Society of Systematic Biologists

Transposable elements (TEs) are mobile genetic sequences that can jump around the genome from one location to another, behaving as genomic parasites. TEs have been particularly effective in colonizing mammalian genomes, and such heavy TE load is expected to have conditioned genome evolution. Indeed, studies conducted both at the gene and genome levels have uncovered TE insertions that seem to have been co-opted—or exapted—by providing transcription factor binding sites (TFBSs) that serve as promoters and enhancers, leading to the hypothesis that TE exaptation is a major factor in the evolution of gene regulation. Here, we critically review the evidence for exaptation of TE-derived sequences as TFBSs, promoters, enhancers, and silencers/insulators both at the gene and genome levels. We classify the functional impact attributed to TE insertions into four categories of increasing complexity and argue that so far very few studies have conclusively demonstrated exaptation of TEs as transcriptional regulatory regions. We also contend that many genome-wide studies dealing with TE exaptation in recent lineages of mammals are still inconclusive and that the hypothesis of rapid transcriptional regulatory rewiring mediated by TE mobilization must be taken with caution. Finally, we suggest experimental approaches that may help attributing higher-order functions to candidate exapted TEs.

Computational predictions have become indispensable for evaluating the disease-related impact of nonsynonymous single-nucleotide variants discovered in exome sequencing. Many such methods have their roots in molecular evolution, as they use information derived from multiple sequence alignments. We show that the performance of current methods (e.g., PolyPhen-2 and SIFT) is improved significantly by optimizing their statistical models on evolutionarily balanced training data, where equal numbers of positive and negative controls within each evolutionary conservation class are used. Evolutionary balancing significantly reduces the false-positive rates for variants observed at highly conserved sites and false-negative rates for variants observed at fast evolving sites. Use of these improved methods enables more accurate forecasting when concordant diagnosis from multiple methods is regarded as a more reliable indicator of the prediction. Applied to a large exome variation data set, we find that the current methods produce concordant predictions for less than half of the population variants. These advances are implemented in a web resource for use in practical applications (www.mypeg.info, last accessed March 13, 2013).

Large-scale, genome-level molecular phylogenetic analyses present both opportunities and challenges for bacterial evolutionary and ecological studies. We constructed a phylum-level bacterial phylogenetic marker database by surveying all complete bacterial genomes and identifying single-copy genes that were widely distributed in each of the 20 bacterial phyla. We showed that phylum trees made using these markers were highly resolved and were more robust than the bacterial genome tree based on 31 universal bacterial marker genes. In addition, using the Global Ocean Sampling data set as an example, we demonstrated that the expanded marker database greatly increased the power of metagenomic phylotyping. We incorporated the database into an automated phylogenomic inference application (Phyla-AMPHORA) and made it publicly available. We believe that this centralized resource should have broad applicability in bacterial systematics, phylogenetics, and metagenomic studies.

Gene duplication generates genetic novelty and redundancy and is a major mechanism of evolutionary change in bacteria and eukaryotes. To date, however, gene duplication has been reported only rarely in RNA viruses. Using a conservative BLAST approach we systematically screened for the presence of duplicated (i.e., paralogous) proteins in all RNA viruses for which full genome sequences are publicly available. Strikingly, we found only nine significantly supported cases of gene duplication, two of which are newly described here—in the 25 and 26 kDa proteins of Beet necrotic yellow vein virus (genus Benyvirus) and in the U1 and U2 proteins of Wongabel virus (family Rhabdoviridae). Hence, gene duplication has occurred at a far lower frequency in the recent evolutionary history of RNA viruses than in other organisms. Although the rapidity of RNA virus evolution means that older gene duplication events will be difficult to detect through sequence-based analyses alone, it is likely that specific features of RNA virus biology, and particularly intrinsic constraints on genome size, reduce the likelihood of the fixation and maintenance of duplicated genes.

Markov models of codon substitution naturally incorporate the structure of the genetic code and the selection intensity at the protein level, providing a more realistic representation of protein-coding sequences compared with nucleotide or amino acid models. Thus, for protein-coding genes, phylogenetic inference is expected to be more accurate under codon models. So far, phylogeny reconstruction under codon models has been elusive due to computational difficulties of dealing with high dimension matrices. Here, we present a fast maximum likelihood (ML) package for phylogenetic inference, CodonPhyML offering hundreds of different codon models, the largest variety to date, for phylogeny inference by ML. CodonPhyML is tested on simulated and real data and is shown to offer excellent speed and convergence properties. In addition, CodonPhyML includes most recent fast methods for estimating phylogenetic branch supports and provides an integral framework for models selection, including amino acid and DNA models.

The Candida Gene Order Browser (CGOB) was developed as a tool to visualize and analyze synteny relationships in multiple Candida species, and to provide an accurate, manually curated set of orthologous Candida genes for evolutionary analyses. Here, we describe major improvements to CGOB. The underlying structure of the database has been changed significantly. Genomic features are now based directly on genome annotations rather than on protein sequences, which allows non-protein features such as centromere locations in Candida albicans and tRNA genes in all species to be included. The data set has been expanded to 13 species, including genomes of pathogens (C. albicans, C. parapsilosis, C. tropicalis, and C. orthopsilosis), and those of xylose-degrading species with important biotechnological applications (C. tenuis, Scheffersomyces stipitis, and Spathaspora passalidarum). Updated annotations of C. parapsilosis, C. dubliniensis, and Debaryomyces hansenii have been incorporated. We discovered more than 1,500 previously unannotated genes among the 13 genomes, ranging in size from 29 to 3,850 amino acids. Poorly conserved and rapidly evolving genes were also identified. Re-analysis of the mating type loci of the xylose degraders suggests that C. tenuis is heterothallic, whereas both Spa. passalidarum and S. stipitis are homothallic. As well as hosting the browser, the CGOB website (http://cgob.ucd.ie) gives direct access to all the underlying genome annotations, sequences, and curated orthology data.

It is currently unclear whether the amino acid substitutions that occur during protein evolution are primarily driven by adaptation, or reflect the random accumulation of neutral changes. When estimated from genomic data, the proportion of adaptive amino acid substitutions, called α, was found to vary greatly across species, from nearly zero in humans to above 0.5 in Drosophila. These variations have been interpreted as reflecting differences in effective population size, adaptation being supposedly more efficient in large populations. Here, we investigate the influence of effective population size and other biological parameters on the rate of adaptive evolution by simulating the evolution of a coding sequence under Fisher’s geometric formalism. We explicitly model recurrent environmental changes and the subsequent adaptive walks, followed by periods of stasis during which purifying selection dominates. We show that, under a variety of conditions, the effective population size has only a moderate influence on α, and an even weaker influence on the per generation rate of selective sweeps, modifying the prevalent view in current literature. The rate of environmental change and, interestingly, the dimensionality of the phenotypic space (organismal complexity) affect the adaptive rate more deeply than does the effective population size. We discuss the reasons why verbal arguments have been misleading on that subject and revisit the empirical evidence. Our results question the relevance of the "α" parameter as an indicator of the efficiency of molecular adaptation.