Evaluating purifying selection in the mitochondrial DNA of various mammalian species

Mitochondrial DNA (mtDNA), the circular DNA molecule inside the mitochondria of all eukaryotic cells, has been shown to be under the effect of purifying selection in several species. Traditional testing of purifying selection has been based simply on ratios of nonsynonymous to synonymous mutations, without considering the relative age of each mutation, which can be determined by phylogenetic analysis of this non-recombining molecule. The incorporation of a mutation time-ordering from phylogeny and of predicted pathogenicity scores for nonsynonymous mutations allow a quantitative evaluation of the effects of purifying selection in human mtDNA. Here, by using this additional information, we show that purifying selection undoubtedly acts upon the mtDNA of other mammalian species/genera, namely Bos sp., Canis lupus, Mus musculus, Orcinus orca, Pan sp. and Sus scrofa. The effects of purifying selection were comparable in all species, leading to a significant major proportion of nonsynonymous variants with higher pathogenicity scores in the younger branches of the tree. We also derive recalibrated mutation rates for age estimates of ancestors of these various species and proposed a correction curve in order to take into account the effects of selection. Understanding this selection is fundamental to evolutionary studies and to the identification of deleterious mutations

High frequency of JCV DNA detection in prostate cancer tissues

BACKGROUND: Prostate cancer (PC) represents the most frequently diagnosed cancer in men. Exposure to infectious agents has been considered to induce prostatic inflammation and cancerous transformation. Controversial data exist concerning the role of the human polyomaviruses BK (BKV) and JC (JCV) in PC etiology. Therefore, a possible association between these polyomaviruses and PC was investigated. MATERIALS AND METHODS: Urine, blood and fresh prostatic tissue specimens were collected from 26 patients with PC. The presence of BKV and JCV, the possible non-coding control region (NCCR) variations and the genotyping analysis of viral protein 1 (VP1) of both viruses were assessed. RESULTS: Data showed a preferential viral re-activation in the urinary compartment and a statistically significant prevalence of JC viruria and of BKV in PC tissues. A BKV DDP-like NCCR sequence was isolated in two patients, whereas JCV NCCR was consistently of an archetypal structural organization. A prevalence of the European genotypes was observed for both viruses. CONCLUSION: Our data demonstrated the presence of JCV DNA in 14/24 (58.3%) cancerous prostatic tissue specimens, confirming the results obtained in a previous study, in which JCV has been defined as common inhabitant of the prostate, and opening the discussion about its potential role in PC

motifDiverge: a model for assessing the statistical significance of gene regulatory motif divergence between two DNA sequences

Next-generation sequencing technology enables the identification of thousands of gene regulatory sequences in many cell types and organisms. We consider the problem of testing if two such sequences differ in their number of binding site motifs for a given transcription factor (TF) protein. Binding site motifs impart regulatory function by providing TFs the opportunity to bind to genomic elements and thereby affect the expression of nearby genes. Evolutionary changes to such functional DNA are hypothesized to be major contributors to phenotypic diversity within and between species; but despite the importance of TF motifs for gene expression, no method exists to test for motif loss or gain. Assuming that motif counts are Binomially distributed, and allowing for dependencies between motif instances in evolutionarily related sequences, we derive the probability mass function of the difference in motif counts between two nucleotide sequences. We provide a method to numerically estimate this distribution from genomic data and show through simulations that our estimator is accurate. Finally, we introduce the R package {\tt motifDiverge} that implements our methodology and illustrate its application to gene regulatory enhancers identified by a mouse developmental time course experiment. While this study was motivated by analysis of regulatory motifs, our results can be applied to any problem involving two correlated Bernoulli trials

An alternative marginal likelihood estimator for phylogenetic models

Bayesian phylogenetic methods are generating noticeable enthusiasm in the field of molecular systematics. Many phylogenetic models are often at stake and different approaches are used to compare them within a Bayesian framework. The Bayes factor, defined as the ratio of the marginal likelihoods of two competing models, plays a key role in Bayesian model selection. We focus on an alternative estimator of the marginal likelihood whose computation is still a challenging problem. Several computational solutions have been proposed none of which can be considered outperforming the others simultaneously in terms of simplicity of implementation, computational burden and precision of the estimates. Practitioners and researchers, often led by available software, have privileged so far the simplicity of the harmonic mean estimator (HM) and the arithmetic mean estimator (AM). However it is known that the resulting estimates of the Bayesian evidence in favor of one model are biased and often inaccurate up to having an infinite variance so that the reliability of the corresponding conclusions is doubtful. Our new implementation of the generalized harmonic mean (GHM) idea recycles MCMC simulations from the posterior, shares the computational simplicity of the original HM estimator, but, unlike it, overcomes the infinite variance issue. The alternative estimator is applied to simulated phylogenetic data and produces fully satisfactory results outperforming those simple estimators currently provided by most of the publicly available software

