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The effects of codon bias and optimality on mRNA and protein regulation

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Abstract

The central dogma of molecular biology entails that genetic information is transferred from nucleic acid to proteins. Notwithstanding retro-transcribing genetic elements, DNA is transcribed to RNA which in turn is translated into proteins. Recent advancements have shown that each stage is regulated to control protein abundances for a variety of essential physiological processes. In this regard, mRNA regulation is essential in fine-tuning or calibrating protein abundances. In this review, we would like to discuss one of several mRNA-intrinsic features of mRNA regulation that has been gaining traction of recent—codon bias and optimality. Specifically, we address the effects of codon bias with regard to codon optimality in several biological processes centred on translation, such as mRNA stability and protein folding among others. Finally, we examine how different organisms or cell types, through this system, are able to coordinate physiological pathways to respond to a variety of stress or growth conditions.

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References

  1. Goel NS et al (1972) A method for calculating codon frequencies in DNA. J Theor Biol 35(3):399–457

    CAS  PubMed  Google Scholar 

  2. Post LE et al (1979) Nucleotide sequence of the ribosomal protein gene cluster adjacent to the gene for RNA polymerase subunit beta in Escherichia coli. Proc Natl Acad Sci USA 76(4):1697–1701

    CAS  PubMed  PubMed Central  Google Scholar 

  3. Grantham R et al (1980) Codon catalog usage and the genome hypothesis. Nucleic Acids Res 8(1):r49-62

    CAS  PubMed  PubMed Central  Google Scholar 

  4. Presnyak V et al (2015) Codon optimality is a major determinant of mRNA stability. Cell 160(6):1111–1124

    CAS  PubMed  PubMed Central  Google Scholar 

  5. Pechmann S, Frydman J (2013) Evolutionary conservation of codon optimality reveals hidden signatures of cotranslational folding. Nat Struct Mol Biol 20(2):237–243

    CAS  PubMed  Google Scholar 

  6. Bazzini AA et al (2016) Codon identity regulates mRNA stability and translation efficiency during the maternal-to-zygotic transition. EMBO J. 35(9):2087–2103

    CAS  PubMed  PubMed Central  Google Scholar 

  7. Hershberg R, Petrov DA (2008) Selection on codon bias. Annu Rev Genet 42:287–299

    CAS  PubMed  Google Scholar 

  8. Drummond DA, Wilke CO (2008) Mistranslation-induced protein misfolding as a dominant constraint on coding-sequence evolution. Cell 134(2):341–352

    CAS  PubMed  PubMed Central  Google Scholar 

  9. Zhou T, Weems M, Wilke CO (2009) Translationally optimal codons associate with structurally sensitive sites in proteins. Mol Biol Evol 26(7):1571–1580

    CAS  PubMed  PubMed Central  Google Scholar 

  10. Stoletzki N, Eyre-Walker A (2007) Synonymous codon usage in Escherichia coli: selection for translational accuracy. Mol Biol Evol 24(2):374–381

    CAS  PubMed  Google Scholar 

  11. Ran W, Higgs PG (2012) Contributions of speed and accuracy to translational selection in bacteria. PloS One 7:e51652

    CAS  PubMed  PubMed Central  Google Scholar 

  12. Ran W, Higgs PG (2010) The influence of anticodon-codon interactions and modified bases on codon usage bias in bacteria. Mol Biol Evol 27:2129–2140

    CAS  PubMed  Google Scholar 

  13. Shabalina SA, Spiridonov NA, Kashina A (2013) Sounds of silence: synonymous nucleotides as a key to biological regulation and complexity. Nucleic Acids Res 41, 2073–2094

  14. Rodnina MV (2016) The ribosome in action: Tuning of translational efficiency and protein folding. Protein Sci 25:1390–1406

    CAS  PubMed  PubMed Central  Google Scholar 

  15. Hanson G, Coller J (2018) Codon optimality, bias and usage in translation and mRNA decay. Nat Rev Mol Cell Biol 19(1):20–30

    CAS  PubMed  Google Scholar 

  16. Brule CE, Grayhack EJ (2017) Synonymous codons: choose wisely for expression. Trends Genet 33(4):283–297

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Dever TE, Dinman JD, Green R (2018) Translation elongation and recoding in Eukaryotes. Cold Spring Harb Perspect Biol 10(8):a032649

    PubMed  PubMed Central  Google Scholar 

  18. Sauna ZE, Chava K-S (2011) Understanding the contribution of synonymous mutations to human disease. Nat Rev Genet 12:683–691

    CAS  PubMed  Google Scholar 

  19. Chaney JL, Clark PL (2015) Roles for synonymous codon usage. Protein Biog. https://doi.org/10.1146/annurev-biophys-060414-034333

    Article  Google Scholar 

  20. Quax TE et al (2015) Codon bias as a means to fine-tune gene expression. Mol Cell 59(2):149–161

    CAS  PubMed  PubMed Central  Google Scholar 

  21. Tuller T, Zur H (2015) Multiple roles of the coding sequence 5′ end in gene expression regulation. Nucleic Acids Res 43(1):13–28

    CAS  PubMed  Google Scholar 

  22. Zur H et al (2020) Predictive biophysical modeling and understanding of the dynamics of mRNA translation and its evolution. Nucleic Acids Res 44(19):9031–9049

