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Leucyl-tRNA synthetase is a tumour suppressor in breast cancer and regulates codon-dependent translation dynamics

Nature Cell Biology volume 24, pages 307–315 (2022)Cite this article

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Abstract

Tumourigenesis and cancer progression require enhanced global protein translation1,2,3. Such enhanced translation is caused by oncogenic and tumour-suppressive events that drive the synthesis and activity of translational machinery4,5. Here we report the surprising observation that leucyl-tRNA synthetase (LARS) becomes repressed during mammary cell transformation and in human breast cancer. Monoallelic genetic deletion of LARS in mouse mammary glands enhanced breast cancer tumour formation and proliferation. LARS repression reduced the abundance of select leucine tRNA isoacceptors, leading to impaired leucine codon-dependent translation of growth suppressive genes, including epithelial membrane protein 3 (EMP3) and gamma-glutamyltransferase 5 (GGT5). Our findings uncover a tumour-suppressive tRNA synthetase and reveal that dynamic repression of a specific tRNA synthetase—along with its downstream cognate tRNAs—elicits a downstream codon-biased translational gene network response that enhances breast tumour formation and growth.

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Fig. 1: Clinical evidence of LARS reduction upon malignant transformation.
Fig. 2: LARS suppresses tumourigenesis and proliferation.
Fig. 3: Charged tRNA profiling reveals reduction in select tRNA-Leu isoacceptors.
Fig. 4: LARS promotes translation and expression of Leu-enriched genes.
Fig. 5: EMP3 and GGT5 regulate growth downstream of LARS in a codon-dependent manner.

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Data availability

The data that support the findings of this study have been deposited in the Gene Expression Omnibus (GE) under the accession code GSE176130.

Mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE partner repository with the dataset identifier PXD026609.

The human breast cancer data were derived from the TCGA Research Network: http://cancergenome.nih.gov/. The dataset derived from this resource that supports the findings of this study is available from UCSC Xena.

All other data supporting the findings of this study are available from the corresponding author upon reasonable request. Source data are provided with this paper.

Code availability

Code generated is available from the authors upon reasonable request.

References

  1. Hanahan, D. & Weinberg, R. A. Hallmarks of cancer: the next generation. Cell 144, 646–674 (2011).

    Article  CAS  PubMed  Google Scholar 

  2. Ruggero, D. Translational control in cancer etiology. Cold Spring Harb. Perspect. Biol. 5, a012336 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  3. Leprivier, G., Rotblat, B., Khan, D., Jan, E. & Sorensen, P. H. Stress-mediated translational control in cancer cells. Biochim. Biophys. Acta Gene Regul. Mech. 1849, 845–860 (2015).

    Article  CAS  Google Scholar 

  4. Loayza-Puch, F. et al. Tumour-specific proline vulnerability uncovered by differential ribosome codon reading. Nature 530, 490–494 (2016).

    Article  CAS  PubMed  Google Scholar 

  5. Ebright, R. Y. et al. Deregulation of ribosomal protein expression and translation promotes breast cancer metastasis. Science 367, 1468–1473 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  6. Novoa, E. M. & Ribas de Pouplana, L. Speeding with control: codon usage, tRNAs, and ribosomes. Trends Genet. 28, 574–581 (2012).

    Article  CAS  PubMed  Google Scholar 

  7. Knott, S. R. V. et al. Asparagine bioavailability governs metastasis in a model of breast cancer. Nature 554, 378–381 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Goodarzi, H. et al. Modulated expression of specific tRNAs drives gene expression and cancer progression. Cell 165, 1416–1427 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Pavon-Eternod, M. et al. tRNA over-expression in breast cancer and functional consequences. Nucleic Acids Res. 37, 7268–7280 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  10. Kirchner, S. & Ignatova, Z. Emerging roles of tRNA in adaptive translation, signalling dynamics and disease. Nat. Rev. Genet. 16, 98–112 (2014).

    Article  PubMed  CAS  Google Scholar 

  11. Zhang, Z. et al. Global analysis of tRNA and translation factor expression reveals a dynamic landscape of translational regulation in human cancers. Commun. Biol. 1, 1–11 (2018).

