Methods for incorporating the hypermutability of CpG dinucleotides in detecting natural selection operating at the amino acid sequence level

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.

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.

Polymorphism Data Assist Estimation of the Nonsynonymous over Synonymous Fixation Rate Ratio ω for Closely Related Species

The ratio of nonsynonymous over synonymous sequence divergence, dN/dS, is a widely used estimate of the nonsynonymous over synonymous fixation rate ratio ω, which measures the extent to which natural selection modulates protein sequence evolution. Its computation is based on a phylogenetic approach and computes sequence divergence of protein-coding DNA between species, traditionally using a single representative DNA sequence per species. This approach ignores the presence of polymorphisms and relies on the indirect assumption that new mutations fix instantaneously, an assumption which is generally violated and reasonable only for distantly related species. The violation of the underlying assumption leads to a time-dependence of sequence divergence, and biased estimates of ω in particular for closely related species, where the contribution of ancestral and lineage-specific polymorphisms to sequence divergence is substantial. We here use a time-dependent Poisson random field model to derive an analytical expression of dN/dS as a function of divergence time and sample size. We then extend our framework to the estimation of the proportion of adaptive protein evolution α. This mathematical treatment enables us to show that the joint usage of polymorphism and divergence data can assist the inference of selection for closely related species. Moreover, our analytical results provide the basis for a protocol for the estimation of ω and α for closely related species. We illustrate the performance of this protocol by studying a population data set of four corvid species, which involves the estimation of ω and α at different time-scales and for several choices of sample sizes.

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).

Biased Inference of Selection Due to GC-Biased Gene Conversion and the Rate of Protein Evolution in Flycatchers When Accounting for It

The rate of recombination impacts on rates of protein evolution for at least two reasons: it affects the efficacy of selection due to linkage and influences sequence evolution through the process of GC-biased gene conversion (gBGC). We studied how recombination, via gBGC, affects inferences of selection in gene sequences using comparative genomic and population genomic data from the collared flycatcher (Ficedula albicollis). We separately analyzed different mutation categories (“strong”-to-“weak,” “weak-to-strong,” and GC-conservative changes) and found that gBGC impacts on the distribution of fitness effects of new mutations, and leads to that the rate of adaptive evolution and the proportion of adaptive mutations among nonsynonymous substitutions are underestimated by 22–33%. It also biases inferences of demographic history based on the site frequency spectrum. In light of this impact, we suggest that inferences of selection (and demography) in lineages with pronounced gBGC should be based on GC-conservative changes only. Doing so, we estimate that 10% of nonsynonymous mutations are effectively neutral and that 27% of nonsynonymous substitutions have been fixed by positive selection in the flycatcher lineage. We also find that gene expression level, sex-bias in expression, and the number of protein–protein interactions, but not Hill–Robertson interference (HRI), are strong determinants of selective constraint and rate of adaptation of collared flycatcher genes. This study therefore illustrates the importance of disentangling the effects of different evolutionary forces and genetic factors in interpretation of sequence data, and from that infer the role of natural selection in DNA sequence evolution.

Inferring Indel Parameters using a Simulation-based Approach

In this study, we present a novel methodology to infer indel parameters from multiple sequence alignments (MSAs) based on simulations. Our algorithm searches for the set of evolutionary parameters describing indel dynamics which best fits a given input MSA. In each step of the search, we use parametric bootstraps and the Mahalanobis distance to estimate how well a proposed set of parameters fits input data. Using simulations, we demonstrate that our methodology can accurately infer the indel parameters for a large variety of plausible settings. Moreover, using our methodology, we show that indel parameters substantially vary between three genomic data sets: Mammals, bacteria, and retroviruses. Finally, we demonstrate how our methodology can be used to simulate MSAs based on indel parameters inferred from real data sets.

PARP-1 inhibitors and radiotherapy sensitivity: future prospects for therapy?

SUMMARY 

Despite the efficacy of multimodality treatment for women with breast cancer, sustained locoregional control remains a significant issue. Efforts to identify effective targeted therapies for treatment of these patients have intensified in recent years. The PARP family of proteins represents one potential target. PARP-1 is a DNA repair enzyme that plays a critical role in base excision repair, homologous recombination, nonhomologous end joining and transcriptional regulation. In this review, we discuss the rationale for using PARP-1 inhibitors clinically, the role of PARP-1 in DNA damage repair and the potential clinical utility of using PARP-1 inhibitors as a radiosensitization strategy. We will also review the most relevant clinical trials using various PARP-1 inhibitors and the future of biomarker development to predict response to PARP-1 inhibition.

