Codon usage bias

Codon usage bias refers to differences in the frequency of occurrence of synonymous codons in coding DNA. A codon is a series of three nucleotides (a triplet) that encodes a specific amino acid residue in a polypeptide chain or for the termination of translation (stop codons). Codon usage bias refers to differences in the frequency of occurrence of synonymous codons in coding DNA. A codon is a series of three nucleotides (a triplet) that encodes a specific amino acid residue in a polypeptide chain or for the termination of translation (stop codons). There are 64 different codons (61 codons encoding for amino acids plus 3 stop codons) but only 20 different translated amino acids. The overabundance in the number of codons allows many amino acids to be encoded by more than one codon. Because of such redundancy it is said that the genetic code is degenerate. The genetic codes of different organisms are often biased towards using one of the several codons that encode the same amino acid over the others—that is, a greater frequency of one will be found than expected by chance. How such biases arise is a much debated area of molecular evolution. Codon usage tables detailing genomic codon usage bias for most organisms in GenBank and RefSeq can be found in the HIVE-Codon Usage Table database. It is generally acknowledged that codon biases reflect a balance between mutational biases and natural selection for translational optimization. Optimal codons in fast-growing microorganisms, like Escherichia coli or Saccharomyces cerevisiae (baker's yeast), reflect the composition of their respective genomic tRNA pool. It is thought that optimal codons help to achieve faster translation rates and high accuracy. As a result of these factors, translational selection is expected to be stronger in highly expressed genes, as is indeed the case for the above-mentioned organisms. In other organisms that do not show high growing rates or that present small genomes, codon usage optimization is normally absent, and codon preferences are determined by the characteristic mutational biases seen in that particular genome. Examples of this are Homo sapiens (human) and Helicobacter pylori. Organisms that show an intermediate level of codon usage optimization include Drosophila melanogaster (fruit fly), Caenorhabditis elegans (nematode worm), Strongylocentrotus purpuratus (sea urchin) or Arabidopsis thaliana (thale cress). Several viral families (herpesvirus, lentivirus, papillomavirus, polyomavirus, adenovirus, and parvovirus) are known to encode structural proteins that display heavily skewed codon usage compared to the host cell. The suggestion has been made that these codon biases play a role in the temporal regulation of their late proteins. The nature of the codon usage-tRNA optimization has been fiercely debated. It is not clear whether codon usage drives tRNA evolution or vice versa. At least one mathematical model has been developed where both codon usage and tRNA expression co-evolve in feedback fashion (i.e., codons already present in high frequencies drive up the expression of their corresponding tRNAs, and tRNAs normally expressed at high levels drive up the frequency of their corresponding codons). However, this model does not seem to yet have experimental confirmation. Another problem is that the evolution of tRNA genes has been a very inactive area of research. Different factors have been proposed to be related to codon usage bias, including gene expression level (reflecting selection for optimizing translation process by tRNA abundance), %G+C composition (reflecting horizontal gene transfer or mutational bias), GC skew (reflecting strand-specific mutational bias), amino acid conservation, protein hydropathy, transcriptional selection, RNA stability, optimal growth temperature, hypersaline adaptation and dietary nitrogen. Although the mechanism of codon bias selection remains controversial, possible explanations for this bias fall into two general categories. One explanation revolves around the selectionist theory, in which codon bias contributes to the efficiency and/or accuracy of protein expression and therefore undergoes positive selection. The selectionist model also explains why more frequent codons are recognized by more abundant tRNA molecules, as well as the correlation between preferred codons, tRNA levels and gene copy numbers. Although it has been shown that the rate of amino acid incorporation at more frequent codons occurs at a much higher rate than that of rare codons, the speed of translation has not been shown to be directly affected and therefore the bias towards more frequent codons may not be directly advantageous. However, the increase in translation elongation speed may still be indirectly advantageous by increasing the cellular concentration of free ribosomes and potentially the rate of initiation for messenger RNAs. The second explanation for codon usage can be explained by mutational bias, a theory which posits that codon bias exists because of nonrandomness in the mutational patterns. In other words, some codons can undergo more changes and therefore result in lower equilibrium frequencies, also known as “rare” codons. Different organisms also exhibit different mutational biases, and there is growing evidence that the level of genome-wide GC content is the most significant parameter in explaining codon bias differences between organisms. Additional studies have demonstrated that codon biases can be statistically predicted in prokaryotes using only intergenic sequences, arguing against the idea of selective forces on coding regions and further supporting the mutation bias model. However, this model alone cannot fully explain why preferred codons are recognized by more abundant tRNAs. To reconcile the evidence from both mutational pressures and selection, the prevailing hypothesis for codon bias can be explained by the mutation-selection-drift balance model. This hypothesis states that selection favors major codons over minor codons, but minor codons are able to persist due to mutation pressure and genetic drift. It also suggests that selection is generally weak, but that selection intensity scales to higher expression and more functional constraints of coding sequences. Because secondary structure of the 5’ end of mRNA influences translational efficiency, synonymous changes at this region on the mRNA can result in profound effects on gene expression. Codon usage in noncoding DNA regions can therefore play a major role in RNA secondary structure and downstream protein expression, which can undergo further selective pressures. In particular, strong secondary structure at the ribosome-binding site or initiation codon can inhibit translation, and mRNA folding at the 5’ end generates a large amount of variation in protein levels.