Fluctuating selection models and Mcdonald-Kreitman type analyses

It is likely that the strength of selection acting upon a mutation varies through time due to changes in the environment. However, most population genetic theory assumes that the strength of selection remains constant. Here we investigate the consequences of fluctuating selection pressures on the quantification of adaptive evolution using McDonald-Kreitman (MK) style approaches. In agreement with previous work, we show that fluctuating selection can generate evidence of adaptive evolution even when the expected strength of selection on a mutation is zero. However, we also find that the mutations, which contribute to both polymorphism and divergence tend, on average, to be positively selected during their lifetime, under fluctuating selection models. This is because mutations that fluctuate, by chance, to positive selected values, tend to reach higher frequencies in the population than those that fluctuate towards negative values. Hence the evidence of positive adaptive evolution detected under a fluctuating selection model by MK type approaches is genuine since fixed mutations tend to be advantageous on average during their lifetime. Never-the-less we show that methods tend to underestimate the rate of adaptive evolution when selection fluctuates

Selective Constraints on Amino Acids Estimated by a Mechanistic Codon Substitution Model with Multiple Nucleotide Changes

Empirical substitution matrices represent the average tendencies of substitutions over various protein families by sacrificing gene-level resolution. We develop a codon-based model, in which mutational tendencies of codon, a genetic code, and the strength of selective constraints against amino acid replacements can be tailored to a given gene. First, selective constraints averaged over proteins are estimated by maximizing the likelihood of each 1-PAM matrix of empirical amino acid (JTT, WAG, and LG) and codon (KHG) substitution matrices. Then, selective constraints specific to given proteins are approximated as a linear function of those estimated from the empirical substitution matrices. Akaike information criterion (AIC) values indicate that a model allowing multiple nucleotide changes fits the empirical substitution matrices significantly better. Also, the ML estimates of transition-transversion bias obtained from these empirical matrices are not so large as previously estimated. The selective constraints are characteristic of proteins rather than species. However, their relative strengths among amino acid pairs can be approximated not to depend very much on protein families but amino acid pairs, because the present model, in which selective constraints are approximated to be a linear function of those estimated from the JTT/WAG/LG/KHG matrices, can provide a good fit to other empirical substitution matrices including cpREV for chloroplast proteins and mtREV for vertebrate mitochondrial proteins. The present codon-based model with the ML estimates of selective constraints and with adjustable mutation rates of nucleotide would be useful as a simple substitution model in ML and Bayesian inferences of molecular phylogenetic trees, and enables us to obtain biologically meaningful information at both nucleotide and amino acid levels from codon and protein sequences.Comment: Table 9 in this article includes corrections for errata in the Table 9 published in 10.1371/journal.pone.0017244. Supporting information is attached at the end of the article, and a computer-readable dataset of the ML estimates of selective constraints is available from 10.1371/journal.pone.001724

A Strong Deletion Bias in Nonallelic Gene Conversion

Gene conversion is the unidirectional transfer of genetic information between orthologous (allelic) or paralogous (nonallelic) genomic segments. Though a number of studies have examined nucleotide replacements, little is known about length difference mutations produced by gene conversion. Here, we investigate insertions and deletions produced by nonallelic gene conversion in 338 Drosophila and 10,149 primate paralogs. Using a direct phylogenetic approach, we identify 179 insertions and 614 deletions in Drosophila paralogs, and 132 insertions and 455 deletions in primate paralogs. Thus, nonallelic gene conversion is strongly deletion-biased in both lineages, with almost 3.5 times as many conversion-induced deletions as insertions. In primates, the deletion bias is considerably stronger for long indels and, in both lineages, the per-site rate of gene conversion is orders of magnitudes higher than that of ordinary mutation. Due to this high rate, deletion-biased nonallelic gene conversion plays a key role in genome size evolution, leading to the cooperative shrinkage and eventual disappearance of selectively neutral paralogs

