The RNA N6-methyladenosine modification landscape of human fetal tissues. Nat. Cell Biol. 21, 651–661 (2019).
Lee, J. H. et al. Enhancer RNA m6A methylation facilitates transcriptional condensate formation and gene activation. Mol. Cell 81, 3368–3385 (2021).
UniProt Consortium. UniProt: the universal protein knowledgebase in 2021. Nucleic Acids Res. 49, 480‒489 (2021).
Jassal, B. et al. The reactome pathway knowledgebase. Nucleic Acids Res. 48, 498–503 (2020).
Keshava Prasad, T. S. et al. Human protein reference database—2009 update. Nucleic Acids Res. 37, 767–772 (2009).
Lee, M. H., Kundu, J. K., Chae, J. I. & Shim, J. H. Targeting ROCK/LIMK/cofilin signaling pathway in cancer. Arch. Pharm. Res. 42, 481–491 (2019).
Samsonraj, R. M. et al. Validation of osteogenic properties of cytochalasin D by high-resolution RNA-sequencing in mesenchymal stem cells derived from bone marrow and adipose tissues. Stem Cells Dev. 27, 1136–1145 (2018).
Huang, F. Y. et al. Cytochalasin D promotes pulmonary metastasis of B16 melanoma through expression of tissue factor. Oncol. Rep. 30, 478–484 (2013).
Schiller, Z. A., Schiele, N. R., Sims, J. K., Lee, K. & Kuo, C. K. Adipogenesis of adipose-derived stem cells may be regulated via the cytoskeleton at physiological oxygen levels in vitro. Stem Cell Res. Ther. 4, 79 (2013).
Bao, Y. et al. Early committed clockwise cell chirality upregulates adipogenic differentiation of mesenchymal stem cells. Adv. Biosyst. 4, e2000161 (2020).
Yue, Y. et al. VIRMA mediates preferential m6A mRNA methylation in 3′UTR and near stop codon and associates with alternative polyadenylation. Cell Discov. 4, 10 (2018).
Tan, Y., Li, Y. & Tang, F. Oncogenic seRNA functional activation: a novel mechanism of tumorigenesis. Mol. Cancer 19, 74 (2020).
Mousavi, K. et al. eRNAs promote transcription by establishing chromatin accessibility at defined genomic loci. Mol. Cell 51, 606–617 (2013).
Li, Y. et al. FGFR-inhibitor-mediated dismissal of SWI/SNF complexes from YAP-dependent enhancers induces adaptive therapeutic resistance. Nat. Cell Biol. 23, 1187–1198 (2021).
Patil, D. P. et al. m6A RNA methylation promotes XIST-mediated transcriptional repression. Nature 537, 369–373 (2016).
Peng, J. et al. Single-cell RNA-seq highlights intra-tumoral heterogeneity and malignant progression in pancreatic ductal adenocarcinoma. Cell Res. 29, 725–738 (2019).
Struhl, K. Transcriptional noise and the fidelity of initiation by RNA polymerase II. Nat. Struct. Mol. Biol. 14, 103–105 (2007).
Xiang, J. F. et al. Human colorectal cancer-specific CCAT1-L lncRNA regulates long-range chromatin interactions at the MYC locus. Cell Res. 24, 513–531 (2014).
Schaukowitch, K. et al. Enhancer RNA facilitates NELF release from immediate early genes. Mol. Cell 56, 29–42 (2014).
Zhou, Y., Xu, S., Zhang, M. & Wu, Q. Systematic functional characterization of antisense eRNA of protocadherin α composite enhancer. Genes Dev. 35, 1383–1394 (2021).
Maruyama, A., Mimura, J. & Itoh, K. Non-coding RNA derived from the region adjacent to the human HO-1 E2 enhancer selectively regulates HO-1 gene induction by modulating Pol II binding. Nucleic Acids Res. 42, 13599–13614 (2014).
