Measuring the relative contribution to predictive power of modern nucleotide substitution modeling approaches.

Traditional approaches to probabilistic phylogenetic inference have relied on information-theoretic criteria to select among a relatively small set of substitution models. These model selection criteria have recently been called into question when applied to richer models, including models that invoke mixtures of nucleotide frequency profiles. At the nucleotide level, we are therefore left without a clear picture of mixture models' contribution to overall predictive power relative to other modeling approaches. Here, we utilize a Bayesian cross-validation method to directly measure the predictive performance of a wide range of nucleotide substitution models. We compare the relative contributions of free nucleotide exchangeability parameters, gamma-distributed rates across sites, and mixtures of nucleotide frequencies with both finite and infinite mixture frameworks. We find that the most important contributor to a model's predictive power is the use of a sufficiently rich mixture of nucleotide frequencies. These results suggest that mixture models should be given greater consideration in nucleotide-level phylogenetic inference.

Genes and sites under adaptation at the phylogenetic scale also exhibit adaptation at the population-genetic scale.

Adaptation in protein-coding sequences can be detected from multiple sequence alignments across species or alternatively by leveraging polymorphism data within a population. Across species, quantification of the adaptive rate relies on phylogenetic codon models, classically formulated in terms of the ratio of nonsynonymous over synonymous substitution rates. Evidence of an accelerated nonsynonymous substitution rate is considered a signature of pervasive adaptation. However, because of the background of purifying selection, these models are potentially limited in their sensitivity. Recent developments have led to more sophisticated mutation-selection codon models aimed at making a more detailed quantitative assessment of the interplay between mutation, purifying, and positive selection. In this study, we conducted a large-scale exome-wide analysis of placental mammals with mutation-selection models, assessing their performance at detecting proteins and sites under adaptation. Importantly, mutation-selection codon models are based on a population-genetic formalism and thus are directly comparable to the McDonald and Kreitman test at the population level to quantify adaptation. Taking advantage of this relationship between phylogenetic and population genetics analyses, we integrated divergence and polymorphism data across the entire exome for 29 populations across 7 genera and showed that proteins and sites detected to be under adaptation at the phylogenetic scale are also under adaptation at the population-genetic scale. Altogether, our exome-wide analysis shows that phylogenetic mutation-selection codon models and the population-genetic test of adaptation can be reconciled and are congruent, paving the way for integrative models and analyses across individuals and populations.

Bayesian Cross-Validation Comparison of Amino Acid Replacement Models: Contrasting Profile Mixtures, Pairwise Exchangeabilities, and Gamma-Distributed Rates-Across-Sites.

Models of amino acid replacement are central to modern phylogenetic inference, particularly so when dealing with deep evolutionary relationships. Traditionally, a single, empirically derived matrix was utilized, so as to keep the degrees-of-freedom of the inference low, and focused on topology. With the growing size of data sets, however, an amino acid-level general-time-reversible matrix has become increasingly feasible, treating amino acid exchangeabilities and frequencies as free parameters. Moreover, models based on mixtures of multiple matrices are increasingly utilized, in order to account for across-site heterogeneities in amino acid requirements of proteins. Such models exist as finite empirically-derived amino acid profile (or frequency) mixtures, free finite mixtures, as well as free Dirichlet process-based infinite mixtures. All of these approaches are typically combined with a gamma-distributed rates-across-sites model. In spite of the availability of these different aspects to modeling the amino acid replacement process, no study has systematically quantified their relative contributions to their predictive power of real data. Here, we use Bayesian cross-validation to establish a detailed comparison, while activating/deactivating each modeling aspect. For most data sets studied, we find that amino acid mixture models can outrank all single-matrix models, even when the latter include gamma-distributed rates and the former do not. We also find that free finite mixtures consistently outperform empirical finite mixtures. Finally, the Dirichlet process-based mixture model tends to outperform all other approaches.

