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Cited by 20RNA and protein synthesis mechanismsRNA modifications and cancerGenomics and Phylogenetic Studies

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Dynamic Landscapes of tRNA Transcriptomes and Translatomes in Diverse Mouse Tissues

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Yu, Peng, Zhou, Siting, Gao, Yan, Liang, Yu, Guo, Wenbing, Wang, Dan Ohtan, Ding, Shuaiwen, Lin, Shuibin, Wang, Jinkai, Cun, Yixian·2023·Genomics, Proteomics & Bioinformatics
DOI 10.1016/j.gpb.2022.07.006PMC PMC10787195

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Abstract

Although the function of tRNAs in the translational process is well established, it remains controversial whether tRNA abundance is tightly associated with translational efficiency (TE) in mammals. Moreover, how critically the expression of tRNAs contributes to the establishment of tissue-specific proteomes in mammals has not been well addressed. Here, we measured both tRNA expression using demethylase-tRNA sequencing (DM-tRNA-seq) and TE of mRNAs using ribosome-tagging sequencing (RiboTag-seq) in the brain, heart, and testis of mice. Remarkable variation in the expression of tRNA isodecoders was observed among different tissues. When the statistical effect of isodecoder-grouping on reducing variations is considered through permutating the anticodons, we observed an expected reduction in the variation of anticodon expression across all samples, an unexpected smaller variation of anticodon usage bias, and an unexpected larger variation of tRNA isotype expression at amino acid level. Regardless of whether or not they share the same anticodons, the isodecoders encoding the same amino acids are co-expressed across different tissues. Based on the expression of tRNAs and the TE of mRNAs, we find that the tRNA adaptation index (tAI) and TE are significantly correlated in the same tissues but not between tissues; and tRNA expression and the amino acid composition of translating peptides are positively correlated in the same tissues but not between tissues. We therefore hypothesize that the tissue-specific expression of tRNAs might be due to post-transcriptional mechanisms. This study provides a resource for tRNA and translation studies, as well as novel insights into the dynamics of tRNAs and their roles in translational regulation.

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Research works printed but not linked: 52