Hsieh, C. L. et al. Enhancer RNAs participate in androgen receptor-driven looping that selectively enhances gene activation. Proc. Natl Acad. Sci. USA 111, 7319–7324 (2014).
Huang, H. et al. Histone H3 trimethylation at lysine 36 guides m6A RNA modification co-transcriptionally. Nature 567, 414–419 (2019).
Yao, B. et al. Hypoxia-induced cofilin 1 promotes hepatocellular carcinoma progression by regulating the PLD1/AKT pathway. Clin. Transl. Med. 11, 366 (2021).
Watts, J. A. et al. A common transcriptional mechanism involving R-loop and RNA abasic site regulates an enhancer RNA of APOE. Nucleic Acids Res. 50, 12497–12514 (2022).
Chen, H. & Liang, H. A high-resolution map of human enhancer RNA loci characterizes super-enhancer activities in cancer. Cancer Cell 38, 701–715 (2020).
Dobin, A. et al. STAR: ultrafast universal RNA-seq aligner. Bioinformatics 29, 15–21 (2013).
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).
Ye, Y. et al. Genome-wide identification and characterization of circular RNA m6A modification in pancreatic cancer. Genome Med. 13, 183 (2021).
Zhang, Y. et al. Model-based analysis of ChIP-Seq (MACS). Genome Biol. 9, 137 (2008).
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, 378–385 (2016).
Quinlan, A. R. & Hall, I. M. BEDTools: a flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).
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).
Ramírez, F. et al. DeepTools2: a next generation web server for deep-sequencing data analysis. Nucleic Acids Res. 44, 160–165 (2016).
Li, H. et al. The sequence alignment/map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).
Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
Buenrostro, J. D., Giresi, P. G., Zaba, L. C., Chang, H. Y. & Greenleaf, W. J. Transposition of native chromatin for fast and sensitive epigenomic profiling of open chromatin, DNA-binding proteins and nucleosome position. Nat. Methods 10, 1213–1218 (2013).
Whyte, W. A. et al. Master transcription factors and mediator establish super-enhancers at key cell identity genes. Cell 153, 307–319 (2013).
Krakau, S., Richard, H. & Marsico, A. PureCLIP: capturing target-specific protein-RNA interaction footprints from single-nucleotide CLIP-seq data. Genome Biol. 18, 240 (2017).
Satija, R., Farrell, J. A., Gennert, D., Schier, A. F. & Regev, A. Spatial reconstruction of single-cell gene expression data. Nat. Biotechnol. 33, 495–502 (2015).
Patel, A. P. et al. Single-cell RNA-seq highlights intratumoral heterogeneity in primary glioblastoma. Science 344, 1396–1401 (2014).
Cao, L. et al. Proteogenomic characterization of pancreatic ductal adenocarcinoma. Cell 184, 5031–5052.e5026 (2021).
Li, R. Analysis code of CFL1-m6A-seRNA-YTHDC2-MLL1 axis in PDAC. Zenodo https://doi.org/10.5281/zenodo.8394749 (2023).
Acknowledgements
The present study was supported by the National Key R&D Program of China (grant no. 2021YFA1302100), National Natural Science Foundation of China Projects (grant nos. 82325037 and 82072617 to J. Zheng, 82003162 to J. Zhang and 82272694 to X.H.), the Program for Guangdong Introducing Innovative and Entrepreneurial Teams (grant no. 2017ZT07S096 to D.L.) and Sun Yat-sen University Intramural Funds (to D.L. and J. Zheng).
