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5-methylcytosine promotes pathogenesis of bladder cancer through stabilizing mRNAs

Nature Cell Biology volume 21, pages 978–990 (2019)Cite this article

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Abstract

Although 5-methylcytosine (m5C) is a widespread modification in RNAs, its regulation and biological role in pathological conditions (such as cancer) remain unknown. Here, we provide the single-nucleotide resolution landscape of messenger RNA m5C modifications in human urothelial carcinoma of the bladder (UCB). We identify numerous oncogene RNAs with hypermethylated m5C sites causally linked to their upregulation in UCBs and further demonstrate YBX1 as an m5C ‘reader’ recognizing m5C-modified mRNAs through the indole ring of W65 in its cold-shock domain. YBX1 maintains the stability of its target mRNA by recruiting ELAVL1. Moreover, NSUN2 and YBX1 are demonstrated to drive UCB pathogenesis by targeting the m5C methylation site in the HDGF 3′ untranslated region. Clinically, a high coexpression of NUSN2, YBX1 and HDGF predicts the poorest survival. Our findings reveal an unprecedented mechanism of RNA m5C-regulated oncogene activation, providing a potential therapeutic strategy for UCB.

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Fig. 1: RNA m5C hypermethylation in UCBs.
Fig. 2: m5C hypermethylation correlates positively with mRNA expression.
Fig. 3: YBX1 is a specific m5C reader.
Fig. 4: YBX1 regulates mRNA stability through recruiting ELAVL1.
Fig. 5: Overexpression of NSUN2 and YBX1 in UCB patients in the SYSUCC cohort correlates with poor prognosis.
Fig. 6: NSUN2 and YBX1 promote UCB pathogenesis.
Fig. 7: NSUN2 and YBX1 target HDGF m5C and stabilize its mRNA to promote UCB pathogenesis.

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Data availability

Deep-sequencing (RNA-Seq, RNA-BisSeq and PAR-CLIP-Seq) data that support the findings of this study have been deposited in the Gene Expression Omnibus database under accession number GSE133671, and also the Genome Sequence Archive under the accession number CRA001050 linked to the project PRJCA000975. The coordinates and structure factors have been deposited in the Protein Data Bank under accession code 6A6L. The data for human UCB, kidney renal clear cell carcinoma, prostate adenocarcinoma, pancreatic adenocarcinoma, breast invasive carcinoma, liver hepatocellular carcinoma and uterine corpus endometrial carcinoma were derived from the TCGA Research Network (http://cancergenome.nih.gov/). The source data for Figs. 1b,d,f, 3j,l, 4d,f, 5d,e, 6a–i, 7d,f and Supplementary Figs. 1g, 5c,e–i, 6c,f–h have been provided in Supplementary Table 10. All other data supporting the findings of this study are available from the corresponding author on reasonable request.

References

  1. Roundtree, I. A. et al. Dynamic RNA modifications in gene expression regulation. Cell 169, 1187–1200 (2017).

    Article  CAS  Google Scholar 

  2. Squires, J. E. et al. Widespread occurrence of 5-methylcytosine in human coding and non-coding RNA. Nucleic Acids Res. 40, 5023–5033 (2012).

    Article  CAS  Google Scholar 

  3. Hussain, S. et al. NSun2-mediated cytosine-5 methylation of vault noncoding RNA determines its processing into regulatory small RNAs. Cell Rep. 4, 255–261 (2013).

    Article  CAS  Google Scholar 

  4. Khoddami, V. & Cairns, B. R. Identification of direct targets and modified bases of RNA cytosine methyltransferases. Nat. Biotechnol. 31, 458–464 (2013).

    Article  CAS  Google Scholar 

  5. Amort, T. et al. Distinct 5-methylcytosine profiles in poly(A) RNA from mouse embryonic stem cells and brain. Genome Biol. 18, 1 (2017).

    Article  Google Scholar 

  6. Cui, X. et al. 5-methylcytosine RNA methylation in Arabidopsis thaliana. Mol. Plant 10, 1387–1399 (2017).

    Article  CAS  Google Scholar 

  7. David, R. et al. Transcriptome-wide mapping of RNA 5-methylcytosine in Arabidopsis mRNAs and non-coding RNAs. Plant Cell 29, 445–460 (2017).

    Article  CAS  Google Scholar 

  8. Yang, X. et al. 5-methylcytosine promotes mRNA export—NSUN2 as the methyltransferase and ALYREF as an m5C reader. Cell Res. 27, 606–625 (2017).