  1. Rodnina, 2011, The ribosome as a molecular machine: the mechanism of tRNA–mRNA movement in translocation, Biochem Soc Trans, 39, 658, 10.1042/BST0390658 · DOI
  2. Kirchner, 2015, Emerging roles of tRNA in adaptive translation, signalling dynamics and disease, Nat Rev Genet, 16, 98, 10.1038/nrg3861 · DOI
  3. Chan, 2016, GtRNAdb 2.0: an expanded database of transfer RNA genes identified in complete and draft genomes, Nucleic Acids Res, 44, D184, 10.1093/nar/gkv1309 · DOI
  4. Goodenbour, 2006, Diversity of tRNA genes in eukaryotes, Nucleic Acids Res, 34, 6137, 10.1093/nar/gkl725 · DOI
  5. Chan, 2009, GtRNAdb: a database of transfer RNA genes detected in genomic sequence, Nucleic Acids Res, 37, D93, 10.1093/nar/gkn787 · DOI
  6. Kutter, 2011, Pol III binding in six mammals shows conservation among amino acid isotypes despite divergence among tRNA genes, Nat Genet, 43, 948, 10.1038/ng.906 · DOI
  7. Kanaya, 1999, Studies of codon usage and tRNA genes of 18 unicellular organisms and quantification of Bacillus subtilis tRNAs: gene expression level and species-specific diversity of codon usage based on multivariate analysis, Gene, 238, 143, 10.1016/S0378-1119(99)00225-5 · DOI
  8. Dong, 1996, Co-variation of tRNA abundance and codon usage in Escherichia coli at different growth rates, J Mol Biol, 260, 649, 10.1006/jmbi.1996.0428 · DOI · Paper page
  9. Ikemura, 1985, Codon usage and tRNA content in unicellular and multicellular organisms, Mol Biol Evol, 2, 13
  10. Gingold, 2014, A dual program for translation regulation in cellular proliferation and differentiation, Cell, 158, 1281, 10.1016/j.cell.2014.08.011 · DOI
  11. Waldman, 2010, Translation efficiency in humans: tissue specificity, global optimization, and differences between developmental stages, Nucleic Acids Res, 38, 2964, 10.1093/nar/gkq009 · DOI
  12. Hernandez-Alias, 2020, Translational efficiency across healthy and tumor tissues is proliferation-related, Mol Syst Biol, 16, e9275, 10.15252/msb.20199275 · DOI
  13. Najafabadi, 2009, Universal function-specificity of codon usage, Nucleic Acids Res, 37, 7014, 10.1093/nar/gkp792 · DOI
  14. Sémon, 2006, No evidence for tissue-specific adaptation of synonymous codon usage in humans, Mol Biol Evol, 23, 523, 10.1093/molbev/msj053 · DOI
  15. Kanaya, 2001, Codon usage and tRNA genes in eukaryotes: correlation of codon usage diversity with translation efficiency and with CG-dinucleotide usage as assessed by multivariate analysis, J Mol Evol, 53, 290, 10.1007/s002390010219 · DOI
  16. Erber, 2020, LOTTE-seq (long hairpin oligonucleotide based tRNA high-throughput sequencing): specific selection of tRNAs with 3′-CCA end for high-throughput sequencing, RNA Biol, 17, 23, 10.1080/15476286.2019.1664250 · DOI
  17. Hu, 2021, Quantitative mapping of the cellular small RNA landscape with AQRNA-seq, Nat Biotechnol, 39, 978, 10.1038/s41587-021-00874-y · DOI
  18. Zhang, 2022, tRNA modification dynamics from individual organisms to metaepitranscriptomics of microbiomes, Mol Cell, 82, 891, 10.1016/j.molcel.2021.12.007 · DOI
  19. Pinkard, 2020, Quantitative tRNA-sequencing uncovers metazoan tissue-specific tRNA regulation, Nat Commun, 11, 4104, 10.1038/s41467-020-17879-x · DOI
  20. Behrens, 2021, High-resolution quantitative profiling of tRNA abundance and modification status in eukaryotes by mim-tRNAseq, Mol Cell, 81, 1802, 10.1016/j.molcel.2021.01.028 · DOI
  21. Suzuki, 2021, The expanding world of tRNA modifications and their disease relevance, Nat Rev Mol Cell Biol, 22, 375, 10.1038/s41580-021-00342-0 · DOI
  22. Berg, 2021, Transfer RNAs: diversity in form and function, RNA Biol, 18, 316, 10.1080/15476286.2020.1809197 · DOI
  23. Schimmel, 2018, The emerging complexity of the tRNA world: mammalian tRNAs beyond protein synthesis, Nat Rev Mol Cell Biol, 19, 45, 10.1038/nrm.2017.77 · DOI
  24. Svenningsen, 2017, Transfer RNA is highly unstable during early amino acid starvation in Escherichia coli, Nucleic Acids Res, 45, 793, 10.1093/nar/gkw1169 · DOI
  25. Zheng, 2015, Efficient and quantitative high-throughput tRNA sequencing, Nat Methods, 12, 835, 10.1038/nmeth.3478 · DOI · Paper page
  26. Sanz, 2009, Cell-type-specific isolation of ribosome-associated mRNA from complex tissues, Proc Natl Acad Sci U S A, 106, 13939, 10.1073/pnas.0907143106 · DOI · Paper page
  27. Jühling, 2009, tRNAdb 2009: compilation of tRNA sequences and tRNA genes, Nucleic Acids Res, 37, D159, 10.1093/nar/gkn772 · DOI
  28. Anders, 2010, Differential expression analysis for sequence count data, Genome Biol, 11, R106, 10.1186/gb-2010-11-10-r106 · DOI · Paper page
  29. Sanz, 2019, RiboTag: ribosomal tagging strategy to analyze cell-type-specific mRNA expression in vivo, Curr Protoc Neurosci, 88, e77, 10.1002/cpns.77 · DOI
  30. Sharp, 1986, Codon usage in yeast: cluster analysis clearly differentiates highly and lowly expressed genes, Nucleic Acids Res, 14, 5125, 10.1093/nar/14.13.5125 · DOI
  31. Hoernes, 2018, Translation of non-standard codon nucleotides reveals minimal requirements for codon–anticodon interactions, Nat Commun, 9, 4865, 10.1038/s41467-018-07321-8 · DOI
  32. Saint-Léger, 2015, The importance of codon–anticodon interactions in translation elongation, Biochimie, 114, 72, 10.1016/j.biochi.2015.04.013 · DOI
  33. Sharp, 1987, The codon adaptation index – a measure of directional synonymous codon usage bias, and its potential applications, Nucleic Acids Res, 15, 1281, 10.1093/nar/15.3.1281 · DOI · Paper page
  34. dos Reis, 2004, Solving the riddle of codon usage preferences: a test for translational selection, Nucleic Acids Res, 32, 5036, 10.1093/nar/gkh834 · DOI
  35. Chamary, 2006, Hearing silence: non-neutral evolution at synonymous sites in mammals, Nat Rev Genet, 7, 98, 10.1038/nrg1770 · DOI
  36. Ingolia, 2009, Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling, Science, 324, 218, 10.1126/science.1168978 · DOI · Paper page
  37. Szavits-Nossan, 2020, Inferring efficiency of translation initiation and elongation from ribosome profiling, Nucleic Acids Res, 48, 9478, 10.1093/nar/gkaa678 · DOI
  38. Suzuki, 2002, Taurine as a constituent of mitochondrial tRNAs: new insights into the functions of taurine and human mitochondrial diseases, EMBO J, 21, 6581, 10.1093/emboj/cdf656 · DOI
  39. Suzuki, 2020, Complete chemical structures of human mitochondrial tRNAs, Nat Commun, 11, 4269, 10.1038/s41467-020-18068-6 · DOI
  40. Asano, 2018, Metabolic and chemical regulation of tRNA modification associated with taurine deficiency and human disease, Nucleic Acids Res, 46, 1565, 10.1093/nar/gky068 · DOI
  41. Rojas-Benítez, 2017, Modulation of the proteostasis machinery to overcome stress caused by diminished levels of t6A-modified tRNAs in Drosophila, Biomolecules, 7, 25
  42. Alexandrov, 2006, Rapid tRNA decay can result from lack of nonessential modifications, Mol Cell, 21, 87, 10.1016/j.molcel.2005.10.036 · DOI
  43. Motorin, 2010, tRNA stabilization by modified nucleotides, Biochemistry, 49, 4934, 10.1021/bi100408z · DOI
  44. Zhang, 2018, Global analysis of tRNA and translation factor expression reveals a dynamic landscape of translational regulation in human cancers, Commun Biol, 1, 234, 10.1038/s42003-018-0239-8 · DOI
  45. Suzuki, 2014, A complete landscape of post-transcriptional modifications in mammalian mitochondrial tRNAs, Nucleic Acids Res, 42, 7346, 10.1093/nar/gku390 · DOI
  46. Chen, 2021, Origins and evolving functionalities of tRNA-derived small RNAs, Trends Biochem Sci, 46, 790, 10.1016/j.tibs.2021.05.001 · DOI
  47. Shi, 2021, PANDORA-seq expands the repertoire of regulatory small RNAs by overcoming RNA modifications, Nat Cell Biol, 23, 424, 10.1038/s41556-021-00652-7 · DOI
  48. Wang, 2021, CPA-seq reveals small ncRNAs with methylated nucleosides and diverse termini, Cell Discov, 7, 25, 10.1038/s41421-021-00265-2 · DOI
  49. Lin, 2019, Nucleotide resolution profiling of m7G tRNA modification by TRAC-Seq, Nat Protoc, 14, 3220, 10.1038/s41596-019-0226-7 · DOI
  50. Wang, 2018, miR-372 and miR-373 enhance the stemness of colorectal cancer cells by repressing differentiation signaling pathways, Mol Oncol, 12, 1949, 10.1002/1878-0261.12376 · DOI
  51. Langmead, 2012, Fast gapped-read alignment with Bowtie 2, Nat Methods, 9, 357, 10.1038/nmeth.1923 · DOI · Paper page
  52. Chen, 2021, The Genome Sequence Archive Family: toward explosive data growth and diverse data types, Genomics Proteomics Bioinformatics, 19, 578, 10.1016/j.gpb.2021.08.001 · DOI