    Google Scholar 

  23. Bali V, Bebok Z (2015) Decoding mechanisms by which silent codon changes influence protein biogenesis and function. Int J Biochem Cell Biol 64:58–74

    CAS  PubMed  PubMed Central  Google Scholar 

  24. Komar AA (2016) The Yin and Yang of codon usage. Hum Mol Genet 25(R2):R77–R85

    CAS  PubMed  PubMed Central  Google Scholar 

  25. Goz E, Zur H, Tuller T (2017) Hidden silent codes in viral genomes. Evolutionary biology: self/nonself evolution, species and complex traits evolution methods and concepts. Springer, Cham, pp 87–110

    Google Scholar 

  26. Bergman S, Tuller T (2020) Widespread non-modular overlapping codes in the coding regions. Phys Biol 17(3):031002

    CAS  PubMed  Google Scholar 

  27. Sharp PM, Li WH (1987) The codon Adaptation Index–a measure of directional synonymous codon usage bias, and its potential applications. Nucleic Acids Res 15(3):1281–1295

    CAS  PubMed  PubMed Central  Google Scholar 

  28. dos Reis M, Savva R, Wernisch L (2004) Solving the riddle of codon usage preferences: a test for translational selection. Nucleic Acids Res 32(17):5036–5044

    PubMed  PubMed Central  Google Scholar 

  29. Ikemura T (1981) Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes. J Mol Biol 146(1):1–21

    CAS  PubMed  Google Scholar 

  30. Percudani R, Pavesi A, Ottonello S (1997) Transfer RNA gene redundancy and translational selection in Saccharomyces cerevisiae. J Mol Biol 268(2):322–330

    CAS  PubMed  Google Scholar 

  31. Duret L (2000) tRNA gene number and codon usage in the C. elegans genome are co-adapted for optimal translation of highly expressed genes. Trends Genet 16(7):287–289

    CAS  PubMed  Google Scholar 

  32. Sabi R, Tuller T (2014) Modelling the efficiency of codon–tRNA interactions based on codon usage bias. DNA Res 21:511–526

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Sabi R, Volvovitch Daniel R, Tuller T (2017) stAIcalc: tRNA adaptation index calculator based on species-specific weights. Bioinformatics 33(4):589–591

    CAS  PubMed  Google Scholar 

  34. Zhang G et al (2010) Global and local depletion of ternary complex limits translational elongation. Nucleic Acids Res 38(14):4778–4787

    CAS  PubMed  PubMed Central  Google Scholar 

  35. Dana A, Tuller T (2014) The effect of tRNA levels on decoding times of mRNA codons. Nucleic Acids Res 42(14):9171–9181

    CAS  PubMed  PubMed Central  Google Scholar 

  36. Elf J et al (2003) Selective charging of tRNA isoacceptors explains patterns of codon usage. Science 300(5626):1718–1722

    CAS  PubMed  Google Scholar 

  37. Wu Q et al (2019) Translation affects mRNA stability in a codon-dependent manner in human cells. Elife 8:e45396. https://doi.org/10.7554/eLife.45396

    Article  PubMed  PubMed Central  Google Scholar 

  38. Forrest ME et al (2020) Codon and amino acid content are associated with mRNA stability in mammalian cells. PLoS One 15:e0228730

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Narula A, Ellis J, Taliaferro JM, Rissland OS (2019) Coding regions affect mRNA stability in human cells. RNA 25:1751–1764

    CAS  PubMed  PubMed Central  Google Scholar 

  40. Hia F et al (2019) Codon bias confers stability to human mRNAs. EMBO Rep 20(11):e48220

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Pop C et al (2014) Causal signals between codon bias, mRNA structure, and the efficiency of translation and elongation. Mol Syst Biol 10:770

    PubMed  PubMed Central  Google Scholar 

  42. Pringle ES, McCormick C, Cheng Z (2019) Polysome profiling analysis of mRNA and associated proteins engaged in translation. Curr Protoc Mol Biol 125(1):e79

    PubMed  Google Scholar 

  43. Riba A et al (2019) Protein synthesis rates and ribosome occupancies reveal determinants of translation elongation rates. Proc Natl Acad Sci USA 116:15023–15032

    CAS  PubMed  PubMed Central  Google Scholar 

  44. Shine J, Dalgarno L (1975) Determinant of cistron specificity in bacterial ribosomes. Nature 254(5495):34–38

    CAS  PubMed  Google Scholar 

  45. Marilyn K (1989) The scanning model for translation: an update. J Cell Biol 108:229–241

    Google Scholar 

  46. Bentele K, Saffert P, Rauscher R, Ignatova Z, Blüthgen N (2013) Efficient translation initiation dictates codon usage at gene start. Mol Syst Biol 9:675

    PubMed  PubMed Central  Google Scholar 

  47. Goodman DB, Church GM, Kosuri S (2013) Causes and effects of N-terminal codon bias in bacterial genes. Science 342(6157):475–479

    CAS  PubMed  Google Scholar 

  48. Kudla G et al (2009) Coding-sequence determinants of gene expression in Escherichia coli. Science 324(5924):255–258

    CAS  PubMed  PubMed Central  Google Scholar 

  49. Verma M et al (2019) A short translational ramp determines the efficiency of protein synthesis. Nat Commun 10(1):5774