Jump-Chain Simulation of Markov Substitution Processes Over Phylogenies.

We draw attention to an under-appreciated simulation method for generating artificial data in a phylogenetic context. The approach, which we refer to as jump-chain simulation, can invoke rich models of molecular evolution having intractable likelihood functions. As an example, we simulate data under a context-dependent model allowing for CpG hypermutability and show how such a feature can mislead common codon models used for detecting positive selection. We discuss more generally how this method can serve to elucidate the ways by which currently used models for inference are susceptible to violations of their underlying assumptions. Finally, we show how the method could serve as an inference engine in the Approximate Bayesian Computation framework.

A Bayesian Mutation-Selection Framework for Detecting Site-Specific Adaptive Evolution in Protein-Coding Genes.

In recent years, codon substitution models based on the mutation-selection principle have been extended for the purpose of detecting signatures of adaptive evolution in protein-coding genes. However, the approaches used to date have either focused on detecting global signals of adaptive regimes-across the entire gene-or on contexts where experimentally derived, site-specific amino acid fitness profiles are available. Here, we present a Bayesian site-heterogeneous mutation-selection framework for site-specific detection of adaptive substitution regimes given a protein-coding DNA alignment. We offer implementations, briefly present simulation results, and apply the approach on a few real data sets. Our analyses suggest that the new approach shows greater sensitivity than traditional methods. However, more study is required to assess the impact of potential model violations on the method, and gain a greater empirical sense its behavior on a broader range of real data sets. We propose an outline of such a research program.

Evolution of Carthamus species revealed through sequence analyses of the fad2 gene family.

The diversity of 11 fatty acid desaturase (fad2) genes has not been investigated between cultivated and wild species in the Carthamus genus. In this study, 17 C. tinctorius accessions and 28 accessions from other Carthamus species were subjected to sequence analyses of this fad2 gene family. Results showed that among these genes, fad2-1 had a major role in the conversion of oleic acid to linoleic acid. Grouping of all studied wild polyploid species and the wild diploid C. leucocaulos suggested that C. lanatus transferred its fad2-1 gene to C. turkestanicus and C. lanatus. A phylogenetic tree based on fad2-1 gene sequences also showed that C. palaestinus and C. oxyacanthus grouped with C. tinctorius individuals, suggesting that C. tinctorius is closely related to both wild species. A one base pair deletion at position 604 in the fad2-1 gene coding region correlated with high levels of oleic acid content in five mutant phenotypes of the evaluated C. tinctorius accessions. Grouping of fad2-1 and fad2-8 (Ctfad2-10) indicated that both of these genes are involved in oleate desaturases activity. The fad2-3 (Ctfad2-3) and Ctfad2-4 had the highest sequence similarity among the other fad2 genes, indicating the conservative nature of these two genes among all the studied species. Our results suggest that C. lanatus is the likely progenitor of C. turkestanicus and C. creticus (Synonym C. baeticus). Also, C. palaestinus is genetically closer to C. tinctorius but the involvement of C. oxyacanthus cannot be excluded and, this requires further investigation.

A Not-So-Long Introduction to Computational Molecular Evolution.

In this chapter, we give a not-so-long and self-contained introduction to computational molecular evolution. In particular, we present the emergence of the use of likelihood-based methods, review the standard DNA substitution models, and introduce how model choice operates. We also present recent developments in inferring absolute divergence times and rates on a phylogeny, before showing how state-of-the-art models take inspiration from diffusion theory to link population genetics, which traditionally focuses at a taxonomic level below that of the species, and molecular evolution. Although this is not a cookbook chapter, we try and point to popular programs and implementations along the way.

Detecting amino acid preference shifts with codon-level mutation-selection mixture models.