Author information
Authors and Affiliations
Contributions
J. Zheng and D.L. conceived and designed the entire project. R.L., X.H. and H.Z. designed and supervised the research. X.H. and H.Z. prepared all samples for high-throughput sequencing. J. Zhang and R.B. performed CUT&Tag-seq and ATAC-seq. S. Wu. and S. Wen. performed m6A-seq, PAR-CLIP-seq, RNA-seq and qPCR. H.Z. and L. Zhuang performed immunofluorescence staining, coIP, recombinant protein pulldown, ELISA, liquid chromatography–tandem MS, dot blotting, western blotting and REMSA blots. H.Z., S.Z. and J.S. performed RNA pulldown, ChIP assays and cell proliferation, migration and invasion assays. X.H. and H.Z. designed single guide RNAs and prepared dCas13b cell samples. R.L. performed statistical and bioinformatics analyses of high-throughput sequencing data. L. Zeng and S.D. were engaged in the analysis of public data. Z.Z. supervised all bioinformatics analyses. Q.Z., M.L. and R.C. were responsible for tissue sample preparation. R.L., H.Z., X.H., J. Zheng and D.L. wrote the manuscript and all authors proofed it.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Genetics thanks Alexey Ruzov and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.
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 Identification of super-enhancers in PDAC.
a, Workflow of identification of super-enhancers and m6A-seRNAs in PDAC. These steps identified 17,467 seRNAs. b, Venn plot showing overlap of super-enhancers identified in this study and reported in the published databases. c, The profile of H3K27ac, H3K4me3, H3K4me1 and H3K27me3 modifications at the center of seRNAs and its 2 kilobases flanking regions in PANC-1 cells. The y axis represents the ratio of histone modification counts and input counts.
Extended Data Fig. 2 Aberrant levels of m6A-seRNAs and CFL1 in PDAC.
a, Top 2 motifs enriched at m6A-seRNAs in pancreatic tissue m6A-seq. P value was from Homer based on binomial distribution. b, The number of significantly differential seRNAs in PDAC and normal tissues. The blue line shows the number of differentially m6A-seRNAs from 1,000 permutation test and the red line represents realistic number of differentially m6A-seRNAs. P value by permutation test (one-sided). c, Snapshots of UCSC genome browser showing m6A enrichment over two representative seRNAs in PDAC versus normal tissues. Grey, input; red, IP in PDAC; blue, IP in normal. d, Different m6A levels of five representative seRNAs in PDAC and normal tissues (paired PDAC-normal, n = 33; unpaired PDAC, n = 32; all PDAC, n = 65). e, Hazard ratios (HRs) for PFS of hyper-m6A seRNAs in 65 PDAC patients. Bars represent 95% confidence interval. Purple line, −log2(FDR). f, Significantly higher CFL1 levels in PDAC than normal tissues. Data were from TCGA-PAAD and GTEx (Normal, n = 171; PDAC, n = 178). g, Kaplan-Meier estimate of PFS time by different CFL1 levels in 65 PDAC patients, with the HR (95% CI) of high CFL1 level being 1.99 (1.15–3.44). h, Kaplan-Meier estimate of overall survival (OS) time by different CFL1 levels in TCGA-PAAD patients, with the HR (95% CI) for death of high CFL1 level being 1.60 (1.05–2.43). i, Western blot analysis of CFL1 in paired PDAC and normal tissues from randomly selected 30 patients showing that most PDAC had higher CFL1 levels than normal tissues. N, normal; T, tumor and R, reference for loading control. Each band was semi-quantitated by gray density relative to β-ACTIN and R bands. Quantitative results using image J software are shown in Fig. 1j and Supplementary Table 4. j, Higher CFL1 protein levels in PDAC than normal tissues (Normal, n = 74; PDAC, n = 132). Data were derived from CPTAC PDAC proteomics dataset. P in (d), (f) and (j) values by two-sided Wilcoxon rank-sum tests.
