SENCA: A Multilayered Codon Model to Study the Origins and Dynamics of Codon Usage

Next-generation development and application of codon model in evolution

To date, numerous nucleotide, amino acid, and codon substitution models have been developed to estimate the evolutionary history of any sequence/organism in a more comprehensive way. Out of these three, the codon substitution model is the most powerful. These models have been utilized extensively to detect selective pressure on a protein, codon usage bias, ancestral reconstruction and phylogenetic reconstruction. However, due to more computational demanding, in comparison to nucleotide and amino acid substitution models, only a few studies have employed the codon substitution model to understand the heterogeneity of the evolutionary process in a genome-scale analysis. Hence, there is always a question of how to develop more robust but less computationally demanding codon substitution models to get more accurate results. In this review article, the authors attempted to understand the basis of the development of different types of codon-substitution models and how this information can be utilized to develop more robust but less computationally demanding codon substitution models. The codon substitution model enables to detect selection regime under which any gene or gene region is evolving, codon usage bias in any organism or tissue-specific region and phylogenetic relationship between different lineages more accurately than nucleotide and amino acid substitution models. Thus, in the near future, these codon models can be utilized in the field of conservation, breeding and medicine.

Evaluation of Methods to Detect Shifts in Directional Selection at the Genome Scale

Identifying the footprints of selection in coding sequences can inform about the importance and function of individual sites. Analyses of the ratio of nonsynonymous to synonymous substitutions (dN/dS) have been widely used to pinpoint changes in the intensity of selection, but cannot distinguish them from changes in the direction of selection, that is, changes in the fitness of specific amino acids at a given position. A few methods that rely on amino-acid profiles to detect changes in directional selection have been designed, but their performances have not been well characterized. In this paper, we investigate the performance of six of these methods. We evaluate them on simulations along empirical phylogenies in which transition events have been annotated and compare their ability to detect sites that have undergone changes in the direction or intensity of selection to that of a widely used dN/dS approach, codeml's branch-site model A. We show that all methods have reduced performance in the presence of biased gene conversion but not CpG hypermutability. The best profile method, Pelican, a new implementation of Tamuri AU, Hay AJ, Goldstein RA. (2009. Identifying changes in selective constraints: host shifts in influenza. PLoS Comput Biol. 5(11):e1000564), performs as well as codeml in a range of conditions except for detecting relaxations of selection, and performs better when tree length increases, or in the presence of persistent positive selection. It is fast, enabling genome-scale searches for site-wise changes in the direction of selection associated with phenotypic changes.

Codon Usage Optimization in the Prokaryotic Tree of Life: How Synonymous Codons Are Differentially Selected in Sequence Domains with Different Expression Levels and Degrees of Conservation

The prokaryotic genomes—the current heritage of the most ancient life forms on earth—are comprised of diverse gene sets, all characterized by varied origins, ancestries, and spatial-temporal expression patterns. Such genetic diversity has for a long time raised the question of how cells shape their coding strategies to optimize protein demands (i.e., product abundance) and accuracy (i.e., translation fidelity) through the use of the same genetic code in genomes with GC contents that range from less than 20 to more than 80%. Here, we present evidence on how codon usage is adjusted in the prokaryotic tree of life and on how specific biases have operated to improve translation. Through the use of proteome data, we characterized conserved and variable sequence domains in genes of either high or low expression level and quantitated the relative weight of efficiency and accuracy—as well as their interaction—in shaping codon usage in prokaryotes.

Prokaryote genomes exhibit a wide range of GC contents and codon usages, both resulting from an interaction between mutational bias and natural selection. In order to investigate the basis underlying specific codon changes, we performed a comprehensive analysis of 29 different prokaryote families. The analysis of core gene sets with increasing ancestries in each family lineage revealed that the codon usages became progressively more adapted to the tRNA pools. While, as previously reported, highly expressed genes presented the most optimized codon usage, the singletons contained the less selectively favored codons. The results showed that usually codons with the highest translational adaptation were preferentially enriched. In agreement with previous reports, a C bias in 2- to 3-fold pyrimidine-ending codons, and a U bias in 4-fold codons occurred in all families, irrespective of the global genomic GC content. Furthermore, the U biases suggested that U₃-mRNA–U₃₄-tRNA interactions were responsible for a prominent codon optimization in both the most ancestral core and the highly expressed genes. A comparative analysis of sequences that encode conserved (cr) or variable (vr) translated products, with each one being under high (HEP) and low (LEP) expression levels, demonstrated that the efficiency was more relevant (by a factor of 2) than accuracy to modeling codon usage. Finally, analysis of the third position of codons (GC3) revealed that in genomes with global GC contents higher than 35 to 40%, selection favored a GC3 increase, whereas in genomes with very low GC contents, a decrease in GC3 occurred. A comprehensive final model is presented in which all patterns of codon usage variations are condensed in four distinct behavioral groups.

