Nucleotide Sequence Evolution Research Articles

Impacts of Taxon-Sampling Schemes on Bayesian Tip Dating Under the Fossilized Birth-Death Process.

Evolutionary timescales can be inferred by molecular-clock analyses of genetic data and fossil evidence. Bayesian phylogenetic methods such as tip dating provide a powerful framework for inferring evolutionary timescales, but the most widely used priors for tree topologies and node times often assume that present-day taxa have been sampled randomly or exhaustively. In practice, taxon sampling is often carried out so as to include representatives of major lineages, such as orders or families. We examined the impacts of different densities of diversified sampling on Bayesian tip dating on unresolved fossilized birth-death (FBD) trees, in which fossil taxa are topologically constrained but their exact placements are averaged out. We used synthetic data generated by simulations of nucleotide sequence evolution, fossil occurrences, and diversified taxon sampling. Our analyses under the diversified-sampling FBD process show that increasing taxon-sampling density does not necessarily improve divergence-time estimates. However, when informative priors were specified for the root age or when tree topologies were fixed to those used for simulation, the performance of tip dating on unresolved FBD trees maintains its accuracy and precision or improves with taxon-sampling density. By exploring three situations in which models are mismatched, we find that including all relevant fossils, without pruning off those that are incompatible with the diversified-sampling FBD process, can lead to underestimation of divergence times. Our reanalysis of a eutherian mammal data set confirms some of the findings from our simulation study, and reveals the complexity of diversified taxon sampling in phylogenomic data sets. In highlighting the interplay of taxon-sampling density and other factors, the results of our study have practical implications for using Bayesian tip dating to infer evolutionary timescales across the Tree of Life. [Bayesian tip dating; eutherian mammals; fossilized birth-death process; phylogenomics; taxon sampling.].

HexSE: Simulating evolution in overlapping reading frames.

Gene overlap occurs when two or more genes are encoded by the same nucleotides. This phenomenon is found in all taxonomic domains, but is particularly common in viruses, where it may provide a mechanism to increase the information content of compact genomes. The presence of overlapping reading frames (OvRFs) can skew estimates of selection based on the rates of non-synonymous and synonymous substitutions, since a substitution that is synonymous in one reading frame may be non-synonymous in another and vice versa. To understand the impact of OvRFs on molecular evolution, we implemented a versatile simulation model of nucleotide sequence evolution along a phylogeny with any distribution of open reading frames in linear or circular genomes. We use a custom data structure to track the substitution rates at every nucleotide site, which is determined by the stationary nucleotide frequencies, transition bias and the distribution of selection biases (dN/dS) in the respective reading frames. Our simulation model is implemented in the Python scripting language. All source code is released under the GNU General Public License version 3 and are available at https://github.com/PoonLab/HexSE.

Gene transfer and nucleotide sequence evolution by Gossypium cytoplasmic genomes indicates novel evolutionary characteristics.

The DNA fragments transferred among cotton cytoplasmic genomes are highly differentiated. The wild D group cotton species have undergone much greater evolution compared with cultivated AD group. Cotton (Gossypium spp.) is one of the most economically important fiber crops worldwide. Gene transfer, nucleotide evolution, and the codon usage preferences in cytoplasmic genomes are important evolutionary characteristics of high plants. In this study, we analyzed the nucleotide sequence evolution, codon usage, and transfer of cytoplasmic DNA fragments in Gossypium chloroplast (cp) and mitochondrial (mt) genomes, including the A genome group, wild D group, and cultivated AD group of cotton species. Our analyses indicated that the differences in the length of transferred cytoplasmic DNA fragments were not significant in mitochondrial and chloroplast sequences. Analysis of the transfer of tRNAs found that trnQ and nine other tRNA genes were commonly transferred between two different cytoplasmic genomes. The Codon Adaptation Index values showed that Gossypium cp genomes prefer A/T-ending codons. Codon preference selection was higher in the D group than the other two groups. Nucleotide sequence evolution analysis showed that intergenic spacer sequences were more variable than coding regions and nonsynonymous mutations were clearly more common in cp genomes than mt genomes. Evolutionary analysis showed that the substitution rate was much higher in cp genomes than mt genomes. Interestingly, the D group cotton species have undergone much faster evolution compared with cultivated AD groups, possibly due to the selection and domestication of diverse cotton species. Our results demonstrate that gene transfer and differential nucleotide sequence evolution have occurred frequently in cotton cytoplasmic genomes.

