Codon usage bias

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Codons are triplets of nucleotides that together specify an amino acid residue in a polypeptide chain. Most organisms use 20 or 21 amino acids to make their polypeptides, which are proteins or protein precursors.

Because there are four possible nucleotides, adenine (A), guanine (G), cytosine (C) and thymine (T) in DNA, there are 64 possible triplets to recognize only 20 amino acids plus the translation termination signal. Because of this redundancy, all but two amino acids are coded for by more than one triplet. Different organisms often show particular preferences for one of the several codons that encode the same given amino acid. How these 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) or Arabidopsis thaliana (wall 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.

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[edit] Factors contributing 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. [1] [2] [3]

[edit] 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. [4] Methods such as the 'frequency of optimal codons' (Fop) and 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. [5] [6] Multivariate statistical methods, such as correspondence analysis and principal component analysis, are widely used to analyze variations in codon usage among genes. [7] There are many computer programs to implement the statistical analyses enumerated above, including CodonW, [8] and G-language GAE. [9]

[edit] References

  1. ^ "Synonymous codon usage in bacteria."
  2. ^ "Synonymous codon usage is subject to selection in thermophilic bacteria."
  3. ^ "Molecular Signature of Hypersaline Adaptation: Insights from Genome and Proteome Composition of Halophilic Prokaryotes."
  4. ^ Comeron JM, Aguadé M. (1998) J Mol Evol. 47(3):268-74: "An evaluation of measures of synonymous codon usage bias."
  5. ^ "Codon usage indices"
  6. ^ "The 'weighted sum of relative entropy': a new index for synonymous codon usage bias."
  7. ^ Perrière G, Thioulouse J. (2002) Nucleic Acids Res. 30(20):4548-55. "Use and misuse of correspondence analysis in codon usage studies."
  8. ^ CodonW
  9. ^ G-language Genome Analysis Environment (G-language GAE)

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