Abstract

N6-methyladenosine (m6A) messenger RNA methylation is a gene regulatory mechanism affecting cell differentiation and proliferation in development and cancer. To study the roles of m6A mRNA methylation in cell proliferation and tumorigenicity, we investigated human endometrial cancer in which a hotspot R298P mutation is present in a key component of the methyltransferase complex (METTL14). We found that about 70% of endometrial tumours exhibit reductions in m6A methylation that are probably due to either this METTL14 mutation or reduced expression of METTL3, another component of the methyltransferase complex. These changes lead to increased proliferation and tumorigenicity of endometrial cancer cells, likely through activation of the AKT pathway. Reductions in m6A methylation lead to decreased expression of the negative AKT regulator PHLPP2 and increased expression of the positive AKT regulator mTORC2. Together, these results reveal reduced m6A mRNA methylation as an oncogenic mechanism in endometrial cancer and identify m6A methylation as a regulator of AKT signalling.

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Acknowledgements

We thank Martin Aryee (Massachusetts General Hospital) for initial discussions and Angela Andersen for editing the manuscript. This work was supported by a Marsha Rivkin Foundation award (M.A.E.); University of Chicago Institute for Biophysical Dynamics Yen Fellowship (B.T.H.); National Natural Science Foundation of China grants 81472023 and 81271919 (S.L.); The National Basic Research Programme grants 2012CB720600 and 2012CB720605 (S.L.); the National Key Research and Development Programme of China 2017YFA0506800 (Jz.L.); the Thousands Young Talents Plan of China and Hundreds Talents Programme of Zhejiang University (Jz.L.); National Cancer Institute grants CA111882 (E.L.) and F32 CA221007 (B.T.H.); National Institutes of Health grants R01 HG008688 and RM1 HG008935 (C.H.); and University of Chicago Cancer Center Support Grant P30CA014599. M.A.E. thanks the Harris Family Foundation for their generous support. C.H. is an investigator of the Howard Hughes Medical Institute.

Author information

Author notes

  1. These authors contributed equally: Jun Liu, Mark A. Eckert, Bryan T. Harada, Song-Mei Liu.

Affiliations

  1. Department of Chemistry and Institute for Biophysical Dynamics, The University of Chicago, Chicago, IL, USA

    • Jun Liu
    • , Bryan T. Harada
    • , Zhike Lu
    • , Kangkang Yu
    • , Allen C. Zhu
    •  & Chuan He
  2. Howard Hughes Medical Institute, Chicago, IL, USA

    • Jun Liu
    • , Bryan T. Harada
    • , Zhike Lu
    • , Kangkang Yu
    • , Allen C. Zhu
    •  & Chuan He
  3. Department of Obstetrics and Gynecology/Section of Gynecologic Oncology, The University of Chicago, Chicago, IL, USA

    • Mark A. Eckert
    • , Samantha M. Tienda
    • , Agnieszka Chryplewicz
    •  & Ernst Lengyel
  4. Center for Gene Diagnosis, Zhongnan Hospital of Wuhan University, Wuhan, China

    • Song-Mei Liu
    • , Ying Yang
    • , Jing-Tao Huang
    •  & Shao-Min Chen
  5. College of Chemistry, Sichuan University, Chengdu, China

    • Kangkang Yu
  6. Committee on Cancer Biology and Medical Scientist Training Program, The University of Chicago, Chicago, IL, USA

    • Allen C. Zhu
  7. Department of Pathology, Zhongnan Hospital of Wuhan University, Wuhan, China

    • Zhi-Gao Xu
  8. Hubei Key Laboratory of Tumor Biological Behaviors & Hubei Cancer Clinical Study Center, Zhongnan Hospital of Wuhan University, Wuhan, China

    • Xiao-Hua Leng
  9. Department of Obstetrics and Gynecology, Zhongnan Hospital of Wuhan University, Wuhan, China

    • Xue-Chen Yu
  10. MOE Key Laboratory of Macromolecular Synthesis and Functionalization, Department of Polymer Science and Engineering, Zhejiang University, Hangzhou, China

    • Jie Cao
    • , Zezhou Zhang
    •  & Jianzhao Liu
  11. Department of Biochemistry and Molecular Biology, The University of Chicago, Chicago, IL, USA

    • Chuan He

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Contributions

Jun L., M.A.E., B.T.H., E.L. and C.H. designed the experiments. M.A.E., S.M.T., S.L., Y.Y., J.H., S.C., Z.X, X.L., X.Y. and E.L. collected the patient samples with assistance from J.C., Z.Z. and Jz.L. Jun L., M.A.E. and B.T.H. carried out the experiments with help from K.Y., S.M.T., A.C. and A.C.Z. Jun L., M.A.E., B.T.H., E.L. and C.H. analysed the data and interpreted the findings. Z.L. aided with analysis of the sequencing data. Jz.L aided in the early design of experiments. Jun L. and B.T.H. wrote the manuscript with input from M.A.E., E.L. and C.H.

