Abstract
Aberrant RNA modifications lead to dysregulated gene expression and cancer progression. Ribosomal RNA (rRNA) accounts for more than 80% of a cell’s total RNA, but the functions and molecular mechanisms underlying rRNA modifications in cancers are poorly understood. Here, we show that the 18S rRNA N6-methyladenosine (m6A) methyltransferase complex METTL5–TRMT112 is upregulated in various cancer types and correlated with poor prognosis. In addition, we demonstrate the critical functions of METTL5 in promoting hepatocellular carcinoma (HCC) tumorigenesis in vitro and in mouse models. Mechanistically, depletion of METTL5-mediated 18S rRNA m6A modification results in impaired 80S ribosome assembly and decreased translation of mRNAs involved in fatty acid metabolism. We further reveal that ACSL4 mediates the function of METTL5 on fatty acid metabolism and HCC progression, and targeting ACSL4 and METTL5 synergistically inhibits HCC tumorigenesis in vivo. Our study uncovers mechanistic insights underlying mRNA translation control and HCC tumorigenesis through lipid metabolism remodeling and provides a molecular basis for the development of therapeutic strategies for HCC treatment.
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 digital issues and online access to articles
$119.00 per year
only $9.92 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
The public data reanalyzed in our study were from TCGA datasets (https://www.cancer.gov/about-nci/organization/ccg/research/structural-genomics/tcga). The raw sequencing data generated in this study are available at the NCBI GEO (GSE175827). Source data are provided with this paper.
Code availability
All the software and algorithms employed in this study are presented in Supplementary Table 6 and are available without any access restrictions.
References
Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).
Pelletier, J., Thomas, G. & Volarevic, S. Ribosome biogenesis in cancer: new players and therapeutic avenues. Nat. Rev. Cancer 18, 51–63 (2018).
Zink, D., Fischer, A. H. & Nickerson, J. A. Nuclear structure in cancer cells. Nat. Rev. Cancer 4, 677–687 (2004).
Lafontaine, D. L. J., Riback, J. A., Bascetin, R. & Brangwynne, C. P. The nucleolus as a multiphase liquid condensate. Nat. Rev. Mol. Cell Biol. 22, 165–182 (2021).
Bursac, S., Prodan, Y., Pullen, N., Bartek, J. & Volarevic, S. Dysregulated ribosome biogenesis reveals therapeutic liabilities in cancer. Trends cancer 7, 57–76 (2021).
Barbieri, I. & Kouzarides, T. Role of RNA modifications in cancer. Nat. Rev. Cancer 20, 303–322 (2020).
Maden, B. E. Identification of the locations of the methyl groups in 18 S ribosomal RNA from Xenopus laevis and man. J. Mol. Biol. 189, 681–699 (1986).
Maden, B. E. Locations of methyl groups in 28 S rRNA of Xenopus laevis and man. Clustering in the conserved core of molecule. J. Mol. Biol. 201, 289–314 (1988).
van Tran, N. et al. The human 18S rRNA m6A methyltransferase METTL5 is stabilized by TRMT112. Nucleic Acids Res. 47, 7719–7733 (2019).
Ma, H. et al. N6-Methyladenosine methyltransferase ZCCHC4 mediates ribosomal RNA methylation. Nat. Chem. Biol. 15, 88–94 (2019).
Ignatova, V. V. et al. The rRNA m6A methyltransferase METTL5 is involved in pluripotency and developmental programs. Genes Dev. 34, 715–729 (2020).
Xing, M. et al. The 18S rRNA m6A methyltransferase METTL5 promotes mouse embryonic stem cell differentiation. EMBO Rep. 21, e49863 (2020).
Rong, B. et al. Ribosome 18S m6A methyltransferase METTL5 promotes translation initiation and breast cancer cell growth. Cell Rep. 33, 108544 (2020).
Wang, L. et al. Mettl5 mediated 18S rRNA N6-methyladenosine (m6A) modification controls stem cell fate determination and neural function. Genes Dis. 9, 268–274 (2022).
Xiao, Y. et al. An elongation- and ligation-based qPCR amplification method for the radiolabeling-free detection of locus-specific N6-methyladenosine modification. Angew. Chem. Int. Ed. Engl. 57, 15995–16000 (2018).
Ho, C. et al. AKT (v-akt murine thymoma viral oncogene homolog 1) and N-Ras (neuroblastoma ras viral oncogene homolog) coactivation in the mouse liver promotes rapid carcinogenesis by way of mTOR (mammalian target of rapamycin complex 1), FOXM1 (forkhead box M1)/SKP2, and c-Myc pathways. Hepatology 55, 833–845 (2012).