BACKGROUND: In recent years, increasing attention has been placed on the development of phylogeny-based statistical methodologies for uncovering site-specific changes in amino acid fitness profiles over time. The few available random-effects approaches, modelling across-site variation in amino acid profiles as random variables drawn from a statistical law, either lack a mechanistic codon-level formulation, or pose significant computational challenges. RESULTS: Here, we bring together a few existing ideas to explore a simple and fast method based on a predefined finite mixture of amino acid profiles within a codon-level substitution model following the mutation-selection formulation. Our study is focused on the detection of site-specific shifts in amino acid profiles over a known sub-clade of a tree, using simulations with and without shifts over the sub-clade to study the properties of the method. Through modifications of the values of the amino acid profiles, our simulations show different levels of reliability under different forms of finite mixture models. Sites identified by our method in a real data set show obvious overlap with those identified using previous methods, with some notable differences. CONCLUSION: Overall, our results show that when a site-specific shift in amino acid profile is strongly pronounced, involving two clearly different sets of profiles, the method performs very well; but shifts between profiles that share many features are difficult to correctly identify, highlighting the challenging nature of the problem.

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).

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).

Clonal evolution and genome stability in a 2500-year-old fungal individual.

Individuals of the basidiomycete fungus Armillaria are well known for their ability to spread from woody substrate to substrate on the forest floor through the growth of rhizomorphs. Here, we made 248 collections of A. gallica in one locality in Michigan's Upper Peninsula. To identify individuals, we genotyped collections with molecular markers and somatic compatibility testing. We found several different individuals in proximity to one another, but one genetic individual stood out as exceptionally large, covering hundreds of tree root systems over approximately 75 hectares of the forest floor. Based on observed growth rates of the fungus, we estimate the minimum age of the large individual as 2500 years. With whole-genome sequencing and variant discovery, we also found that mutation had occurred within the somatic cells of the individual, reflecting its historical pattern of growth from a single point. The overall rate of mutation over the 90 mb genome, however, was extremely low. This same individual was first discovered in the late 1980s, but its full spatial extent and internal mutation dynamic was unknown at that time. The large individual of A. gallica has been remarkably resistant to genomic change as it has persisted in place.

COMPASS: the COMPletely Arbitrary Sequence Simulator.

SUMMARY: Simulated sequence alignments are frequently used to test bioinformatics tools, but current sequence simulators are limited to defined state spaces. Here, we present the COMPletely Arbitrary Sequence Simulator (COMPASS), which is able to simulate the evolution of absolutely any discrete state space along a tree, for any form of time-reversible model. AVAILABILITY AND IMPLEMENTATION: COMPASS is implemented in Python 2.7, and is freely available for all platforms with the Supplementary Information, as well as at http://labs.carleton.ca/eme/software-and-data. CONTACT: alex_wong@carleton.ca. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Genomics of Compensatory Adaptation in Experimental Populations of Aspergillus nidulans.

Knowledge of the number and nature of genetic changes responsible for adaptation is essential for understanding and predicting evolutionary trajectories. Here, we study the genomic basis of compensatory adaptation to the fitness cost of fungicide resistance in experimentally evolved strains of the filamentous fungus Aspergillus nidulans The original selection experiment tracked the fitness recovery of lines founded by an ancestral strain that was resistant to fludioxonil, but paid a fitness cost in the absence of the fungicide. We obtained whole-genome sequence data for the ancestral A. nidulans strain and eight experimentally evolved strains. We find that fludioxonil resistance in the ancestor was likely conferred by a mutation in histidine kinase nikA, part of the two-component signal transduction system of the high-osmolarity glycerol (HOG) stress response pathway. To compensate for the pleiotropic negative effects of the resistance mutation, the subsequent fitness gains observed in the evolved lines were likely caused by secondary modification of HOG pathway activity. Candidate genes for the compensatory fitness increases were significantly overrepresented by stress response functions, and some were specifically associated with the HOG pathway itself. Parallel evolution at the gene level was rare among evolved lines. There was a positive relationship between the predicted number of adaptive steps, estimated from fitness data, and the number of genomic mutations, determined by whole-genome sequencing. However, the number of genomic mutations was, on average, 8.45 times greater than the number of adaptive steps inferred from fitness data. This research expands our understanding of the genetic basis of adaptation in multicellular eukaryotes and lays out a framework for future work on the genomics of compensatory adaptation in A. nidulans.

