Codon usage bias

<h2 id="contributing-factors">Contributing factors</h2>
Different factors have been proposed to be related to codon usage bias, including gene expression level (reflecting selection for optimizing the translation process by tRNA abundance), <a href="/facts/Guanine-cytosine_content/nRoVl7gw">guanine-cytosine content</a> (GC content, reflecting <a href="/facts/Horizontal_gene_transfer/FPyUIsf2">horizontal gene transfer</a> or mutational bias), <a href="/facts/Guanine-cytosine_skew/Y18tNEjw">guanine-cytosine skew</a> (GC skew, reflecting strand-specific mutational bias), <a href="/facts/Conserved_sequence/YAxy2Xa2">amino acid conservation</a>, <a href="/facts/Hydrophilicity_plot/RGNpz8mH">protein hydropathy</a>, transcriptional selection, RNA stability, optimal growth temperature, hypersaline adaptation, and dietary nitrogen.<a class="footnote-ref" id="fnref:14" href="#fn:14">14</a><a class="footnote-ref" id="fnref:15" href="#fn:15">15</a><a class="footnote-ref" id="fnref:16" href="#fn:16">16</a><a class="footnote-ref" id="fnref:17" href="#fn:17">17</a><a class="footnote-ref" id="fnref:18" href="#fn:18">18</a><a class="footnote-ref" id="fnref:19" href="#fn:19">19</a>

<h2 id="evolutionary-theories">Evolutionary theories</h2>
<h3>Mutational bias versus selection</h3>
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 <a href="/facts/Positive_selection/gHPnrflX">positive selection</a>. 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 <a href="/facts/Gene_copy_number/g7GPKrsx">gene copy numbers</a>. 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 <a href="/facts/Ribosomes/CF2u2gtg">ribosomes</a> and potentially the rate of initiation for <a href="/facts/Messenger_RNA/oUL6qxQn">messenger RNAs</a> (mRNAs).<a class="footnote-ref" id="fnref:20" href="#fn:20">20</a>
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 <a href="/facts/Prokaryotes/oYyvaDDH">prokaryotes</a> using only <a href="/facts/Intergenic_region/magkGnx7">intergenic sequences</a>, arguing against the idea of selective forces on <a href="/facts/Coding_regions/eSR9aIKr">coding regions</a> and further supporting the mutation bias model. However, this model alone cannot fully explain why preferred codons are recognized by more abundant tRNAs.<a class="footnote-ref" id="fnref:21" href="#fn:21">21</a>

<h3>Mutation-selection-drift balance model</h3>
To reconcile the evidence from both <a href="/facts/Mutation_pressure/9mDsWUET">mutational pressures</a> 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 <a href="/facts/Genetic_drift/lvoEEctX">genetic drift</a>. It also suggests that selection is generally weak, but that selection intensity scales to higher expression and more functional constraints of coding sequences.<a class="footnote-ref" id="fnref:22" href="#fn:22">22</a>

<h2 id="consequences-of-codon-composition">Consequences of codon composition</h2>
<h3>Effect on RNA secondary structure</h3>
Because <a href="/facts/Nucleic_acid_secondary_structure/qXjlsFK5">secondary structure</a> of the <a href="/facts/5%27_end/TIAXEQmY">5’ end</a> of mRNA influences translational efficiency, synonymous changes at this region on the mRNA can result in profound effects on gene expression. Codon usage in <a href="/facts/Noncoding_DNA/zyTU8q8M">noncoding DNA</a> 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 <a href="/facts/Ribosomal_binding_site/K2IuCHly">ribosome-binding site</a> or <a href="/facts/Start_codon/Rn2L6gXQ">initiation codon</a> can inhibit translation, and mRNA folding at the 5’ end generates a large amount of variation in protein levels.<a class="footnote-ref" id="fnref:23" href="#fn:23">23</a>

<h3>Effect on transcription or gene expression</h3>
<a href="/facts/Heterologous_expression/AUTNhevE">Heterologous gene expression</a> is used in many biotechnological applications, including protein production and <a href="/facts/Metabolic_engineering/quTXJR6r">metabolic engineering</a>. Because tRNA pools vary between different organisms, the rate of <a href="/facts/Transcription_(genetics)/bDgp9HDN">transcription</a> and translation of a particular coding sequence can be less efficient when placed in a non-native context. For an overexpressed <a href="/facts/Transgene/8DvIYSGt">transgene</a>, the corresponding mRNA makes a large percent of total cellular RNA, and the presence of rare codons along the <a href="/facts/Primary_transcript/4UXtKPHw">transcript</a> can lead to inefficient use and depletion of ribosomes and ultimately reduce levels of heterologous protein production. In addition, the composition of the gene (e.g. the total number of rare codons and the presence of consecutive rare codons) may also affect translation accuracy.<a class="footnote-ref" id="fnref:24" href="#fn:24">24</a><a class="footnote-ref" id="fnref:25" href="#fn:25">25</a> 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 <a href="/facts/Codon_optimization/UfMw2mde">codon optimization</a>, 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, codon harmonization or codon correlations.<a class="footnote-ref" id="fnref:26" href="#fn:26">26</a><a class="footnote-ref" id="fnref:27" href="#fn:27">27</a> With the number of nucleotide changes introduced, <a href="/facts/Artificial_gene_synthesis/ELoepoLA">artificial gene synthesis</a> is often necessary for the creation of such an optimized gene.
Specialized codon bias is further seen in some <a href="/facts/Endogenous/DFkSLkRI">endogenous</a> genes such as those involved in amino acid starvation. For example, <a href="/facts/Amino_acid_synthesis/u4vsv3jG">amino acid biosynthetic</a> 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.<a class="footnote-ref" id="fnref:28" href="#fn:28">28</a>

