62 research outputs found

    Increased Affinity for RNA Targets Evolved Early in Animal and Plant Dicer Lineages through Different Structural Mechanisms

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    Understanding the structural basis for evolutionary changes in protein function is central to molecular evolutionary biology and can help determine the extent to which functional convergence occurs through similar or different structural mechanisms. Here, we combine ancestral sequence reconstruction with functional characterization and structural modeling to directly examine the evolution of sequence-structure-function across the early differentiation of animal and plant Dicer/DCL proteins, which perform the first molecular step in RNA interference by identifying target RNAs and processing them into short interfering products. We found that ancestral Dicer/DCL proteins evolved similar increases in RNA target affinities as they diverged independently in animal and plant lineages. In both cases, increases in RNA target affinities were associated with sequence changes that anchored the RNA's 5'phosphate, but the structural bases for 5'phosphate recognition were different in animal versus plant lineages. These results highlight how molecular-functional evolutionary convergence can derive from the evolution of unique protein structures implementing similar biochemical mechanisms

    Robustness of Ancestral Sequence Reconstruction to Phylogenetic Uncertainty

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    Ancestral sequence reconstruction (ASR) is widely used to formulate and test hypotheses about the sequences, functions, and structures of ancient genes. Ancestral sequences are usually inferred from an alignment of extant sequences using a maximum likelihood (ML) phylogenetic algorithm, which calculates the most likely ancestral sequence assuming a probabilistic model of sequence evolution and a specific phylogeny—typically the tree with the ML. The true phylogeny is seldom known with certainty, however. ML methods ignore this uncertainty, whereas Bayesian methods incorporate it by integrating the likelihood of each ancestral state over a distribution of possible trees. It is not known whether Bayesian approaches to phylogenetic uncertainty improve the accuracy of inferred ancestral sequences. Here, we use simulation-based experiments under both simplified and empirically derived conditions to compare the accuracy of ASR carried out using ML and Bayesian approaches. We show that incorporating phylogenetic uncertainty by integrating over topologies very rarely changes the inferred ancestral state and does not improve the accuracy of the reconstructed ancestral sequence. Ancestral state reconstructions are robust to uncertainty about the underlying tree because the conditions that produce phylogenetic uncertainty also make the ancestral state identical across plausible trees; conversely, the conditions under which different phylogenies yield different inferred ancestral states produce little or no ambiguity about the true phylogeny. Our results suggest that ML can produce accurate ASRs, even in the face of phylogenetic uncertainty. Using Bayesian integration to incorporate this uncertainty is neither necessary nor beneficial

    Parallel Geographic Variation in Drosophila melanogaster

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    Drosophila melanogaster, an ancestrally African species, has recently spread throughout the world, associated with human activity. The species has served as the focus of many studies investigating local adaptation relating to latitudinal variation in non-African populations, especially those from the United States and Australia. These studies have documented the existence of shared, genetically determined phenotypic clines for several life history and morphological traits. However, there are no studies designed to formally address the degree of shared latitudinal differentiation at the genomic level. Here we present our comparative analysis of such differentiation. Not surprisingly, we find evidence of substantial, shared selection responses on the two continents, probably resulting from selection on standing ancestral variation. The polymorphic inversion In(3R)P has an important effect on this pattern, but considerable parallelism is also observed across the genome in regions not associated with inversion polymorphism. Interestingly, parallel latitudinal differentiation is observed even for variants that are not particularly strongly differentiated, which suggests that very large numbers of polymorphisms are targets of spatially varying selection in this species

