Plastid endosymbiosis, genome evolution and the origin of green plants

Evolutionary relationships among complex, multicellular eukaryotes are generally interpreted within the framework of molecular sequence-based phylogenies that suggest green plants and animals are only distantly related on the eukaryotic tree. However, important anomalies have been reported in phylogenomic analyses, including several that relate specifically to green plant evolution. In addition, plants and animals share molecular, biochemical and genome-level features that suggest a relatively close relationship between the two groups. This article explores the impacts of plastid endosymbioses on nuclear genomes, how they can explain incongruent phylogenetic signals in molecular data sets and reconcile conflicts among different sources of comparative data. Specifically, I argue that the large influx of plastid DNA into plant and algal nuclear genomes has resulted in tree-building artifacts that obscure a relatively close evolutionary relationship between green plants and animals.     

Phylogenomic analyses predict sistergroup relationship of nucleariids and Fungi and paraphyly of zygomycetes with significant support

Background  

Resolving the evolutionary relationships among Fungi remains challenging because of their highly variable evolutionary rates, and lack of a close phylogenetic outgroup. Nucleariida, an enigmatic group of amoeboids, have been proposed to emerge close to the fungal-metazoan divergence and might fulfill this role. Yet, published phylogenies with up to five genes are without compelling statistical support, and genome-level data should be used to resolve this question with confidence.     

Evolutionary relationships of the human immunodeficiency viruses

The rapid accumulation of nucleotide sequence data on viral genes has allowed, for the first time, the development of detailed phylogenies of viruses based on an objective criterion. This has been demonstrated clearly in the recent analysis of the evolutionary relationships of HIV - the AIDS virus. When first characterized, HIV seemed aberrant and almost unique in many features. Now it is known to be one of a large group of immunodeficiency viruses, which are widely distributed among primates and other mammals.     

Sequential adaptive variables and subject selection for GEE methods

Modeling correlated or highly stratified multiple-response data is a common data analysis task in many applications, such as those in large epidemiological studies or multisite cohort studies. The generalized estimating equations method is a popular statistical method used to analyze these kinds of data, because it can manage many types of unmeasured dependence among outcomes. Collecting large amounts of highly stratified or correlated response data is time-consuming; thus, the use of a more aggressive sampling strategy that can accelerate this process—such as the active-learning methods found in the machine-learning literature—will always be beneficial. In this study, we integrate adaptive sampling and variable selection features into a sequential procedure for modeling correlated response data. Besides reporting the statistical properties of the proposed procedure, we also use both synthesized and real data sets to demonstrate the usefulness of our method.     

Networks in a Large-Scale Phylogenetic Analysis: Reconstructing Evolutionary History of Asparagales (Lilianae) Based on Four Plastid Genes

Phylogenetic analysis aims to produce a bifurcating tree, which disregards conflicting signals and displays only those that are present in a large proportion of the data. However, any character (or tree) conflict in a dataset allows the exploration of support for various evolutionary hypotheses. Although data-display network approaches exist, biologists cannot easily and routinely use them to compute rooted phylogenetic networks on real datasets containing hundreds of taxa. Here, we constructed an original neighbour-net for a large dataset of Asparagales to highlight the aspects of the resulting network that will be important for interpreting phylogeny. The analyses were largely conducted with new data collected for the same loci as in previous studies, but from different species accessions and greater sampling in many cases than in published analyses. The network tree summarised the majority data pattern in the characters of plastid sequences before tree building, which largely confirmed the currently recognised phylogenetic relationships. Most conflicting signals are at the base of each group along the Asparagales backbone, which helps us to establish the expectancy and advance our understanding of some difficult taxa relationships and their phylogeny. The network method should play a greater role in phylogenetic analyses than it has in the past. To advance the understanding of evolutionary history of the largest order of monocots Asparagales, absolute diversification times were estimated for family-level clades using relaxed molecular clock analyses.     