    Article  CAS  Google Scholar 

  9. Delaunay, S. & Frye, M. RNA modifications regulating cell fate in cancer. Nat. Cell Biol. 21, 552–559 (2019).

    Article  CAS  Google Scholar 

  10. Robertson, A.G. et al. Comprehensive molecular characterization of muscle-invasive bladder cancer. Cell 171, 540–556 (2017).

    Article  CAS  Google Scholar 

  11. Choi, W. et al. Intrinsic basal and luminal subtypes of muscle-invasive bladder cancer. Nat. Rev. Urol. 11, 400–410 (2014).

    Article  CAS  Google Scholar 

  12. Pachmayr, E., Treese, C. & Stein, U. Underlying mechanisms for distant metastasis - molecular biology. Visc. Med. 33, 11–20 (2017).

    Article  Google Scholar 

  13. Zhang, Y. et al. Function of the c-Met receptor tyrosine kinase in carcinogenesis and associated therapeutic opportunities. Mol. Cancer 17, 45 (2018).

    Article  Google Scholar 

  14. Kandimalla, R., van Tilborg, A. A. & Zwarthoff, E. C. DNA methylation-based biomarkers in bladder cancer. Nat. Rev. Urol. 10, 327–335 (2013).

    Article  CAS  Google Scholar 

  15. Zhao, J. et al. Anti-HDGF targets cancer and cancer stromal stem cells resistant to chemotherapy. Clin. Cancer Res. 19, 3567–3576 (2013).

    Article  CAS  Google Scholar 

  16. Cheng, G. Z. et al. Regulation of cancer cell survival, migration, and invasion by Twist: AKT2 comes to interplay. Cancer Res. 68, 957–960 (2008).

    Article  CAS  Google Scholar 

  17. Li, H., Zhang, G. Y., Pan, C. H., Zhang, X. Y. & Su, X. Y. LncRNA MAFG-AS1 promotes the aggressiveness of breast carcinoma through regulating miR-339-5p/MMP15. Eur. Rev. Med. Pharmacol. Sci. 23, 2838–2846 (2019).

    CAS  PubMed  Google Scholar 

  18. Muller, M. et al. MMP19 is upregulated during melanoma progression and increases invasion of melanoma cells. Mod. Pathol. 23, 511–521 (2010).

    Article  Google Scholar 

  19. Guo, Y. et al. Blocking Wnt/LRP5 signaling by a soluble receptor modulates the epithelial to mesenchymal transition and suppresses met and metalloproteinases in osteosarcoma Saos-2 cells. J. Orthop. Res. 25, 964–971 (2007).

    Article  CAS  Google Scholar 

  20. Flores, I. L. et al. EEF1D modulates proliferation and epithelial-mesenchymal transition in oral squamous cell carcinoma. Clin. Sci. 130, 785–799 (2016).

    Article  CAS  Google Scholar 

  21. Choudhry, H. et al. Tumor hypoxia induces nuclear paraspeckle formation through HIF-2α dependent transcriptional activation of NEAT1 leading to cancer cell survival. Oncogene 34, 4482–4490 (2015).

    Article  CAS  Google Scholar 

  22. Hu, F. et al. Hypoxia upregulates Rab11-family interacting protein 4 through HIF-1α to promote the metastasis of hepatocellular carcinoma. Oncogene 34, 6007–6017 (2015).

    Article  CAS  Google Scholar 

  23. Xiong, Y. et al. MIR29B regulates expression of MLLT11 (AF1Q), an MLL fusion partner, and low MIR29B expression associates with adverse cytogenetics and poor overall survival in AML. Br. J. Haematol. 153, 753–757 (2011).

    Article  CAS  Google Scholar 

  24. Wang, X. et al. N 6-methyladenosine-dependent regulation of messenger RNA stability. Nature 505, 117–120 (2014).

    Article  Google Scholar 

  25. Lyabin, D. N., Eliseeva, I. A. & Ovchinnikov, L. P. YB-1 protein: functions and regulation. WIREs RNA 5, 95–110 (2014).

    Article  CAS  Google Scholar 

  26. Mo, D. et al. Human helicase RECQL4 drives cisplatin resistance in gastric cancer by activating an AKT–YB1–MDR1 signaling pathway. Cancer Res. 76, 3057–3066 (2016).

