Ambiguity Coding Allows Accurate Inference of Evolutionary Parameters from Alignments in an Aggregated State-Space.

How can we best learn the history of a protein's evolution? Ideally, a model of sequence evolution should capture both the process that generates genetic variation and the functional constraints determining which changes are fixed. However, in practical terms the most suitable approach may simply be the one that combines the convenience of easily available input data with the ability to return useful parameter estimates. For example, we might be interested in a measure of the strength of selection (typically obtained using a codon model) or an ancestral structure (obtained using structural modeling based on inferred amino acid sequence and side chain configuration). But what if data in the relevant state-space are not readily available? We show that it is possible to obtain accurate estimates of the outputs of interest using an established method for handling missing data. Encoding observed characters in an alignment as ambiguous representations of characters in a larger state-space allows the application of models with the desired features to data that lack the resolution that is normally required. This strategy is viable because the evolutionary path taken through the observed space contains information about states that were likely visited in the "unseen" state-space. To illustrate this, we consider two examples with amino acid sequences as input. We show that $$\omega$$, a parameter describing the relative strength of selection on nonsynonymous and synonymous changes, can be estimated in an unbiased manner using an adapted version of a standard 61-state codon model. Using simulated and empirical data, we find that ancestral amino acid side chain configuration can be inferred by applying a 55-state empirical model to 20-state amino acid data. Where feasible, combining inputs from both ambiguity-coded and fully resolved data improves accuracy. Adding structural information to as few as 12.5% of the sequences in an amino acid alignment results in remarkable ancestral reconstruction performance compared to a benchmark that considers the full rotamer state information. These examples show that our methods permit the recovery of evolutionary information from sequences where it has previously been inaccessible. [Ancestral reconstruction; natural selection; protein structure; state-spaces; substitution models.].

Physicochemical Amino Acid Properties Better Describe Substitution Rates in Large Populations.

Substitutions between chemically distant amino acids are known to occur less frequently than those between more similar amino acids. This knowledge, however, is not reflected in most codon substitution models, which treat all nonsynonymous changes as if they were equivalent in terms of impact on the protein. A variety of methods for integrating chemical distances into models have been proposed, with a common approach being to divide substitutions into radical or conservative categories. Nevertheless, it remains unclear whether the resulting models describe sequence evolution better than their simpler counterparts. We propose a parametric codon model that distinguishes between radical and conservative substitutions, allowing us to assess if radical substitutions are preferentially removed by selection. Applying our new model to a range of phylogenomic data, we find differentiating between radical and conservative substitutions provides significantly better fit for large populations, but see no equivalent improvement for smaller populations. Comparing codon and amino acid models using these same data shows that alignments from large populations tend to select phylogenetic models containing information about amino acid exchangeabilities, whereas the structure of the genetic code is more important for smaller populations. Our results suggest selection against radical substitutions is, on average, more pronounced in large populations than smaller ones. The reduced observable effect of selection in smaller populations may be due to stronger genetic drift making it more challenging to detect preferences. Our results imply an important connection between the life history of a phylogenetic group and the model that best describes its evolution.

Protein evolution depends on multiple distinct population size parameters.

That population size affects the fate of new mutations arising in genomes, modulating both how frequently they arise and how efficiently natural selection is able to filter them, is well established. It is therefore clear that these distinct roles for population size that characterize different processes should affect the evolution of proteins and need to be carefully defined. Empirical evidence is consistent with a role for demography in influencing protein evolution, supporting the idea that functional constraints alone do not determine the composition of coding sequences.Given that the relationship between population size, mutant fitness and fixation probability has been well characterized, estimating fitness from observed substitutions is well within reach with well-formulated models. Molecular evolution research has, therefore, increasingly begun to leverage concepts from population genetics to quantify the selective effects associated with different classes of mutation. However, in order for this type of analysis to provide meaningful information about the intra- and inter-specific evolution of coding sequences, a clear definition of concepts of population size, what they influence, and how they are best parameterized is essential.Here, we present an overview of the many distinct concepts that "population size" and "effective population size" may refer to, what they represent for studying proteins, and how this knowledge can be harnessed to produce better specified models of protein evolution.