A method for inferring the rate of evolution of homologous characters that can potentially improve phylogenetic inference, resolve deep divergence and correct systematic biases

Current phylogenetic methods attempt to account for evolutionary rate variation across characters in a matrix. This is generally achieved by the use of sophisticated evolutionary models, combined with dense sampling of large numbers of characters. However, systematic biases and superimposed substitutions make this task very difficult. Model adequacy can sometimes be achieved at the cost of adding large numbers of free parameters, with each parameter being optimized according to some criterion, resulting in increased computation times and large variances in the model estimates. In this study, we develop a simple approach that estimates the relative evolutionary rate of each homologous character. The method that we describe uses the similarity between characters as a proxy for evolutionary rate. In this article, we work on the premise that if the character-state distribution of a homologous character is similar to many other characters, then this character is likely to be relatively slowly evolving. If the character-state distribution of a homologous character is not similar to many or any of the rest of the characters in a data set, then it is likely to be the result of rapid evolution. We show that in some test cases, at least, the premise can hold and the inferences are robust. Importantly, the method does not use a "starting tree" to make the inference and therefore is tree independent. We demonstrate that this approach can work as well as a maximum likelihood (ML) approach, though the ML method needs to have a known phylogeny, or at least a very good estimate of that phylogeny. We then demonstrate some uses for this method of analysis, including the improvement in phylogeny reconstruction for both deep-level and recent relationships and overcoming systematic biases such as base composition bias. Furthermore, we compare this approach to two well-established methods for reweighting or removing characters. These other methods are tree-based and we show that they can be systematically biased. We feel this method can be useful for phylogeny reconstruction, understanding evolutionary rate variation, and for understanding selection variation on different characters.     

Increasing phylogenetic resolution at low taxonomic levels using massively parallel sequencing of chloroplast genomes

Background  

Molecular evolutionary studies share the common goal of elucidating historical relationships, and the common challenge of adequately sampling taxa and characters. Particularly at low taxonomic levels, recent divergence, rapid radiations, and conservative genome evolution yield limited sequence variation, and dense taxon sampling is often desirable. Recent advances in massively parallel sequencing make it possible to rapidly obtain large amounts of sequence data, and multiplexing makes extensive sampling of megabase sequences feasible. Is it possible to efficiently apply massively parallel sequencing to increase phylogenetic resolution at low taxonomic levels?     

Detection of broadly expressed neuronal genes in C. elegans

The genes that are expressed in most or all types of neurons define generic neuronal features and provide a window into the developmental origin and function of the nervous system. Few such genes (sometimes referred to as pan-neuronal or broadly expressed neuronal genes) have been defined to date and the mechanisms controlling their regulation are not well understood. As a first step in investigating their regulation, we used a computational approach to detect sequences overrepresented in their promoter elements. We identified a ten-nucleotide cis-regulatory motif shared by many broadly expressed neuronal genes and demonstrated that it is involved in control of neuronal expression. Our results further suggest that global and cell-type-specific controls likely act in concert to establish pan-neuronal gene expression. Using the newly discovered motif and genome-level gene expression data, we identified a set of 234 candidate broadly expressed genes. The known involvement of many of these genes in neurogenesis and physiology of the nervous system supports the utility of this set for future targeted analyses.     

Targeted Enrichment: Maximizing Orthologous Gene Comparisons across Deep Evolutionary Time

Estimated phylogenies of evolutionarily diverse taxa will be well supported and more likely to be historically accurate when the analysis contains large amounts of data–many genes sequenced across many taxa. Inferring such phylogenies for non-model organisms is challenging given limited resources for whole-genome sequencing. We take advantage of genomic data from a single species to test the limits of hybridization-based enrichment of hundreds of exons across frog species that diverged up to 250 million years ago. Enrichment success for a given species depends greatly on the divergence time between it and the reference species, and the resulting alignment contains a significant proportion of missing data. However, our alignment generates a well-supported phylogeny of frogs, suggesting that this technique is a practical solution towards resolving relationships across deep evolutionary time.     

Targeted sequence capture as a powerful tool for evolutionary analysis1

Next-generation sequencing technologies (NGS) have revolutionized biological research by significantly increasing data generation while simultaneously decreasing the time to data output. For many ecologists and evolutionary biologists, the research opportunities afforded by NGS are substantial; even for taxa lacking genomic resources, large-scale genome-level questions can now be addressed, opening up many new avenues of research. While rapid and massive sequencing afforded by NGS increases the scope and scale of many research objectives, whole genome sequencing is often unwarranted and unnecessarily complex for specific research questions. Recently developed targeted sequence enrichment, coupled with NGS, represents a beneficial strategy for enhancing data generation to answer questions in ecology and evolutionary biology. This marriage of technologies offers researchers a simple method to isolate and analyze a few to hundreds, or even thousands, of genes or genomic regions from few to many samples in a relatively efficient and effective manner. These strategies can be applied to questions at both the infra- and interspecific levels, including those involving parentage, gene flow, divergence, phylogenetics, reticulate evolution, and many more. Here we provide a brief overview of targeted sequence enrichment, and emphasize the power of this technology to increase our ability to address a wide range of questions of interest to ecologists and evolutionary biologists, particularly for those working with taxa for which few genomic resources are available.     