    Article  CAS  Google Scholar 

  12. Zhou, Z., Sun, B., Nie, A., Yu, D. & Bian, M. Roles of aminoacyl-tRNA synthetases in cancer. Front. Cell Dev. Biol. 8, 1–12 (2020).

    Article  Google Scholar 

  13. Park, S. G., Schimmel, P. & Kim, S. Aminoacyl tRNA synthetases and their connections to disease. Proc. Natl Acad. Sci. USA 105, 11043–11049 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Vo, M.-N. et al. ANKRD16 prevents neuron loss caused by an editing-defective tRNA synthetase. Nature 557, 510–515 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  15. Antonellis, A. et al. Glycyl tRNA synthetase mutations in Charcot–Marie–Tooth disease type 2D and distal spinal muscular atrophy type V. Am. J. Hum. Genet. 72, 1293–1299 (2003).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Komarov, P. G. et al. A chemical inhibitor of p53 that protects mice from the side effects of cancer therapy. Science 285, 1733 LP–1731737 (1999).

    Article  Google Scholar 

  17. Guy, C. T., Cardiff, R. D. & Muller, W. J. Induction of mammary tumors by expression of polyomavirus middle T oncogene: a transgenic mouse model for metastatic disease. Mol. Cell. Biol. 12, 954–961 (1992).

    CAS  PubMed  PubMed Central  Google Scholar 

  18. Testa, G. et al. A reliable lacZ expression reporter cassette for multipurpose, knockout-first alleles. Genesis 38, 151–158 (2004).

    Article  CAS  PubMed  Google Scholar 

  19. Tavora, B. et al. Tumoural activation of TLR3–SLIT2 axis in endothelium drives metastasis. Nature 586, 299–304 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  20. Padmanaban, V. et al. Organotypic culture assays for murine and human primary and metastatic-site tumors. Nat. Protoc. 15, 2413–2442 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  21. Han, J. M. et al. Leucyl-tRNA synthetase is an intracellular leucine sensor for the mTORC1-signaling pathway. Cell 149, 410–424 (2012).

    Article  CAS  PubMed  Google Scholar 

  22. He, X. D. I. et al. Sensing and transmitting intracellular amino acid signals through reversible lysine aminoacylations. Cell Metab. 27, 151–166.e6 (2018).

    Article  CAS  PubMed  Google Scholar 

  23. Pavlova, N. N. et al. Translation in amino-acid-poor environments is limited by tRNA Gln charging. eLife https://doi.org/10.7554/eLife.62307 (2020).

  24. Gobet, C. et al. Robust landscapes of ribosome dwell times and aminoacyl-tRNAs in response to nutrient stress in liver. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.1918145117 (2020).

  25. Evans, M. E., Clark, W. C., Zheng, G. & Pan, T. Determination of tRNA aminoacylation levels by high-throughput sequencing. Nucleic Acids Res. 45, e133 (2017).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  26. Lund, Elsebet & Dahlberg, J. E. Proofreading and aminoacylation of tRNAs before export from the nucleus. Science 282, 2082–2085 (1998).

    Article  CAS  PubMed  Google Scholar 

  27. Elf, J., Nilsson, D., Tenson, T. & Ehrenberg, M. Selective charging of tRNA isoacceptors explains patterns of codon usage. Science 300, 1718–1722 (2003).

    Article  CAS  PubMed  Google Scholar 

  28. Gilbert, L. A. et al. Genome-scale CRISPR-mediated control of gene repression and activation. Cell 159, 647–661 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  29. Chan, P. P. & Lowe, T. M. GtRNAdb: a database of transfer RNA genes detected in genomic sequence. Nucleic Acids Res. 37, 93–97 (2009).

    Article  CAS  Google Scholar 

  30. Chan, P. P. & Lowe, T. M. GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes. Nucleic Acids Res. 44, D184–D189 (2016).