A new parameter-rich structure-aware mechanistic model for amino acid substitution during evolution.

Improvements in the description of amino acid substitution are required to develop better pseudo-energy-based protein structure-aware models for use in phylogenetic studies. These models are used to characterize the probabilities of amino acid substitution and enable better simulation of protein sequences over a phylogeny. A better characterization of amino acid substitution probabilities in turn enables numerous downstream applications, like detecting positive selection, ancestral sequence reconstruction, and evolutionarily-motivated protein engineering. Many existing Markov models for amino acid substitution in molecular evolution disregard molecular structure and describe the amino acid substitution process over longer evolutionary periods poorly. Here, we present a new model upgraded with a site-specific parameterization of pseudo-energy terms in a coarse-grained force field, which describes local heterogeneity in physical constraints on amino acid substitution better than a previous pseudo-energy-based model with minimum cost in runtime. The importance of each weight term parameterization in characterizing underlying features of the site, including contact number, solvent accessibility, and secondary structural elements was evaluated, returning both expected and biologically reasonable relationships between model parameters. This results in the acceptance of proposed amino acid substitutions that more closely resemble those observed site-specific frequencies in gene family alignments. The modular site-specific pseudo-energy function is made available for download through the following website: https://liberles.cst.temple.edu/Software/CASS/index.html.

ProtASR: An Evolutionary Framework for Ancestral Protein Reconstruction with Selection on Folding Stability.

The computational reconstruction of ancestral proteins provides information on past biological events and has practical implications for biomedicine and biotechnology. Currently available tools for ancestral sequence reconstruction (ASR) are often based on empirical amino acid substitution models that assume that all sites evolve at the same rate and under the same process. However, this assumption is frequently violated because protein evolution is highly heterogeneous due to different selective constraints among sites. Here, we present ProtASR, a new evolutionary framework to infer ancestral protein sequences accounting for selection on protein stability. First, ProtASR generates site-specific substitution matrices through the structurally constrained mean-field (MF) substitution model, which considers both unfolding and misfolding stability. We previously showed that MF models outperform empirical amino acid substitution models, as well as other structurally constrained substitution models, both in terms of likelihood and correctly inferring amino acid distributions across sites. In the second step, ProtASR adapts a well-established maximum-likelihood (ML) ASR procedure to infer ancestral proteins under MF models. A known bias of ML ASR methods is that they tend to overestimate the stability of ancestral proteins by underestimating the frequency of deleterious mutations. We compared ProtASR under MF to two empirical substitution models (JTT and CAT), reconstructing the ancestral sequences of simulated proteins. ProtASR yields reconstructed proteins with less biased stabilities, which are significantly closer to those of the simulated proteins. Analysis of extant protein families suggests that folding stability evolves through time across protein families, potentially reflecting neutral fluctuation. Some families exhibit a more constant protein folding stability, while others are more variable. ProtASR is freely available from https://github.com/miguelarenas/protasr and includes detailed documentation and ready-to-use examples. It runs in seconds/minutes depending on protein length and alignment size. [Ancestral sequence reconstruction; folding stability; molecular adaptation; phylogenetics; protein evolution; protein structure.].

GC-biased gene conversion links the recombination landscape and demography to genomic base composition: GC-biased gene conversion drives genomic base composition across a wide range of species.

The origin and evolutionary dynamics of the spatial heterogeneity in genomic base composition have been debated since its discovery in the 1970s. With the recent availability of numerous genome sequences from a wide range of species it has been possible to address this question from a comparative perspective, and similarities and differences in base composition between groups of organisms are becoming evident. Ample evidence suggests that the contrasting dynamics of base composition are driven by GC-biased gene conversion (gBGC), a process that is associated with meiotic recombination. In line with this hypothesis, base composition is associated with the rate of recombination and the evolutionary dynamics of the recombination landscape, therefore, governs base composition. In addition, and at first sight perhaps surprisingly, the relationship between demography and genomic base composition is in agreement with the gBGC hypothesis: organisms with larger populations have higher GC content than those with smaller populations.