Overestimation of nonsynonymous/synonymous rate ratio by reverse-translation of aligned amino acid sequences

In the analysis of protein-coding nucleotide sequences, the ratio of the number of nonsynonymous substitutions to that of synonymous substitutions (d(N)/d(S)) is used as an indicator for the direction and magnitude of natural selection operating at the amino acid sequence level. The d(S) and d(N) values are estimated based on the comparison of homologous codons, which are often identified by converting (reverse-translating) aligned amino acid sequences into codon sequences. In this method, however, homologous codons may be mis-identified when frame-shifts occurred or amino acid sequences were mis-aligned, which may lead to overestimation of the d(N)/d(S) ratio. Here the effect of reverse-translating aligned amino acid sequences on the estimation of d(N)/d(S) ratio was examined through a large-scale analysis of protein-coding nucleotide sequences from vertebrate species. Apparently, 1-9% of codon sites that were identified as homologous with reverse-translation contained non-homologous codons, where the d(N)/d(S) ratio was unduly high. By correcting the d(N)/d(S) ratio for these codon sites, it was inferred that the ratio was 5-43% overestimated with reverse-translation. These results suggest that caution should be exerted in the study of natural selection using the d(N)/d(S) ratio by reverse-translating aligned amino acid sequences.

CpG site degeneration triggered by the loss of functional constraint created a highly polymorphic macaque drug-metabolizing gene, CYP1A2

Background

Elucidating the pattern of evolutionary changes in drug-metabolizing genes is an important subject not only for evolutionary but for biomedical research. We investigated the pattern of divergence and polymorphisms of macaque CYP1A1 and CYP1A2 genes, which are major drug-metabolizing genes in humans. In humans, CYP1A2 is specifically expressed in livers while CYP1A1 has a wider gene expression pattern in extrahepatic tissues. In contrast, macaque CYP1A2 is expressed at a much lower level than CYP1A1 in livers. Interestingly, a previous study has shown that Macaca fascicularis CYP1A2 harbored unusually high genetic diversity within species. Genomic regions showing high genetic diversity within species is occasionally interpreted as a result of balancing selection, where natural selection maintains highly diverged alleles with different functions. Nevertheless many other forces could create such signatures.

Results

We found that the CYP1A1/2 gene copy number and orientation has been highly conserved among mammalian genomes. The signature of gene conversion between CYP1A1 and CYP1A2 was detected, but the last gene conversion event in the simian primate lineage occurred before the Catarrhini-Platyrrhini divergence. The high genetic diversity of macaque CYP1A2 therefore cannot be explained by gene conversion between CYP1A1 and CYP1A2. By surveying CYP1A2 polymorphisms in total 91 M. fascicularis and M. mulatta, we found several null alleles segregating in these species, indicating functional constraint on CYP1A2 in macaques may have weakened after the divergence between humans and macaques. We propose that the high genetic diversity in macaque CYP1A2 is partly due to the degeneration of CpG sites, which had been maintained at a high level by purifying selection, and the rapid degeneration process was initiated by the loss of functional constraint on macaque CYP1A2.

Conclusions

Our findings show that the highly polymorphic CYP1A2 gene in macaques has not been created by balancing selection but by the burst of CpG site degeneration after loss of functional constraint. Because the functional importance of CYP1A1/2 genes is different between humans and macaques, we have to be cautious in extrapolating a drug-testing data using substrates metabolized by CYP1A genes from macaques to humans, despite of their somewhat overlapping substrate specificity.