    Article  CAS  Google Scholar 

  27. Wei, W. J. et al. YB-1 binds to CAUC motifs and stimulates exon inclusion by enhancing the recruitment of U2AF to weak polypyrimidine tracts. Nucleic Acids Res. 40, 8622–8636 (2012).

    Article  CAS  Google Scholar 

  28. Damian, L. Isothermal titration calorimetry for studying protein-ligand interactions. Methods Mol. Biol. 1008, 103–118 (2013).

    Article  CAS  Google Scholar 

  29. Xu, C. et al. Structural basis for selective binding of m6A RNA by the YTHDC1 YTH domain. Nat. Chem. Biol. 10, 927–929 (2014).

    Article  CAS  Google Scholar 

  30. Wang, L. et al. LIN28 zinc knuckle domain is required and sufficient to induce let-7 oligouridylation. Cell Rep. 18, 2664–2675 (2017).

    Article  CAS  Google Scholar 

  31. Cappellen, D. et al. Frequent activating mutations of FGFR3 in human bladder and cervix carcinomas. Nat. Genet. 23, 18–20 (1999).

    Article  CAS  Google Scholar 

  32. Knowles, M. A., Habuchi, T., Kennedy, W. & Cuthbert-Heavens, D. Mutation spectrum of the 9q34 tuberous sclerosis gene TSC1 in transitional cell carcinoma of the bladder. Cancer Res. 63, 7652–7656 (2003).

    CAS  PubMed  Google Scholar 

  33. Al-Ahmadie, H. A. et al. Frequent somatic CDH1 loss-of-function mutations in plasmacytoid variant bladder cancer. Nat. Genet. 48, 356–358 (2016).

    Article  CAS  Google Scholar 

  34. Cancer Genome Atlas Research Network. Comprehensive molecular characterization of urothelial bladder carcinoma. Nature 507, 315–322 (2014).

  35. Sun, M. et al. Phosphatidylinositol-3-OH Kinase (PI3K)/AKT2, activated in breast cancer, regulates and is induced by estrogen receptor α (ERα) via interaction between ERα and PI3K. Cancer Res. 61, 5985–5991 (2001).

    CAS  PubMed  Google Scholar 

  36. Samatar, A. A. & Poulikakos, P. I. Targeting RAS–ERK signalling in cancer: promises and challenges. Nat. Rev. Drug Discov. 13, 928–942 (2014).

    Article  CAS  Google Scholar 

  37. Walavalkar, N. M. et al. Solution structure and intramolecular exchange of methyl-cytosine binding domain protein 4 (MBD4) on DNA suggests a mechanism to scan for mCpG/TpG mismatches. Nucleic Acids Res. 42, 11218–11232 (2014).

    Article  CAS  Google Scholar 

  38. Liu, Y. et al. Structural basis for Klf4 recognition of methylated DNA. Nucleic Acids Res. 42, 4859–4867 (2014).

    Article  CAS  Google Scholar 

  39. Evdokimova, V. et al. The major mRNA-associated protein YB-1 is a potent 5′ cap-dependent mRNA stabilizer. EMBO J. 20, 5491–5502 (2001).

    Article  CAS  Google Scholar 

  40. Mukherjee, N. et al. Integrative regulatory mapping indicates that the RNA-binding protein HuR couples pre-mRNA processing and mRNA stability. Mol. Cell 43, 327–339 (2011).

    Article  CAS  Google Scholar 

  41. Goodarzi, H. et al. Endogenous tRNA-derived fragments suppress breast cancer progression via YBX1 displacement. Cell 161, 790–802 (2015).

    Article  CAS  Google Scholar 

  42. Lyons, S. M. et al. YB-1 regulates tiRNA-induced stress granule formation but not translational repression. Nucleic Acids Res. 44, 6949–6960 (2016).

    Article  CAS  Google Scholar 

  43. Ren, H. et al. Expression of hepatoma-derived growth factor is a strong prognostic predictor for patients with early-stage non-small-cell lung cancer. J. Clin. Oncol. 22, 3230–3237 (2004).

    Article  CAS  Google Scholar 

  44. Uyama, H. et al. Hepatoma-derived growth factor is a novel prognostic factor for patients with pancreatic cancer. Clin. Cancer Res. 12, 6043–6048 (2006).

    Article  CAS  Google Scholar 

  45. Chen, S. C. et al. Hepatoma-derived growth factor regulates breast cancer cell invasion by modulating epithelial–mesenchymal transition. J. Pathol. 228, 158–169 (2012).