Detecting Adaptation in Protein-Coding Genes Using a Bayesian Site-Heterogeneous Mutation-Selection Codon Substitution Model.

Codon substitution models have traditionally attempted to uncover signatures of adaptation within protein-coding genes by contrasting the rates of synonymous and non-synonymous substitutions. Another modeling approach, known as the mutation-selection framework, attempts to explicitly account for selective patterns at the amino acid level, with some approaches allowing for heterogeneity in these patterns across codon sites. Under such a model, substitutions at a given position occur at the neutral or nearly neutral rate when they are synonymous, or when they correspond to replacements between amino acids of similar fitness; substitutions from high to low (low to high) fitness amino acids have comparatively low (high) rates. Here, we study the use of such a mutation-selection framework as a null model for the detection of adaptation. Following previous works in this direction, we include a deviation parameter that has the effect of capturing the surplus, or deficit, in non-synonymous rates, relative to what would be expected under a mutation-selection modeling framework that includes a Dirichlet process approach to account for across-codon-site variation in amino acid fitness profiles. We use simulations, along with a few real data sets, to study the behavior of the approach, and find it to have good power with a low false-positive rate. Altogether, we emphasize the potential of recent mutation-selection models in the detection of adaptation, calling for further model refinements as well as large-scale applications.

Whole genome sequencing and comparative genomics of closely related Fusarium Head Blight fungi: Fusarium graminearum, F. meridionale and F. asiaticum.

BACKGROUND: The Fusarium graminearum species complex is composed of many distinct fungal species that cause several diseases in economically important crops, including Fusarium Head Blight of wheat. Despite being closely related, these species and individuals within species have distinct phenotypic differences in toxin production and pathogenicity, with some isolates reported as non-pathogenic on certain hosts. In this report, we compare genomes and gene content of six new isolates from the species complex, including the first available genomes of F. asiaticum and F. meridionale, with four other genomes reported in previous studies. RESULTS: A comparison of genome structure and gene content revealed a 93-99% overlap across all ten genomes. We identified more than 700 k base pairs (kb) of single nucleotide polymorphisms (SNPs), insertions, and deletions (indels) within common regions of the genome, which validated the species and genetic populations reported within species. We constructed a non-redundant pan gene list containing 15,297 genes from the ten genomes and among them 1827 genes or 12% were absent in at least one genome. These genes were co-localized in telomeric regions and select regions within chromosomes with a corresponding increase in SNPs and indels. Many are also predicted to encode for proteins involved in secondary metabolism and other functions associated with disease. Genes that were common between isolates contained high levels of nucleotide variation and may be pseudogenes, allelic, or under diversifying selection. CONCLUSIONS: The genomic resources we have contributed will be useful for the identification of genes that contribute to the phenotypic variation and niche specialization that have been reported among members of the F. graminearum species complex.

Grouping substitution types into different relaxed molecular clocks.

Different types of nucleotide substitutions experience different patterns of rate change over time. We propose clustering context-dependent (or context-independent) nucleotide substitution types according to how their rates change and then using the grouping for divergence time estimation. With our models, relative rates among types that are in the same group are fixed, whereas absolute rates of the types within a group change over time according to a shared relaxed molecular clock. We illustrate our procedure by analysing a 0.15 Mb intergenic region to infer divergence times relating eight primates. The different groupings of substitution types that we explore have little effect on the posterior means of divergence times, but the widths of the credibility intervals decrease as the number of groups increases.This article is part of the themed issue 'Dating species divergences using rocks and clocks'.

Relaxing the Molecular Clock to Different Degrees for Different Substitution Types.