    Long-Branch Attraction Bias and Inconsistency in Bayesian Phylogenetics

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    Bayesian inference (BI) of phylogenetic relationships uses the same probabilistic models of evolution as its precursor maximum likelihood (ML), so BI has generally been assumed to share ML's desirable statistical properties, such as largely unbiased inference of topology given an accurate model and increasingly reliable inferences as the amount of data increases. Here we show that BI, unlike ML, is biased in favor of topologies that group long branches together, even when the true model and prior distributions of evolutionary parameters over a group of phylogenies are known. Using experimental simulation studies and numerical and mathematical analyses, we show that this bias becomes more severe as more data are analyzed, causing BI to infer an incorrect tree as the maximum a posteriori phylogeny with asymptotically high support as sequence length approaches infinity. BI's long branch attraction bias is relatively weak when the true model is simple but becomes pronounced when sequence sites evolve heterogeneously, even when this complexity is incorporated in the model. This bias—which is apparent under both controlled simulation conditions and in analyses of empirical sequence data—also makes BI less efficient and less robust to the use of an incorrect evolutionary model than ML. Surprisingly, BI's bias is caused by one of the method's stated advantages—that it incorporates uncertainty about branch lengths by integrating over a distribution of possible values instead of estimating them from the data, as ML does. Our findings suggest that trees inferred using BI should be interpreted with caution and that ML may be a more reliable framework for modern phylogenetic analysis

    Multiple Recombination Events Drive the Current Genetic Structure of Xanthomonas perforans in Florida

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    Prior to the identification of Xanthomonas perforans associated with bacterial spot of tomato in 1991, X. euvesicatoria was the only known species in Florida. Currently, X. perforans is the Xanthomonas sp. associated with tomato in Florida. Changes in pathogenic race and sequence alleles over time signify shifts in the dominant X. perforans genotype in Florida. We previously reported recombination of X. perforans strains with closely related Xanthomonas species as a potential driving factor for X. perforans evolution. However, the extent of recombination across the X. perforans genomes was unknown. We used a core genome multilocus sequence analysis approach to identify conserved genes and evaluated recombination-associated evolution of these genes in X. perforans. A total of 1,356 genes were determined to be “core” genes conserved among the 58 X. perforans genomes used in the study. Our approach identified three genetic groups of X. perforans in Florida based on the principal component analysis (PCA) using core genes. Nucleotide variation in 241 genes defined these groups, that are referred as Phylogenetic-group Defining (PgD) genes. Furthermore, alleles of many of these PgD genes showed 100% sequence identity with X. euvesicatoria, suggesting that variation likely has been introduced by recombination at multiple locations throughout the bacterial chromosome. Site-specific recombinase genes along with plasmid mobilization and phage associated genes were observed at different frequencies in the three phylogenetic groups and were associated with clusters of recombinant genes. Our analysis of core genes revealed the extent, source, and mechanisms of recombination events that shaped the current population and genomic structure of X. perforans in Florida

    Bacteroides dorei dominates gut microbiome prior to autoimmunity in Finnish children at high risk for type 1 diabetes

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    The incidence of the autoimmune disease, type 1 diabetes (T1D), has increased dramatically over the last half century in many developed countries and is particularly high in Finland and other Nordic countries. Along with genetic predisposition, environmental factors are thought to play a critical role in this increase. As with other autoimmune diseases, the gut microbiome is thought to play a potential role in controlling progression to T1D in children with high genetic risk, but we know little about how the gut microbiome develops in children with high genetic risk for T1D. In this study, the early development of the gut microbiomes of 76 children at high genetic risk for T1D was determined using high-throughput 16S rRNA gene sequencing. Stool samples from children born in the same hospital in Turku, Finland were collected at monthly intervals beginning at 4-6 months after birth until 2.2 years of age. Of those 76 children, 29 seroconverted to T1D-related autoimmunity (cases) including 22 who later developed T1D, the remaining 47 subjects remained healthy (controls). While several significant compositional differences in low abundant species prior to seroconversion were found, one highly abundant group composed of two closely related species, Bacteroides dorei and Bacteroides vulgatus, was significantly higher in cases compared to controls prior to seroconversion. Metagenomic sequencing of samples high in the abundance of the B. dorei/vulgatus group before seroconversion, as well as longer 16S rRNA sequencing identified this group as Bacteroides dorei. The abundance of B. dorei peaked at 7.6 months in cases, over 8 months prior to the appearance of the first islet autoantibody, suggesting that early changes in the microbiome may be useful for predicting T1D autoimmunity in genetically susceptible infants. The cause of increased B. dorei abundance in cases is not known but its timing appears to coincide with the introduction of solid food.</p