    CAS  PubMed  PubMed Central  Google Scholar 

  50. Tuller T et al (2010) An evolutionarily conserved mechanism for controlling the efficiency of protein translation. Cell 141(2):344–354

    CAS  PubMed  Google Scholar 

  51. Dobrzynski M, Bruggeman FJ (2009) Elongation dynamics shape bursty transcription and translation. Proc Natl Acad Sci USA 106(8):2583–2588

    CAS  PubMed  PubMed Central  Google Scholar 

  52. Keller TE et al (2012) Reduced mRNA secondary-structure stability near the start codon indicates functional genes in prokaryotes. Genome Biol Evol 4(2):80–88

    CAS  PubMed  Google Scholar 

  53. Grosjean H, Fiers W (1982) Preferential codon usage in prokaryotic genes: the optimal codon-anticodon interaction energy and the selective codon usage in efficiently expressed genes. Gene 18(3):199–209

    CAS  PubMed  Google Scholar 

  54. Grosjean H et al (1978) Bacteriophage MS2 RNA: a correlation between the stability of the codon: anticodon interaction and the choice of code words. J Mol Evol 12(2):113–119

    CAS  PubMed  Google Scholar 

  55. Gauss DH, Sprinzl M (1981) Compilation of tRNA sequences. Nucleic Acids Res 9(1):r1–r23

    CAS  PubMed  PubMed Central  Google Scholar 

  56. Pedersen S (1984) Escherichia coli ribosomes translate in vivo with variable rate. Embo j 3(12):2895–2898

    CAS  PubMed  PubMed Central  Google Scholar 

  57. Sorensen MA, Pedersen S (1991) Absolute in vivo translation rates of individual codons in Escherichia coli. The two glutamic acid codons GAA and GAG are translated with a threefold difference in rate. J Mol Biol 222(2):265–280

    CAS  PubMed  Google Scholar 

  58. Sorensen MA, Kurland CG, Pedersen S (1989) Codon usage determines translation rate in Escherichia coli. J Mol Biol 207(2):365–377

    CAS  PubMed  Google Scholar 

  59. Frumkin I et al (2018) Codon usage of highly expressed genes affects proteome-wide translation efficiency. Proc Natl Acad Sci USA 115(21):E4940-e4949

    PubMed  PubMed Central  Google Scholar 

  60. Gobet C et al (2020) Robust landscapes of ribosome dwell times and aminoacyl-tRNAs in response to nutrient stress in liver. Proc Natl Acad Sci USA 117(17):9630–9641

    CAS  PubMed  PubMed Central  Google Scholar 

  61. Carlini DB, Stephan W (2003) In vivo introduction of unpreferred synonymous codons into the Drosophila Adh gene results in reduced levels of ADH protein. Genetics 163(1):239–243

    CAS  PubMed  PubMed Central  Google Scholar 

  62. Mishima Y, Tomari Y (2016) Codon usage and 3’ UTR length determine maternal mRNA stability in zebrafish. Mol Cell 61(6):874–885

    CAS  PubMed  Google Scholar 

  63. Zhou Z et al (2016) Codon usage is an important determinant of gene expression levels largely through its effects on transcription. Proc Natl Acad Sci USA 113:E6117–E6125

    CAS  PubMed  PubMed Central  Google Scholar 

  64. Yu CH et al (2015) Codon usage influences the local rate of translation elongation to regulate co-translational protein folding. Mol Cell 59(5):744–754

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Jin HY, Xiao C (2018) An integrated polysome profiling and ribosome profiling method to investigate in vivo translatome. Methods Mol Biol 1712:1–18

    CAS  PubMed  PubMed Central  Google Scholar 

  66. Lampson BL et al (2013) Rare codons regulate KRas oncogenesis. Curr Biol 23(1):70–75

    CAS  PubMed  Google Scholar 

  67. Fu J, Dang Y, Counter C, Liu Y (2018) Codon usage regulates human KRAS expression at both transcriptional and translational levels. J Biol Chem 293:17929–17940

    CAS  PubMed  PubMed Central  Google Scholar 

  68. Ingolia NT et al (2009) Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324(5924):218–223

    CAS  PubMed  PubMed Central  Google Scholar 

  69. McGlincy NJ, Ingolia NT (2017) Transcriptome-wide measurement of translation by ribosome profiling. Methods 126:112–129

    PubMed  PubMed Central  Google Scholar 

  70. Charneski CA, Hurst LD (2013) Positively charged residues are the major determinants of ribosomal velocity. PLoS Biol 11(3):e1001508

    CAS  PubMed  PubMed Central  Google Scholar 

  71. Ingolia NT, Lareau LF, Weissman JS (2011) Ribosome profiling of mouse embryonic stem cells reveals the complexity and dynamics of mammalian proteomes. Cell 147(4):789–802

    CAS  PubMed  PubMed Central  Google Scholar 

  72. Li GW, Oh E, Weissman JS (2012) The anti-Shine-Dalgarno sequence drives translational pausing and codon choice in bacteria. Nature 484(7395):538–541

    CAS  PubMed  PubMed Central  Google Scholar 

  73. Qian W et al (2012) Balanced codon usage optimizes eukaryotic translational efficiency. PLoS Genet 8(3):e1002603

    CAS  PubMed  PubMed Central  Google Scholar 

  74. Artieri CG, Fraser HB (2014) Accounting for biases in riboprofiling data indicates a major role for proline in stalling translation. Genome Res 24(12):2011–2021

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Weinberg DE et al (2016) Improved ribosome-footprint and mRNA measurements provide insights into dynamics and regulation of yeast translation. Cell Rep 14(7):1787–1799

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Gardin J et al (2014) Measurement of average decoding rates of the 61 sense codons in vivo. Elife 3.

