Measuring and Detecting Molecular Adaptation in Codon Usage Against Nonsense Errors During Protein Translation

Abstract

Codon usage bias (CUB) has been documented across a wide range of taxa and is the subject of numerous studies. While most explanations of CUB invoke some type of natural selection, most measures of CUB adaptation are heuristically defined. In contrast, we present a novel and mechanistic method for defining and contextualizing CUB adaptation to reduce the cost of nonsense errors during protein translation. Using a model of protein translation, we develop a general approach for measuring the protein production cost in the face of nonsense errors of a given allele as well as the mean and variance of these costs across its coding synonyms. We then use these results to define the nonsense error adaptation index (NAI) of the allele or a contiguous subset thereof. Conceptually, the NAI value of an allele is a relative measure of its elevation on a specific and well-defined adaptive landscape. To illustrate its utility, we calculate NAI values for the entire coding sequence and across a set of nonoverlapping windows for each gene in the Saccharomyces cerevisiae S288c genome. Our results provide clear evidence of adaptation to reduce the cost of nonsense errors and increasing adaptation with codon position and expression. The magnitude and nature of this adaptation are also largely consistent with simulation results in which nonsense errors are the only selective force driving CUB evolution. Because NAI is derived from mechanistic models, it is both easier to interpret and more amenable to future refinement than other commonly used measures of codon bias. Further, our approach can also be used as a starting point for developing other mechanistically derived measures of adaptation such as for translational accuracy.

Explaining complex codon usage patterns with selection for translational efficiency, mutation bias, and genetic drift

“…Because codons that minimize missense errors may not necessarily be the ones that minimize elongation times (13), our model is likely insufficient to explain the codon usage at these sites. Similarly, adaptation against nonsense errors has been documented in S. cerevisiae (14,15) and other organisms (43). In addition, factors indirectly related to protein translation, such as mRNA secondary structures at the 5′ region of a gene, have been shown to be under selection for efficient binding of ribosomes to mRNAs, and hence can affect the frequency of codon usage at these sites (10,11).…”

“…5, 6, 13, and 14). Although most theories of codon usage predict that the degree of bias in codon usage should increase with gene expression (1,4,15), they lack any specific quantitative predictions about the rate and nature of these changes. This is because most commonly used indices of CUB, such as frequency of optimal codons (F op ) (1), codon adaptation index (CAI) (16), and codon bias index (CBI) (17), are both heuristic and aggregate measures of CUB and fail to define explicitly the factors responsible for the evolution of CUB.…”

Entropy and Information Approaches to Genetic Diversity and its Expression: Genomic Geography

“…Sanchez and Grau [130] extend the idea of DNA's triplet code or amino acids as a Boolean information system, and from this suggest that the distribution of amino acids in a protein might follow a Boltzmann distribution. Long ago, Iwasa [112] wrote theory for evolution of codon usage bias in entropic terms, and this has recently been rediscovered in analysis of codon usage in yeast [131]. Loewenstern and Yianilos [132] show that the entropy of natural DNA is less than its theoretical maximum of 2 bits per site.…”

ChloroMitoCU: Codon patterns across organelle genomes for functional genomics and evolutionary applications

“…One such evolutionary force is codon usage, which has evolved independently in organelle genomes. Codon usage, a pattern that describes the degeneracy of the genetic code and illustrates differential usage of synonymous codons, is thought to be under the influence of several factors such as mutational pressure, 3 translational selection, 4 and tRNA dependencies 5 , 6 . In model organisms, the evolutionary forces shaping codon usage have been assessed using either genome-wide sets of predicted coding regions or subsets of highly expressed ribosomal proteins 7 .…”