    Article  CAS  PubMed  Google Scholar 

  31. Novoa, E. M., Pavon-Eternod, M., Pan, T. & Ribas De Pouplana, L. A role for tRNA modifications in genome structure and codon usage. Cell 149, 202–213 (2012).

    Article  CAS  PubMed  Google Scholar 

  32. Sanz, E. et al. Cell-type-specific isolation of ribosome-associated mRNA from complex tissues. Proc. Natl Acad. Sci. USA 106, 13939–13944 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  33. McGlincy, N. J. & Ingolia, N. T. Transcriptome-wide measurement of translation by ribosome profiling. Methods 126, 112–129 (2017).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  34. Zecha, J. et al. TMT labeling for the masses: a robust and cost-efficient, in-solution labeling approach. Mol. Cell. Proteom. 18, 1468–1478 (2019).

    Article  CAS  Google Scholar 

  35. Wang, Y. W., Cheng, H. L., Ding, Y. R., Chou, L. H. & Chow, N. H. EMP1, EMP 2, and EMP3 as novel therapeutic targets in human cancer. Biochim. Biophys. Acta Rev. Cancer 1868, 199–211 (2017).

    Article  CAS  PubMed  Google Scholar 

  36. Alaminos, M. et al. EMP3, a myelin-related gene located in the critical 19q13.3 region, is epigenetically silenced and exhibits features of a candidate tumor suppressor in glioma and neuroblastoma. Cancer Res. 65, 2565–2571 (2005).

    Article  CAS  PubMed  Google Scholar 

  37. Wang, W. et al. Effector T cells abrogate stroma-mediated chemoresistance in ovarian cancer. Cell 165, 1092–1105 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  38. Xu, Y. et al. Translation control of the immune checkpoint in cancer and its therapeutic targeting. Nat. Med. 25, 301–311 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Dittmar, K. A., Sørensen, M. A., Elf, J., Ehrenberg, M. & Pan, T. Selective charging of tRNA isoacceptors induced by amino-acid starvation. EMBO Rep. 6, 151–157 (2005).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Darnell, A. M., Subramaniam, A. R. & O’Shea, E. K. Translational control through differential ribosome pausing during amino acid limitation in mammalian cells. Mol. Cell 71, 229–243.e11 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  41. Huh, D. et al. A stress‐induced tyrosine‐tRNA depletion response mediates codon‐based translational repression and growth suppression. EMBO J. 40, 1–16 (2021).

    Article  CAS  Google Scholar 

  42. Shin, S.-H. et al. Implication of leucyl-tRNA synthetase 1 (LARS1) over-expression in growth and migration of lung cancer cells detected by siRNA targeted knock-down analysis. Exp. Mol. Med. 40, 229–236 (2008).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  43. Gomez, M. A. R. & Ibba, M. Aminoacyl-tRNA synthetases. RNA 26, 910–936 (2020).

    Article  CAS  Google Scholar 

  44. Perona, J. J. & Hou, Y.-M. Indirect readout of tRNA for aminoacylation. Biochemistry 46, 10419–10432 (2007).

    Article  CAS  PubMed  Google Scholar 

  45. Tocchini-Valentini, G., Saks, M. E. & Abelson, J. tRNA leucine identity and recognition sets. J. Mol. Biol. 298, 779–793 (2000).

    Article  CAS  PubMed  Google Scholar 

  46. Park, S. J. & Schimmel, P. Evidence for interaction of an aminoacyl transfer RNA synthetase with a region important for the identity of its cognate transfer RNA. J. Biol. Chem. 263, 16527–16530 (1988).

    Article  CAS  PubMed  Google Scholar 

  47. Fender, A., Sissler, M., Florentz, C. & Giegé, R. Functional idiosyncrasies of tRNA isoacceptors in cognate and noncognate aminoacylation systems. Biochimie 86, 21–29 (2004).