  77. Nakahigashi K et al (2014) Effect of codon adaptation on codon-level and gene-level translation efficiency in vivo. BMC Genomics 15:1115

    PubMed  PubMed Central  Google Scholar 

  78. Gerashchenko MV, Glagyshev VN (2017) Ribonuclease selection for ribosome profiling. Nucleic Acids Res 45:e6

    PubMed  Google Scholar 

  79. Gerashchenko MV, Gladyshev VN (2014) Translation inhibitors cause abnormalities in ribosome profiling experiments. Nucleic Acids Res 42:e134

    PubMed  PubMed Central  Google Scholar 

  80. Wright G et al (2020) Analysis of computational codon usage models and their association with translationally slow codons. PLoS ONE 15(4):e0232003

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Diament A, Tuller T (2016) Estimation of ribosome profiling performance and reproducibility at various levels of resolution. Biology Direct. https://doi.org/10.1186/s13062-016-0127-4

    Article  PubMed  PubMed Central  Google Scholar 

  82. Lareau LF et al (2014) Distinct stages of the translation elongation cycle revealed by sequencing ribosome-protected mRNA fragments. Elife 3:e01257. https://doi.org/10.7554/eLife.01257

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  83. Hussmann JA et al (2015) Understanding biases in ribosome profiling experiments reveals signatures of translation dynamics in yeast. PLoS Genet 11(12):e1005732

    PubMed  PubMed Central  Google Scholar 

  84. Santos DA et al (2019) Cycloheximide can distort measurements of mRNA levels and translation efficiency. Nucleic Acids Res 47(10):4974–4985

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Pelechano V, Wei W, Steinmetz LM (2015) Widespread co-translational RNA decay reveals ribosome dynamics. Cell 161(6):1400–1412

    CAS  PubMed  PubMed Central  Google Scholar 

  86. Pelechano V, Wei W, Steinmetz LM (2016) Genome-wide quantification of 5’-phosphorylated mRNA degradation intermediates for analysis of ribosome dynamics. Nat Protoc 11(2):359–376

    CAS  PubMed  PubMed Central  Google Scholar 

  87. Ingolia NT et al (2012) The ribosome profiling strategy for monitoring translation in vivo by deep sequencing of ribosome-protected mRNA fragments. Nat Protoc 7(8):1534–1550

    CAS  PubMed  PubMed Central  Google Scholar 

  88. Young DJ et al (2015) Rli1/ABCE1 recycles terminating ribosomes and controls translation reinitiation in 3′UTRs in vivo. Cell 162(4):872–884

    CAS  PubMed  PubMed Central  Google Scholar 

  89. Guydosh NR, Green R (2014) Dom34 rescues ribosomes in 3’ untranslated regions. Cell 156(5):950–962

    CAS  PubMed  PubMed Central  Google Scholar 

  90. Shah P et al (2013) Rate-limiting steps in yeast protein translation. Cell 153(7):1589–1601

    CAS  PubMed  PubMed Central  Google Scholar 

  91. Archer SK et al (2016) Dynamics of ribosome scanning and recycling revealed by translation complex profiling. Nature 535(7613):570–574

    CAS  PubMed  Google Scholar 

  92. Zlotorynski E (2016) Profiling ribosome dynamics. Nat Rev Mol Cell Biol 17(9):535–535

    CAS  PubMed  Google Scholar 

  93. Oh E et al (2011) Selective ribosome profiling reveals the cotranslational chaperone action of trigger factor in vivo. Cell 147(6):1295–1308

    CAS  PubMed  PubMed Central  Google Scholar 

  94. Schibich D et al (2016) Global profiling of SRP interaction with nascent polypeptides. Nature 536(7615):219–223

    CAS  PubMed  Google Scholar 

  95. Galmozzi CV et al (2019) Selective ribosome profiling to study interactions of translating ribosomes in yeast. Nat Protoc 14(8):2279–2317

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Shiber A et al (2018) Cotranslational assembly of protein complexes in eukaryotes revealed by ribosome profiling. Nature 561(7722):268–272

    CAS  PubMed  PubMed Central  Google Scholar 

  97. Wu CC et al (2020) Ribosome collisions trigger general stress responses to regulate cell fate. Cell 182(2):404-416e14

    CAS  PubMed  PubMed Central  Google Scholar 

  98. Ikeuchi K et al (2019) Collided ribosomes form a unique structural interface to induce Hel2-driven quality control pathways. EMBO J. https://doi.org/10.15252/embj.2018100276

    Article  PubMed  PubMed Central  Google Scholar 

  99. Tesina P et al (2020) Molecular mechanism of translational stalling by inhibitory codon combinations and poly(A) tracts. EMBO J. https://doi.org/10.15252/embj.2019103365

    Article  PubMed  Google Scholar 

  100. Diament A et al (2018) The extent of ribosome queuing in budding yeast. PLoS Comput Biol 14(1):e1005951

    PubMed  PubMed Central  Google Scholar 

  101. Gamble CE et al (2016) Adjacent codons act in concert to modulate translation efficiency in yeast. Cell 166(3):679–690