Phylogenomic analyses data of the avian phylogenomics project.

BACKGROUND: Determining the evolutionary relationships among the major lineages of extant birds has been one of the biggest challenges in systematic biology. To address this challenge, we assembled or collected the genomes of 48 avian species spanning most orders of birds, including all Neognathae and two of the five Palaeognathae orders. We used these genomes to construct a genome-scale avian phylogenetic tree and perform comparative genomic analyses. FINDINGS: Here we present the datasets associated with the phylogenomic analyses, which include sequence alignment files consisting of nucleotides, amino acids, indels, and transposable elements, as well as tree files containing gene trees and species trees. Inferring an accurate phylogeny required generating: 1) A well annotated data set across species based on genome synteny; 2) Alignments with unaligned or incorrectly overaligned sequences filtered out; and 3) Diverse data sets, including genes and their inferred trees, indels, and transposable elements. Our total evidence nucleotide tree (TENT) data set (consisting of exons, introns, and UCEs) gave what we consider our most reliable species tree when using the concatenation-based ExaML algorithm or when using statistical binning with the coalescence-based MP-EST algorithm (which we refer to as MP-EST*). Other data sets, such as the coding sequence of some exons, revealed other properties of genome evolution, namely convergence. CONCLUSIONS: The Avian Phylogenomics Project is the largest vertebrate phylogenomics project to date that we are aware of. The sequence, alignment, and tree data are expected to accelerate analyses in phylogenomics and other related areas.

Whole-genome analyses resolve early branches in the tree of life of modern birds.

To better determine the history of modern birds, we performed a genome-scale phylogenetic analysis of 48 species representing all orders of Neoaves using phylogenomic methods created to handle genome-scale data. We recovered a highly resolved tree that confirms previously controversial sister or close relationships. We identified the first divergence in Neoaves, two groups we named Passerea and Columbea, representing independent lineages of diverse and convergently evolved land and water bird species. Among Passerea, we infer the common ancestor of core landbirds to have been an apex predator and confirm independent gains of vocal learning. Among Columbea, we identify pigeons and flamingoes as belonging to sister clades. Even with whole genomes, some of the earliest branches in Neoaves proved challenging to resolve, which was best explained by massive protein-coding sequence convergence and high levels of incomplete lineage sorting that occurred during a rapid radiation after the Cretaceous-Paleogene mass extinction event about 66 million years ago.

Early mesozoic coexistence of amniotes and hepadnaviridae.

Hepadnaviridae are double-stranded DNA viruses that infect some species of birds and mammals. This includes humans, where hepatitis B viruses (HBVs) are prevalent pathogens in considerable parts of the global population. Recently, endogenized sequences of HBVs (eHBVs) have been discovered in bird genomes where they constitute direct evidence for the coexistence of these viruses and their hosts from the late Mesozoic until present. Nevertheless, virtually nothing is known about the ancient host range of this virus family in other animals. Here we report the first eHBVs from crocodilian, snake, and turtle genomes, including a turtle eHBV that endogenized >207 million years ago. This genomic "fossil" is >125 million years older than the oldest avian eHBV and provides the first direct evidence that Hepadnaviridae already existed during the Early Mesozoic. This implies that the Mesozoic fossil record of HBV infection spans three of the five major groups of land vertebrates, namely birds, crocodilians, and turtles. We show that the deep phylogenetic relationships of HBVs are largely congruent with the deep phylogeny of their amniote hosts, which suggests an ancient amniote-HBV coexistence and codivergence, at least since the Early Mesozoic. Notably, the organization of overlapping genes as well as the structure of elements involved in viral replication has remained highly conserved among HBVs along that time span, except for the presence of the X gene. We provide multiple lines of evidence that the tumor-promoting X protein of mammalian HBVs lacks a homolog in all other hepadnaviruses and propose a novel scenario for the emergence of X via segmental duplication and overprinting of pre-existing reading frames in the ancestor of mammalian HBVs. Our study reveals an unforeseen host range of prehistoric HBVs and provides novel insights into the genome evolution of hepadnaviruses throughout their long-lasting association with amniote hosts.

