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).
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. Different organisms often show particular preferences for one of the several codons that encode the same amino acid- that is, a greater frequency of one will be found than expected by chance. How such preferences arise is a much debated area of molecular evolution.
It is generally acknowledged that codon preferences 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).
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.
- 1 Factors contributing to codon usage bias
- 2 Evolutionary theories for codon bias
- 3 Consequences of codon composition
- 4 Methods of analyzing codon usage bias
- 5 References
- 6 External links
Factors contributing to codon usage bias
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 and hypersaline adaptation.
Evolutionary theories for codon bias
Mutational bias versus selection
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. 
Mutation-selection-drift balance model
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.
Consequences of codon composition
Effect on RNA secondary structure
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 non-coding 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. 
Effect on transcription/gene expression
Heterologous gene expression is used in many biotechnological applications, including protein production and metabolic engineering. Because tRNA pools vary between different organisms, the rate of transcription and translation of a particular coding sequence can be less efficient when placed in a non-native context. For an overexpressed transgene, the corresponding mRNA makes a large percent of total cellular RNA, and the presence of rare codons along the transcript can lead to inefficient use and depletion of ribosomes and ultimately reduce levels of heterologous protein production. However, using codons that are optimized for tRNA pools in a particular host to overexpress a heterologous gene may also cause amino acid starvation and alter the equilibrium of tRNA pools. This method of adjusting codons to match host tRNA abundances, called codon optimization, has traditionally been used for expression of a heterologous gene. However, new strategies for optimization of heterologous expression consider global nucleotide content such as local mRNA folding, codon pair bias, a codon ramp or codon correlations. 
Specialized codon bias is further seen in some endogenous genes such as those involved in amino acid starvation. For example, amino acid biosynthetic enzymes preferentially use codons that are poorly adapted to normal tRNA abundances, but have codons that are adapted to tRNA pools under starvation conditions. Thus, codon usage can introduce an additional level of transcriptional regulation for appropriate gene expression under specific cellular conditions.
Effect on speed of translation elongation
Generally speaking for highly expressed genes, translation elongation rates are faster along transcripts with higher codon adaptation to tRNA pools, and slower along transcripts with rare codons. This correlation between codon translation rates and cognate tRNA concentrations provides additional modulation of translation elongation rates, which can provide several advantages to the organism. Specifically, codon usage can allow for global regulation of these rates, and rare codons may contribute to the accuracy of translation at the expense of speed. 
Effect on protein folding
Protein folding in vivo is vectorial, such that the N-terminus of a protein exits the translating ribosome and becomes solvent-exposed before its more C-terminal regions. As a result, co-translational protein folding introduces several spatial and temporal constraints on the nascent polypeptide chain in its folding trajectory. Because mRNA translation rates are coupled to protein folding, and codon adaption is linked to translation elongation, it has been hypothesized that manipulation at the sequence level may be an effective strategy to regulate or improve protein folding. Several studies have shown that pausing of translation as a result of local mRNA structure occurs for certain proteins, which may be necessary for proper folding. Furthermore, synonymous mutations have been shown to have significant consequences in the folding process of the nascent protein and can even change substrate specificity of enzymes. These studies suggest that codon usage influences the speed at which polypeptides emerge vectorially from the ribosome, which may further impact protein folding pathways throughout the available structural space. 
Methods of analyzing codon usage bias
In the field of bioinformatics and computational biology, many statistical methods have been proposed and used to analyze codon usage bias. Methods such as the 'frequency of optimal codons' (Fop), the Relative Codon Adaptation (RCA)  or the 'Codon Adaptation Index' (CAI)  are used to predict gene expression levels, while methods such as the 'effective number of codons' (Nc) and Shannon entropy from information theory are used to measure codon usage evenness. Multivariate statistical methods, such as correspondence analysis and principal component analysis, are widely used to analyze variations in codon usage among genes. There are many computer programs to implement the statistical analyses enumerated above, including CodonW, GCUA, INCA, etc. Codon optimization has applications in designing synthetic genes and DNA vaccines. Several software packages are available online for this purpose (refer to external links). Optimizing the occurrence of desired/undesired motifs and sequence composition in all possible reverse translated gene sequences increases the search space exponentially with respect to gene length. For those reasons, the problem could be addressed using optimization algorithms like genetic algorithms (Sandhu et al., In Silico Biol. 2008;8(2):187-92).