类群取样与系统发育分析精确度之探索

Appropriate and extensive taxon sampling is one of the most important determinants of accurate phylogenetic estimation. In addition, accuracy of inferences about evolutionary processes obtained from phylogenetic analyses is improved significantly by thorough taxon sampling efforts. Many recent efforts to improve phylogenetic estimates have focused instead on increasing sequence length or the number of overall characters in the analysis, and this often does have a beneficial effect on the accuracy of phylogenetic analyses. However, phylogenetic analyses of few taxa (but each represented by many characters) can be subject to strong systematic biases, which in turn produce high measures of repeatability (such as bootstrap proportions) in support of incorrect or misleading phylogenetic results. Thus, it is important for phylogeneticists to consider both the sampling of taxa, as well as the sampling of characters, in designing phylogenetic studies. Taxon sampling also improves estimates of evolutionary parameters derived from phylogenetic trees, and is thus important for improved applications of phylogenetic analyses. Analysis of sensitivity to taxon inclusion, the possible effects of long-branch attraction, and sensitivity of parameter estimation for model-based methods should be a part of any careful and thorough phylogenetic analysis. Furthermore, recent improvements in phylogenetic algorithms and in computational power have removed many constraints on analyzing large, thoroughly sampled data sets. Thorough taxon sampling is thus one of the most practical ways to improve the accuracy of phylogenetic estimates, as well as the accuracy of biological inferences that are based on these phylogenetic trees.     

Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation

Accurate modeling of geographic distributions of species is crucial to various applications in ecology and conservation. The best performing techniques often require some parameter tuning, which may be prohibitively time‐consuming to do separately for each species, or unreliable for small or biased datasets. Additionally, even with the abundance of good quality data, users interested in the application of species models need not have the statistical knowledge required for detailed tuning. In such cases, it is desirable to use “default settings”, tuned and validated on diverse datasets. Maxent is a recently introduced modeling technique, achieving high predictive accuracy and enjoying several additional attractive properties. The performance of Maxent is influenced by a moderate number of parameters. The first contribution of this paper is the empirical tuning of these parameters. Since many datasets lack information about species absence, we present a tuning method that uses presence‐only data. We evaluate our method on independently collected high‐quality presence‐absence data. In addition to tuning, we introduce several concepts that improve the predictive accuracy and running time of Maxent. We introduce “hinge features” that model more complex relationships in the training data; we describe a new logistic output format that gives an estimate of probability of presence; finally we explore “background sampling” strategies that cope with sample selection bias and decrease model‐building time. Our evaluation, based on a diverse dataset of 226 species from 6 regions, shows: 1) default settings tuned on presence‐only data achieve performance which is almost as good as if they had been tuned on the evaluation data itself; 2) hinge features substantially improve model performance; 3) logistic output improves model calibration, so that large differences in output values correspond better to large differences in suitability; 4) “target‐group” background sampling can give much better predictive performance than random background sampling; 5) random background sampling results in a dramatic decrease in running time, with no decrease in model performance.     

Molecular phylogeny revealed complex evolutionary process in Elymus species

Recent molecular phylogenetic studies on Elymus have added to our understanding of the origination of Elymus species. However, evolutionary dynamics and speciation of most species in Elymus are unclear. Molecular phylogeny has demonstrated that reticulate evolution has occurred extensively in the genus, as an example, the largest subunit of RNA polymerase II (rpb2) and phosphoenolpyruvate carboxylase (pepC) data revealed two versions of the St genome, St1 and St2contributing to speciation of E. caninus. Phylogenetic analyses of E. pendulinus uncovered additional genome-level complexity. Our data indicated that both chloroplast and nuclear gene introgression have occurred in the evolutionary process of E. pendulinus. Non-donor species genomes have been detected in severalElymus species, such as in allohexaploid E. repens (StStStStHH), a Taeniatherum-like (Ta genome in Triticeae) GBSSI sequence, Bromus- (Bromeae) and Panicum-like (Paniceae) ITS sequences have been detected. The chloroplast DNA data indicated that Pseudoroegneria is the maternal genome donor to Elymus species, but whether different Elymus species originated from different St donors remains an open question. The origin of the Y genome in Elymus is puzzling. It is clear that the Ygenome is distinct from the St genome, but unclear on the relationships of Y to other genomes in Triticeae. Introgressive hybridization may be an important factor complicating the evolutionary history of the species in Elymus. The extent of introgression and its role in creating diversity in Elymus species should be the objective of further investigations.     