Evidence for GC-biased gene conversion as a driver of between-lineage differences in avian base composition.

BACKGROUND: While effective population size (Ne) and life history traits such as generation time are known to impact substitution rates, their potential effects on base composition evolution are less well understood. GC content increases with decreasing body mass in mammals, consistent with recombination-associated GC biased gene conversion (gBGC) more strongly impacting these lineages. However, shifts in chromosomal architecture and recombination landscapes between species may complicate the interpretation of these results. In birds, interchromosomal rearrangements are rare and the recombination landscape is conserved, suggesting that this group is well suited to assess the impact of life history on base composition. RESULTS: Employing data from 45 newly and 3 previously sequenced avian genomes covering a broad range of taxa, we found that lineages with large populations and short generations exhibit higher GC content. The effect extends to both coding and non-coding sites, indicating that it is not due to selection on codon usage. Consistent with recombination driving base composition, GC content and heterogeneity were positively correlated with the rate of recombination. Moreover, we observed ongoing increases in GC in the majority of lineages. CONCLUSIONS: Our results provide evidence that gBGC may drive patterns of nucleotide composition in avian genomes and are consistent with more effective gBGC in large populations and a greater number of meioses per unit time; that is, a shorter generation time. Thus, in accord with theoretical predictions, base composition evolution is substantially modulated by species life history.

Kr/Kc but not dN/dS correlates positively with body mass in birds, raising implications for inferring lineage-specific selection.

BACKGROUND: The ratio of the rates of non-synonymous and synonymous substitution (dN/dS) is commonly used to estimate selection in coding sequences. It is often suggested that, all else being equal, dN/dS should be lower in populations with large effective size (Ne) due to increased efficacy of purifying selection. As Ne is difficult to measure directly, life history traits such as body mass, which is typically negatively associated with population size, have commonly been used as proxies in empirical tests of this hypothesis. However, evidence of whether the expected positive correlation between body mass and dN/dS is consistently observed is conflicting. RESULTS: Employing whole genome sequence data from 48 avian species, we assess the relationship between rates of molecular evolution and life history in birds. We find a negative correlation between dN/dS and body mass, contrary to nearly neutral expectation. This raises the question whether the correlation might be a method artefact. We therefore in turn consider non-stationary base composition, divergence time and saturation as possible explanations, but find no clear patterns. However, in striking contrast to dN/dS, the ratio of radical to conservative amino acid substitutions (Kr/Kc) correlates positively with body mass. CONCLUSIONS: Our results in principle accord with the notion that non-synonymous substitutions causing radical amino acid changes are more efficiently removed by selection in large populations, consistent with nearly neutral theory. These findings have implications for the use of dN/dS and suggest that caution is warranted when drawing conclusions about lineage-specific modes of protein evolution using this metric.

Late-replicating domains have higher divergence and diversity in Drosophila melanogaster.