Detecting Clusters of Mutations

Positive selection for protein function can lead to multiple mutations within a small stretch of DNA, i.e., to a cluster of mutations. Recently, Wagner proposed a method to detect such mutation clusters. His method, however, did not take into account that residues with high solvent accessibility are inherently more variable than residues with low solvent accessibility. Here, we propose a new algorithm to detect clustered evolution. Our algorithm controls for different substitution probabilities at buried and exposed sites in the tertiary protein structure, and uses random permutations to calculate accurate P values for inferred clusters. We apply the algorithm to genomes of bacteria, fly, and mammals, and find several clusters of mutations in functionally important regions of proteins. Surprisingly, clustered evolution is a relatively rare phenomenon. Only between 2% and 10% of the genes we analyze contain a statistically significant mutation cluster. We also find that not controlling for solvent accessibility leads to an excess of clusters in terminal and solvent-exposed regions of proteins. Our algorithm provides a novel method to identify functionally relevant divergence between groups of species. Moreover, it could also be useful to detect artifacts in automatically assembled genomes

DNA sequence diversity and the efficiency of natural selection in animal mitochondrial DNA

Selection is expected to be more efficient in species that are more diverse because both the efficiency of natural selection and DNA sequence diversity are expected to depend upon the effective population size. We explore this relationship across a data set of 751 mammal species for which we have mitochondrial polymorphism data. We introduce a method by which we can examine the relationship between our measure of the efficiency of natural selection, the nonsynonymous relative to the synonymous nucleotide site diversity (πN/πS), and synonymous nucleotide diversity (πS), avoiding the statistical non-independence between the two quantities. We show that these two variables are strongly negatively and linearly correlated on a log scale. The slope is such that as πS doubles, πN/πS is reduced by 34%. We show that the slope of this relationship differs between the two phylogenetic groups for which we have the most data, rodents and bats, and that it also differs between species with high and low body mass, and between those with high and low mass-specific metabolic rate

A systematic review and meta-analysis of low intensity CBT for psychosis

Sixteen sessions of individual cognitive behavior therapy for people with psychosis (CBTp) is recommended. However, access to CBTp is poor, so the potential of low intensity CBTp (fewer than 16 sessions of face-to-face contact) is being explored. A systematic review and meta-analysis was conducted of 10 controlled trials evaluating low intensity CBTp. Significant between-group effects were found on the primary outcome, symptoms of psychosis, at post-intervention (d = − 0.46, 95% CI: − 0.06, − 0.86) and follow-up (d = − 0.40, 95% CI: − 0.06, − 0.74). Study quality did not moderate post-intervention psychosis outcomes, nor did contact time/number of sessions or therapy format (individual versus group). Between-group effects on secondary outcomes (depression, anxiety and functioning) were not significant at post-intervention, but became significant at follow-up for depression and functioning outcomes (but not for anxiety). Overall, findings suggest that low intensity CBTp shows promise with effect sizes comparable to those found in meta-analyses of CBTp more broadly. We suggest that low intensity CBTp could help widen access. Future research is called for to identify mechanisms of change and to ascertain moderators of outcome so that low intensity CBTp targets key mechanisms (so that scarce therapy time is used effectively) and so that interventions offered are matched to patient need

Correlated Evolution of Nearby Residues in Drosophilid Proteins

Here we investigate the correlations between coding sequence substitutions as a function of their separation along the protein sequence. We consider both substitutions between the reference genomes of several Drosophilids as well as polymorphisms in a population sample of Zimbabwean Drosophila melanogaster. We find that amino acid substitutions are “clustered” along the protein sequence, that is, the frequency of additional substitutions is strongly enhanced within ≈10 residues of a first such substitution. No such clustering is observed for synonymous substitutions, supporting a “correlation length” associated with selection on proteins as the causative mechanism. Clustering is stronger between substitutions that arose in the same lineage than it is between substitutions that arose in different lineages. We consider several possible origins of clustering, concluding that epistasis (interactions between amino acids within a protein that affect function) and positional heterogeneity in the strength of purifying selection are primarily responsible. The role of epistasis is directly supported by the tendency of nearby substitutions that arose on the same lineage to preserve the total charge of the residues within the correlation length and by the preferential cosegregation of neighboring derived alleles in our population sample. We interpret the observed length scale of clustering as a statistical reflection of the functional locality (or modularity) of proteins: amino acids that are near each other on the protein backbone are more likely to contribute to, and collaborate toward, a common subfunction

