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. 2010 Sep 16;6(9):e1001128.
doi: 10.1371/journal.pgen.1001128.

Effect of correlated tRNA abundances on translation errors and evolution of codon usage bias

Premal Shah  1 , Michael A Gilchrist
Affiliations

Effect of correlated tRNA abundances on translation errors and evolution of codon usage bias

Premal Shah et al. PLoS Genet. .

Abstract

Despite the fact that tRNA abundances are thought to play a major role in determining translation error rates, their distribution across the genetic code and the resulting implications have received little attention. In general, studies of codon usage bias (CUB) assume that codons with higher tRNA abundance have lower missense error rates. Using a model of protein translation based on tRNA competition and intra-ribosomal kinetics, we show that this assumption can be violated when tRNA abundances are positively correlated across the genetic code. Examining the distribution of tRNA abundances across 73 bacterial genomes from 20 different genera, we find a consistent positive correlation between tRNA abundances across the genetic code. This work challenges one of the fundamental assumptions made in over 30 years of research on CUB that codons with higher tRNA abundances have lower missense error rates and that missense errors are the primary selective force responsible for CUB.

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Conflict of interest statement

The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Correlation between a focal tRNA's abundance and the abundance of its neighbors , across 73 prokaryotic genomes.
Each point in panels (A–C) represents a tRNA species that encodes an amino acid with degeneracy formula image. The solid lines represent the regression lines between formula image and formula image for each genome. Genomes with a negative formula image are coded in red, while genomes with a positive formula image are represented by blue lines. Panels (D–F) present the distribution of correlation coefficients formula image between formula image and formula image across all the genomes. The mean of the distribution of formula image values for all the three degenerate classes differ significantly from 0 (Wilcox test, formula image).
Figure 2
Figure 2. Model of translation errors.
During translation, a ribosome pauses at a codon (ACA in this case) waiting for a cognate tRNA. During this pause, one of the three processes can take place: elongation by cognate tRNAs leading to no translation error, elongation by a near-cognate tRNA leading to a missense error with rate formula image or premature termination of translation due to recognition by release factors, spontaneous ribosome drop-off or frameshifting leading to a nonsense error with a rate formula image.
Figure 3
Figure 3. Correlation of translation error rates with cognate elongation rate in E. coli.
We find that rates of both (A) missense formula image and (B) nonsense errors formula image are negatively correlated with the rate of elongation by cognate tRNAs at that codon. The dashed line indicates the regression line between formula image and formula image. This is consistent with expectations under the standard model. However, in the case of twofold degenerate amino acids (formula image), whose two codons are joined together by solid lines, we see that formula image increases with formula image for 8 out of 10 amino acids. In the case of formula image every amino acid showed a decrease in formula image with formula image.
Figure 4
Figure 4. Frequencies of negative relationships between cognate elongation rate and translation errors .
Panels (A–D) represent the distribution of E. coli strains that show amino acid specific negative relationship between formula image and formula image, while panels (E–H) represent the distribution of 73 genomes for the same. Amino acids in every degenerate class (formula image) show a negative relationship between cognate elongation rate formula image and nonsense error rates (formula image) both intra-specifically as well as inter-specifically. A majority of amino acids in the 2-fold degenerate class (formula image) show an increase in missense error rate formula image with formula image across genomes. As the degeneracy of amino acids increases, we see an increase in the frequency of the expected negative relationship between formula image and formula image across E. coli strains as well as other bacterial species.
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