Competing interests

C.H. is a scientific founder of Accent Therapeutics and a member of its scientific advisory board. All other authors declare no competing interests.

Corresponding authors

Correspondence to Ernst Lengyel or Chuan He.

Integrated supplementary information

  1. Supplementary Figure 1 Expression of m6A-regulatory genes in endometrial cancer patients.

    (a) Scatter plots showing the correlation of m6A methylation levels with the expression of m6A writers, erasers and readers in tumor tissue. Linear least squares line is shown in red. The Pearson correlation coefficient (r) and p-value (p) from a two-tailed t-test of r = 0 are shown. n = 22 tumor-normal pairs for FTO and METTL14, n = 38 tumor-normal pairs for the others. (b) Box plots showing the expression of METTL3 in TCGA endometrial cancer patients with tumors wild-type for (black) or containing mutations in (red) the indicated genes. p-values were calculated by two-tailed t-test. n = 232 tumors. For the box plots, the center line represents the median, the box limits show the upper and lower quartiles, whiskers represent 1.5x the interquartile range, and outliers are represented as individual data points. (c) Survival curves showing the correlation between METTL3 expression and patient overall survival in the TCGA datasets for endometrial cancer, high grade serous ovarian cancer, and pancreatic adenocarcinoma. n indicates the number of patients in each group. p-values were calculated by two-tailed log-rank test.

  2. Supplementary Figure 2 Effects of alterations to m6A mRNA methylation on the HEC-1-A endometrial cancer cell line.

    (a) Immunoblot showing expression levels of METTL14 in the wild-type HEC-1-A cell line, the METTL14+/- knockout cell line, knockout cells rescued with stable expression of wild-type and mutant METTL14, and HEC-1-A cells stably transfected with control shRNA or shRNA targeting METTL14. Three independent experiments have been repeated with similar results. (b) Transwell invasion and migration were assessed for wild-type HEC-1-A cells, METTL14+/- knockout cells, and knockout cells rescued with wild-type or mutant METL14. (c) LC-MS/MS quantification of the m6A/A ratio from polyA-RNA purified from the METTL14 knockdown and control cells. (d-h) Cell proliferation in an MTS assay (d), anchorage-independent cell growth (e), colony formation (f), migration in a wound healing assay (g), and transwell invasion and migration (h) of HEC-1-A cells expressing control shRNA or shRNA targeting METTL14. (i) Immunoblot showing the expression of METTL3 in HEC-1-A cells expressing a control shRNA or two different shRNAs targeting METTL3. Three independent experiments have been repeated with similar results. (j) Transwell invasion and migration were assessed for HEC-1-A cells stably expressing control shRNA or shRNA targeting METTL3. For panels c-g, n = 3 biological replicates. Error bars indicate mean ± s.e.m. p-values determined by two-tailed t-test. For panel b, h and j, the bar shows the mean from n = 3 technical replicates. Raw gel images for panels a and i are provided in Supplementary Fig. 8.

  3. Supplementary Figure 3 m6A-seq of endometrial tumors and cell lines.

    (a) Relative expression of METTL3 in tumor and tumor-adjacent samples were measured by RT-qPCR. The bar shows the mean from n = 3 technical replicates, (b) m6A/A ratio of polyA RNAs isolated from tumor and tumor-adjacent samples were measured by LC-MS/MS. The bar shows the mean from n = 3 technical replicates. (c) Metagene plots showing the average distribution of m6A peaks identified across all transcripts in the tumor and tumor-adjacent samples from 5 patients. (d) Sequence logo showing the top motifs enriched across all m6A-peaks identified from n = 5 patients. (e) The number of significant m6A peaks detected and the number of transcripts containing a significant m6A peak are reported. The number of genes containing a significant m6A peak in each pair of samples is reported in the table. (f) For each pair of samples, we identified the set of transcripts showing a significant m6A peak in both normal tissue samples. We then calculated the fraction of these transcripts showing a > 2-fold decrease in enrichment in both tumor samples and divided by all transcripts showing a > 2-fold decrease in enrichment in at least one tumor sample. (g) Histograms showing the changes in m6A enrichment in the METTL3 knockdown versus control HEC-1-A cells (left) or mutant METTL14(R298P) versus wild-type METTL14 HEC-1-A cells (right).

  4. Supplementary Figure 4 GO term analysis and RT-qPCR validation for m6A-seq results.

    (a) GO term analysis of transcripts with reduced m6A in knockdown METTL3 and mutant METTL14 HEC-1-A cells versus control. (b-e) m6A-IP combined with RT-qPCR was used to quantify the relative m6A levels (b,d) and mRNA levels (c,e) in the METTL14 mutant cell line (b,c) and METTL3 knockdown cells (d,e) versus control. For panels b-e, error bars indicate mean ± s.e.m from n = 3 biological replicates. p-values determined by two-tailed t-test. (f) Quantification of the immunoblots in Fig. 4a,b. Error bars indicate mean ± s.e.m from n = 3 biological replicates. p-values determined by two-tailed t-test. (g,h) Left: Immunohistochemical staining of tissue microarray cores for PRR5L (g) and p-mTOR(S2481) (h). Right: Quantification of IHC staining in normal endometrium (n = 10) and endometrial tumors (n = 30). Staining was assessed using automated software51 and scored on a scale of 0 (no staining) to 3 (high staining). The p-value was determined by a χ2-test. Scale bar = 50μm.