The Embedding Problem for Markov Models of Nucleotide Substitution

10.1371/journal.pone.0069187PLoS ONE87-POLN

Correlated Mutations: A Hallmark of Phenotypic Amino Acid Substitutions

Point mutations resulting in the substitution of a single amino acid can cause severe functional consequences, but can also be completely harmless. Understanding what determines the phenotypical impact is important both for planning targeted mutation experiments in the laboratory and for analyzing naturally occurring mutations found in patients. Common wisdom suggests using the extent of evolutionary conservation of a residue or a sequence motif as an indicator of its functional importance and thus vulnerability in case of mutation. In this work, we put forward the hypothesis that in addition to conservation, co-evolution of residues in a protein influences the likelihood of a residue to be functionally important and thus associated with disease. While the basic idea of a relation between co-evolution and functional sites has been explored before, we have conducted the first systematic and comprehensive analysis of point mutations causing disease in humans with respect to correlated mutations. We included 14,211 distinct positions with known disease-causing point mutations in 1,153 human proteins in our analysis. Our data show that (1) correlated positions are significantly more likely to be disease-associated than expected by chance, and that (2) this signal cannot be explained by conservation patterns of individual sequence positions. Although correlated residues have primarily been used to predict contact sites, our data are in agreement with previous observations that (3) many such correlations do not relate to physical contacts between amino acid residues. Access to our analysis results are provided at http://webclu.bio.wzw.tum.de/~pagel/supplements/correlated-positions/

Reduced Selective Constraint in Endosymbionts: Elevation in Radical Amino Acid Replacements Occurs Genome-Wide

As predicted by the nearly neutral model of evolution, numerous studies have shown that reduced Ne accelerates the accumulation of slightly deleterious changes under genetic drift. While such studies have mostly focused on eukaryotes, bacteria also offer excellent models to explore the effects of Ne. Most notably, the genomes of host-dependent bacteria with small Ne show signatures of genetic drift, including elevated Ka/Ks. Here, I explore the utility of an alternative measure of selective constraint: the per-site rate of radical and conservative amino acid substitutions (Dr/Dc). I test the hypothesis that purifying selection against radical amino acid changes is less effective in two insect endosymbiont groups (Blochmannia of ants and Buchnera of aphids), compared to related gamma-Proteobacteria. Genome comparisons demonstrate a significant elevation in Dr/Dc in endosymbionts that affects the majority (66–79%) of shared orthologs examined. The elevation of Dr/Dc in endosymbionts affects all functional categories examined. Simulations indicate that Dr/Dc estimates are sensitive to codon frequencies and mutational parameters; however, estimation biases occur in the opposite direction as the patterns observed in genome comparisons, thereby making the inference of elevated Dr/Dc more conservative. Increased Dr/Dc and other signatures of genome degradation in endosymbionts are consistent with strong effects of genetic drift in their small populations, as well as linkage to selected sites in these asexual bacteria. While relaxed selection against radical substitutions may contribute, genome-wide processes such as genetic drift and linkage best explain the pervasive elevation in Dr/Dc across diverse functional categories that include basic cellular processes. Although the current study focuses on a few bacterial lineages, it suggests Dr/Dc is a useful gauge of selective constraint and may provide a valuable alternative to Ka/Ks when high sequence divergences preclude estimates of Ks. Broader application of Dr/Dc will benefit from approaches less prone to estimation biases