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Matsuo Y et al (2017) Ubiquitination of stalled ribosome triggers ribosome-associated quality control. Nat Commun. https://doi.org/10.1038/s41467-017-00188-10

    Article  PubMed  PubMed Central  Google Scholar 

  103. Han P et al (2020) Genome-wide survey of ribosome collision. Cell Rep 31(5):107610

    CAS  PubMed  PubMed Central  Google Scholar 

  104. Meydon S, Guydosh NR (2020) Disome and trisome profiling reveal genome-wide targets of ribosome quality control. Mol Cell 79(4):588–602.e6. https://doi.org/10.1016/j.molcel.2020.06.010

    Article  CAS  Google Scholar 

  105. Rooijers K et al (2013) Ribosome profiling reveals features of normal and disease-associated mitochondrial translation. Nat Commun. https://doi.org/10.1038/ncomms3886

    Article  PubMed  Google Scholar 

  106. Gonzalez C et al (2014) Ribosome profiling reveals a cell-type-specific translational landscape in brain tumors. J Neurosci. 34(33):10924–10936

    PubMed  PubMed Central  Google Scholar 

  107. Stern-Ginossar N, Ingolia NT (2015) Ribosome profiling as a tool to decipher viral complexity. Annu Rev Virol. https://doi.org/10.1146/annurev-virology-100114-054854

    Article  PubMed  Google Scholar 

  108. Peltz SW, Donahue JL, Jacobson A (1992) A mutation in the tRNA nucleotidyltransferase gene promotes stabilization of mRNAs in Saccharomyces cerevisiae. Mol Cell Biol 12(12):5778–5784

    CAS  PubMed  PubMed Central  Google Scholar 

  109. Herrick D, Parker R, Jacobson A (1990) Identification and comparison of stable and unstable mRNAs in Saccharomyces cerevisiae. Mol Cell Biol 10(5):2269–2284

    CAS  PubMed  PubMed Central  Google Scholar 

  110. Kurosaki T, Myers JR, Maquat LE (2019) Defining nonsense-mediated mRNA decay intermediates in human cells. Methods 155:68–76

    CAS  PubMed  Google Scholar 

  111. Antic S et al (2015) General and microRNA-mediated mRNA degradation occurs on ribosome complexes in drosophila cells. Mol Cell Biol 35(13):2309–2320

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Graille M, Seraphin B (2012) Surveillance pathways rescuing eukaryotic ribosomes lost in translation. Nat Rev Mol Cell Biol 13(11):727–735

    CAS  PubMed  Google Scholar 

  113. Shoemaker CJ, Green R (2012) Translation drives mRNA quality control. Nat Struct Mol Biol 19(6):594–601

    CAS  PubMed  PubMed Central  Google Scholar 

  114. Hu W et al (2009) Co-translational mRNA decay in Saccharomyces cerevisiae. Nature 461(7261):225–229

    CAS  PubMed  PubMed Central  Google Scholar 

  115. Coller J, Parker R (2005) General translational repression by activators of mRNA decapping. Cell 122(6):875–886

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Coller JM et al (2001) The DEAD box helicase, Dhh1p, functions in mRNA decapping and interacts with both the decapping and deadenylase complexes. RNA 7(12):1717–1727

    CAS  PubMed  PubMed Central  Google Scholar 

  117. Sweet T, Kovalak C, Coller J (2012) The DEAD-box protein Dhh1 promotes decapping by slowing ribosome movement. PLoS Biol 10(6):e1001342

    CAS  PubMed  PubMed Central  Google Scholar 

  118. Harigaya Y, Parker R (2016) Analysis of the association between codon optimality and mRNA stability in Schizosaccharomyces pombe. BMC Genomics 17(1):895

    PubMed  PubMed Central  Google Scholar 

  119. de Freitas NJ et al (2018) Codon choice directs constitutive mRNA levels in trypanosomes. Elife. https://doi.org/10.7554/eLife.32467

    Article  Google Scholar 

  120. Jeacock L, Faria J, Horn D (2018) Codon usage bias controls mRNA and protein abundance in trypanosomatids. Elife. https://doi.org/10.7554/eLife.32496

    Article  PubMed  PubMed Central  Google Scholar 

  121. Buschauer R et al (2020) The Ccr4-Not complex monitors the translating ribosome for codon optimality. Science 368(6488):eaay6912. https://doi.org/10.1126/science.aay6912

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Tesina P et al (2019) Structure of the 80S ribosome-Xrn1 nuclease complex. Nat Struct Mol Biol 26(4):275–280

    CAS  PubMed  Google Scholar 

  123. Radhakrishnan A et al (2016) The DEAD-box protein Dhh1p couples mRNA decay and translation by monitoring codon optimality. Cell 167(1):122-132.e9

    CAS  PubMed  PubMed Central  Google Scholar 

  124. He F, Celik A, Wu C, Jacobson A (2018) General decapping activators target different subsets of inefficiently translated mRNAs. Elife 7:e34409. https://doi.org/10.7554/eLife.34409

    Article  PubMed  PubMed Central  Google Scholar 

  125. Freimer JW, Hu T, Blelloch R (2018) Decoupling the impact of microRNAs on translational repression versus RNA degradation in embryonic stem cells. Elife 7:e38014. https://doi.org/10.7554/eLife.38014

    Article  PubMed  PubMed Central  Google Scholar 

  126. Courel M et al (2019) GC content shapes mRNA storage and decay in human cells. Elife 8:e49708. https://doi.org/10.7554/eLife.49708

    Article  PubMed  PubMed Central  Google Scholar 

  127. Hanson G et al (2018) Translation elongation and mRNA stability are coupled through the ribosomal A-site. RNA 1377–1389.