Simulation of Genomes: A Review

There is an increasing role of population genetics in human genetic research linking empirical observations with hypotheses about sequence variation due to historical and evolutionary causes. In addition, the data sets are increasing in size, with genome-wide data becoming a common place in many empirical studies. As far as more information is available, it becomes clear that simplest hypotheses are not consistent with data. Simulations will provide the key tool to contrast complex hypotheses on real data by generating simulated data under the hypothetical historical and evolutionary conditions that we want to contrast. Undoubtedly, developing tools for simulating large sequences that at the same time allow simulate natural selection, recombination and complex demography patterns will be of great interest in order to better understanding the trace left on the DNA by different interacting evolutionary forces. Simulation tools will be also essential to evaluate the sampling properties of any statistics used on genome-wide association studies and to compare performance of methods applied at genome-wide scales. Several recent simulation tools have been developed. Here, we review some of the currently existing simulators which allow for efficient simulation of large sequences on complex evolutionary scenarios. In addition, we will point out future directions in this field which are already a key part of the current research in evolutionary biology and it seems that it will be a primary tool in the future research of genome and post-genomic biology.     

Congruence,non‐homology,and the phylogeny of basal turtles

Joyce, W.G. and Sterli J. 2010. Congruence, non‐homology, and the phylogeny of basal turtles.–Acta Zoologica (Stockholm) Modern cladistic analysis is characterized by the assembly of increasingly larger data sets coupled with the use of congruence as the final test of homology. Some critics of this development have recently called for a return to more detailed primary homology analysis while questioning the utility of congruence. This discussion appears to be central to the debate regarding the phylogenetic relationships of basal turtles, as the large data sets developed by us have been criticized recently for utilizing poorly constructed characters and including too many homoplasy‐prone characters. Our analysis of this critique reveals that (1) new information regarding poorly understood taxa has a greater impact on the outcome of turtle phylogenies than the characters under dispute; (2) most current turtle phylogenies differ in taxon sampling, not character sampling, and so it appears illogical to condemn a particular analysis for its character sampling; (3) even evolutionary taxonomists should agree that key characters utilized to resolve basal turtle relationships cannot be thought to be ‘infallible’; (4) whereas various criteria provide positive evidence for homology, only congruence provides positive evidence for non‐homology; and (5) a stalemate between conflicting camps within a congruence frame work is preferable to the ad hoc dismissal of data sets, because authoritative statements are untestable.     

Digest: Resolving phylogenomic conflicts in characiform fishes†

How can taxonomists best resolve the challenge of curating and analyzing large phylogenomic datasets that produce incongruent but highly supported topologies? Betancur‐R et al. used a recently established hypothesis‐testing procedure on a large dataset of genes and species to study the evolutionary relationships of characiform fishes, finding that past conclusions of non‐monophyly may have been problematic and establishing monophyly with high confidence. The new findings highlight the importance of using dense taxon sampling to resolve conflicting relationships with phylogenomic data.     

Horizontal gene transfer between bacteria and animals

Horizontal gene transfer is increasingly described between bacteria and animals. Such transfers that are vertically inherited have the potential to influence the evolution of animals. One classic example is the transfer of DNA from mitochondria and chloroplasts to the nucleus after the acquisition of these organelles by eukaryotes. Even today, many of the described instances of bacteria-to-animal transfer occur as part of intimate relationships such as those of endosymbionts and their invertebrate hosts, particularly insects and nematodes, while numerous transfers are also found in asexual animals. Both of these observations are consistent with modern evolutionary theory, in particular the serial endosymbiotic theory and Muller's ratchet. Although it is tempting to suggest that these particular lifestyles promote horizontal gene transfer, it is difficult to ascertain given the nonrandom sampling of animal genome sequencing projects and the lack of a systematic analysis of animal genomes for such transfers.     

MEGA: a biologist-centric software for evolutionary analysis of DNA and protein sequences

The Molecular Evolutionary Genetics Analysis (MEGA) software is a desktop application designed for comparative analysis of homologous gene sequences either from multigene families or from different species with a special emphasis on inferring evolutionary relationships and patterns of DNA and protein evolution. In addition to the tools for statistical analysis of data, MEGA provides many convenient facilities for the assembly of sequence data sets from files or web-based repositories, and it includes tools for visual presentation of the results obtained in the form of interactive phylogenetic trees and evolutionary distance matrices. Here we discuss the motivation, design principles and priorities that have shaped the development of MEGA. We also discuss how MEGA might evolve in the future to assist researchers in their growing need to analyze large data set using new computational methods.