Several reports from mammals indicate that an increase in the mutation rate in late-replicating regions may, in part, be responsible for the observed genomic heterogeneity in neutral substitution rates and levels of diversity, although the mechanisms for this remain poorly understood. Recent evidence also suggests that late replication is associated with high mutability in yeast. This then raises the question as to whether a similar effect is operating across all eukaryotes. Limited evidence from one chromosome arm in Drosophila melanogaster suggests the opposite pattern, with regions overlapping early-firing origins showing increased levels of diversity and divergence. Given the availability of genome-wide replication timing profiles for D. melanogaster, we now return to this issue. Consistent with what is seen in other taxa, we find that divergence at synonymous sites in exon cores, as well as divergence at putatively unconstrained intronic sites, is elevated in late-replicating regions. Analysis of genes with low codon usage bias suggests a ∼30% difference in mutation rate between the earliest and the latest replicating sequence. Intronic sequence suggests a more modest difference. We additionally show that an increase in diversity in late-replicating sequences is not owing to replication timing covarying with the local recombination rate. If anything, the effects of recombination mask the impact of replication timing. We conclude that, contrary to prior reports and consistent with what is seen in mammals and yeast, there is indeed a relationship between rates of nucleotide divergence and diversity and replication timing that is consistent with an increase in the mutation rate during late S-phase in D. melanogaster. It is therefore plausible that such an effect might be common among eukaryotes. The result may have implications for the inference of positive selection.

Support for multiple classes of local expression clusters in Drosophila melanogaster, but no evidence for gene order conservation.

BACKGROUND: Gene order in eukaryotic genomes is not random, with genes with similar expression profiles tending to cluster. In yeasts, the model taxon for gene order analysis, such syntenic clusters of non-homologous genes tend to be conserved over evolutionary time. Whether similar clusters show gene order conservation in other lineages is, however, undecided. Here, we examine this issue in Drosophila melanogaster using high-resolution chromosome rearrangement data. RESULTS: We show that D. melanogaster has at least three classes of expression clusters: first, as observed in mammals, large clusters of functionally unrelated housekeeping genes; second, small clusters of functionally related highly co-expressed genes; and finally, as previously defined by Spellman and Rubin, larger domains of co-expressed but functionally unrelated genes. The latter are, however, not independent of the small co-expression clusters and likely reflect a methodological artifact. While the small co-expression and housekeeping/essential gene clusters resemble those observed in yeast, in contrast to yeast, we see no evidence that any of the three cluster types are preserved as synteny blocks. If anything, adjacent co-expressed genes are more likely to become rearranged than expected. Again in contrast to yeast, in D. melanogaster, gene pairs with short intergene distance or in divergent orientations tend to have higher rearrangement rates. These findings are consistent with co-expression being partly due to shared chromatin environment. CONCLUSIONS: We conclude that, while similar in terms of cluster types, gene order evolution has strikingly different patterns in yeasts and in D. melanogaster, although recombination is associated with gene order rearrangement in both.

Intronic AT skew is a defendable proxy for germline transcription but does not predict crossing-over or protein evolution rates in Drosophila melanogaster.

Recent evidence suggests that germline transcription may affect both protein evolutionary rates, possibly mediated by repair processes, and recombination rates, possibly mediated by chromatin and epigenetic modification. Here, we test these propositions in Drosophila melanogaster. The challenge for such analyses is to provide defendable measures of germline gene expression. Intronic AT skew is a good candidate measure as it is thought to be a consequence, at least in part, of transcription-coupled repair. Prior evidence suggests that intronic AT skew in D. melanogaster is not affected by proximity to intron extremities and differs between transcribed DNA and flanking sequence. We now also establish that intronic AT skew is a defendable proxy for germline expression as (a) it is more similar than expected by chance between introns of the same gene (which is not accounted for by physical proximity), (b) is correlated with male germline expression, and (c) is more pronounced in broadly expressed genes. Furthermore, (d) a trend for intronic skew to differ between 3' and 5' ends of genes is particular to broadly expressed genes. Finally, (e) controlling for physical distance, introns of proximate genes are most different in skew if they have different tissue specificity. We find that intronic AT skew, employed as a proxy for germline transcription, correlates neither with recombination rates nor with the rate of protein evolution. We conclude that there is no prima facie evidence that germline expression modulates recombination rates or monotonically affects protein evolution rates in D. melanogaster.

Why there is more to protein evolution than protein function: splicing, nucleosomes and dual-coding sequence.