  128. Dao Duc K, Song YS (2018) The impact of ribosomal interference, codon usage, and exit tunnel interactions on translation elongation rate variation. PLoS Genet

  129. Schwartz DC, Parker R (1999) Mutations in translation initiation factors lead to increased rates of deadenylation and decapping of mRNAs in Saccharomyces cerevisiae. Mol Cell Biol 5247–5256.

  130. Schwartz DC, Parker R (2000) mRNA decapping in yeast requires dissociation of the cap binding protein, eukaryotic translation initiation factor 4E. Mol Cell Biol 20:7933–7942

    CAS  PubMed  PubMed Central  Google Scholar 

  131. Edri S, Tuller T (2014) Quantifying the effect of ribosomal density on mRNA stability. PLoS One 9:e102308

    PubMed  PubMed Central  Google Scholar 

  132. Chan LY, Mugler CF, Heinrich S, Vallotton P, Weis K (2018) Non-invasive measurement of mRNA decay reveals translation initiation as the major determinant of mRNA stability. Elife 7:e32536. https://doi.org/10.7554/eLife.32536

    Article  PubMed  PubMed Central  Google Scholar 

  133. Neymotin B, Ettorre V, Gresham D (2016) Multiple Transcript Properties Related to Translation Affect mRNA Degradation Rates in Saccharomyces cerevisiae. G3 (Bethesda) 6(11):3475-3483. https://doi.org/10.1534/g3.116.032276

    Article  CAS  Google Scholar 

  134. Purvis IJ et al (1987) The efficiency of folding of some proteins is increased by controlled rates of translation in vivo. A hypothesis. J Mol Biol 193(2):413–417

    CAS  PubMed  Google Scholar 

  135. Thanaraj TA, Argos P (1996a) Ribosome-mediated translational pause and protein domain organization. Protein Sci 5(8):1594–1612

    CAS  PubMed  PubMed Central  Google Scholar 

  136. Thanaraj TA, Argos P (1996b) Protein secondary structural types are differentially coded on messenger RNA. Protein Sci 5(10):1973–1983

    CAS  PubMed  PubMed Central  Google Scholar 

  137. Krasheninnikov IA, Komar AA, Adzhubei IA (1991) Nonuniform size distribution of nascent globin peptides, evidence for pause localization sites, and a contranslational protein-folding model. J Protein Chem 10(5):445–453

    CAS  PubMed  Google Scholar 

  138. Chartier M, Gaudreault F, Najmanovich R (2012) Large-scale analysis of conserved rare codon clusters suggests an involvement in co-translational molecular recognition events. Bioinformatics 28(11):1438–1445. https://doi.org/10.1093/bioinformatics/bts149

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  139. Fluman N, Navon S, Bibi E, Pilpel Y (2014) mRNA-programmed translation pauses in the targeting of E. coli membrane proteins. Elife 3:e03440. https://doi.org/10.7554/eLife.03440

    Article  PubMed Central  Google Scholar 

  140. Bitran A et al (2020) Cotranslational folding allows misfolding-prone proteins to circumvent deep kinetic traps. Proc Natl Acad Sci USA 117(3):1485–1495

    CAS  PubMed  PubMed Central  Google Scholar 

  141. Zhao F, Yu CH, Liu Y (2017) Codon usage regulates protein structure and function by affecting translation elongation speed in Drosophila cells. Nucleic Acids Res 45(14):8484–8492

    CAS  PubMed  PubMed Central  Google Scholar 

  142. Oresic M, Shalloway D (1998) Specific correlations between relative synonymous codon usage and protein secondary structure. J Mol Biol 281(1):31–48

    CAS  PubMed  Google Scholar 

  143. Adzhubei IA, Adzhubei AA, Neidle S (1998) An Integrated Sequence-Structure Database incorporating matching mRNA sequence, amino acid sequence and protein three-dimensional structure data. Nucleic Acids Res 26(1):327–331

    CAS  PubMed  PubMed Central  Google Scholar 

  144. Smith DW (1996) Problems of translating heterologous genes in expression systems: the role of tRNA. Biotechnol Prog 12(4):417–422

    CAS  PubMed  Google Scholar 

  145. Kurland C, Gallant J (1996) Errors of heterologous protein expression. Curr Opin Biotechnol 7(5):489–493

    CAS  PubMed  Google Scholar 

  146. Komar AA, Jaenicke R (1995) Kinetics of translation of gamma B crystallin and its circularly permutated variant in an in vitro cell-free system: possible relations to codon distribution and protein folding. FEBS Lett 376(3):195–198

    CAS  PubMed  Google Scholar 

  147. Komar AA, Lesnik T, Reiss C (1999) Synonymous codon substitutions affect ribosome traffic and protein folding during in vitro translation. FEBS Lett 462(3):387–391