Extended Data Fig. 3 CFL1 promotes seRNA m6A formation.
a, Metagene plot shows RPKM values of m6A-seq over m6A-seRNA. b, The number (histogram plot) and proportion (pie plot) of CFL1 PAR-CLIP peaks in different genomic regions based on m6A-seq of total RNA. c, The top 2 motifs identified by CFL1 CLIP peak analysis, with enrichment P values by Homer based on binomial distribution. d, The overlap between the CFL1-binding peaks in seRNAs and the hyper-m6A seRNAs in PDAC. e, The effect of CFL1 KO on seRNA m6A formation. Data were determined by m6A-seq of nuclear RNA from PANC-1 cells. f, The number (histogram plot) and proportion (pie plot) of CFL1 PAR-CLIP peaks in different genomic regions by PAR-CLIP-seq of PANC-1 nuclear RNA. g, Distribution of CFL1 PAR-CLIP-seq tags around the m6A sites of seRNAs in PANC-1 cells and top 1 motif enriched by CFL1 PAR-CLIP-seq of nuclear RNA (P value by Homer based on binomial distribution). h, Metagene plots showing the distribution of m6A peaks in mRNA in CFL1 KO or KO-control cells. i, Histogram showing the m6A enrichment alterations at mRNA m6A peaks in CFL1 KO or KO-control cells. Red, hyper-m6A (n = 541); gray, m6A unchanged (n = 14,137) and blue, hypo-m6A (n = 768). j, LC-MS/MS quantification of m6A abundance in poly A+ RNA from CFL1 KO or KO-control cells. Data are mean ± SD of three replications. P by two-sided Student’s t-test. k, Dot blots of m6A in different amounts of poly A+ RNA from CFL1 KO or KO-control cells. MB: methylene blue as loading control. l, Heatmap showing differentially expressed genes in corresponding pathways between CytoD treated cells and control cells. m, The effect of CytoD treatment on seRNA m6A formation of PANC-1 cells determined by m6A-seq.
Extended Data Fig. 4 CFL1 is a METTL3 cofactor required for seRNA m6A formation in PANC-1 cells.
a, Immunoblotting of reciprocal co-immunoprecipitation products showing CFL1 and METTL3 interaction independent on RNA or DNA. IgG served as non-specificity antibody control. b, In vitro pulldown assays showing direct interaction of CFL1-His and METTL3-Flag. c, Mander’s coefficient analysis showing the colocalization of CFL1 and METTL3 in PANC-1 cells (Data are mean ± SD; n = 8), P values by one-way ANOVA with Dunnett’s T3 multiple-comparison test. d, The effect of METTL3 KO on seRNA m6A formation in PANC-1 nuclear RNA by m6A-seq. e, The motifs identified in CFL1 KO, METTL3 KO and KO-control PANC-1 cells, P values by Homer based on binomial distribution. f, The overlap between CFL1 KO hypo-m6A seRNAs and METTL3 KO hypo-m6A seRNAs in PANC-1 nuclear RNA by m6A-seq. g, Correlation of the m6A levels of overlapped hypo-m6A seRNAs (n = 2,968) shown in Fig. 2h. P value by two-sided Spearman correlation. h, The effect of METTL3 or CFL1 KO on the m6A levels in seRNAs (n = 2,968) shown in Fig. 2h. P values by two-sided Wilcoxon rank-sum tests. i, The profile of METTL3 PAR-CLIP read density at the center of seRNAs and top 2 motifs identified by METTL3 CLIP peaks (P values by Homer based on binomial distribution). j, The profile of CFL1 binding at METTL3 binding center and its 1 kilobases flanking regions of seRNAs in PANC-1 nuclear RNA by PAR-CLIP-seq. k, Aggregation plots showing m6A enrichment over the TSS of CFL1 KO hypo-m6A seRNAs or seRNA without m6A site in PANC-1 cells. l, Aggregation plots showing CFL1 and METTL3 binding levels over the TSS of CFL1 KO hypo-m6A seRNAs or seRNA without m6A site in PANC-1 cells. m, Aggregation plots showing m6A enrichment over the TSS of CFL1-bound seRNAs or CFL1-unbound seRNAs in PANC-1 cells. n, Snapshots of UCSC genome browser showing the m6A levels decreased in two representative seRNAs in CFL1 or METTL3 KO cells. Grey, input; light blue, IP in KO-Control cells; rosy, IP in CFL1 KO cells; orange, IP in METTL3 KO cells.
