Stop codons in bacteria are not selectively equivalent

Abstract

Background: The evolution and genomic stop codon frequencies have not been rigorously studied with the exception of coding of non-canonical amino acids. Here we study the rate of evolution and frequency distribution of stop codons in bacterial genomes.

Results: We show that in bacteria stop codons evolve slower than synonymous sites, suggesting the action of weak negative selection. However, the frequency of stop codons relative to genomic nucleotide content indicated that this selection regime is not straightforward. The frequency of TAA and TGA stop codons is GC-content dependent, with TAA decreasing and TGA increasing with GC-content, while TAG frequency is independent of GC-content. Applying a formal, analytical model to these data we found that the relationship between stop codon frequencies and nucleotide content cannot be explained by mutational biases or selection on nucleotide content. However, with weak nucleotide content-dependent selection on TAG, -0.5 < Nes < 1.5, the model fits all of the data and recapitulates the relationship between TAG and nucleotide content. For biologically plausible rates of mutations we show that, in bacteria, TAG stop codon is universally associated with lower fitness, with TAA being the optimal for G-content < 16% while for G-content > 16% TGA has a higher fitness than TAG.

Conclusions: Our data indicate that TAG codon is universally suboptimal in the bacterial lineage, such that TAA is likely to be the preferred stop codon for low GC content while the TGA is the preferred stop codon for high GC content. The optimization of stop codon usage may therefore be useful in genome engineering or gene expression optimization applications.

Figure 1

The relationship between stop codon frequencies in 736 bacterial genomes and G content in GA-type twofold synonymous codons. The pattern is shown for all genomes (A) and as an average measure for bins of 10% of G-content with SD (B).

Figure 2

The model, with rates of mutation and selection coefficients on nucleotide content and stop codons.

Figure 3

The estimated selection coefficients on TAG, S2, for individual genome measurements (blue) and the average for bins of 10% in magenta (A). The average of the estimated values of S2 (black points) and the red line approximating the average estimated values as S2 ~ ln(3.6fG + 0.4) (B).

Figure 4

Expected stop codon frequencies based on expressions (4) with the approximation${\mathit{S}}_{\mathbf{2}}\approx \mathbf{ln}\left(\mathbf{3.6}{\mathit{f}}_{\mathit{G}}+\mathbf{0.4}\mathbf{\text{bf}}\right)$. Points represent average observed stop codon frequencies for TAA (blue), TGA (green) and TAG (red) across binds of 10% G-content while the approximations are shown with the lines.

Figure 5

The predicted values of S₂based on the f_TAGand f_G,${\mathbf{S}}_{\mathbf{2}}=\mathbf{ln}\left(\frac{f_G\left(1-f_{\mathit{\text{TAG}}}\right)}{f_{\mathit{\text{TAG}}}}\right)$(red) and based on f_TAGand f_TGA, S₂ = ln (f_TGA/f_TAG) (blue).

Figure 6

The relationship between stop codon frequencies in 736 bacterial genomes and G content in GA-type twofold synonymous codons. The pattern is shown for all codons with A (A), T (B), G (C) and C (D) nucleotides in the position immediately posterior to the stop codon.

Figure 7

The relationship between the ratio μ₂/μ₁, and S₁for G-content of 5% (red) and 16% (blue) with the values of the parameters at which there is no selective difference between TAG and TAA (S₁-S₂ = 0) are indicated by straight lines.