Khatter, H., Myasnikov, A. G., Natchiar, S. K. & Klaholz, B. P. Structure of the human 80S ribosome. Nature 520, 640–645 (2015).
Khajuria, R. K. et al. Ribosome levels selectively regulate translation and lineage commitment in human hematopoiesis. Cell 173, 90–103 (2018).
Yamashita, R. et al. Comprehensive detection of human terminal oligo-pyrimidine (TOP) genes and analysis of their characteristics. Nucleic Acids Res. 36, 3707–3715 (2008).
Padanad, M. S. et al. Fatty acid oxidation mediated by acyl-coa synthetase long chain 3 is required for mutant KRAS lung tumorigenesis. Cell Rep. 16, 1614–1628 (2016).
Beatty, A. et al. Ferroptotic cell death triggered by conjugated linolenic acids is mediated by ACSL1. Nat. Commun. 12, 2244 (2021).
Chen, J. et al. ACSL4 reprograms fatty acid metabolism in hepatocellular carcinoma via c-Myc/SREBP1 pathway. Cancer Lett. 502, 154–165 (2021).
Zhang, Q. et al. Metabolic reprogramming of ovarian cancer involves ACSL1-mediated metastasis stimulation through upregulated protein myristoylation. Oncogene 40, 97–111 (2021).
Wang, L. et al. Mettl5 mediated 18S rRNA N6-methyladenosine (m6A) modification controls stem cell fatedetermination and neural function. Genes Dis. 9, 268–274 (2020).
Leismann, J. et al. The 18S ribosomal RNA m6A methyltransferase Mettl5 is required for normal walking behavior in Drosophila. EMBO Rep. 21, e49443 (2020).
Klinge, S. & Woolford, J. L. Jr. Ribosome assembly coming into focus. Nat. Rev. Mol. Cell Biol. 20, 116–131 (2019).
Liu, N. et al. N6-methyladenosine-dependent RNA structural switches regulate RNA–protein interactions. Nature 518, 560–564 (2015).
Alarcon, C. R., Lee, H., Goodarzi, H., Halberg, N. & Tavazoie, S. F. N6-methyladenosine marks primary microRNAs for processing. Nature 519, 482–485 (2015).
Thoreen, C. C. et al. A unifying model for mTORC1-mediated regulation of mRNA translation. Nature 485, 109–113 (2012).
Ward, P. S. & Thompson, C. B. Metabolic reprogramming: a cancer hallmark even warburg did not anticipate. Cancer Cell 21, 297–308 (2012).
Faubert, B., Solmonson, A. & DeBerardinis, R.J. Metabolic reprogramming and cancer progression. Science 368, eaaw5473 (2020).
Sangineto, M. et al. Lipid metabolism in development and progression of hepatocellular carcinoma. Cancers 12, 1419 (2020).
Carracedo, A., Cantley, L. C. & Pandolfi, P. P. Cancer metabolism: fatty acid oxidation in the limelight. Nat. Rev. Cancer 13, 227–232 (2013).
Caro, P. et al. Metabolic signatures uncover distinct targets in molecular subsets of diffuse large B cell lymphoma. Cancer Cell 22, 547–560 (2012).
Ma, Y. et al. Long-chain fatty acyl-CoA synthetase 1 promotes prostate cancer progression by elevation of lipogenesis and fatty acid beta-oxidation. Oncogene 40, 1806–1820 (2021).
Dominissini, D., Moshitch-Moshkovitz, S., Salmon-Divon, M., Amariglio, N. & Rechavi, G. Transcriptome-wide mapping of N6-methyladenosine by m6A-seq based on immunocapturing and massively parallel sequencing. Nat. Protoc. 8, 176–189 (2013).
Lin, S., Liu, Q., Jiang, Y. Z. & Gregory, R. I. Nucleotide resolution profiling of m7G tRNA modification by TRAC-seq. Nat. Protoc. 14, 3220–3242 (2019).
Philippe, L., van den Elzen, A. M. G., Watson, M. J. & Thoreen, C. C. Global analysis of LARP1 translation targets reveals tunable and dynamic features of 5′ TOP motifs. Proc. Natl Acad. Sci. USA 117, 5319–5328 (2020).
Dai, Z. et al. N7-Methylguanosine tRNA modification enhances oncogenic mRNA translation and promotes intrahepatic cholangiocarcinoma progression. Mol. Cell 81, 3339–3355 e3338 (2021).