Extended Data Fig. 5 The effect of seRNA m6A on H3K4me3 modification in PDAC cells.
a, The RPKM values of H3K4me3 (left), H3K27ac (middle) and H3K4me1 (right) ChIP-seq within the center of seRNA-associated chromatin and its 2 kilobases flanking regions categorized into top-50% or bottom-50% based on the m6A levels. b, Density of the distance between H3K4me3 modification determined by ChIP-seq and the seRNA m6A site. Data from GSE31755. c, Reduced H3K4me3 modification probability (curve) and level (box plot, n = 3,217) in hypo-m6A seRNA related chromatin in CFL1 KO cells. d, The averaged H3K4me3 signals in seRNA without m6A site related chromatin in CFL1 KO and KO-control cells (n = 12,092). The y axis represents log2 of read counts. e, The genomic distribution of upregulated (Up) or downregulated (Down) H3K4me3 peaks in CFL1 KO cells. f, Overlap between CFL1 KO unchanged H3K4me3 regions and CFL1 KO hypo-m6A-seRNAs. g, Boxplots showing the differential H3K4me3 levels (CFL1 KO vs non-KO) within super-enhancers with different number of RRACH motifs (high: n = 2,128; low: n = 2,128). h, Density of the H3K4me3 CUT&Tag-seq signals as function of m6A peak intensity. m6A peaks were categorized into low (n = 1,410), medium (n = 1,418), and high groups (n = 1,428). P value by Kruskal-Wallis’s test. i, The portions of downregulated, upregulated and unchanged seRNAs in CFL1 KO versus KO-control cells. j, The overlap between CFL1 KO downregulated seRNAs and CFL1 KO hypo-m6A seRNAs (left), and CFL1 KO downregulated H3K4me3 regions (middle) or CFL1 CLIP peaks in seRNAs (right). k, Percentage of differentially expressed CFL1-related m6A seRNAs in 65 PDAC samples (left) and CFL1 KO cells (right). l, Boxplots showing the differential H3K4me3 levels (CFL1 KO versus non-KO) within the indicated subgroups, sample size: CFL1 KO: n = 2. KO-Control: n = 2. m, Snapshots of UCSC genome browser showing m6A levels and H3K4me3 levels in two seRNAs and their associated chromatin regions. Grey, input; blue, IP in non-KO; red, IP in CFL1 KO; orange, H3K4me3 CUT&Tag. IgG as negative control. P in (c), (d), (g) and (l) values by two-sided Wilcoxon rank-sum tests.
Extended Data Fig. 6 YTHDC2 recognition of seRNA m6A.
a, Read densities of YTHDC1 and YTHDC2 iCLIP-seq at the center and 1 kilobases flanking regions of m6A-seRNAs in HEK293T cells. Data from GSE51633, GSE156400, GSE92879, GSE114543 and GSE78030. P value was examined by two-sided Wilcoxon rank-sum test. b, Overlap between YTHDC2-binding peaks in seRNAs and hyper-m6A seRNAs in PDAC samples. c, Venn plots of overlap between YTHDC1 CLIP peaks in seRNAs and CFL1-related m6A and H3K4me3 regions in PANC-1 cells (left) or hyper-m6A seRNAs in PDAC samples (right). d, REMSA showing no interaction between m6A-seRNA oligo or unmethylated-seRNA oligo and recombinant YTHDC1 protein. e, Western blot analysis of RNA pull-down products of cell lysate with oligo-A or oligo-m6A shows binding of YTHDC2 but not YTHDC1 to oligo-m6A. f, The genomic distribution of YTHDC2 PAR-CLIP-seq peaks in PANC-1 nuclear RNA. g, YTHDC2 enrichment with five representative seRNAs or seRNA without m6A site in CFL1 or METTL3 KO PDAC cells. h, The absence of YTHDC1 enrichment in five representative seRNAs or seRNA without m6A site in CFL1 or METTL3 KO PDAC cells. i−k, Enrichment of m6A (i), YTHDC2 (j) and H3K4me3 (k) with five representative seRNAs or seRNA without m6A site in CFL1 KO cells transfected with dPspCas13b-METTL3 gRNA or its control dPspCas13b-METTL3 NT. l, The absence of YTHDC1 enrichment in five representative seRNAs or seRNA without m6A site in CFL1 KO along with dPspCas13b-METTL3 system transfection. Results in (g-l) are mean ± SD from 3 independent experiments. P by one-way ANOVA with Dunnett’s T3 multiple-comparison test in g−l. n.s., not significant.