  5. Supplementary Figure 5 Polysome profiling of m6A methylated transcripts.

    (a) Quantification of the immunoblots in Fig. 5a. Error bars indicate mean ± s.e.m. from n = 3 biological replicates. p-values determined by two-tailed t-test. (b) The absorbance at 260 nm was measured during fractionation of polysomes from HEC-1-A cells transiently transfected with control siRNA or siRNA targeting YTHDF1. (c) The abundance of PHLPP2 transcripts was measured by RT-qPCR in each fraction from the polysome profiling. (d-f) Top: The abundance of PRR5 (d), PRR5L (e), and mTOR (f) transcripts were measured by RT-qPCR in each fraction from the polysome profiling. Bottom: Fractions from the polysome fractionation corresponding to non-ribosomal RNAs, ribosome-associated RNAs, and polysome-associated RNAs were pooled and the abundance of the PRR5 (d), PRR5L (e), and mTOR (f) transcripts were quantified by RT-qPCR. For c-f, n = 2 biological replicates, bar represents the mean value.

  6. Supplementary Figure 6 Time course of AKT activation during EGF stimulation and effects of m6A methylation on the RL95-2 cell line.

    (a-b) Immunoblots showing the time course of AKT(S473) phosphorylation after EGF stimulation in shControl versus shMETTL3 HEC-1-A cells (a) or HEC-1-A cells expressing wild-type METTL14 versus mutant METTL14 (b). Cells were either incubated in media with 10% FBS (medium) or no FBS (starved). After 16 h of starvation, cells were stimulated with 10 ng/mL of recombinant EGF for the indicated amounts of time. Plots quantifying the time-course of EGF activation show mean ± s.e.m. from n = 3 biological replicates. p-values determined by two-tailed t-test. (c-f) Effects of alterations to m6A methylation on RL95-2 endometrial cancer cells were examined after transient transfection with control siRNA, siRNA targeting METTL3, siRNA targeting METTL14, empty vector, plasmid encoding METTL3 or plasmid encoding METTL14. (c) LC-MS/MS quantification of the m6A/A ratio in polyA-RNA after transient transfection of RL95-2 cells with the indicated reagents. (d,e) Cell proliferation measured by MTS assay of RL95-2 cells transfected with the indicated reagents. Cell numbers were normalized to the MTS signal ~ 5 h after cell seeding. For panels c-e, error bars indicate mean ± s.e.m from n = 3 biological replicates. p-values determined by two-tailed t-test. (f) Immunoblot showing the effects of the indicated perturbations to m6A methylation on the expression and phosphorylation of proteins involved in the AKT pathway in RL95-2 cells. Three independent experiments have been repeated with similar results. Raw gel images for panels a, b and f are provided in Supplementary Fig. 8.

  7. Supplementary Figure 7 Alteration of the AKT pathway rescues changes in cell proliferation due to reduced m6A methylation.

    (a,b) Immunoblots analyzing the effect of FLAG-PHLPP2 overexpression (a) or RICTOR knockdown (b) on AKT phosphorylation in wild-type and METTL14+/- HEC-1-A cells. Three independent experiments have been repeated with similar results. (c-d) Proliferation measured by MTS assay of wild-type versus METTL14+/- HEC-1-A cells. Cells were transiently transfected with a PHLPP2 overexpression plasmid versus empty vector (c) or siRNAs targeting RICTOR versus negative control siRNAs (d). Error bars show mean ± s.e.m. from n = 3 biological replicates. (e) Immunoblots showing phosphorylation status of AKT(S473) and FOXO1(S256), a downstream target of AKT, after treatment of the indicated HEC-1-A cell lines with 5 µM MK-2206 for 24 h. Three independent experiments have been repeated with similar results. Plots quantifying the phosphorylation status of FOXO1(S256) show mean ± s.e.m. from n = 3 biological replicates. p-values determined by two-tailed t-test. (f) Cell proliferation measured by MTS assay for the indicated cell lines in the presence of 5 µM MK-2206 or DMSO. For proliferation assays, cell numbers were normalized to the MTS signal ~ 5 h after cell seeding. n = 3 biological replicates; error bars indicate mean ± s.e.m. Raw gel images for panels a, b and e are provided in Supplementary Fig. 8.

  8. Supplementary Figure 8 Unprocessed original scans of blots.

    Unprocessed images of all immunoblots. Molecular weight markers in kDa

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https://doi.org/10.1038/s41556-018-0174-4