A novel framework for evaluating the performance of codon usage bias metrics

The protein domains of vertebrate species in which selection is more effective have greater intrinsic structural disorder

The nearly neutral theory of molecular evolution posits variation among species in the effectiveness of selection. In an idealized model, the census population size determines both this minimum magnitude of the selection coefficient required for deleterious variants to be reliably purged, and the amount of neutral diversity. Empirically, an "effective population size" is often estimated from the amount of putatively neutral genetic diversity and is assumed to also capture a species' effectiveness of selection. A potentially more direct measure of the effectiveness of selection is the degree to which selection maintains preferred codons. However, past metrics that compare codon bias across species are confounded by among-species variation in %GC content and/or amino acid composition. Here we propose a new Codon Adaptation Index of Species (CAIS), based on Kullback-Leibler divergence, that corrects for both confounders. We demonstrate the use of CAIS correlations, as well as the Effective Number of Codons, to show that the protein domains of more highly adapted vertebrate species evolve higher intrinsic structural disorder.

Translation Comes First: Ancient and Convergent Selection of Codon Usage Bias Across Prokaryotic Genomes

Codon usage is the outcome of different evolutionary processes and can inform us about the conditions in which organisms live and evolve. Here, we present R_ENC', which is an improvement to the original S index developed by dos Reis et al. (2004). Our index is less sensitive to G+C content, which greatly affects synonymous codon usage in prokaryotes, making it better suited to detect selection acting on codon usage. We used R_ENC' to estimate the extent of selected codon usage bias in 1800 genomes representing 26 prokaryotic phyla. We found that Gammaproteobacteria, Betaproteobacteria, Actinobacteria, and Firmicutes are the phyla/subphyla showing more genomes with selected codon usage bias. In particular, we found that several lineages within Gammaproteobacteria and Firmicutes show a similar set of functional terms enriched in genes under selected codon usage bias, indicating convergent evolution. We also show that selected codon usage bias tends to evolve in genes coding for the translation machinery before other functional GO terms. Finally, we discuss the possibility to use R_ENC' to predict whether lineages evolved in copiotrophic or oligotrophic environments.

Enhanced effective codon numbers to understand codon usage bias

Codon usage bias is a well recognized phenomenon but the relative influence of its major causes: G+C content, mutational biases, and selection, are often difficult to disentangle. This paper presents methods to calculate modified effective codon numbers that allow the investigation of the sources of codon bias and how genes or organisms have their codon biases shaped. In particular, it demonstrates that variation in codon usage bias across organisms is likely driven more by likely mutational forces while the variation in codon usage bias within genomes is likely driven by codon selectional forces.

Protein Abundance Prediction Through Machine Learning Methods

Proteins are responsible for most physiological processes, and their abundance provides crucial information for systems biology research. However, absolute protein quantification, as determined by mass spectrometry, still has limitations in capturing the protein pool. Protein abundance is impacted by translation kinetics, which rely on features of codons. In this study, we evaluated the effect of codon usage bias of genes on protein abundance. Notably, we observed differences regarding codon usage patterns between genes coding for highly abundant proteins and genes coding for less abundant proteins. Analysis of synonymous codon usage and evolutionary selection showed a clear split between the two groups. Our machine learning models predicted protein abundances from codon usage metrics with remarkable accuracy, achieving strong correlation with experimental data. Upon integration of the predicted protein abundance in enzyme-constrained genome-scale metabolic models, the simulated phenotypes closely matched experimental data, which demonstrates that our predictive models are valuable tools for systems metabolic engineering approaches.

Codon-based indices for modeling gene expression and transcript evolution

Codon usage bias (CUB) refers to the phenomena that synonymous codons are used in different frequencies in most genes and organisms. The general assumption is that codon biases reflect a balance between mutational biases and natural selection. Today we understand that the codon content is related and can affect all gene expression steps. Starting from the 1980s, codon-based indices have been used for answering different questions in all biomedical fields, including systems biology, agriculture, medicine, and biotechnology. In general, codon usage bias indices weigh each codon or a small set of codons to estimate the fitting of a certain coding sequence to a certain phenomenon (e.g., bias in codons, adaptation to the tRNA pool, frequencies of certain codons, transcription elongation speed, etc.) and are usually easy to implement. Today there are dozens of such indices; thus, this paper aims to review and compare the different codon usage bias indices, their applications, and advantages. In addition, we perform analysis that demonstrates that most indices tend to correlate even though they aim to capture different aspects. Due to the centrality of codon usage bias on different gene expression steps, it is important to keep developing new indices that can capture additional aspects that are not modeled with the current indices.