    CAS  PubMed  Google Scholar 

  148. Spencer PS et al (2012) Silent substitutions predictably alter translation elongation rates and protein folding efficiencies. J Mol Biol 422(3):328–335

    CAS  PubMed  PubMed Central  Google Scholar 

  149. Zhang G, Hubalewska M, Ignatova Z (2009) Transient ribosomal attenuation coordinates protein synthesis and co-translational folding. Nat Struct Mol Biol 16(3):274–280

    CAS  PubMed  Google Scholar 

  150. Zhou M et al (2015) Nonoptimal codon usage influences protein structure in intrinsically disordered regions. Mol Microbiol 97(5):974–987

    CAS  PubMed  PubMed Central  Google Scholar 

  151. Buhr F et al (2016) Synonymous codons direct cotranslational folding toward different protein conformations. Mol Cell 61(3):341–351

    CAS  PubMed  PubMed Central  Google Scholar 

  152. Yang JR, Chen X, Zhang J (2014) Codon-by-codon modulation of translational speed and accuracy via mRNA folding. PLoS Biol. 12(7):e1001910

    PubMed  PubMed Central  Google Scholar 

  153. Faure G et al (2016) Role of mRNA structure in the control of protein folding. Nucleic Acids Res 44(22):10898–10911

    CAS  PubMed  PubMed Central  Google Scholar 

  154. Sharp PM et al (2005) Variation in the strength of selected codon usage bias among bacteria. Nucleic Acids Res 33(4):1141–1153

    CAS  PubMed  PubMed Central  Google Scholar 

  155. Saibil H (2013) Chaperone machines for protein folding, unfolding and disaggregation. Nat Rev Mol Cell Biol 14(10):630–642

    CAS  PubMed  PubMed Central  Google Scholar 

  156. Xia X (1996) Maximizing transcription efficiency causes codon usage bias. Genetics 144:1309–1320

    CAS  PubMed  PubMed Central  Google Scholar 

  157. Cohen E, Zafrir Z, Tuller T (2018) A code for transcription elongation speed. RNA Biol 15(1):81–94

    PubMed  Google Scholar 

  158. Kudla G et al (2006) High guanine and cytosine content increases mRNA levels in mammalian cells. PLoS Biol 4(6):e180

    PubMed  PubMed Central  Google Scholar 

  159. Newman ZR et al (2016) Differences in codon bias and GC content contribute to the balanced expression of TLR7 and TLR9. Proc Natl Acad Sci USA 113(10):E1362–E1371

    CAS  PubMed  PubMed Central  Google Scholar 

  160. Mordstein C et al (2020) Codon usage and splicing jointly influence mRNA localization. Cell Syst 10(4):351-362 e8

    CAS  PubMed  PubMed Central  Google Scholar 

  161. Fontrodona N et al (2019) Interplay between coding and exonic splicing regulatory sequences. Genome Res 29(5):711–722

    CAS  PubMed  PubMed Central  Google Scholar 

  162. Stergachis AB et al (2013) Exonic transcription factor binding directs codon choice and affects protein evolution. Science 342(6164):1367–1372

    CAS  PubMed  PubMed Central  Google Scholar 

  163. Crick FH (1966) Codon–anticodon pairing: the wobble hypothesis. J Mol Biol 19(2):548–555

    CAS  PubMed  Google Scholar 

  164. Roth AC (2012) Decoding properties of tRNA leave a detectable signal in codon usage bias. Bioinformatics 28(18):i340–i348

    CAS  PubMed  PubMed Central  Google Scholar 

  165. Gromadski KB, Daviter T, Rodnina MV (2006) A uniform response to mismatches in codon-anticodon complexes ensures ribosomal fidelity. Mol Cell 21(3):369–377

    CAS  PubMed  Google Scholar 

  166. Stadler M, Fire A (2011) Wobble base-pairing slows in vivo translation elongation in metazoans. RNA 17(12):2063–2073

    CAS  PubMed  PubMed Central  Google Scholar 

  167. Dedon PC, Begley TJ (2014) A system of RNA modifications and biased codon use controls cellular stress response at the level of translation. Chem Res Toxicol 27(3):330–337

    CAS  PubMed  PubMed Central  Google Scholar 

  168. Boccaletto P et al (2018) MODOMICS: a database of RNA modification pathways. 2017 update. Nucleic Acids Res 46:D303–D307

    CAS  PubMed  Google Scholar 

  169. Deng W et al (2015) Trm9-catalyzed tRNA modifications regulate global protein expression by codon-biased translation. PLoS Genet 11(12):e1005706

    PubMed  PubMed Central  Google Scholar 

  170. Jaroensuk J et al (2016) Methylation at position 32 of tRNA catalyzed by TrmJ alters oxidative stress response in Pseudomonas aeruginosa. Nucleic Acids Res 44(22):10834–10848

    CAS  PubMed  PubMed Central  Google Scholar 

  171. Gu C, Begley TJ, Dedon PC (2014) tRNA modifications regulate translation during cellular stress. FEBS Lett 588(23):4287–4296

    CAS  PubMed  PubMed Central  Google Scholar 

  172. Chan CT et al (2012) Reprogramming of tRNA modifications controls the oxidative stress response by codon-biased translation of proteins. Nat Commun 3:937

    PubMed  Google Scholar 

  173. Chionh YH et al (2016) tRNA-mediated codon-biased translation in mycobacterial hypoxic persistence. Nat Commun 7:13302