    Article  CAS  Google Scholar 

  46. Wittekind, C. 2010 TNM system: on the 7th edition of TNM classification of malignant tumors (in German). Pathologe 31, 331–332 (2010).

    Article  CAS  Google Scholar 

  47. Bossi, D. et al. In vivo genetic screens of patient-derived tumors revealed unexpected frailty of the transformed phenotype. Cancer Discov. 6, 650–663 (2016).

    Article  CAS  Google Scholar 

  48. Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 17, 10–12 (2011).

    Article  Google Scholar 

  49. Bolger, A. M. et al. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).

    Article  CAS  Google Scholar 

  50. Rieder, D. et al. meRanTK: methylated RNA analysis ToolKit. Bioinformatics 32, 782–785 (2016).

    Article  CAS  Google Scholar 

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

    Article  CAS  Google Scholar 

  52. Kim, D. et al. HISAT: a fast spliced aligner with low memory requirements. Nat. Methods 12, 357–360 (2015).

    Article  CAS  Google Scholar 

  53. Anders, S. et al. HTSeq–a Python framework to work with high-throughput sequencing data. Bioinformatics 31, 166–169 (2015).

    Article  CAS  Google Scholar 

  54. Love, M. I. et al. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 15, 550 (2014).

    Article  Google Scholar 

  55. Veschi, V. et al. Epigenetic siRNA and chemical screens identify SETD8 inhibition as a therapeutic strategy for p53 activation in high-risk neuroblastoma. Cancer Cell 31, 50–63 (2017).

    Article  CAS  Google Scholar 

  56. Otwinowski, Z. & Minor, W. Processing of X-ray diffraction data collected in oscillation mode. Methods Enzymol. 276, 307–326 (1997).

    Article  CAS  Google Scholar 

  57. Emsley, P. & Cowtan, K. Coot: model-building tools for molecular graphics. Acta Crystallogr. D 60, 2126–2132 (2004).

    Article  Google Scholar 

  58. Adams, P. D. et al. PHENIX: a comprehensive Python-based system for macromolecular structure solution. Acta Crystallogr. D 66, 213–221 (2010).

    Article  CAS  Google Scholar 

  59. Adiconis, X. et al. Comparative analysis of RNA sequencing methods for degraded or low-input samples. Nat. Methods 10, 623–629 (2013).

    Article  CAS  Google Scholar 

  60. Zeng, Y. et al. Refined RIP-seq protocol for epitranscriptome analysis with low input materials. PLoS Biol. 16, e2006092 (2018).

    Article  Google Scholar 

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

    Article  Google Scholar 

  62. Langmead, B., Trapnell, C., Pop, M. & Salzberg, S. L. Ultrafast and memory-efficient alignment of short DNA sequences to the human genome. Genome Biol. 10, R25 (2009).

    Article  Google Scholar 

  63. Corcoran, D. L. et al. PARalyzer: definition of RNA binding sites from PAR-CLIP short-read sequence data. Genome Biol. 12, R79 (2011).

    Article  CAS  Google Scholar 

  64. Thorvaldsdóttir, H. et al. Integrative genomics viewer (IGV): high-performance genomics data visualization and exploration. Brief. Bioinform. 14, 178–192 (2013).

    Article  Google Scholar 

  65. Li, X. D. et al. Overexpression of maelstrom promotes bladder urothelial carcinoma cell aggressiveness by epigenetically downregulating MTSS1 through DNMT3B. Oncogene 35, 6281–6292 (2016).

    Article  CAS  Google Scholar 

  66. Muller, P. A. et al. Mutant p53 drives invasion by promoting integrin recycling. Cell 139, 1327–1341 (2009).

    Article  Google Scholar 

  67. Liu, J. et al. An integrated TCGA pan-cancer clinical data resource to drive high-quality survival outcome analytics. Cell 173, 400–416 (2018).

    Article  CAS  Google Scholar 

  68. Hudis, C. A. et al. Proposal for standardized definitions for efficacy end points in adjuvant breast cancer trials: the STEEP system. J. Clin. Oncol. 25, 2127–2132 (2007).

    Article  Google Scholar 

  69. Punt, C. J. et al. Endpoints in adjuvant treatment trials: a systematic review of the literature in colon cancer and proposed definitions for future trials. J. Natl Cancer Inst. 99, 998–1003 (2007).