Chen, B. et al. N7-methylguanosine tRNA modification promotes tumorigenesis and chemoresistance through WNT/beta-catenin pathway in nasopharyngeal carcinoma. Oncogene 41, 2239–2253 (2022).
Wang, T. et al. Translating mRNAs strongly correlate to proteins in a multivariate manner and their translation ratios are phenotype specific. Nucleic Acids Res. 41, 4743–4754 (2013).
Huang da, W., Sherman, B. T. & Lempicki, R. A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res. 37, 1–13 (2009).
Huang da, W., Sherman, B. T. & Lempicki, R. A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc. 4, 44–57 (2009).
Acknowledgements
We thank all the individuals who generously donated tissues, and we thank H. Hu (The First Affiliated Hospital of Sun Yat-sen University) for assistance in data analysis. The work was funded by the National Natural Science Foundation of China (81922052, 81974435 and 81772999), a Distinguished Young Scholars grant from the Natural Science Foundation of Guangdong (2019B151502011), and the Guangzhou People’s Livelihood Science and Technology Project (201903010006) awarded to S. B. L., the National Science Fund for Distinguished Young Scholars (81825013) and Key Program (82130083) of National Natural Science Foundation of China awarded to M. K. and National Natural Science Foundation of China (82002981) awarded to H. P.
Author information
Authors and Affiliations
Contributions
H. P., S. P., M. K. and S. L. designed and supervised the study. H. P., B. C., W. W., H. H., S. G. and L. W. performed the in vitro assays. H. P., L. W. and J. M. performed the in vivo assays. S. P., M. K. and S. L. were responsible for the clinical sample tissues. All other data analyses were performed by H. P., C. Y. and H. H. S. L. and H. P. wrote the paper, and all authors reviewed the final manuscript.
Corresponding authors
Ethics declarations
Competing interests
The authors declare no competing interests.
Peer review
Peer review information
Nature Metabolism thanks Michael Roden and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Alfredo Gimenez-Cassina, in collaboration with the Nature Metabolism team.
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 Determining m6A fractions at the 18 S rRNA A1832 site in THLE-2, HepG2 and Huh7 cells using SELECT.
a-c, Quantification of m6A fractions at the 18 S rRNA A1832 site in THLE-2 (a), HepG2 (b) and Huh7 (c) cells. A series of indicated ratios of 18 S rRNA m6A-oligo were used to calculate the quantity of 18 S rRNA and the percentage of m6A-modified 18 S rRNA in THLE-2, HepG2 and Huh7 cells. The upper panels show fluorescence amplification curves, and the lower panels show the standard curves for calculation of m6A fractions at the 18 S rRNA A1832 site. Each point on the standard curves is indicated by the mean value from three independent experiments.
Extended Data Fig. 2 Determining m6A fractions at the 18 S rRNA A1832 site in patient liver samples using SELECT.
a-c, Upper panels: fluorescence amplification curves of the indicated ratio of 18 S rRNA m6A-oligo and 100 ng of total RNA of peritumor tissues. Lower panels: standard curves for calculation of m6A fractions at the 18 S rRNA A1832 site in peritumor tissues. Each point on the standard curves is indicated by the mean value from three independent experiments. d-f, Upper panels: fluorescence amplification curves of the indicated ratios of 18 S rRNA m6A-oligo and 100 ng of total RNA of tumor tissues. Lower panels: standard curves for calculation of m6A fractions at the 18 S rRNA A1832 site in HCC tumor tissues. Each point on the standard curves is indicated by the mean value from three independent experiments.
Extended Data Fig. 3 METTL5/TRMT112-mediated 18 S rRNA m6A modification regulates 80 S ribosome assembly and global mRNA translation.
a, Western blot analysis of METTL5 and TRMT112 protein expression in HCC cells with or without METTL5 knockdown. b, Western blot analysis of METTL5 and TRMT112 protein expression in HCC cells with or without TRMT112 knockdown. c, Anti-m6A and anti-IgG IP-qPCR analysis of 18 S rRNA m6A modification of HepG2 cells with or without METTL5 knockdown. n = 3. (c-e,i) the unit for n is “replicates”. d,e, Anti-m6A and anti-IgG IP-qPCR analysis of 18 S rRNA m6A modification of HepG2 and Huh7 cells with or without TRMT112 depletion. n = 3. f, TAE agarose gel electrophoresis of RNAs from control and METTL5-knockdown HepG2 cells. g, Western blot analysis of puromycin incorporated into nascent peptides to monitor global mRNA translation in HepG2 cells with or without METTL5 depletion. Coomassie bright blue gel in the right panel serves as a control. h, Polysome profiling analysis showed that 80 S subunit peaks were significantly decreased upon METTL5 knockdown. i, Anti-RPL24 RIP-qPCR assay revealed that METTL5 knockdown significantly decreases the interaction between 18 S rRNA and RPL24. n = 3. In the histograms, the data are shown as the mean ± SD with P-values labeled on individual panels. P values were indicated by two-tailed unpaired Student’s t test for (c-e and i) in this figure.