A Simulation-Based Evaluation of Tip-Dating Under the Fossilized Birth-Death Process.

Bayesian molecular dating is widely used to study evolutionary timescales. This procedure usually involves phylogenetic analysis of nucleotide sequence data, with fossil-based calibrations applied as age constraints on internal nodes of the tree. An alternative approach is tip-dating, which explicitly includes fossil data in the analysis. This can be done, for example, through the joint analysis of molecular data from present-day taxa and morphological data from both extant and fossil taxa. In the context of tip-dating, an important development has been the fossilized birth–death process, which allows non-contemporaneous tips and sampled ancestors while providing a model of lineage diversification for the prior on the tree topology and internal node times. However, tip-dating with fossils faces a number of considerable challenges, especially, those associated with fossil sampling and evolutionary models for morphological characters. We conducted a simulation study to evaluate the performance of tip-dating using the fossilized birth–death model. We simulated fossil occurrences and the evolution of nucleotide sequences and morphological characters under a wide range of conditions. Our analyses of these data show that the number and the maximum age of fossil occurrences have a greater influence than the degree of among-lineage rate variation or the number of morphological characters on estimates of node times and the tree topology. Tip-dating with the fossilized birth–death model generally performs well in recovering the relationships among extant taxa but has difficulties in correctly placing fossil taxa in the tree and identifying the number of sampled ancestors. The method yields accurate estimates of the ages of the root and crown group, although the precision of these estimates varies with the probability of fossil occurrence. The exclusion of morphological characters results in a slight overestimation of node times, whereas the exclusion of nucleotide sequences has a negative impact on inference of the tree topology. Our results provide an overview of the performance of tip-dating using the fossilized birth–death model, which will inform further development of the method and its application to key questions in evolutionary biology.

Putative Independent Evolutionary Reversals from Genotypic to Temperature-Dependent Sex Determination are Associated with Accelerated Evolution of Sex-Determining Genes in Turtles.

The evolutionary lability of sex-determining mechanisms across the tree of life is well recognized, yet the extent of molecular changes that accompany these repeated transitions remain obscure. Most turtles retain the ancestral temperature-dependent sex determination (TSD) from which multiple transitions to genotypic sex determination (GSD) occurred independently, and two contrasting hypotheses posit the existence or absence of reversals back to TSD. Here we examined the molecular evolution of the coding regions of a set of gene regulators involved in gonadal development in turtles and several other vertebrates. We found slower molecular evolution in turtles and crocodilians compared to other vertebrates, but an acceleration in Trionychia turtles and at some phylogenetic branches demarcating major taxonomic diversification events. Of all gene classes examined, hormone signaling genes, and Srd5a1 in particular, evolve faster in many lineages and especially in turtles. Our data show that sex-linked genes do not follow a ubiquitous nor uniform pattern of molecular evolution. We then evaluated turtle nucleotide and protein evolution under two evolutionary hypotheses with or without GSD-to-TSD reversals, and found that when GSD-to-TSD reversals are considered, all transitional branches irrespective of direction, exhibit accelerated molecular evolution of nucleotide sequences, while GSD-to-TSD transitional branches also show acceleration in protein evolution. Significant changes in predicted secondary structure that may affect protein function were identified in three genes that exhibited hastened evolution in turtles compared to other vertebrates or in transitional versus non-transitional branches within turtles, rendering them candidates for a key role during SDM evolution in turtles.

