Phylogenetic analysis of mutational robustness based on codon usage supports that the standard genetic code does not prefer extreme environments

Abstract

The mutational robustness of the genetic code is rarely discussed in the context of biological diversity, such as codon usage and related factors, often considered as independent of the actual organism's proteome. Here we put the living beings back to picture and use distortion as a metric of mutational robustness. Distortion estimates the expected severities of non-synonymous mutations measuring it by amino acid physicochemical properties and weighting for codon usage. Using the biological variance of codon frequencies, we interpret the mutational robustness of the standard genetic code with regards to their corresponding environments and genomic compositions (GC-content). Employing phylogenetic analyses, we show that coding fidelity in physicochemical properties can deteriorate with codon usages adapted to extreme environments and these putative effects are not the artefacts of phylogenetic bias. High temperature environments select for codon usages with decreased mutational robustness of hydrophobic, volumetric, and isoelectric properties. Selection at high saline concentrations also leads to reduced fidelity in polar and isoelectric patterns. These show that the genetic code performs best with mesophilic codon usages, strengthening the view that LUCA or its ancestors preferred lower temperature environments. Taxonomic implications, such as rooting the tree of life, are also discussed.

Figure 1

The large-scale hierarchy of the Bayesian phylogenetic tree based on S16 rRNAs sequences. The Bayesian tree was constructed using Beast v1.10.4. The numbers at each node represent posterior probability values (only values < 1.00 are shown). Collapsed nodes contain multiple species (see Supplementary Data S1 online for more details).

Figure 2

The effect of GC-content and environmental variables (temperature, salt concentration and pH) on the expected distortion measures (κ = 2.5) of different amino acid physicochemical properties. The effects were estimated by PGLS models using the inferred phylogenetic relationships shown on Fig. 1. Partial regression lines show significant trends (continuous line: p < 0.001, dashed line: p < 0.05). In each case, the remaining independent variables were set as [GC = 0.5; T = 30 °C; cc_NaCl = 2.5 w/V%; pH = 7].