    Article  CAS  PubMed  Google Scholar 

  48. McFarland, M. R. et al. The molecular aetiology of tRNA synthetase depletion: induction of a GCN4 amino acid starvation response despite homeostatic maintenance of charged tRNA levels. Nucleic Acids Res. 48, 3071–3088 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  49. Guo, M. & Schimmel, P. Essential nontranslational functions of tRNA synthetases. Nat. Chem. Biol. 9, 145–153 (2013).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  50. Gandin, V. et al. Polysome fractionation and analysis of mammalian translatomes on a genome-wide scale. J. Vis. Exp. https://doi.org/10.3791/51455 (2014).

  51. Xiao, Z., Zou, Q., Liu, Y. & Yang, X. Genome-wide assessment of differential translations with ribosome profiling data. Nat. Commun. 7, 11194 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  52. Lauria, F. et al. riboWaltz: optimization of ribosome P-site positioning in ribosome profiling data. PLoS Comput. Biol. 14, 1–20 (2018).

    Article  CAS  Google Scholar 

  53. Hickernell, F. J. Quadrature error bound. Math. Comput. 67, 299–322 (1998).

    Article  Google Scholar 

  54. Tyanova, S. et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat. Methods 13, 731–740 (2016).

    Article  CAS  PubMed  Google Scholar 

  55. Wagner, K. U. et al. Cre-mediated gene deletion in the mammary gland. Nucleic Acids Res. 25, 4323–4330 (1997).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

We thank members of the Tavazoie laboratory for thoughtful feedback on previous versions of the manuscript. We also thank The Rockefeller University resource centers: C. Zhao and C. Lai from the Genomics Resource Center, A. North and staff at the Bio-Imaging resource facility, and V. Francis from the Comparative Bioscience Center and veterinary staff for animal husbandry and care. M.C.P was supported by a Medical Scientist Training Program grant from the National Institute of General Medical Sciences of the National Institutes of Health under award number T32GM007739 to the Weill Cornell/Rockefeller/Sloan Kettering Tri-Institutional MD-PhD programme, and by an F30 Predoctoral Fellowship from the National Cancer Institute of the National Institutes of Health under award number 1F30CA247026-01. H.A. was supported by a training grant from the National Institutes of Health under award number F32GM133118. H.G. was supported by an R01 from the National Cancer Institute of the National Institutes of Health under award number R01CA240984. S.F.T. was supported by the Breast Cancer Research Foundation award, the Reem-Kayden award, the Department of Defense Collaborative Scholars and Innovators award, a U54 award from the National Cancer Institute of the National Institute of Health under award number U54CA261701, and a Faculty Scholars award from the Howard Hughes Medical Institute. S.F.T. and the Tavazoie lab were supported by the Black Family and the Black Family Metastasis Research Center. The results published here are in part based on data generated by the TCGA Research Network: https://www.cancer.gov/tcga. Some figures were generated with assistance from biorender.com.

Author information

Author notes

  1. These authors jointly supervised this work: Hani Goodarzi and Sohail F. Tavazoie.

Authors and Affiliations

  1. Laboratory of Systems Cancer Biology, The Rockefeller University, New York, NY, USA

    Maria C. Passarelli, Alexandra M. Pinzaru, Maria V. Liberti & Sohail F. Tavazoie

  2. Department of Biochemistry and Biophysics, University of California, San Francisco, CA, USA

    Hosseinali Asgharian & Hani Goodarzi

  3. Department of Urology, University of California, San Francisco, CA, USA

    Hosseinali Asgharian & Hani Goodarzi

  4. Helen Diller Family Comprehensive Cancer Center, University of California, San Francisco, CA, USA

    Hosseinali Asgharian & Hani Goodarzi

  5. Proteomics Resource Center, The Rockefeller University, New York, NY, USA

    Søren Heissel & Henrik Molina

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Contributions

M.C.P and S.F.T. designed the experiments. M.C.P., A.P., M.V.L, S.H. and H.M. performed the experiments. H.G. and H.A. performed ribosome profiling and polysome profiling sequencing analyses. M.C.P. and S.F.T. wrote the paper with input from the co-authors.

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Correspondence to Hani Goodarzi or Sohail F. Tavazoie.