Genetic coding algorithm for sense and antisense peptide interactions

Sense and antisense peptides, i.e. peptides specified by complementary DNA and RNA sequences, interact with increased probability. Biro, Blalock, Mekler, Root-Bernstein and Siemion investigated the recognition rules of peptide—peptide interaction based on the complementary coding of DNA and RNA sequences in 3′→5′ and 5′→3′ directions. After more than three decades of theoretical and experimental investigations, the efficiency of this approach to predict peptide—peptide binding has been experimentally verified for more than 50 ligand—receptor systems, and represents a promising field of research. The natural genetic coding algorithm for sense and antisense peptide interactions combines following elements: of amino acid physico-chemical properties, stereochemical interaction, and bidirectional transcription. The interplay of these factors influences the specificity of sense—antisense peptide interactions, and affects the selection and evolution of peptide ligand—receptor systems. Complementary mRNA codon—tRNA anticodon complexes, and recently discovered Carter-Wolfenden tRNA acceptor-stem code, provide the basis for the rational modeling of peptide interactions based on their hydrophobic and lipophilic amino acid physico-chemical properties. It is shown that the interactions of complementary amino acid pairs according to the hydrophobic and lipophilic properties strongly depend on the central (second) purine base of the mRNA codon and its pyrimidine complement of the tRNA anticodon. This enables the development of new algorithms for the analysis of structure, function and evolution of protein and nucleotide sequences that take into account the residue's tendency to leave water and enter a nonpolar condensed phase considering its mass, size and accessible surface area. The practical applications of the sense—antisense peptide modeling are illustrated using different interaction assay types based on: microscale thermophoresis (MST), tryptophan fluorescence spectroscopy (TFS), nuclear magnetic resonance spectroscopy (NMR), and magnetic particles enzyme immunoassay (MPEIA). Various binding events and circumstances were considered, e.g., in situations with—short antisense peptide ligand (MST), L- and D-enantiomer acceptors (TFS), in low affinity conditions (NMR), and with more than one antisense peptide targeting hormone (MPEIA).

Pyvolve: A Flexible Python Module for Simulating Sequences along Phylogenies.

We introduce Pyvolve, a flexible Python module for simulating genetic data along a phylogeny using continuous-time Markov models of sequence evolution. Easily incorporated into Python bioinformatics pipelines, Pyvolve can simulate sequences according to most standard models of nucleotide, amino-acid, and codon sequence evolution. All model parameters are fully customizable. Users can additionally specify custom evolutionary models, with custom rate matrices and/or states to evolve. This flexibility makes Pyvolve a convenient framework not only for simulating sequences under a wide variety of conditions, but also for developing and testing new evolutionary models. Pyvolve is an open-source project under a FreeBSD license, and it is available for download, along with a detailed user-manual and example scripts, from http://github.com/sjspielman/pyvolve.

Mixture models of nucleotide sequence evolution that account for heterogeneity in the substitution process across sites and across lineages.

Molecular phylogenetic studies of homologous sequences of nucleotides often assume that the underlying evolutionary process was globally stationary, reversible, and homogeneous (SRH), and that a model of evolution with one or more site-specific and time-reversible rate matrices (e.g., the GTR rate matrix) is enough to accurately model the evolution of data over the whole tree. However, an increasing body of data suggests that evolution under these conditions is an exception, rather than the norm. To address this issue, several non-SRH models of molecular evolution have been proposed, but they either ignore heterogeneity in the substitution process across sites (HAS) or assume it can be modeled accurately using the distribution. As an alternative to these models of evolution, we introduce a family of mixture models that approximate HAS without the assumption of an underlying predefined statistical distribution. This family of mixture models is combined with non-SRH models of evolution that account for heterogeneity in the substitution process across lineages (HAL). We also present two algorithms for searching model space and identifying an optimal model of evolution that is less likely to over- or underparameterize the data. The performance of the two new algorithms was evaluated using alignments of nucleotides with 10 000 sites simulated under complex non-SRH conditions on a 25-tipped tree. The algorithms were found to be very successful, identifying the correct HAL model with a 75% success rate (the average success rate for assigning rate matrices to the tree's 48 edges was 99.25%) and, for the correct HAL model, identifying the correct HAS model with a 98% success rate. Finally, parameter estimates obtained under the correct HAL-HAS model were found to be accurate and precise. The merits of our new algorithms were illustrated with an analysis of 42 337 second codon sites extracted from a concatenation of 106 alignments of orthologous genes encoded by the nuclear genomes of Saccharomyces cerevisiae, S. paradoxus, S. mikatae, S. kudriavzevii, S. castellii, S. kluyveri, S. bayanus, and Candida albicans. Our results show that second codon sites in the ancestral genome of these species contained 49.1% invariable sites, 39.6% variable sites belonging to one rate category (V1), and 11.3% variable sites belonging to a second rate category (V2). The ancestral nucleotide content was found to differ markedly across these three sets of sites, and the evolutionary processes operating at the variable sites were found to be non-SRH and best modeled by a combination of eight edge-specific rate matrices (four for V1 and four for V2). The number of substitutions per site at the variable sites also differed markedly, with sites belonging to V1 evolving slower than those belonging to V2 along the lineages separating the seven species of Saccharomyces. Finally, sites belonging to V1 appeared to have ceased evolving along the lineages separating S. cerevisiae, S. paradoxus, S. mikatae, S. kudriavzevii, and S. bayanus, implying that they might have become so selectively constrained that they could be considered invariable sites in these species.

