Good codons, bad transcript: large reductions in gene expression and fitness arising from synonymous mutations in a key enzyme

Abstract

Biased codon usage in protein-coding genes is pervasive, whereby amino acids are largely encoded by a specific subset of possible codons. Within individual genes, codon bias is stronger at evolutionarily conserved residues, favoring codons recognized by abundant tRNAs. Although this observation suggests an overall pattern of selection for translation speed and/or accuracy, other work indicates that transcript structure or binding motifs drive codon usage. However, our understanding of codon bias evolution is constrained by limited experimental data on the fitness effects of altering codons in functional genes. To bridge this gap, we generated synonymous variants of a key enzyme-coding gene in Methylobacterium extorquens. We found that mutant gene expression, enzyme production, enzyme activity, and fitness were all significantly lower than wild-type. Surprisingly, encoding the gene using only rare codons decreased fitness by 40%, whereas an allele coded entirely by frequent codons decreased fitness by more than 90%. Increasing gene expression restored mutant fitness to varying degrees, demonstrating that the fitness disadvantage of synonymous mutants arose from a lack of beneficial protein rather than costs of protein production. Protein production was negatively correlated with the frequency of motifs with high affinity for the anti-Shine-Dalgarno sequence, suggesting ribosome pausing as the dominant cause of low mutant fitness. Together, our data support the idea that, although a particular set of codons are favored on average across a genome, in an individual gene selection can either act for or against codons depending on their local context.

Fig. 1.

Codon usage bias metrics. The relationship between commonly used indices of codon bias and the index of relative codon usage in Methylobacterium extorquens AM1 used in this study (odds ratio of finding a specific codon at conserved rather than variable residues in protein-coding genes across the genome). (A) Relative frequency of codon usage for each amino acid, calculated for a list of 39 highly expressed ribosomal protein coding genes (from the codon usage bias database, http://cub-db.cs.umt.edu/index.shtml, last accessed December 16, 2012). (B) Relative frequency of codon usage for each amino acid, calculated for 1,022 coding regions (GenBank) and (C) relative synonymous codon usage (RSCU), the frequency of each codon normalized by the total number of synonymous codons for that amino acid. (D) The frequency of optimal codons (Fop) for each of the synonymous fae alleles used in this study; bars are colored by strain. In (A–C), red points indicate the most commonly used codon in the respective set of genes. Data in (B) and (C) are based on the entire M. extorquens genome.

Fig. 2.

Fitness effects of synonymous fae alleles. Points show mean ± SE (standard error) (n = 4) for each strain, labeled and colored as in table 1. The open triangle represents the WT strain without a FLAG tag (WT*) and the dashed line shows data for a Δfae (knockout) strain (Del). (B–E) Data are shown relative to WT. (A) Growth rate (per hour) as a function of the proportion of rare codons in fae. (B) Growth rate as a function of the amount of mRNA, as measured from quantitative real time PCR. (C) Growth rate as a function of the amount of FLAG-tagged FAE protein as quantified from Western blots. (D) Average amount of FAE protein produced per mRNA in each strain. (E) Enzyme activity in protein extracts from different strains (rate of conversion of substrate to product per milligram total protein; n = 3). We did not measure enzyme activity for strains AC and AF because they did not produce detectable FAE protein; however, we measured activity in the fae knockout strain (Del) to serve as a control for the spontaneous reaction that occurs in cell extracts but that is insufficient to permit growth on methanol (Vorholt et al. 2000).

Fig. 3.

Frequency of hexamers in fae alleles with different predicted affinity to the anti-SD sequence. More negative values indicate stronger binding. Strains are colored as in table 1. The dashed line indicates maximum affinity to anti-SD observed in the WT allele. For clarity, hexamers that are absent in a given allele are omitted.

Fig. 4.

Protein production in mutant strains as a function of the total number of hexamers with high binding affinity to anti-SD, with two different thresholds for high affinity (more negative affinity indicates stronger binding). Points are colored and labeled as in table 1.

Fig. 5.

Growth rate (mean ± 2 SE [standard error]) of synonymous fae mutants (strains are labeled as in table 1) on permissive succinate medium (diamonds) and selective methanol medium (filled circles). All pairwise differences in growth rate on succinate are nonsignificant (Tukey–HSD correction, P > 0.7).

Fig. 6.

Fitness as a function of induced gene expression. Growth rate (mean ± SE; n = 3) of fae knockout strains carrying plasmid-borne fae alleles on a regulated promoter in methanol, as a function of inducer concentration. The strain carrying the WT allele shows growth even in the absence of cumate inducer, due to high background gene expression from the regulated plasmid (∼40% of native chromosomal level). The dashed line indicates the growth rate of the chromosomal mutant carrying the WT allele (from fig. 2A). Note that due to the cost of plasmid carriage (Chou and Marx 2012), none of the alleles—including the WT version—achieved the expression level of the chromosomal WT strain.