    Article  Google Scholar 

  70. Camp, R. L. et al. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin. Cancer Res. 10, 7252–7259 (2004).

    Article  CAS  Google Scholar 

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Acknowledgements

This work was supported by grants from the National Key R&D Program of China (grant no. 2017YFC1309001 to D.X.), the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA16000000 to Y.-G.Y.), the National Natural Science Foundation of China (grant nos 31625016 to Y.-G.Y., 81672530 to F.-J.Z., 31870741 to Y.H. and 31670824 to B.-F.S.), the National Key R&D Program of China Stem Cell and Translational Research (grant no. 2018YFA0109700 to Y.Y.), the Youth Innovation Promotion Association of CAS (grant nos 2016097 to B.-F.S. and 2018133 to Y.Y.), Shanghai Municipal Science and Technology Major Project (grant no. 2017SHZDZX01 to Y.-G.Y.), the NSFC consulting grant (grant no. 91753000 to Y.-G.Y.). We thank the staff from BL18U1 at the Shanghai Synchrotron Radiation Facility and the staff from the National Facility for Protein Science in Shanghai, Zhangjiang Laboratory for their technical support and assistance. We thank the staff from the Nanjing Biomedical Research Institute of Nanjing University for their assistance with data collection. We thank X. Ding and M. Zhang at the Laboratory of Proteomics core facility at the Institute of Biophysics, CAS for their technical support for the LC-MS analysis, the TCGA research network for providing the data analysed in this manuscript and the BIG CAS genomic platform for sequencing.

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Author notes

  1. These authors contributed equally: Xin Chen, Ang Li, Bao-Fa Sun, Ying Yang, Ya-Nan Han, Xun Yuan.

Authors and Affiliations

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

    Xin Chen, Ri-Xin Chen, Wen-Su Wei, Xiao-Dan Ma, Zhuo-Wei Liu, Jun-Hang Luo, Fang-Jian Zhou & Dan Xie

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

    Xin Chen, Wen-Su Wei, Zhuo-Wei Liu & Fang-Jian Zhou

  3. CAS Key Laboratory of Genomic and Precision Medicine, Collaborative Innovation Center of Genetics and Development, College of Future Technology, Beijing Institute of Genomics, Chinese Academy of Sciences, Beijing, China

    Ang Li, Bao-Fa Sun, Ying Yang, Ya-Nan Han, Chun-Chun Gao, Yu-Sheng Chen, Yong-Liang Zhao & Yun-Gui Yang

  4. University of Chinese Academy of Sciences, Beijing, China

    Ang Li, Bao-Fa Sun, Ying Yang, Ya-Nan Han, Chun-Chun Gao, Yu-Sheng Chen, Cong Lyu, Hai-Lin Wang, Yong-Liang Zhao & Yun-Gui Yang

  5. Institute of Stem Cell and Regeneration, Chinese Academy of Sciences, Beijing, China

    Bao-Fa Sun, Ying Yang, Yong-Liang Zhao & Yun-Gui Yang

  6. State Key Laboratory of Molecular Biology, Shanghai Key Laboratory of Molecular Andrology, CAS Center for Excellence in Molecular Cell Science, Shanghai Institute of Biochemistry and Cell Biology, Chinese Academy of Sciences, University of Chinese Academy of Sciences, Shanghai, China

    Xun Yuan, Yanchao Liu & Ying Huang

  7. State Key Laboratory of Genetic Engineering, Collaborative Innovation Centre of Genetics and Development, Multiscale Research Institute for Complex Systems, Department of Biochemistry, School of Life Sciences, Fudan University, Shanghai, China

    Mengmeng Zhang & Jinbiao Ma

  8. State Key Laboratory of Environmental Chemistry and Ecotoxicology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China

    Cong Lyu & Hai-Lin Wang

Authors
  1. Xin Chen
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  2. Ang Li
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Contributions

Y.-G.Y., D.X. and F.-J.Z. conceived this project, and designed and supervised the experiments. X.C., A.L., Y.Y. and X.Y. carried out the experiments with help from R.-X.C., W.-S.W., X.-D.M., Z.-W.L., J.-H.L., Y.L., M.Z., C.L., H.-L.W. and J.M. B.-F.S., Y.-N.H., W.-S.W., C.-C.G. and Y.-S.C. analysed the data. D.X., Y.H., F.-J.Z., Y.-L.Z. and Y.-G.Y. wrote the manuscript.

Corresponding authors

Correspondence to Ying Huang, Dan Xie or Yun-Gui Yang.

