Stops making sense: translational trade-offs and stop codon reassignment

Louise J Johnson,Richard A Nichols,Greg S Elgar,Conrad P Lichtenstein,Steven C Le Comber,P David Polly,James A Cotton

doi:10.1186/1471-2148-11-227

Abstract

BackgroundEfficient gene expression involves a trade-off between (i) premature termination of protein synthesis; and (ii) readthrough, where the ribosome fails to dissociate at the terminal stop. Sense codons that are similar in sequence to stop codons are more susceptible to nonsense mutation, and are also likely to be more susceptible to transcriptional or translational errors causing premature termination. We therefore expect this trade-off to be influenced by the number of stop codons in the genetic code. Although genetic codes are highly constrained, stop codon number appears to be their most volatile feature.ResultsIn the human genome, codons readily mutable to stops are underrepresented in coding sequences. We construct a simple mathematical model based on the relative likelihoods of premature termination and readthrough. When readthrough occurs, the resultant protein has a tail of amino acid residues incorrectly added to the C-terminus. Our results depend strongly on the number of stop codons in the genetic code. When the code has more stop codons, premature termination is relatively more likely, particularly for longer genes. When the code has fewer stop codons, the length of the tail added by readthrough will, on average, be longer, and thus more deleterious. Comparative analysis of taxa with a range of stop codon numbers suggests that genomes whose code includes more stop codons have shorter coding sequences.ConclusionsWe suggest that the differing trade-offs presented by alternative genetic codes may result in differences in genome structure. More speculatively, multiple stop codons may mitigate readthrough, counteracting the disadvantage of a higher rate of nonsense mutation. This could help explain the puzzling overrepresentation of stop codons in the canonical genetic code and most variants.

Highlights

Efficient gene expression involves a trade-off between (i) premature termination of protein synthesis; and (ii) readthrough, where the ribosome fails to dissociate at the terminal stop
Influences of genetic codes on genome structure Intriguingly, a relationship between overall genome size and codon reassignment has been noted before, but in the opposite direction to that suggested by our model: TGA is frequently reassigned to a sense codon in small genomes [15,16], including mitochondrial genomes, for which there is a higher possibility that any one stop codon will drift to complete loss
Codon reassignment imposes a new regime of mutational and transcriptional pressures, and new selective pressures on gene length. We find it implausible that the changes in average gene length seen in Figure 2 are selectively neutral, since they represent fundamental pervasive change to whole proteomes

Summary

Introduction

Efficient gene expression involves a trade-off between (i) premature termination of protein synthesis; and (ii) readthrough, where the ribosome fails to dissociate at the terminal stop. Sense codons that are similar in sequence to stop codons are more susceptible to nonsense mutation, and are likely to be more susceptible to transcriptional or translational errors causing premature termination. We expect this trade-off to be influenced by the number of stop codons in the genetic code. Readthrough - a failure to terminate protein production at the stop codon -appears to be selected against Both prokaryotes and eukaryotes use conserved release factors to recognise stop codons and ensure the prompt release of ribosomes [4,5], and the occurrence of termination sequences in Saccharomyces cerevisiae is negatively correlated with readthrough [6]. The existence of ‘tandem stops’, in-frame secondary stop codons which are conserved between species [7] and correspond to variations in the genetic code [8] suggests that selection acts at the sequence level to ameliorate readthrough (but see [9])

Methods

Results

Discussion

Conclusion