<h3>Effect on speed of translation elongation</h3>
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.<a class="footnote-ref" id="fnref:29" href="#fn:29">29</a>

<h3>Effect on protein folding</h3>
<a href="/facts/Protein_folding/rf3nR2Y1">Protein folding</a> in vivo is <a href="/facts/Vectorial_space/M1pxMLx2">vectorial</a>, such that the <a href="/facts/N-terminus/yKYpuaBJ">N-terminus</a> of a protein exits the translating ribosome and becomes solvent-exposed before its more <a href="/facts/C-terminal/ws4scHgT">C-terminal</a> 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 adaptation 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, <a href="/facts/Synonymous_mutations/Dkf1vtDb">synonymous mutations</a> 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 <a href="/facts/Peptide/C4ltc7UG">polypeptides</a> emerge vectorially from the ribosome, which may further impact protein folding pathways throughout the available structural space.<a class="footnote-ref" id="fnref:30" href="#fn:30">30</a>

<h2 id="methods-of-analysis">Methods of analysis</h2>
In the field of <a href="/facts/Bioinformatics/D5x2L8ee">bioinformatics</a> and <a href="/facts/Computational_biology/GxgNfwcX">computational biology</a>, many statistical methods have been proposed and used to analyze codon usage bias.<a class="footnote-ref" id="fnref:31" href="#fn:31">31</a> Methods such as the 'frequency of optimal codons' (Fop),<a class="footnote-ref" id="fnref:32" href="#fn:32">32</a> the relative codon adaptation (RCA)<a class="footnote-ref" id="fnref:33" href="#fn:33">33</a> or the <a href="/facts/Codon_Adaptation_Index/keENGFrn">codon adaptation index</a> (CAI)<a class="footnote-ref" id="fnref:34" href="#fn:34">34</a> are used to predict gene expression levels, while methods such as the '<a href="/facts/Effective_number_of_codons/tx6y4bKp">effective number of codons</a>' (Nc) and <a href="/facts/Shannon_entropy/NLg4NLvt">Shannon entropy</a> from <a href="/facts/Information_theory/qNLH3U5P">information theory</a> are used to measure codon usage evenness.<a class="footnote-ref" id="fnref:35" href="#fn:35">35</a> Multivariate statistical methods, such as <a href="/facts/Correspondence_analysis/xWXNuCr4">correspondence analysis</a> and <a href="/facts/Principal_component_analysis/pCbVTqdR">principal component analysis</a>, are widely used to analyze variations in codon usage among genes.<a class="footnote-ref" id="fnref:36" href="#fn:36">36</a> 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 <a href="/facts/Dna_vaccines/qlmX0fwQ">DNA vaccines</a>. Several software packages are available online for this purpose (refer to external links).

<h2 id="external-links">External links</h2>
<ul><li><a href="http://cbb.big.ac.cn/Software#CAT">Composition Analysis Toolkit</a> <a href="https://web.archive.org/web/20200726115926/http://cbb.big.ac.cn/Software#CAT">Archived</a> 2020-07-26 at the <a href="/facts/Wayback_Machine/nmQ3a6JC">Wayback Machine</a>: estimating codon usage bias and its statistical significance</li>
<li><a href="https://hive.biochemistry.gwu.edu/review/codon">HIVE-Codon Usage Table database</a></li>
<li><a href="http://www.kazusa.or.jp/codon/">Codon Usage Database</a></li>
<li><a href="http://codonw.sourceforge.net/">CodonW</a></li>
<li><a href="http://mcinerneylab.com/software/gcua">GCUA - General Codon Usage Analysis</a></li>
<li><a href="http://gcua.schoedl.de">Graphical Codon Usage Analyser</a></li>
<li><a href="http://www.jcat.de/">JCat - Java Codon Usage Adaptation Tool</a></li>
<li><a href="http://bioinfo.hr/research/inca/">INCA - Interactive Codon Analysis software</a></li>
<li><a href="http://www.bioinsilico.com/acua">ACUA - Automated Codon Usage Analysis Tool</a> <a href="https://web.archive.org/web/20200726121411/http://www.bioinsilico.com/acua">Archived</a> 2020-07-26 at the <a href="/facts/Wayback_Machine/nmQ3a6JC">Wayback Machine</a></li>
<li><a href="https://web.archive.org/web/20170625045731/http://genomes.urv.cat/OPTIMIZER/">OPTIMIZER - Codon usage optimization</a></li>
<li><a href="https://web.archive.org/web/20180817095021/http://genomes.urv.cat/HEG-DB/">HEG-DB - Highly Expressed Genes Database</a></li>
<li><a href="https://web.archive.org/web/20170630120028/http://genomes.urv.cat/CAIcal/E-CAI/">E-CAI - Expected value of Codon Adaptation Index</a></li>
<li><a href="https://web.archive.org/web/20170630113458/http://genomes.urv.cat/CAIcal/">CAIcal -Set of tools to assess codon usage adaptation</a></li>
<li><a href="http://compbio.umbc.edu/1774/">scRCA - Automatic determination of translational codon usage bias</a></li>
<li><a href="http://pbil.univ-lyon1.fr/datasets/charif04/">Online Synonymous Codon Usage Analyses with the ade4 and seqinR packages</a></li>
<li><a href="https://web.archive.org/web/20101102210801/http://miracle.igib.res.in/gasco/">Genetic Algorithm Simulation for Codon Optimization</a></li></ul>

<h2 id="references">References</h2>

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</ol>

Codon usage bias open-in-new

Codon usage bias