Molecular evolution of the pigmentation gene melanocortin-1 receptor in rodents

Adaptive variation in the melanocortin 1-receptor gene (MC1R), a key locus in melanogenesis, has been identified in some species of rodents. However, in others, MC1R has no causative role in pigmentation phenotypes despite their coat color variation. In this study, we characterized the rates and patterns of MC1R nucleotide and amino acid sequence evolution and, particularly, selective pressures in the separated domains of the protein using a comparative analysis of 43 species representing three major lineages of rodents with variable coat colors. We found high amino acid variation (44% of sites) throughout the protein. Most substitutions were observed in extracellular and transmembrane domains; the intracellular segment was conserved across species. Pairwise non-synonymous substitutions did not vary significantly in different domains among the rodent lineages - i.e., variation was not associated with phylogenetic distance. Phylogeny-based likelihood analysis suggested that purifying selection has mostly shaped the evolutionary course of MC1R. However, a high proportion of sites (27%) were under relaxation of functional constraints (ω = 0.38), and four sites (3, 14, 26, and 251) clearly evolved under positive selection (ω ≅ 2.9). Thus, our data indicate a high proportion of sites evolving under relaxed evolutionary constraints, which might indicate the evolvability of the system in the generation of adaptive changes in specific taxa in rodent lineages.

Parallel Evolution of Auditory Genes for Echolocation in Bats and Toothed Whales

The ability of bats and toothed whales to echolocate is a remarkable case of convergent evolution. Previous genetic studies have documented parallel evolution of nucleotide sequences in Prestin and KCNQ4, both of which are associated with voltage motility during the cochlear amplification of signals. Echolocation involves complex mechanisms. The most important factors include cochlear amplification, nerve transmission, and signal re-coding. Herein, we screen three genes that play different roles in this auditory system. Cadherin 23 (Cdh23) and its ligand, protocadherin 15 (Pcdh15), are essential for bundling motility in the sensory hair. Otoferlin (Otof) responds to nerve signal transmission in the auditory inner hair cell. Signals of parallel evolution occur in all three genes in the three groups of echolocators—two groups of bats (Yangochiroptera and Rhinolophoidea) plus the dolphin. Significant signals of positive selection also occur in Cdh23 in the Rhinolophoidea and dolphin, and Pcdh15 in Yangochiroptera. In addition, adult echolocating bats have higher levels of Otof expression in the auditory cortex than do their embryos and non-echolocation bats. Cdh23 and Pcdh15 encode the upper and lower parts of tip-links, and both genes show signals of convergent evolution and positive selection in echolocators, implying that they may co-evolve to optimize cochlear amplification. Convergent evolution and expression patterns of Otof suggest the potential role of nerve and brain in echolocation. Our synthesis of gene sequence and gene expression analyses reveals that positive selection, parallel evolution, and perhaps co-evolution and gene expression affect multiple hearing genes that play different roles in audition, including voltage and bundle motility in cochlear amplification, nerve transmission, and brain function.