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Integrated supplementary information

Supplementary Figure 1 Overview of m5C patterns and m5C-related pathways in tumour and normal samples.

a-d, RNA categories (a), mRNA region distributions (b), metagene profiles (c) and logo plot (d) of m5C sites in tumour and normal samples. The shaded area represents the range from minimal to maximal values (c). e, Heatmap showing the methylation level change of differential m5C sites (4,126 hypermethylated and 1,154 hypomethylated) between tumour and normal samples. f, The distribution of m5C differential levels (tumour relative to normal). g, UHPLC-MS/MS showing the percentage of mRNA m5C in five paired tumour and adjacent normal samples. T: tumour; N: adjacent normal. Data represent mean ± s.d. (n = 3 independent experiments). The P values were determined by a two-sided unpaired Student's t-test. h, Canonical pathway analysis of hypermethylated mRNAs (n = 2,041) in tumours. i, Heatmap showing the tendency of 103 hypermethylated genes in tumours involving 6 canonical cancer-related pathways (JAK/STAT, ERBB, PI3K/AKT, VEGF, EMT and ERK/MAPK). j, Canonical pathway analysis of hypomethylated mRNAs (n = 504) in tumours. k, Heatmap showing the methylation level change of differential m5C sites (185 sites: higher methylation; 90 sites: lower methylation) in MIBC relative to NMIBC. l, Canonical pathway analysis of mRNAs with higher methylated m5C sites (n = 185, MIBC relative to NMIBC). m, Heatmap showing the methylation tendency of 36 metastasis-related genes in MIBC relative to NMIBC. n, Heatmap showing the methylation level change of differential m5C sites (109 sites: higher methylation; 104 sites: lower methylation) in LM relative to non-LM UCBs. o, Heatmap showing the methylation tendency of 22 metastasis-related genes in LM relative to non-LM UCBs. Heatmaps (e, i, k, m-o) showing the z-score of m5C level. Colours from blue to red indicate low to high. 65 samples (36 UCB and 29 adjacent normal tissues) were used for a-f, h-j; 36 UCB samples (24 MIBC and 12 NMIBC) were used for k-m; 36 samples (15 LM and 21 non-LM) were used for n, o. All P values in h, j and l were calculated by one-sided Fisher’s exact test via IPA software. Source data for g are provided in Supplementary Table 10.

Supplementary Figure 2 Correlation of m5C level with the oncogenic gene expression in bladder cancer samples.

a, Scatter plot showing the correlation of m5C methylation with gene expression levels for MLLT11 transcription factor 7 cofactor (MLLT11) and proteasome 26S subunit, non-ATPase 3 (PSMD3) in 65 tissue samples. The centre line and the shaded regions means the regression line and 95% confidence interval, respectively. Spearman's correlation coefficient and P value was calculated using the function “cor.test” in the statistical language R. b, c, Boxplot showing the distribution of m5C methylation level (b) and the mRNA expression (c) of MLLT11 and PSMD3 in tumours compared to matched normal samples. The P values were determined by a two-sided paired Wilcoxon signed rank test. d, Kaplan-Meier analysis indicating the correlation of the mRNA expression of MLLT11 and PSMD3 with poor DFS rates in UCB patients from TCGA analysis. The P values were determined by a two-sided log-rank test. e, Scatter plot showing the correlation of m5C methylation levels for HDGF, F11R, RAB11FIP4, MLLT11 and PSMD3 with NSUN2 gene expression level in 65 tissue samples. The centre line and the shaded regions means the regression line and 95% confidence interval, respectively. Spearman’s correlation coefficient and P value was calculated using the function “cor.test” in the statistical language R. In boxplots of b and c, central bars represent medians, box edges represent the first and third interquartile ranges. 22 pairs of cancerous and non-cancerous bladder tissues were used for b and c.

Supplementary Figure 3 YBX1 serves as a specific m5C reader.