Metadata

Authors
Yu, P., Zhou, S., Gao, Y., Liang, Y., Guo, W., Wang, D. O., Ding, S., Lin, S., Wang, J., & Cun, Y.
Affiliations
  • Sun Yat-sen University (CN)
  • Shenyang Pharmaceutical University (CN)
Corresponding author
  • Lin, Shuibin
  • Wang, Jinkai
  • Cun, Yixian
ORCID
Venue
Genomics, Proteomics & Bioinformatics
Publisher
Oxford University Press (OUP)
Published
2022-08-08
Volume
21
Issue
4
Pages
834-849
ISSN
  • 1672-0229 (ISSN-L)
  • 2210-3244
DOI
10.1016/j.gpb.2022.07.006
PMC
PMC10787195
OpenAlex
W4300582127
Cited by
20
References
2
Citations by year
2026: 5 · 2025: 7 · 2024: 4 · 2023: 4
FWCI
1.22
Type
journal article
Language
en
Open access
Licence
Locations
  • Genomics Proteomics & Bioinformatics (publishedVersion, cc-by)
  • bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion, cc-by-nc-nd)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Key R&D Program of China (2018YFA0107200)
  • National Natural Science Foundation of China (31970594, 31771446, 81922052, 81974435, 31971335)
  • Natural Science Foundation of Guangdong, China (2019B151502011, 2021A1515110650)
  • China Postdoctoral Science Foundation (2021M703755)
  • National Key Research and Development Program of China (2018YFA0107200)
  • Natural Science Foundation of Guangdong Province (2021A1515110650)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA and protein synthesis mechanisms
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › RNA modifications and cancer
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Genomics and Phylogenetic Studies
Keywords
tRNA expression, translation efficiency, tRNA adaptation index, post-transcriptional regulation
MeSH terms
Amino Acids, Animals, Anticodon, Mammals, RNA, Transfer, Protein Biosynthesis, Mice, Transcriptome

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