Identification of candidate regulatory sequences in mammalian 3' UTRs by statistical analysis of oligonucleotide distributions

3' untranslated regions (3' UTRs) contain binding sites for many regulatory elements, and in particular for microRNAs (miRNAs). The importance of miRNA-mediated post-transcriptional regulation has become increasingly clear in the last few years. We propose two complementary approaches to the statistical analysis of oligonucleotide frequencies in mammalian 3' UTRs aimed at the identification of candidate binding sites for regulatory elements. The first method is based on the identification of sets of genes characterized by evolutionarily conserved overrepresentation of an oligonucleotide. The second method is based on the identification of oligonucleotides showing statistically significant strand asymmetry in their distribution in 3' UTRs. Both methods are able to identify many previously known binding sites located in 3'UTRs, and in particular seed regions of known miRNAs. Many new candidates are proposed for experimental verification.Comment: Added two reference

Meta-analysis of massively parallel reporter assays enables prediction of regulatory function across cell types.

Deciphering the potential of noncoding loci to influence gene regulation has been the subject of intense research, with important implications in understanding genetic underpinnings of human diseases. Massively parallel reporter assays (MPRAs) can measure regulatory activity of thousands of DNA sequences and their variants in a single experiment. With increasing number of publically available MPRA data sets, one can now develop data-driven models which, given a DNA sequence, predict its regulatory activity. Here, we performed a comprehensive meta-analysis of several MPRA data sets in a variety of cellular contexts. We first applied an ensemble of methods to predict MPRA output in each context and observed that the most predictive features are consistent across data sets. We then demonstrate that predictive models trained in one cellular context can be used to predict MPRA output in another, with loss of accuracy attributed to cell-type-specific features. Finally, we show that our approach achieves top performance in the Fifth Critical Assessment of Genome Interpretation "Regulation Saturation" Challenge for predicting effects of single-nucleotide variants. Overall, our analysis provides insights into how MPRA data can be leveraged to highlight functional regulatory regions throughout the genome and can guide effective design of future experiments by better prioritizing regions of interest

Islands of linkage in an ocean of pervasive recombination reveals two-speed evolution of human cytomegalovirus genomes

Human cytomegalovirus (HCMV) infects most of the population worldwide, persisting throughout the host's life in a latent state with periodic episodes of reactivation. While typically asymptomatic, HCMV can cause fatal disease among congenitally infected infants and immunocompromised patients. These clinical issues are compounded by the emergence of antiviral resistance and the absence of an effective vaccine, the development of which is likely complicated by the numerous immune evasins encoded by HCMV to counter the host's adaptive immune responses, a feature that facilitates frequent super-infections. Understanding the evolutionary dynamics of HCMV is essential for the development of effective new drugs and vaccines. By comparing viral genomes from uncultivated or low-passaged clinical samples of diverse origins, we observe evidence of frequent homologous recombination events, both recent and ancient, and no structure of HCMV genetic diversity at the whole-genome scale. Analysis of individual gene-scale loci reveals a striking dichotomy: while most of the genome is highly conserved, recombines essentially freely and has evolved under purifying selection, 21 genes display extreme diversity, structured into distinct genotypes that do not recombine with each other. Most of these hyper-variable genes encode glycoproteins involved in cell entry or escape of host immunity. Evidence that half of them have diverged through episodes of intense positive selection suggests that rapid evolution of hyper-variable loci is likely driven by interactions with host immunity. It appears that this process is enabled by recombination unlinking hyper-variable loci from strongly constrained neighboring sites. It is conceivable that viral mechanisms facilitating super-infection have evolved to promote recombination between diverged genotypes, allowing the virus to continuously diversify at key loci to escape immune detection, while maintaining a genome optimally adapted to its asymptomatic infectious lifecycle

Evolutionary distances in the twilight zone -- a rational kernel approach

Phylogenetic tree reconstruction is traditionally based on multiple sequence alignments (MSAs) and heavily depends on the validity of this information bottleneck. With increasing sequence divergence, the quality of MSAs decays quickly. Alignment-free methods, on the other hand, are based on abstract string comparisons and avoid potential alignment problems. However, in general they are not biologically motivated and ignore our knowledge about the evolution of sequences. Thus, it is still a major open question how to define an evolutionary distance metric between divergent sequences that makes use of indel information and known substitution models without the need for a multiple alignment. Here we propose a new evolutionary distance metric to close this gap. It uses finite-state transducers to create a biologically motivated similarity score which models substitutions and indels, and does not depend on a multiple sequence alignment. The sequence similarity score is defined in analogy to pairwise alignments and additionally has the positive semi-definite property. We describe its derivation and show in simulation studies and real-world examples that it is more accurate in reconstructing phylogenies than competing methods. The result is a new and accurate way of determining evolutionary distances in and beyond the twilight zone of sequence alignments that is suitable for large datasets.Comment: to appear in PLoS ON