a, Nuclear and cytoplasmic fractions of 293T cells were analysed by western blotting. PARP1 and β-Tubulin serve as nuclear and cytoplasmic markers, respectively. b, SDS-PAGE showing the purified wild-type (WT) and mutant (W65F) YBX1 CSD proteins as stained by coomassie brilliant blue. Positions of molecular markers are indicated on the left in kDa. c-e, Comparison of different binding modes for m6A RNA, 5mC DNA, and m5C RNA. c, Recognition of m6A by the YTH domain of YTHDC1.d, Recognition of 5mC by the MBD domain of MBD4. e, Recognition of 5mC by the Zinc-finger domain of Klf4. f, Sequence alignment of YBX1 CSD homologs, including human (hs, homo sapiens, P67809), mouse (mm, mus musculus, P62960), zebrafish (zf, danio rerio, A1A605), frog (xt, xenopus laevis, P21573), chick (gg, gallus, Q06066), fly (dm, drosophila melanogaster, O46173), and silkworm (bm, bombyx mori, Q6F6B1). Identical residues are shaded in red. Secondary structural elements of YBX1 CSD are displayed above. The residues involving π-π stacking interactions are marked by red triangles. N67 and N70 that contact m5C5 through hydrogen bond interactions are indicated with brown diamond. g, h, Western blotting showing the NSUN2 knockdown efficiency and Flag-YBX1 overexpression in NSUN2-depleted HeLa (g) and T24 (h) cells. ACTIN serves as loading control. i, Western blotting showing the levels of NSUN2, Flag-YBX1, siNSUN2-Insensitive Myc-NSUN2 wild-type (Myc-NSUN2-WT) and double-mutant (Myc-NSUN2-DM) in control and NSUN2 knockdown HeLa cells used for PAR-CLIP. ACTIN was used as loading control. j, Overlap of m5C-containing genes with YBX1 and ALYREF binding targets, respectively. The experiments were performed twice independently with similar results. k, Cumulative distribution of m5C levels in input and YBX1-RIP mRNA samples in T24 cells (m5C sites in input: n = 22,104; m5C sites in YBX1-RIP: n = 5,496. Experiments were repeated three times independently with similar results for a, g, h and i. The P value was calculated by a two-sided unpaired Wilcoxon and Mann–Whitney test. Unprocessed gels for a, g, h and i are provided in Supplementary Figs. 8–10.

Supplementary Figure 4 YBX1 regulates mRNA stability through recruiting ELAVL1.

a, Western blotting showing the NSUN2 and YBX1 knockdown efficiency and Myc-ELAVL1 overexpression in NSUN2 or YBX1 knockdown HeLa cells. ACTIN was used as loading control (n = 3 independent experiments). b, PAR-CLIP assay of RNAs pulled down by Myc-ELAVL1 in NSUN2 or YBX1 deficient T24 cells (n = 2 independent experiments). c, Western blotting displaying the NSUN2 and YBX1 knockdown efficiency and Myc-ELAVL1 overexpression in NSUN2 or YBX1 knockdown T24 cells. ACTIN was used as loading control (n = 2 independent experiments). d, Western blotting showing the levels of YBX1, Flag-YBX1, siYBX1-insensitive Flag-YBX1-WT and Flag-YBX1-W65A in control and YBX1 knockdown HeLa cells used for PAR-CLIP. ACTIN was used as loading control (n = 4 independent experiments). e, Overlap of m5C-containing genes with YBX1 and ELAVL1 binding targets. Unprocessed gels for a-d are provided in Supplementary Figs. 8–10.

Supplementary Figure 5 NSUN2 and YBX1 enhance in vitro UCB cell growth and invasion.

a, Western blotting showing NSUN2/YBX1 expression in NSUN2- or YBX1-knockdown or control UCB cells. GAPDH serves as protein loading control. b, Western blotting showing NSUN2 expression in NSUN2-knockdown UCB cells, which stably expressing NSUN2-WT, NSUN2- C321A, or NSUN2-DM. GAPDH was used as loading control. c, NSUN2 WT, not the C321A or DM mutant could restore the decreased colony formation ability of NSUN2-depleted cells. Top: representative images of crystal violet staining of cells; Bottom: histograms of colony numbers. Data are mean ± s.d. (n = 3 independent experiments). d, Western blotting showing YBX1 expression in YBX1-knockdown UCB cells, which stably expressing YBX1-WT or YBX1-W65A mutant. ACTIN was used as loading control. e, YBX1 WT, not the W65A mutant could restore the decreased colony formation ability of YBX1-depleted cells. Top: representative images of crystal violet staining of cells; Bottom: histograms of colony numbers. Data are mean ± s.d. (n = 3 independent experiments). f, NSUN2 WT, not the C321A or DM mutant could restore the decreased cell invasion ability of NSUN2-depleted cells tested by transwell assay. Top: representative images; Bottom: histograms of invasive cell numbers. Scale bar, 100 µm. Data are mean ± s.d. (n = 3 independent experiments). g, Inverted invasion assay showing the rescued cell invasive capacity of NSUN2-depleted UCB cells by reconstitution of NSUN2 WT compared to that of C321A or DM mutant. Data are mean ± s.d. (n = 5 independent experiments). h, YBX1 WT, not the W65A mutant could restore the decreased cell invasion ability of YBX1-depleted cells tested by transwell assay (n = 3 independent experiments). Top: representative images; Bottom: histograms of invasive cell numbers. Scale bar, 100 µm. i, Inverted invasion assay showing the rescued cell invasive in YBX1-knockdown UCB cells by reconstitution of YBX1 WT in relative to W65A mutant. Data are mean ± s.d. (n = 5 independent experiments). The precise P values (two-sided unpaired Student's t-test) and source data for c, e-i are provided in Supplementary Table 10. The experiments were performed twice independently with similar results for a, b and d. Unprocessed gels for a, b and d are provided in Supplementary Figs. 8–10.