Extended Data Fig. 4 METTL5 knockdown impairs fatty acid metabolism.
Nile red staining of lipid droplets in control and METTL5-knockdown HCC cells. n = 3 replicates. Scale bar, 200 μm. The unit for n is “replicates”.
Extended Data Fig. 5 METTL5 depletion decreases PUFAs in HCC cells.
a, Z score plot of long-chain FA expression in control and METTL5-knockout HCC cells. n = 6. (a,b) the unit for n is “replicates”. b, The top 9 decreased PUFAs in METTL5-knockout HCC cells. In the boxplots, the median lines represents the median, the bottom and top lines correspond to the 25th and 75th percentiles, and the whiskers represent the maximum and minimum values. n = 6. The P values were obtained from a two-sided Student’s t-test.
Extended Data Fig. 6 METTL5 knockdown impairs fatty acid β-oxidation.
a, b, OCRs of control and METTL5-knockdown HCC cells treated with BSA, palmitate-BSA (PA) and control medium or etomoxir (ETO). After sequential injection of ETO (4 μM) or control medium, oligomycin (Oligo, 1.5 μM), FCCP (1 μM) and rotenone/antimycin (R/A, 0.5 μM), a Seahorse Extracellular Flux (XF) Analyzer was used to perform this assay. The data are shown as the mean ± SD. n = 6 samples. c, d, Basal respiration of control and METTL5-knockdown HCC cells. n = 6 samples. e, f, Maximal respiration of control and METTL5-knockdown HCC cells. n = 6 samples. g, Acute response (decrease in basal OCR after incubation with etomoxir) of control and METTL5-knockdown HCC cells. n = 6 samples. In the histograms, the data are shown as the mean ± SD with P-values labeled on individual panels. P values were indicated by two-tailed unpaired Student’s t test for (c-g) in this figure. (a-g) the unit for n is “samples”.
Extended Data Fig. 7 METTL5 knockdown inhibits the translation of ACSL family mRNAs.
a-d, Total mRNA expression of the ACSL family in control and METTL5-knockdown HCC cells. n = 3 replicates. e-h, Polyribosome-bound mRNA levels of the ACSL family in control and METTL5-knockdown HCC cells. n = 3 replicates. In the histograms, the data are presented as the mean ± SD with P-values labeled on individual panels. P values are indicated by two-tailed unpaired Student’s t test for all analysis in this figure and only P < 0.05 was shown in the figure. (a-h) the unit for n is “replicates”.
Extended Data Fig. 8 Restoring ACSL4 expression in METTL5-knockout cells promotes fatty acid β-oxidation.
a-d, Intracellular free fatty acids (a), triglycerides (b), cholesterol (c) and lipid droplets (d, e) in METTL5-knockout HepG2 and Huh7 cells with or without ACSL4 overexpression. n = 3 replicates. Scale bar, 200 μm. f, g, OCR of METTL5-knockout HepG2 and Huh7 cells with or without ACSL4 overexpression. The data are shown as the mean ± SD. n = 6 samples. h-m, Basal respiration (h, i), maximal respiration (j, k) and acute responses (l, m) of control and METTL5-knockout HepG2 and Huh7 cells with or without ACSL4 overexpression. n = 6 samples. (a-e) the unit for n is “replicates”. (f-m) the unit for n is “samples”.
Extended Data Fig. 9 Restoring ACSL4 expression in METTL5-knockout cells promotes HCC progression.
a, Growth curves of METTL5-knockout HepG2 cells with or without ACSL4 overexpression. The data are shown as the mean ± SD. n = 6 replicates. b, c, Colony formation assay: representative colony images of METTL5-knockout HepG2 cells with or without ACSL4 overexpression (b) and statistical histogram of colonies (c). n = 3 replicates. d, e, Apoptosis analysis of METTL5-knockout HepG2 cells with or without ACSL4 overexpression. n = 3 replicates. f, g, Cell cycle analysis of METTL5-knockout HepG2 cells with or without ACSL4 overexpression. n = 3 samples. In the histograms, the data are presented as the mean ± SD with P-values labeled on individual panels. P values are indicated by two-tailed unpaired Student’s t-test for (a, c, e and g) in this figure. (a-g) the unit for n is “replicates”.