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S.F.T. is a cofounder, shareholder and member of the scientific advisory board of Inspirna. The remaining authors declare no competing interests.

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Extended data

Extended Data Fig. 1 LARS is repressed during malignant transformation.

a, Aminoacyl tRNA synthetase mRNA levels in non-transformed human mammary epithelial cell line MCF10A (left, dark gray or magenta) compared to HCC1806 (right, light gray or pink), normalized to GAPDH. Magenta and pink-colored expression pairs indicate a significant decrease in aaRS expression between MCF10A and HCC1806 of 50% or greater (statistics calculated by unpaired two-tailed Student’s t-test with Bonferroni correction for multiple comparisons, n = 4 samples per group). b, Soft agar colony formation assays of PyMT-transformed MCF10A and NMuMG cells compared to empty-transduced control. Representative of n = 3 experiments. c, Tumor growth curves for transplanted PyMT transformed NMuMG cells compared to control, statistics calculated by 2-way ANOVA. (n = 4 mice per group) d, Western blot of QARS in PyMT-transformed MCF10A cells compared to empty control. e, Quantification of d. f, Western blot of HARS, NARS, IARS in PyMT-transformed MCF10A cells compared to empty control. g, Quantification of f. d–g, HSC70 as a loading control, representative of n = 2 independent experiments. h, Genomic copy number assay for LARS in MCF10A and HCC1806 cells, using RNAseP as a normalization control. i, qRT-PCR of LARS mRNA levels in MCF10A, HCC1806, MDA-MB-231 and T47D cell lines, normalized to GAPDH. j, LARS mRNA levels in NMuMG, 4T07 and EO771 cell lines, normalized to GAPDH. k, Above, Western blot of LARS in MDA-MB-231 parental cells compared to highly metastatic LM2 cell lines. Below, quantification. l, Above, Western blot of LARS in NMuMG cell lines compared to isogenic low and high metastatic cell lines 67NR, 4T07 and 4T1. Below, quantification. HSC70 is used as a loading control in i–j. h–l Representative of n = 3 independent experiments. All data are mean ± s.e.m., statistics calculated by unpaired two-tailed Student’s t-test.

Source data

Extended Data Fig. 2 LARS depletion promotes tumor growth.

a, Representative images of Ki-67 staining in LARS-depleted PyMT tumours. Scale bar, 100px. b, Quantification of a as mean fluorescence intensity of Ki-67 normalized to DAPI. Cre- n = 15, Cre+ n = 14, where each data point represents staining from an individual animal. Statistics calculated by two-tailed Mann-Whitney test. c, Representative images of LARS-depleted PyMT Cre+ or Cre- tumour-derived organoids cultured in Matrigel, transfected with LARS constructs – wild-type, catalytically inactive (K716A/K719A) ‘CAT’, leucine-binding null (F50A/Y52A) ‘LEU’21,22 or empty vector control. d, Quantification of change in 2D projection of organoid area, normalized to Day 1. 26-30 organoids quantified per experimental group, representative of n = 3 independent experiments e, Representative images of wild-type PyMT tumour-derived organoids cultured in Matrigel, transfected with indicated LARS constructs. f, Quantification of change in 2D projection of organoid area, normalized to Day 1. 44-81 organoids quantified per experimental group, representative of n = 3 experiments c-f, Scale bar, 100 µm, Statistics calculated by unpaired two-tailed Student’s t-test. All data are mean ± s.e.m.

Source data

Extended Data Fig. 3 LARS reduction enhances tumorigenesis through depletion of tRNA-LeuCAG.