Two Stationary Nonhomogeneous Markov Models of Nucleotide Sequence Evolution

The general Markov model (GMM) of nucleotide substitution does not assume the evolutionary process to be stationary, reversible, or homogeneous. The GMM can be simplified by assuming the evolutionary process to be stationary. A stationary GMM is appropriate for analyses of phylogenetic data sets that are compositionally homogeneous; a data set is considered to be compositionally homogeneous if a statistical test does not detect significant differences in the marginal distributions of the sequences. Though the general time-reversible (GTR) model assumes stationarity, it also assumes reversibility and homogeneity. We propose two new stationary and nonhomogeneous models--one constrains the GMM to be reversible, whereas the other does not. The two models, coupled with the GTR model, comprise a set of nested models that can be used to test the assumptions of reversibility and homogeneity for stationary processes. The two models are extended to incorporate invariable sites and used to analyze a seven-taxon hominoid data set that displays compositional homogeneity. We show that within the class of stationary models, a nonhomogeneous model fits the hominoid data better than the GTR model. We note that if one considers a wider set of models that are not constrained to be stationary, then an even better fit can be obtained for the hominoid data. However, the methods for reducing model complexity from an extremely large set of nonstationary models are yet to be developed.

The re-emergence of H1N1 influenza virus in 1977: a cautionary tale for estimating divergence times using biologically unrealistic sampling dates.

In 1977, H1N1 influenza A virus reappeared after a 20-year absence. Genetic analysis indicated that this strain was missing decades of nucleotide sequence evolution, suggesting an accidental release of a frozen laboratory strain into the general population. Recently, this strain and its descendants were included in an analysis attempting to date the origin of pandemic influenza virus without accounting for the missing decades of evolution. Here, we investigated the effect of using viral isolates with biologically unrealistic sampling dates on estimates of divergence dates. Not accounting for missing sequence evolution produced biased results and increased the variance of date estimates of the most recent common ancestor of the re-emergent lineages and across the entire phylogeny. Reanalysis of the H1N1 sequences excluding isolates with unrealistic sampling dates indicates that the 1977 re-emergent lineage was circulating for approximately one year before detection, making it difficult to determine the geographic source of reintroduction. We suggest that a new method is needed to account for viral isolates with unrealistic sampling dates.

Phylogenetic Tree Reconstruction Accuracy and Model Fit when Proportions of Variable Sites Change across the Tree

Commonly used phylogenetic models assume a homogeneous process through time in all parts of the tree. However, it is known that these models can be too simplistic as they do not account for nonhomogeneous lineage-specific properties. In particular, it is now widely recognized that as constraints on sequences evolve, the proportion and positions of variable sites can vary between lineages causing heterotachy. The extent to which this model misspecification affects tree reconstruction is still unknown. Here, we evaluate the effect of changes in the proportions and positions of variable sites on model fit and tree estimation. We consider 5 current models of nucleotide sequence evolution in a Bayesian Markov chain Monte Carlo framework as well as maximum parsimony (MP). We show that for a tree with 4 lineages where 2 nonsister taxa undergo a change in the proportion of variable sites tree reconstruction under the best-fitting model, which is chosen using a relative test, often results in the wrong tree. In this case, we found that an absolute test of model fit is a better predictor of tree estimation accuracy. We also found further evidence that MP is not immune to heterotachy. In addition, we show that increased sampling of taxa that have undergone a change in proportion and positions of variable sites is critical for accurate tree reconstruction.