- Ermolaeva MD (October 2001). "Synonymous codon usage in bacteria". Curr Issues Mol Biol 3 (4): 91–7. PMID 11719972.
- Lynn DJ, Singer GA, Hickey DA (October 2002). "Synonymous codon usage is subject to selection in thermophilic bacteria". Nucleic Acids Res. 30 (19): 4272–7. doi:10.1093/nar/gkf546. PMC 140546. PMID 12364606.
- Paul S, Bag SK, Das S, Harvill ET, Dutta C (2008). "Molecular signature of hypersaline adaptation: insights from genome and proteome composition of halophilic prokaryotes". Genome Biol. 9 (4): R70. doi:10.1186/gb-2008-9-4-r70. PMC 2643941. PMID 18397532.
- Kober, K. M.; Pogson, G. H. (2013). "Genome-Wide Patterns of Codon Bias Are Shaped by Natural Selection in the Purple Sea Urchin, Strongylocentrotus purpuratus". G3: Genes|Genomes|Genetics 3 (7): 1069. doi:10.1534/g3.113.005769.
- Hershberg, R; Petrov, D. A. (2008). "Selection on codon bias". Annual Review of Genetics 42: 287–99. doi:10.1146/annurev.genet.42.110807.091442. PMID 18983258.
- Novoa, E. M.; Ribas De Pouplana, L (2012). "Speeding with control: Codon usage, tRNAs, and ribosomes". Trends in Genetics 28 (11): 574–81. doi:10.1016/j.tig.2012.07.006. PMID 22921354.
- Plotkin, J. B.; Kudla, G (2011). "Synonymous but not the same: The causes and consequences of codon bias". Nature Reviews Genetics 12 (1): 32–42. doi:10.1038/nrg2899. PMC 3074964. PMID 21102527.
- Spencer, P. S.; Barral, J. M. (2012). "Genetic Code Redundancy and Its Influence on the Encoded Polypeptides". Computational and Structural Biotechnology Journal 1: 1. doi:10.5936/csbj.201204006.
- Comeron JM, Aguadé M (September 1998). "An evaluation of measures of synonymous codon usage bias". J. Mol. Evol. 47 (3): 268–74. doi:10.1007/PL00006384. PMID 9732453.
- Ikemura T (September 1981). "Correlation between the abundance of Escherichia coli transfer RNAs and the occurrence of the respective codons in its protein genes: a proposal for a synonymous codon choice that is optimal for the E. coli translational system.". J. Mol. Biol. 151 (3): 389–409. doi:10.1016/0022-2836(81)90003-6. PMID 6175758.
- Fox JM, Erill I (June 2010). "Relative codon adaptation: a generic codon bias index for prediction of gene expression". DNA Res. 17 (3): 185–96. doi:10.1093/dnares/dsq012. PMC 2885275. PMID 20453079.
- Sharp PM, Li WH (February 1987). "The codon Adaptation Index--a measure of directional synonymous codon usage bias, and its potential applications". Nucleic Acids Res. 15 (3): 1281–95. doi:10.1093/nar/15.3.1281. PMC 340524. PMID 3547335.
- Peden J (2005-04-15). "Codon usage indices". Correspondence Analysis of Codon Usage. SourceForge. Retrieved 2010-10-20.
- Suzuki H, Brown CJ, Forney LJ, Top EM (December 2008). "Comparison of correspondence analysis methods for synonymous codon usage in bacteria". DNA Res. 15 (6): 357–65. doi:10.1093/dnares/dsn028. PMC 2608848. PMID 18940873.
- Composition Analysis Toolkit: estimating codon usage bias and its statistical significance
- Codon Usage Database
- GCUA - General Codon Usage Analysis
- Graphical Codon Usage Analyser
- JCat - Java Codon Usage Adaptation Tool
- INCA - Interactive Codon Analysis software
- ACUA - Automated Codon Usage Analysis Tool
- OPTIMIZER - Codon usage optimization
- HEG-DB - Highly Expressed Genes Database
- E-CAI - Expected value of Codon Adaptation Index
- CAIcal -Set of tools to assess codon usage adaptation
- scRCA - Automatic determination of translational codon usage bias
- Online Synonymous Codon Usage Analyses with the ade4 and seqinR packages
- Genetic Algorithm Simulation for Codon Optimization