Supplementary Figure 6 NSUN2 and YBX1 target the m5C of HDGF mRNA.

a,b, Spearman’s correlation analysis demonstrating positive correlation between the expression level of NSUN2 (a) or YBX1 (b) and HDGF mRNA expression in UCB tissues from SYSUCC cohorts and TCGA, respectively. The centre line and the shaded regions indicate the regression line and 95% confidence interval, respectively. Spearman’s correlation coefficient and P value were calculated using the function “cor.test” in the statistical language R. c, Reduced HDGF mRNA half-life by silencing NSUN2 or YBX1 in T24 cells. Data are mean ± s.d. (n = 3 independent experiments). d, Western blotting showing the protein expression of NSUN2 and HDGF in NSUN2-knockdown UCB cells, which stably expressing NSUN2-WT, NSUN2- C321A, or NSUN2-DM. GAPDH was used as loading control. e, Western blotting showing the protein expression of YBX1 and HDGF in YBX1-knockdown UCB cells, which stably expressing YBX1-WT or YBX1-W65A. ACTIN was used as loading control. f, Relative luciferase mRNA expression of luciferase reporter gene with HDGF-m5C WT site (HDGF-WT) or mutant site (HDGF-Mut) in control, NSUN2-knockdown (top) or YBX1-knockdown (bottom) T24 cells. Data are mean ± s.d. (n = 3 independent experiments). g, Relative luciferase activity of luciferase reporter gene with HDGF-m5C WT site (HDGF-WT) or mutant site (HDGF-Mut) in control or NSUN2-knockdown (top) or YBX1-knockdown (bottom) T24 cells (n = 3 independent experiments). h, Knockdown of HDGF through injection of shRNA lentivirus strongly inhibited the subcutaneously growth of PDX tumour in vivo. Left: statistical analysis of xenograft tumour weight of each group. Right: volume-tracking plots. Data are mean ± s.d. (n = 5 animals). All P values were determined by a two-sided unpaired Student's t-test. The experiments were performed twice independently with similar results for d and e. Unprocessed gels for d and e are provided in Supplementary Figs. 8–10. The precise P values (two-sided unpaired Student’s t-test) and source data for c, f-h are presented in Supplementary Table 10.

Supplementary Figure 7 NSUN2/YBX1/HDGF regulatory axis plays a critical role in the pathogenesis of solid tumour.

a-f, Left: Spearman’s correlation analysis demonstrating positive correlation between YBX1 and HDGF mRNA expression levels in Kidney Renal Clear Cell Carcinoma, Prostate Adenocarcinoma, Pancreatic Adenocarcinoma, Breast Invasive Carcinoma, Liver Hepatocellular Carcinoma, Uterine Corpus Endometrial Carcinoma tissues from TCGA analysis, respectively. The centre line and the shaded regions indicate the regression line and 95% confidence interval, respectively. Spearman’s correlation coefficient and P value were calculated using the function “cor.test” in the statistical language R. Right: Kaplan-Meier analysis indicates the correlation of higher co-expression of NSUN2-YBX1-HDGF with poorer DFS rates in the indicated cancer patients. P values were determined by a two-sided log-rank test.

Supplementary Figure 8 Full blots of figures.

The black sections indicate blot results shown in the indicated figures.

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Chen, X., Li, A., Sun, BF. et al. 5-methylcytosine promotes pathogenesis of bladder cancer through stabilizing mRNAs. Nat Cell Biol 21, 978–990 (2019). https://doi.org/10.1038/s41556-019-0361-y

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