There is considerable variation in the rate at which different proteins evolve. Why is this? Classically, it has been considered that the density of functionally important sites must predict rates of protein evolution. Likewise, amino acid choice is usually assumed to reflect optimal protein function. In the present article, we briefly review evidence suggesting that this protein function-centred view is too simplistic. In particular, we concentrate on how selection acting during the protein's production history can also affect protein evolutionary rates and amino acid choice. Exploring the role of selection at the DNA and RNA level, we specifically address how the need (i) to specify exonic splice enhancer motifs in pre-mRNA, and (ii) to ensure nucleosome positioning on DNA have an impact on amino acid choice and rates of evolution. For both, we review evidence that sequence affected by more than one coding demand is particularly constrained. Strikingly, in mammals, splicing-related constraints are quantitatively as important as expression parameters in predicting rates of protein evolution. These results indicate that there is substantially more to protein evolution than protein functional constraints.

Protein rates of evolution are predicted by double-strand break events, independent of crossing-over rates.

Theory predicts that, owing to reduced Hill-Robertson interference, genomic regions with high crossing-over rates should experience more efficient selection. In Saccharomyces cerevisiae a negative correlation between the local recombination rate, assayed as meiotic double-strand breaks (DSBs), and the local rate of protein evolution has been considered consistent with such a model. Although DSBs are a prerequisite for crossing-over, they need not result in crossing-over. With recent high-resolution crossover data, we now return to this issue comparing two species of yeast. Strikingly, even allowing for crossover rates, both the rate of premeiotic DSBs and of noncrossover recombination events predict a gene's rate of evolution. This both questions the validity of prior analyses and strongly suggests that any correlation between crossover rates and rates of protein evolution could be owing to slow-evolving genes being prone to DSBs or a direct effect of DSBs on sequence evolution. To ask if classical theory of recombination has any relevance, we determine whether crossover rates predict rates of protein evolution, controlling for noncrossover DSB events, gene ontology (GO) class, gene expression, protein abundance, nucleotide content, and dispensability. We find that genes with high crossing-over rates have low rates of protein evolution after such control, although any correlation is weaker than that previously reported considering meiotic DSBs as a proxy. The data are consistent both with recombination enhancing the efficiency of purifying selection and, independently, with DSBs being associated with low rates of evolution.

Aging sensitizes toward ROS formation and lipid peroxidation in PS1M146L transgenic mice.

Mutations in the presenilins (PS) account for the majority of familial Alzheimer disease (FAD) cases. To test the hypothesis that oxidative stress can underlie the deleterious effects of presenilin mutations, we analyzed lipid peroxidation products (4-hydroxynonenal (HNE) and malondialdehyde) and antioxidant defenses in brain tissue and levels of reactive oxygen species (ROS) in splenic lymphocytes from transgenic mice bearing human PS1 with the M146L mutation (PS1M146L) compared to those from mice transgenic for wild-type human PS1 (PS1wt) and nontransgenic littermate control mice. In brain tissue, HNE levels were increased only in aged (19-22 months) PS1M146L transgenic animals compared to PS1wt mice and not in young (3-4 months) or middle-aged mice (13-15 months). Similarly, in splenic lymphocytes expressing the transgenic PS1 proteins, mitochondrial and cytosolic ROS levels were elevated to 142.1 and 120.5% relative to controls only in cells from aged PS1M146L animals. Additionally, brain tissue HNE levels were positively correlated with mitochondrial ROS levels in splenic lymphocytes, indicating that oxidative stress can be detected in different tissues of PS1 transgenic mice. Antioxidant defenses (activities of antioxidant enzymes Cu/Zn-SOD, GPx, or GR) or susceptibility to in vitro oxidative stimulation was unaltered. In summary, these results demonstrate that the PS1M146L mutation increases mitochondrial ROS formation and oxidative damage in aged mice. Hence, oxidative stress caused by the combined effects of aging and PS1 mutations may be causative for triggering neurodegenerative events in FAD patients.