Extended Data Fig. 10 ACSL4 depletion in METTL5-overexpressing cells inhibits HCC progression and β-oxidation of fatty acids.
a, METTL5 and ACSL4 protein expression after knockdown of ACSL4 in METTL5-overexpressing HCC cells. b,c, Growth curves of METTL5-overexpressing HCC cells with or without ACSL4 depletion. The data are shown as the mean ± SD. n = 6 replicates. d,e, Colony formation assay: representative colony images of METTL5-overexpressing HCC cells with or without ACSL4 depletion (d) and statistical histogram of colonies (e). n = 3 replicates. f,g, Transwell migration and invasion assays: representative migration and invasion images of METTL5-overexpressing HCC cells with or without ACSL4 depletion (f) and statistical histogram of migratory and invasive cells (g). n = 3 replicates. Scale bar, 100 μm. h,i, Wound healing assay: representative wound healing images of METTL5-overexpressing HCC cells with or without ACSL4 depletion (h) and statistical histogram of migration rates (i). n = 3 replicates. Scale bar, 500 μm. j-q, OCR (j,k), basal respiration (l,m), maximal respiration (n,o) and acute response (p,q) analysis of METTL5-overexpressing HCC cells with or without ACSL4 depletion. n = 6 samples. In the histograms, the data are presented as the mean ± SD with P-values labeled on individual panels. P values are indicated by two-tailed unpaired Student’s t test for (c, e, g, i and l-q) in this figure. (b-i) the unit for n is “replicates”. (j-q) the unit for n is “samples”.
Supplementary information
Supplementary Information
Supplementary Methods, Supplementary Tables 1–6 and Supplementary Figures 1 and 2
Source data
Source Data Fig. 1
Statistical Source Data
Source Data Fig. 1
Unprocessed Western Blots
Source Data Fig. 2
Statistical Source Data
Source Data Fig. 2
Unprocessed Western Blots
Source Data Fig. 3
Statistical Source Data
Source Data Fig. 3
Unprocessed Western Blots and Gels
Source Data Fig. 4
Statistical Source Data
Source Data Fig. 4
Unprocessed Western Blots
Source Data Fig. 5
Statistical Source Data
Source Data Fig. 5
Unprocessed Western Blots
Source Data Fig. 6
Statistical Source Data
Source Data Fig. 6
Unprocessed Western Blots
Source Data Fig. 7
Statistical Source Data
Source Data Fig. 7
Unprocessed Western Blots
Source Data Extended Data Fig. 1
Statistical Source Data
Source Data Extended Data Fig. 2
Statistical Source Data
Source Data Extended Data Fig. 3
Statistical Source Data
Source Data Extended Data Fig. 3
Unprocessed Western Blots and Gels
Source Data Extended Data Fig. 5
Statistical Source Data
Source Data Extended Data Fig. 6
Statistical Source Data
Source Data Extended Data Fig. 7
Statistical Source Data
Source Data Extended Data Fig. 8
Statistical Source Data
Source Data Extended Data Fig. 9
Statistical Source Data
Source Data Extended Data Fig. 10
Statistical Source Data
Source Data Extended Data Fig. 10
Unprocessed Western Blots
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Peng, H., Chen, B., Wei, W. et al. N6-methyladenosine (m6A) in 18S rRNA promotes fatty acid metabolism and oncogenic transformation. Nat Metab 4, 1041–1054 (2022). https://doi.org/10.1038/s42255-022-00622-9
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1038/s42255-022-00622-9
This article is cited by
-
Identification of fatty acids synthesis and metabolism-related gene signature and prediction of prognostic model in hepatocellular carcinoma
Cancer Cell International (2024)
-
A CTL − Lys immune function maintains insect metamorphosis by preventing gut bacterial dysbiosis and limiting opportunistic infections
BMC Biology (2024)
-
RNA modifications in cellular metabolism: implications for metabolism-targeted therapy and immunotherapy
Signal Transduction and Targeted Therapy (2024)
-
RNA modification-mediated mRNA translation regulation in liver cancer: mechanisms and clinical perspectives
Nature Reviews Gastroenterology & Hepatology (2024)
-
m6A/m1A/m5C-Associated Methylation Alterations and Immune Profile in MDD
Molecular Neurobiology (2024)