Extended Data Fig. 7 The seRNA m6A mediated YTHDC2-MLL1 interaction promotes chromatin H3K4me3 methylation.
a, Western blot analysis of immunoprecipitation products showing that YTHDC2 interacts with MLL1 and its conserved binding partners ASH2L, and RBBP5 in PDAC cells. b, Agarose gel electrophoresis showing the effects of DNase or RNase on YTHDC2 immunoprecipitation. c, d, Reciprocal immunoprecipitation assays showing the interaction of YTHDC2 (c) but not YTHDC1 (d) with MLL1. e, Immunofluorescence assays showing co-localization of YTHDC2 and MLL1 in PANC-1 cell nuclei, related to Fig. 4b. Green, YTHDC2; red, MLL1; blue, DAPI. Scale bar, 10 μm (left); 0.4 μm (right). Right panel shows pixel intensity plots along the white line in two cells generated by image J software as shown in left panel. f, Density of the distance between MLL1 peaks determined by CUT&Tag-seq and seRNA m6A sites in PANC-1 cells. g, Overlap between MLL1 CUT&Tag peaks in super-enhancers and CFL1 KO related m6A and H3K4me3 regions in PANC-1 cells (left) and hyper-m6A-seRNAs (right) in PDAC samples. h, Correlations of YTHDC2 CUT&Tag-seq signals with MLL1 genomic features in non-overlapping, non-repetitive windows of varied sizes along the enhancer regions. i, Different MLL1 CUT&Tag-seq signals at m6A-seRNAs associated chromatin regions. The regions were categorized into low (n = 1,425), medium (n = 1,412), or high (n = 1,419) based on YTHDC2 binding density in PANC-1 cells. P was calculated by Kruskal−Wallis test. j, Differential H3K4me3 levels over MLL1+/YTHDC2+ super-enhancers (n = 2,110) and MLL1+/YTHDC2- super-enhancers (n =2,232) in YTHDC2 KO versus KO-control PANC-1 cells. P by two-sided Wilcoxon rank-sum tests. k, Density of the distance from YTHDC2 CUT&Tag peaks to YTHDC2 binding sites in seRNAs.
Extended Data Fig. 8 m6A-seRNAs promote chromatin accessibility and oncogene transcription.
a−c, ATAC gain or loss regions in CFL1 (a), YTHDC2 (b) or MLL1 (c) KO and KO-control PANC-1 cells. Upper panel, aggregation plots; lower panel, heatmaps. DAR, differential accessible region. d, The proportion of significantly differential ATAC peaks in different genomic regions. e, GSEA of the target genes shown in Fig. 5d in PDAC compared with normal pancreatic tissues (GSE71729). f, The map of correlations between enriched pathway scores shown in Fig. 5d and CFL1–YTHDC2–MLL1 signature score or CFL1 level in PDAC scRNA-seq dataset (PRJCA001063). The color and size of circles represent different spearman r values; all P < 0.001. g, HR for PFS of 29 genes regulated by 16 hyper-m6A seRNAs shown in Extended Data Fig. 2e in 65 PDAC patients. The bars represent 95% CI. Purple line, −log2(FDR).
