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N6-methyladenosine (m6A) in 18S rRNA promotes fatty acid metabolism and oncogenic transformation

Nature Metabolism volume 4, pages 1041–1054 (2022)Cite this article

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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.

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Fig. 1: METTL5 is upregulated in various cancers and correlated with poor survival.
Fig. 2: Overexpression of METTL5 promotes HCC tumorigenesis in vitro and in vivo.
Fig. 3: Knockdown of METTL5 inhibits HCC tumorigenesis in vitro.
Fig. 4: METTL5 knockdown impairs fatty acid β-oxidation.
Fig. 5: ACSL4 mediates METTL5’s functions in fatty acid metabolism and HCC progression.
Fig. 6: METTL5 depletion inhibits fatty acid metabolism and HCC tumorigenesis in vivo.
Fig. 7: Targeting ACSL4 and METTL5 synergistically inhibits HCC tumorigenesis in vivo.

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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.

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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

Author notes

  1. These authors contributed equally: Hao Peng, Binbin Chen, Wei Wei, Siyao Guo.

  2. These authors jointly supervised this work: Sui Peng, Ming Kuang, Shuibin Lin.

Authors and Affiliations

  1. Center for Translational Medicine, Precision Medicine Institute, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Hao Peng, Wei Wei, Siyao Guo, Hui Han, Chunlong Yang, Jieyi Ma, Lu Wang & Shuibin Lin

  2. Department of Breast Cancer, Cancer Center, Guangdong Provincial People’s Hospital, Guangdong Academy of Medical Sciences, Guangzhou, China

    Hao Peng

  3. Department of Clinical Nutrition, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center of Cancer Medicine, Guangzhou, China

    Binbin Chen

  4. Institute of Precision Medicine, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Sui Peng, Ming Kuang & Shuibin Lin

  5. Department of Gastroenterology and Hepatology, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Sui Peng

  6. Clinical Trial Unit, the First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Sui Peng

  7. Department of Liver Surgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Ming Kuang

  8. Cancer Center, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China

    Ming Kuang

  9. State Key Laboratory of Oncology in South China, Sun Yat-sen University Cancer Center, Guangzhou, China

    Shuibin Lin

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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.

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Correspondence to Sui Peng, Ming Kuang or Shuibin Lin.

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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.

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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.

Source data

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.

Source data

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.

Source data

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.

Source data

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”.

Source data

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”.

Source data

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”.

Source data

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”.

Source data

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”.

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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

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