    CAS  PubMed  PubMed Central  Google Scholar 

  174. Nedialkova DD, Leidel SA (2015) Optimization of codon translation rates via tRNA modifications maintains proteome integrity. Cell 161(7):1606–1618

    CAS  PubMed  PubMed Central  Google Scholar 

  175. Bornelöv S et al (2019) Codon usage optimization in pluripotent embryonic stem cells. Genome Biol. https://doi.org/10.1186/s13059-019-1726-z

    Article  PubMed  PubMed Central  Google Scholar 

  176. Arango D et al (2018) Acetylation of cytidine in mRNA promotes translation efficiency. Cell 175(7):1872-1886.e24

    CAS  PubMed  PubMed Central  Google Scholar 

  177. Eyler DE et al (2019) Pseudouridinylation of mRNA coding sequences alters translation. Proc Natl Acad Sci USA. 116(46):23068–23074

    CAS  PubMed  PubMed Central  Google Scholar 

  178. Mao Y et al (2019) m(6)A in mRNA coding regions promotes translation via the RNA helicase-containing YTHDC2. Nat Commun 10(1):5332

    PubMed  PubMed Central  Google Scholar 

  179. Liu Z, Zhang J (2018) Most m6A RNA modifications in protein-coding regions are evolutionarily unconserved and likely nonfunctional. Mol Biol Evol. 35(3):666–675

    CAS  PubMed  Google Scholar 

  180. Ditttmar KA et al (2005) Selective charging of tRNA isoacceptors induced by amino-acid starvation. EMBO Rep 6(2):151–157

    Google Scholar 

  181. Gingold H, Dahan O, Pilpel Y (2012) Dynamic changes in translational efficiency are deduced from codon usage of the transcriptome. Nucleic Acids Res 40:10053–10063

    CAS  PubMed  PubMed Central  Google Scholar 

  182. Torrent M et al (2018) Cells alter their tRNA abundance to selectively regulate protein synthesis during stress conditions. Sci Signal

  183. Frenkel-Morgenstern M et al (2012) Genes adopt non-optimal codon usage to generate cell cycle-dependent oscillations in protein levels. Mol Syst Biol 8:572

    PubMed  PubMed Central  Google Scholar 

  184. Sabi R, Tuller T (2019) Novel insights into gene expression regulation during meiosis revealed by translation elongation dynamics. NPJ Syst Biol Appl 5:12

    PubMed  PubMed Central  Google Scholar 

  185. Goodenpour JM, Pan T (2006) Diversity of tRNA genes in eukaryotes. Nucleic Acids Res 34:6137–6146

    Google Scholar 

  186. Kutter C et al (2011) Pol III binding in six mammalian genomes shows high conservation among amino acid isotypes, despite divergence in tRNA gene usage. Nat Genet. 43(10):948–955

    CAS  PubMed  PubMed Central  Google Scholar 

  187. Geslain R, Pan T (2010) Functional analysis of human tRNA isodecoders. J Mol Biol 396(3):821

    CAS  PubMed  Google Scholar 

  188. Zhou M et al (2013) Non-optimal codon usage affects expression, structure and function of clock protein FRQ. Nature 495:111–115

    CAS  PubMed  PubMed Central  Google Scholar 

  189. Xu Y et al (2013) Non-optimal codon usage is a mechanism to achieve circadian clock conditionality. Nature 495:116–120

    CAS  PubMed  PubMed Central  Google Scholar 

  190. Saikia M et al (2016) Codon optimality controls differential mRNA translation during amino acid starvation. RNA 22:1719–1727

    CAS  PubMed  PubMed Central  Google Scholar 

  191. Guimaraes JC et al (2020) A rare codon-based translational program of cell proliferation. Genome Biol. https://doi.org/10.1186/s13059-020-1943-5

    Article  PubMed  PubMed Central  Google Scholar 

  192. Burrow DA et al (2018) Attenuated codon optimality contributes to neural-specific mRNA decay in Drosophila. Cell Rep 24(7):1704–1712

    PubMed Central  Google Scholar 

  193. Begley TJ et al (2004) Hot spots for modulating toxicity identified by genomic phenotyping and localization mapping. Mol Cell 16(1):117–125

    CAS  PubMed  Google Scholar 

  194. Bennett CB et al (2001) Genes required for ionizing radiation resistance in yeast. Nat Genet 29(4):426–434

    CAS  PubMed  Google Scholar 

  195. Kalhor HR, Clarke S (2003) Novel methyltransferase for modified uridine residues at the wobble position of tRNA. Mol Cell Biol 23(24):9283–9292

    CAS  PubMed  PubMed Central  Google Scholar 

  196. Begley U et al (2007) Trm9-catalyzed tRNA modifications link translation to the DNA damage response. Mol Cell 28(5):860–870

    CAS  PubMed  PubMed Central  Google Scholar 

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Acknowledgements

The authors thank all members of our laboratory for discussions. This work is supported by Japan Society for the Promotion of Science (JSPS) KAKENHI Grant Numbers JP18H05278 and 20F20115, and Takeda Science Foundation.

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Hia, F., Takeuchi, O. The effects of codon bias and optimality on mRNA and protein regulation. Cell. Mol. Life Sci. 78, 1909–1928 (2021). https://doi.org/10.1007/s00018-020-03685-7

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