Extended Data Fig. 9 m6A-seRNA-related CFL1 promotes malignant phenotypes of pancreatic cancer cells.
a, Effects of CFL1 OE or KO on the ability of PDAC cell proliferation. Results are means ± SD from 3 independent experiments and each had 6 replicates. b, Effects of CFL1 OE or KO on the ability of PDAC cell migration and invasion (left, PANC-1; right, MiaPaCa-2). Results are means ± SD from 4 random fields. The lower panels show representative transwell pictures. Scale bars, 200 μm. c, d, Effects of CFL1 expression change on tumor burden of mice transplanted orthotopically with PDAC cells (n = 5). Shown are bioluminescence images (c) and statistics (d) of fluorescence intensity. e, Effect of CFL1 expression change on survival time of mice with orthotopic transplantation of PDAC cells (n = 6). f, g, Effects of CFL1 expression change on lung colonization of PDAC in mice (n = 5). Shown are bioluminescence imaging (f) and quantitative statistics of fluorescent intensity (g). Quantitative data in d and g are mean ± SD from 5 mice in each group. P values in a, b, d and g by two-sided Student’s t-test.
Extended Data Fig. 10 METTL3, YTHDC2 and m6A-seRNA-related CFL1 promote malignant phenotypes of pancreatic cancer cells.
a, b, Effects of METTL3 KO (a) or YTHDC2 KO (b) on PDAC cell proliferation. Results are means ± SD from 3 independent experiments and each had 6 replicates. c, Effects of YTHDC2 KO on PDAC cell migration and invasion. Results are means ± SD randomly taken from 4 fields. The right panels show representative transwell pictures. Scale bars, 200 μm. d, e, Rescue assays showing that overexpression of mutant METTL3 without CFL1 binding domain partially restored the abilities of proliferation (d) and migration and invasion (e) of PDAC cells with CFL1 OE and METTL3 KD. Migration and invasion abilities are means ± SD randomly taken from 4 fields. The lower panels show representative transwell pictures. Scale bars, 200 μm. P values in a−e by two-sided Student’s t-test.
Supplementary information
Supplementary Information
Supplementary Figs. 1–5, Methods, Tables 1, 3, 5 and 6, and unprocessed western blots for Supplementary Fig. 5.
Supplementary Table
Supplementary Tables 2 and 4.
Supplementary Data
Statistical source data for Supplementary Figs. 1–3 and 5.
Source data
Source Data Fig. 1
Statistical source data for Fig. 1.
Source Data Fig. 2
Unprocessed western blots for Fig. 2.
Source Data Fig. 2
Statistical source data for Fig. 2.
Source Data Fig. 3
Unprocessed REMSA blots for Fig. 3.
Source Data Fig. 4
Unprocessed western blots 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 dot 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
Unprocessed western blots for Extended Data Fig. 4.
Source Data Extended Data Fig.4
Statistical source data for Extended Data Fig. 4.
Source Data Extended Data Fig.6
Unprocessed REMSA blots and 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 and gels for Extended Data Fig. 7.
Source Data Extended Data Fig. 7
Statistical source data for Extended Data Fig. 7.
Source Data Extended Data Fig. 9
Statistical source data for Extended Data Fig. 9.
Source Data Extended Data Fig. 10
Statistical source data for Extended Data Fig. 10.
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) 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.
About this article
Cite this article
Li, R., Zhao, H., Huang, X. et al. Super-enhancer RNA m6A promotes local chromatin accessibility and oncogene transcription in pancreatic ductal adenocarcinoma. Nat Genet 55, 2224–2234 (2023). https://doi.org/10.1038/s41588-023-01568-8
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s41588-023-01568-8
This article is cited by
-
The biological function of the N6-Methyladenosine reader YTHDC2 and its role in diseases
Journal of Translational Medicine (2024)