a, Volcano plot showing differential expression of total tRNAs in HCC1806 cell line compared to MCF10A (n = 3 per group). b, Northern blot validation of reduction in tRNA-Leu species in LARS-depleted 4T07 cells compared to control. c, Quantification of b, n = 8-9 replicates examined over 3 independent experiments. Statistics calculated by unpaired two-tailed Student’s t-test. d, Western blot depicting LARS knockdown levels in 4T07 cells with two independent shRNAs. Representative of n = 3 independent experiments. e, Northern blot validation of reduction in charged tRNA-Leu species by acid urea PAGE in LARS-depleted 4T07 cells compared to control. Deacyl species are boxed for clarification. Representative of n = 3 independent experiments. f, Quantification of e as a ratio of charged to total tRNA, n = 4-5 replicates examined over 3 independent experiments. Statistics calculated by unpaired one-tailed Student’s t-test. g, Northern blot depicting CRISPRi-mediated tRNA-Leu depletion in MCF10A cells. Representative of n = 2 independent experiments. h, Quantification of g. i–k, In vivo metabolomics of branched chain amino acids in LARS-depleted 4T07 tumours (n = 5 mice per group). n.s. by unpaired two-tailed Student’s t-test. All data are mean ± s.e.m.

Source data

Extended Data Fig. 4 LARS facilitates Leu-rich translation for select isoacceptors in vitro and in vivo.

a, Polysome traces from gradient fractionation and polysome profiling in LARS-depleted 4T07 cells compared to control. (n = 3 samples per group). b–e, Cumulative distribution functions of differentially expressed genes by polysome occupancy, stratified by Leu-CUA (b), Leu-CUU (c), Leu-UUA (d), and Leu-UUG (e) codon content, respectively. p < .4581 (b), p < 3.88e-5 (c), p < 2.20e-16 (d), p < 0.09933 (e), two-sided KS test. f–h, Cumulative distribution functions of differential gene expression by RiboTag (n = 3 mice per group), stratified by abundance of Leu isoacceptors (f) Leu-CUG (g) and Leu-CUC (h). p = 0.0004909 (f), p < 0.003378 (g), p < 2.91e-8 (h), two-sided KS test. i, scatter plot of RiboSeq log2 fold translation efficiency ratios (logTER) in LARS depleted cells compared to control, plotted as a function of fractional CUG codon content (n = 2 samples per group). Regression coefficient R = −0.252, p = 7.7 e-138. j, Ribosome dwell time analysis in RiboSeq data (n = 2 samples per group). Leu codons show increased dwell time compared to other codons in Lars knockdown relative to control. shCtrl vs. shLARS3, p = 0.001642; shCtrl vs. shLARS4, p = 0.02282, Kruskal Wallis rank sum test. k, Analysis of leucine sequence discrepancy, or ‘clumpiness’ on ribosome dwell time in 4T07 shCtrl cells. Regression coefficient = 0.023310, p < 2e-16.

Source data

Extended Data Fig. 5 LARS regulates protein expression of tumor suppressors EMP3 and GGT5.

a,b, Clinical association of EMP3, GGT5 in the TCGA database normal breast tissue samples compared to primary tumor. Statistics calculated by two-tailed Mann-Whitney test. c, Western blot of LARS, EMP3, and GGT5 in LARS-depleted NMuMG cells. HSC70 is used as a loading control. d, Quantification of LARS (n = 3) in c, representative of 3 independent experiments. e–f, Quantification of EMP3 (n = 12, e) or GGT5 (n = 9, f) replicates over n = 3 independent experiments. g,h, mRNA levels of EMP3, GGT5 in Lars-depleted NMuMG cells normalized to GAPDH, by qRT-PCR. Representative of n = 3 independent experiments. d-h, Statistics calculated by unpaired two-tailed Student’s t-test. All data are mean ± s.e.m.

Source data

Supplementary information

Reporting Summary

Supplementary Table

Supplementary Tables 1–4. Supplementary Table 1. CUG-enriched genes in RiboSeq. Supplementary Table 2. Significantly reduced proteins in LARS-depleted PyMT tumours by TMT proteomics. Supplementary Table 3. Sequence alignment of tRNA-Leu Isodecoders. Supplementary Table 4. Probe and primer sequences.

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Passarelli, M.C., Pinzaru, A.M., Asgharian, H. et al. Leucyl-tRNA synthetase is a tumour suppressor in breast cancer and regulates codon-dependent translation dynamics. Nat Cell Biol 24, 307–315 (2022). https://doi.org/10.1038/s41556-022-00856-5

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