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Letter

Codon usage bias from tRNA's point of view: Redundancy, specialization, and efficient decoding for translation optimization

Unité Génétique des Génomes Bactériens, Institut Pasteur, 75724 Paris Cedex 15, France; Atelier de Bioinformatique, Université Pierre et Marie Curie, 75005 Paris, France

The selection-mutation-drift theory of codon usage plays a major role in the theory of molecular evolution by explaining the co-evolution of codon usage bias and tRNA content in the framework of translation optimization. Because most studies have focused only on codon usage, we analyzed the tRNA gene pool of 102 bacterial species. We show that as minimal generation times get shorter, the genomes contain more tRNA genes, but fewer anticodon species. Surprisingly, despite the wide G+C variation of bacterial genomes these anticodons are the same in most genomes. This suggests an optimization of the translation machinery to use a small subset of optimal codons and anticodons in fast-growing bacteria and in highly expressed genes. As a result, the overrepresented codons in highly expressed genes tend to be the same in very different genomes to match the same most-frequent anticodons. This is particularly important in fast-growing bacteria, which have higher codon usage bias in these genes. Three models were tested to understand the choice of codons recognized by the same anticodons, all providing significant fit, but under different classes of genes and genomes. Thus, co-evolution of tRNA gene composition and codon usage bias in genomes seen from tRNA's point of view agrees with the selection-mutation-drift theory. However, it suggests a much more universal trend in the evolution of anticodon and codon choice than previously thought. It also provides new evidence that a selective force for the optimization of the translation machinery is the maximization of growth.

Article and publication are at http://www.genome.org/cgi/doi/10.1101/gr.2896904. Article published online before print in October 2004.

[Supplemental material is available online at www.genome.org.]

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