Statistics and truth in phylogenomics

Phylogenomics refers to the inference of historical relationships among species using genome-scale sequence data and to the use of phylogenetic analysis to infer protein function in multigene families. With rapidly decreasing sequencing costs, phylogenomics is becoming synonymous with evolutionary analysis of genome-scale and taxonomically densely sampled data sets. In phylogenetic inference applications, this translates into very large data sets that yield evolutionary and functional inferences with extremely small variances and high statistical confidence (P value). However, reports of highly significant P values are increasing even for contrasting phylogenetic hypotheses depending on the evolutionary model and inference method used, making it difficult to establish true relationships. We argue that the assessment of the robustness of results to biological factors, that may systematically mislead (bias) the outcomes of statistical estimation, will be a key to avoiding incorrect phylogenomic inferences. In fact, there is a need for increased emphasis on the magnitude of differences (effect sizes) in addition to the P values of the statistical test of the null hypothesis. On the other hand, the amount of sequence data available will likely always remain inadequate for some phylogenomic applications, for example, those involving episodic positive selection at individual codon positions and in specific lineages. Again, a focus on effect size and biological relevance, rather than the P value, may be warranted. Here, we present a theoretical overview and discuss practical aspects of the interplay between effect sizes, bias, and P values as it relates to the statistical inference of evolutionary truth in phylogenomics.

A codon substitution model that incorporates the effect of the GC contents, the gene density and the density of CpG islands of human chromosomes

Background

Developing a model for codon substitutions is essential for the analyses of protein sequences. Recent studies on the mutation rates in the non-coding regions have shown that CpG mutation rates in the human genome are negatively correlated to the local GC content and to the densities of functional elements. This study aimed at understanding the effect of genomic features, namely, GC content, gene density, and frequency of CpG islands, on the rates of codon substitution in human chromosomes.

Results

Codon substitution rates of CpG to TpG mutations, TpG to CpG mutations, and non-CpG transitions and transversions in humans were estimated by comparing the coding regions of thousands of human and chimpanzee genes and inferring their ancestral sequences by using macaque genes as the outgroup. Since the genomic features are depending on each other, partial regression coefficients of these features were obtained.

Conclusion

The substitution rates of codons depend on gene densities of the chromosomes. Transcription-associated mutation is one such pressure. On the basis of these results, a model of codon substitutions that incorporates the effect of genomic features on codon substitution in human chromosomes was developed.

Statistical methods for detecting natural selection from genomic data

In the study of molecular and phenotypic evolution, understanding the relative importance of random genetic drift and positive selection as the mechanisms for driving divergences between populations and maintaining polymorphisms within populations has been a central issue. A variety of statistical methods has been developed for detecting natural selection operating at the amino acid and nucleotide sequence levels. These methods may be largely classified into those aimed at detecting recurrent and/or recent/ongoing natural selection by utilizing the divergence and/or polymorphism data. Using these methods, pervasive positive selection has been identified for protein-coding and non-coding sequences in the genomic analysis of some organisms. However, many of these methods have been criticized by using computer simulation and real data analysis to produce excessive false-positives and to be sensitive to various disturbing factors. Importantly, some of these methods have been invalidated experimentally. These facts indicate that many of the statistical methods for detecting natural selection are unreliable. In addition, the signals that have been believed as the evidence for fixations of advantageous mutations due to positive selection may also be interpreted as the evidence for fixations of deleterious mutations due to random genetic drift. The genomic diversity data are rapidly accumulating in various organisms, and detection of natural selection may play a critical role for clarifying the relative role of random genetic drift and positive selection in molecular and phenotypic evolution. It is therefore important to develop reliable statistical methods that are unbiased as well as robust against various disturbing factors, for inferring natural selection.

Functional conservation of DNA methylation in the pea aphid and the honeybee

DNA methylation is a fundamental epigenetic mark known to have wide-ranging effects on gene regulation in a variety of animal taxa. Comparative genomic analyses can help elucidate the function of DNA methylation by identifying conserved features of methylated genes and other genomic regions. In this study, we used computational approaches to distinguish genes marked by heavy methylation from those marked by little or no methylation in the pea aphid, Acyrthosiphon pisum. We investigated if these two classes had distinct evolutionary histories and functional roles by conducting comparative analysis with the honeybee, Apis (Ap.) mellifera. We found that highly methylated orthologs in A. pisum and Ap. mellifera exhibited greater conservation of methylation status, suggesting that highly methylated genes in ancestral species may remain highly methylated over time. We also found that methylated genes tended to show different rates of evolution than unmethylated genes. In addition, genes targeted by methylation were enriched for particular biological processes that differed from those in relatively unmethylated genes. Finally, methylated genes were preferentially ubiquitously expressed among alternate phenotypes in both species, whereas genes lacking signatures of methylation were preferentially associated with condition-specific gene regulation expression. Overall, our analyses support a conserved role for DNA methylation in insects with comparable methylation systems.