Rates of molecular evolution can vary over time. Diverse statistical techniques for divergence time estimation have been developed to accommodate this variation. These typically require that all sequence (or codon) positions at a locus change independently of one another. They also generally assume that the rates of different types of nucleotide substitutions vary across a phylogeny in the same way. This permits divergence time estimation procedures to employ an instantaneous rate matrix with relative rates that do not differ among branches. However, previous studies have suggested that some substitution types (e.g., CpG to TpG changes in mammals) are more clock-like than others. As has been previously noted, this is biologically plausible given the mutational mechanism of CpG to TpG changes. Through stochastic mapping of sequence histories from context-independent substitution models, our approach allows for context-dependent nucleotide substitutions to change their relative rates over time. We apply our approach to the analysis of a 0.15 Mb intergenic region from eight primates. In accord with previous findings, we find comparatively little rate variation over time for CpG to TpG substitutions but we find more for other substitution types. We conclude by discussing the limitations and prospects of our approach.

The effect of selection environment on the probability of parallel evolution.

Across the great diversity of life, there are many compelling examples of parallel and convergent evolution-similar evolutionary changes arising in independently evolving populations. Parallel evolution is often taken to be strong evidence of adaptation occurring in populations that are highly constrained in their genetic variation. Theoretical models suggest a few potential factors driving the probability of parallel evolution, but experimental tests are needed. In this study, we quantify the degree of parallel evolution in 15 replicate populations of Pseudomonas fluorescens evolved in five different environments that varied in resource type and arrangement. We identified repeat changes across multiple levels of biological organization from phenotype, to gene, to nucleotide, and tested the impact of 1) selection environment, 2) the degree of adaptation, and 3) the degree of heterogeneity in the environment on the degree of parallel evolution at the gene-level. We saw, as expected, that parallel evolution occurred more often between populations evolved in the same environment; however, the extent of parallel evolution varied widely. The degree of adaptation did not significantly explain variation in the extent of parallelism in our system but number of available beneficial mutations correlated negatively with parallel evolution. In addition, degree of parallel evolution was significantly higher in populations evolved in a spatially structured, multiresource environment, suggesting that environmental heterogeneity may be an important factor constraining adaptation. Overall, our results stress the importance of environment in driving parallel evolutionary changes and point to a number of avenues for future work for understanding when evolution is predictable.

Genome-wide patterns of recombination in the opportunistic human pathogen Pseudomonas aeruginosa.

The bacterium Pseudomonas aeruginosa is a significant cause of acute nosocomial infections as well as chronic respiratory infections in patients with cystic fibrosis (CF). Recent reports of the intercontinental spread of a CF-specific epidemic strain, combined with high intrinsic levels of antibiotic resistance, have made this opportunistic pathogen an important public health concern. Strain-specific differences correlate with variation in clinical outcomes of infected CF patients, increasing the urgency to understand the evolutionary origin of genetic factors conferring important phenotypes that enable infection, virulence, or resistance. Here, we describe the genome-wide patterns of homologous and nonhomologous recombination in P. aeruginosa, and the extent to which the genomes are affected by these diversity-generating processes. Based on whole-genome sequence data from 32 clinical isolates of P. aeruginosa, we examined the rate and distribution of recombination along the genome, and its effect on the reconstruction of phylogenetic relationships. Multiple lines of evidence suggested that recombination was common and usually involves short stretches of DNA (200-300 bp). Although mutation was the main source of nucleotide diversity, the import of polymorphisms by homologous recombination contributed nearly as much. We also identified the genomic regions with frequent recombination, and the specific sequences of recombinant origin within epidemic strains. The functional characteristics of the genes contained therein were examined for potential associations with a pathogenic lifestyle or adaptation to the CF lung environment. A common link between many of the high-recombination genes was their functional affiliation with the cell wall, suggesting that the products of recombination may be maintained by selection for variation in cell-surface molecules that allows for evasion of the host immune system.