A Population Genomics Lexicon

Population genomics is a growing field stemming from soon a 100 years of developments in population genetics. Here, we summarize the main concepts and terminology underlying both theoretical and empirical statistical population genomics studies. We provide the reader with pointers toward the original literature as well as methodological and historical reviews.

Conditional Approximate Bayesian Computation: A New Approach for Across-Site Dependency in High-Dimensional Mutation-Selection Models

A key question in molecular evolutionary biology concerns the relative roles of mutation and selection in shaping genomic data. Moreover, features of mutation and selection are heterogeneous along the genome and over time. Mechanistic codon substitution models based on the mutation-selection framework are promising approaches to separating these effects. In practice, however, several complications arise, since accounting for such heterogeneities often implies handling models of high dimensionality (e.g., amino acid preferences), or leads to across-site dependence (e.g., CpG hypermutability), making the likelihood function intractable. Approximate Bayesian Computation (ABC) could address this latter issue. Here, we propose a new approach, named Conditional ABC (CABC), which combines the sampling efficiency of MCMC and the flexibility of ABC. To illustrate the potential of the CABC approach, we apply it to the study of mammalian CpG hypermutability based on a new mutation-level parameter implying dependence across adjacent sites, combined with site-specific purifying selection on amino-acids captured by a Dirichlet process. Our proof-of-concept of the CABC methodology opens new modeling perspectives. Our application of the method reveals a high level of heterogeneity of CpG hypermutability across loci and mild heterogeneity across taxonomic groups; and finally, we show that CpG hypermutability is an important evolutionary factor in rendering relative synonymous codon usage. All source code is available as a GitHub repository (https://github.com/Simonll/LikelihoodFreePhylogenetics.git).

Population Genetics Based Phylogenetics Under Stabilizing Selection for an Optimal Amino Acid Sequence: A Nested Modeling Approach

We present a new phylogenetic approach, selection on amino acids and codons (SelAC), whose substitution rates are based on a nested model linking protein expression to population genetics. Unlike simpler codon models that assume a single substitution matrix for all sites, our model more realistically represents the evolution of protein-coding DNA under the assumption of consistent, stabilizing selection using a cost-benefit approach. This cost-benefit approach allows us to generate a set of 20 optimal amino acid-specific matrix families using just a handful of parameters and naturally links the strength of stabilizing selection to protein synthesis levels, which we can estimate. Using a yeast data set of 100 orthologs for 6 taxa, we find SelAC fits the data much better than popular models by 104-105 Akike information criterion units adjusted for small sample bias. Our results also indicated that nested, mechanistic models better predict observed data patterns highlighting the improvement in biological realism in amino acid sequence evolution that our model provides. Additional parameters estimated by SelAC indicate that a large amount of nonphylogenetic, but biologically meaningful, information can be inferred from existing data. For example, SelAC prediction of gene-specific protein synthesis rates correlates well with both empirical (r=0.33-0.48) and other theoretical predictions (r=0.45-0.64) for multiple yeast species. SelAC also provides estimates of the optimal amino acid at each site. Finally, because SelAC is a nested approach based on clearly stated biological assumptions, future modifications, such as including shifts in the optimal amino acid sequence within or across lineages, are possible.

Multiple Factors Confounding Phylogenetic Detection of Selection on Codon Usage

Detecting selection on codon usage (CU) is a difficult task, since CU can be shaped by both the mutational process and selective constraints operating at the DNA, RNA, and protein levels. Yang and Nielsen (2008) developed a test (which we call CUYN) for detecting selection on CU using two competing mutation-selection models of codon substitution. The null model assumes that CU is determined by the mutation bias alone, whereas the alternative model assumes that both mutation bias and/or selection act on CU. In applications on mammalian-scale alignments, the CUYN test detects selection on CU for numerous genes. This is surprising, given the small effective population size of mammals, and prompted us to use simulations to evaluate the robustness of the test to model violations. Simulations using a modest level of CpG hypermutability completely mislead the test, with 100% false positives. Surprisingly, a high level of false positives (56.1%) resulted simply from using the HKY mutation-level parameterization within the CUYN test on simulations conducted with a GTR mutation-level parameterization. Finally, by using a crude optimization procedure on a parameter controlling the CpG hypermutability rate, we find that this mutational property could explain a very large part of the observed mammalian CU. Altogether, our work emphasizes the need to evaluate the potential impact of model violations on statistical tests in the field of molecular phylogenetic analysis. The source code of the simulator and the mammalian genes used are available as a GitHub repository (https://github.com/Simonll/LikelihoodFreePhylogenetics.git).

Using the Mutation-Selection Framework to Characterize Selection on Protein Sequences

When mutational pressure is weak, the generative process of protein evolution involves explicit probabilities of mutations of different types coupled to their conditional probabilities of fixation dependent on selection. Establishing this mechanistic modeling framework for the detection of selection has been a goal in the field of molecular evolution. Building on a mathematical framework proposed more than a decade ago, numerous methods have been introduced in an attempt to detect and measure selection on protein sequences. In this review, we discuss the structure of the original model, subsequent advances, and the series of assumptions that these models operate under.