Search

Gene duplications and novel genes have been shown to play a major role in helminth adaptation to a parasitic lifestyle because they provide the novelty necessary for adaptation to a changing environment, such as living in multiple hosts. Here we present the de novo sequenced and annotated genome of the parasitic trematode Atriophallophorus winterbourni and its comparative genomic analysis to other major parasitic trematodes. First, we reconstructed the species phylogeny, and dated the split of A. winterbourni from the Opisthorchiata suborder to approximately 237.4 Ma (±120.4 Myr). We then addressed the question of which expanded gene families and gained genes are potentially involved in adaptation to parasitism. To do this, we used hierarchical orthologous groups to reconstruct three ancestral genomes on the phylogeny leading to A. winterbourni and performed a GO (Gene Ontology) enrichment analysis of the gene composition of each ancestral genome, allowing us to characterize the subsequent genomic changes. Out of the 11,499 genes in the A. winterbourni genome, as much as 24% have arisen through duplication events since the speciation of A. winterbourni from the Opisthorchiata, and as much as 31.9% appear to be novel, that is, newly acquired. We found 13 gene families in A. winterbourni to have had more than ten genes arising through these recent duplications; all of which have functions potentially relating to host behavioral manipulation, host tissue penetration, and hiding from host immunity through antigen presentation. We identified several families with genes evolving under positive selection. Our results provide a valuable resource for future studies on the genomic basis of adaptation to parasitism and point to specific candidate genes putatively involved in antagonistic host–parasite adaptation. [ABSTRACT FROM AUTHOR]

Testing how populations are locally adapted and predicting their response to their future environment is of key importance in view of climate change. Landscape genomics is a powerful approach to investigate genes and environmental factors involved in local adaptation. In a pooled amplicon sequencing approach of 94 genes in 71 populations, we tested whether >3500 single nucleotide polymorphisms ( SNPs) in the three most common oak species in Switzerland ( Quercus petraea, Q. pubescens, Q. robur) show an association with abiotic factors related to local topography, historical climate and soil characteristics. In the analysis including all species, the most frequently associated environmental factors were those best describing the habitats of the species. In the species-specific analyses, the most important environmental factors and associated SNPs greatly differed among species. However, we identified one SNP and seven genes that were associated with the same environmental factor across all species. We finally used regressions of allele frequencies of the most strongly associated SNPs along environmental gradients to predict the risk of nonadaptedness ( RONA), which represents the average change in allele frequency at climate-associated loci theoretically required to match future climatic conditions. RONA is considerable for some populations and species (up to 48% in single populations) and strongly differs among species. Given the long generation time of oaks, some of the required allele frequency changes might not be realistic to achieve based on standing genetic variation. Hence, future adaptedness requires gene flow or planting of individuals carrying beneficial alleles from habitats currently matching future climatic conditions. [ABSTRACT FROM AUTHOR]

Recent development of deep sequencing technologies has facilitated de novo genome sequencing projects, now conducted even by individual laboratories. However, this will yield more and more genome sequences that are not well assembled, and will hinder thorough annotation when no closely related reference genome is available. One of the challenging issues is the identification of protein-coding sequences split into multiple unassembled genomic segments, which can confound orthology assignment and various laboratory experiments requiring the identification of individual genes. In this study, using the genome of a cartilaginous fish, Callorhinchus milii, as test case, we performed gene prediction using a model specifically trained for this genome. We implemented an algorithm, designated ESPRIT, to identify possible linkages between multiple protein-coding portions derived from a single genomic locus split into multiple unassembled genomic segments. We developed a validation framework based on an artificially fragmented human genome, improvements between early and recent mouse genome assemblies, comparison with experimentally validated sequences from GenBank, and phylogenetic analyses. Our strategy provided insights into practical solutions for efficient annotation of only partially sequenced (low-coverage) genomes. To our knowledge, our study is the first formulation of a method to link unassembled genomic segments based on proteomes of relatively distantly related species as references. [ABSTRACT FROM PUBLISHER]

Traditionally, codon models of evolution have been parametric, meaning that the 61 × 61 substitution rate matrix was derived from only a handful of parameters, typically the equilibrium frequencies, the ratio of nonsynonymous to synonymous substitution rates and the ratio between transition and transversion rates. These parameters are reasonable choices and are based on observations of what aspects of evolution often vary in coding DNA. However, the choices are relatively arbitrary and no systematic empirical search has ever been performed to identify the best parameters for a codon model. Even for the empirical or semi-empirical models that have been presented recently, only the average substitution rates have been estimated from databases of real coding DNA, but the parameters used were essentially the same as before. In this study we attempted to investigate empirically what the most relevant parameters for a codon model are. By performing a principal component analysis (PCA) on 3666 substitution rate matrices estimated from single gene families, the sets of the most co-varying substitution rates were determined. Interestingly, the two most significant principal components (PCs) describe clearly identifiable parameters: the first PC separates synonymous and nonsynonymous substitutions while the second PC distinguishes between substitutions where only one nucleotide changes and substitutions with two or three nucleotide changes. For the third and subsequent PCs no simple descriptions could be found. [ABSTRACT FROM AUTHOR]