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. 2018 Jan;19(1):20-30.
doi: 10.1038/nrm.2017.91. Epub 2017 Oct 11.

Codon Optimality, Bias and Usage in Translation and mRNA Decay

Free PMC article

Codon Optimality, Bias and Usage in Translation and mRNA Decay

Gavin Hanson et al. Nat Rev Mol Cell Biol. .
Free PMC article


The advent of ribosome profiling and other tools to probe mRNA translation has revealed that codon bias - the uneven use of synonymous codons in the transcriptome - serves as a secondary genetic code: a code that guides the efficiency of protein production, the fidelity of translation and the metabolism of mRNAs. Recent advancements in our understanding of mRNA decay have revealed a tight coupling between ribosome dynamics and the stability of mRNA transcripts; this coupling integrates codon bias into the concept of codon optimality, or the effects that specific codons and tRNA concentrations have on the efficiency and fidelity of the translation machinery. In this Review, we first discuss the evidence for codon-dependent effects on translation, beginning with the basic mechanisms through which translation perturbation can affect translation efficiency, protein folding and transcript stability. We then discuss how codon effects are leveraged by the cell to tailor the proteome to maintain homeostasis, execute specific gene expression programmes of growth or differentiation and optimize the efficiency of protein production.


Figure 1 ∣
Figure 1 ∣. The codon content of an mRNA can influence translation via tRNA-dependent mechanisms.
a ∣ Optimal codons (green; top) allow rapid ribosome translocation, thereby clearing the 5′ end of the coding DNA sequence (CDS) to allow the assembly of the 80S ribosome from its 40S and 60S subunits over the start codon. Stretches of non-optimal codons (red; bottom) can slow ribosome translocation as the ribosomes wait for a rare cognate tRNA. The non-optimal codon stretch can create ribosome crowding that can eventually result in the formation of a queue that, should it stretch back far enough, could inhibit translation initiation, thereby modulating the overall rate of protein production per transcript, or translation efficiency. Thus, although translation initiation may be the rate-limiting step for the majority of transcripts, non-optimal codons can still modulate translation efficiency by directly influencing initiation. b–d ∣ Representation of the modes through which ribosome elongation can be slowed in a codon-dependent manner. When the codon–cognate tRNA species (blue) is rare in the tRNA pool or in low abundance relative to near-cognate tRNA species (yellow), slow translation can result owing to the increased time required until the correct codon is successfully bound by the ribosome and accommodated, as shown in part b. Part c shows that codons that must be decoded by tRNA species that do not form canonical Watson–Crick base pairs at the third position of the codon will often cause a slowing of the ribosome, due to the increased time required to incorporate wobble tRNAs. Two common wobble interactions in yeast are shown, with a G–U wobble interaction in the decoding of the non-optimal Leu codon CUG and a hypoxanthine (I) modification at the wobble position in the tRNA that decodes the non-optimal Arg codon CGA. We contrast these with traditional Watson–Crick base pairs (on the right-hand side), which are associated with more efficient translation. Part d shows that non-optimal codon doublets are associated with the capacity to induce ribosome slowing to a degree greater than the sum of their parts. PP, polypeptide.
Figure 2 ∣
Figure 2 ∣. Codon optimality in a transcript can be used to optimize protein folding.
A stretch of non-optimal codons (red line; top) in an inter-domain linker region can slow translation elongation to allow proper folding of the emerging polypeptide into a functional protein domain,,. When codon optimality is high in this region (green line; bottom), protein misfolding can occur. Conversely, ribosomes quickly translate optimal codons (green boxes; top),,, which often encode highly conserved residues (marked in yellow). Optimal codons are less prone to reading errors because they correspond to tRNA species with a high cognate:near-cognate tRNA ratio, thereby ensuring high translation fidelity of the most functionally important residues. When important amino acids are encoded by non-optimal codons (red boxes; bottom), missense errors are more likely to occur during translation.
Figure 3 ∣
Figure 3 ∣. Non-optimal codons decrease mRNA stability in a Dhh1-dependent mechanism.
In budding yeast, fast translation over optimal codons (green) is coupled with high mRNA stability. The slowing of ribosomes over non-optimal codons (red) recruits the DEAD-box helicase and decapping activator Dhh1 to the ribonucleoprotein, thereby initiating mRNA decay. The polarity of the effect of the association of Dhh1 with the ribosome is emphasized, as stretches of non-optimal codons at the 3′ region of transcripts appear to induce mRNA decay more robustly.
Figure 4 ∣
Figure 4 ∣. Codon content and mRNA stability are matched in transcripts encoding functionally-related proteins.
a,b ∣ Genes that encode interconnected components of biochemical pathways tend to have a similar codon content and similar mRNA half-lives. Each enzyme in the two biochemical pathways is represented by two colours. The colour on the left corresponds to the mRNA half-life of each transcript in yeast, based on previously published mRNA half-life data, and the colour on the right corresponds to the codon content, which is defined as the percentage of optimal codons in the transcript. Codon optimality was determined by tRNA adaptation index (tAI) values calculated in yeast, and codons with corresponding tAI values greater than the median were considered optimal. Part a shows a simplified representation of the glycolysis and gluconeogenesis enzymes, as well as enzymes of the tricarboxylic acid (TCA) cycle; metabolic intermediates were omitted for clarity. Most enzymes are encoded by highly stable transcripts with a high proportion of optimal codons, underscoring the importance of this fundamental pathway in cell growth and survival. By contrast, part b shows that transcripts encoding proteins involved in the mating pheromone response pathway are remarkably unstable and depleted of optimal codons, which is consistent with the transient nature of this response. c ∣ Codon optimality and median mRNA half-life for a number of gene ontology terms, showing that genes with similar functions (for example, in pheromone response, glycolysis and tRNA modification) or that encode components of a large cellular apparatus (including the ribosome (RPL and RPS genes), the mitochondrial ribosome (MRP genes) or the small subunit processome (SSU genes)), tend to share similar codon optimality profiles. RPL, large subunit ribosomal proteins; RPS, small subunit ribosomal proteins. Part c is reproduced with permission from REF. , Elsevier.
Figure 5 ∣
Figure 5 ∣. Various conditions can alter tRNA pools in different ways to support the translation of mRNAs necessary to maintain homeostasis or to favour the current gene expression programme.
a ∣ Amino acid (AA) depletion can alter the relative charging of synonymous tRNA species that favour the production of proteins that help to restore the availability of amino acids, for example, components of the ubiquitin–proteasome system, b ∣ In conditions of oxidative stress in budding yeast, such as an increase in the levels of reactive oxygen species (ROS), Trm4-dependent methylation of the wobble cytidine in tRNALeu(CAA) is upregulated to favour the translation of mRNAs enriched with TTG Leu codons, such as 60S ribosomal protein L22-A (RPL22A), which is known to confer resistance to ROS. c ∣ Proliferating cells such as induced pluripotent stem cells and neoplasm-derived cell lines have a tRNA pool that is distinct from cells that have undergone terminal differentiation and that have entered a postmitotic state. This tRNA pool favours the production of proteins involved in translation, DNA replication and the cell cycle over proteins involved in cell–cell adhesion, tissue patterning and multicellular functions. MEM, minimum essential medium.

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