On Theoretical Models of Gene Expression Evolution with Random Genetic Drift and Natural Selection

BackgroundThe relative contributions of natural selection and random genetic drift are a major source of debate in the study of gene expression evolution, which is hypothesized to serve as a bridge from molecular to phenotypic evolution. It has been suggested that the conflict between views is caused by the lack of a definite model of the neutral hypothesis, which can describe the long-run behavior of evolutionary change in mRNA abundance. Therefore previous studies have used inadequate analogies with the neutral prediction of other phenomena, such as amino acid or nucleotide sequence evolution, as the null hypothesis of their statistical inference.Methodology/Principal FindingsIn this study, we introduced two novel theoretical models, one based on neutral drift and the other assuming natural selection, by focusing on a common property of the distribution of mRNA abundance among a variety of eukaryotic cells, which reflects the result of long-term evolution. Our results demonstrated that (1) our models can reproduce two independently found phenomena simultaneously: the time development of gene expression divergence and Zipf's law of the transcriptome; (2) cytological constraints can be explicitly formulated to describe long-term evolution; (3) the model assuming that natural selection optimized relative mRNA abundance was more consistent with previously published observations than the model of optimized absolute mRNA abundances.Conclusions/SignificanceThe models introduced in this study give a formulation of evolutionary change in the mRNA abundance of each gene as a stochastic process, on the basis of previously published observations. This model provides a foundation for interpreting observed data in studies of gene expression evolution, including identifying an adequate time scale for discriminating the effect of natural selection from that of random genetic drift of selectively neutral variations.

Expansion of signal pathways in the ectomycorrhizal fungus Laccaria bicolor– evolution of nucleotide sequences and expression patterns in families of protein kinases and RAS small GTPases

The ectomycorrhizal fungus Laccaria bicolor has the largest genome of all fungi yet sequenced. The large genome size is partly a result of an expansion of gene family sizes. Among the largest gene families are protein kinases and RAS small guanosine triphosphatases (GTPases), which are key components of signal transduction pathways. Comparative genomics and phylogenetic analyses were used to examine the evolution of the two largest families of protein kinases and RAS small GTPases in L. bicolor. Expression levels in various tissues and growth conditions were inferred from microarray data. The two families possessed a large number of young duplicates (paralogs) that had arisen in the Laccaria lineage following the separation from the saprophyte Coprinopsis cinerea. The protein kinase paralogs were dispersed in many small clades and the majority were pseudogenes. By contrast, the RAS paralogs were found in three large groups of RAS1-, RAS2- and RHO1-like GTPases with few pseudogenes. Duplicates of protein kinases and RAS small GTPase have either retained, gained or lost motifs found in the coding regions of their ancestors. Frequent outcomes during evolution were the formation of pseudogenes (nonfunctionalization) or proteins with novel structures and expression patterns (neofunctionalization).

Gibbs field approach for evolutionary analysis of regulatory signal of gene expression

We propose a new approach to modeling a nucleotide sequence evolution subject to constraints on the secondary structure. The approach is based on the problem of optimizing a functional that involves both standard evolution of the primary structure and a condition of secondary structure conservation. We discuss simulation results in the example of evolution in the case of classical attenuation regulation.

Spatial and Temporal Heterogeneity in Nucleotide Sequence Evolution

Models of nucleotide substitution make many simplifying assumptions about the evolutionary process, including that the same process acts on all sites in an alignment and on all branches on the phylogenetic tree. Many studies have shown that in reality the substitution process is heterogeneous and that this variability can introduce systematic errors into many forms of phylogenetic analyses. I propose a new rigorous approach for describing heterogeneity called a temporal hidden Markov model (THMM), which can distinguish between among site (spatial) heterogeneity and among lineage (temporal) heterogeneity. Several versions of the THMM are applied to 16 sets of aligned sequences to quantitatively assess the different forms of heterogeneity acting within them. The most general THMM provides the best fit in all the data sets examined, providing strong evidence of pervasive heterogeneity during evolution. Investigating individual forms of heterogeneity provides further insights. In agreement with previous studies, spatial rate heterogeneity (rates across sites [RAS]) is inferred to be the single most prevalent form of heterogeneity. Interestingly, RAS appears so dominant that failure to independently include it in the THMM masks other forms of heterogeneity, particularly temporal heterogeneity. Incorporating RAS into the THMM reveals substantial temporal and spatial heterogeneity in nucleotide composition and bias toward transition substitution in all alignments examined, although the relative importance of different forms of heterogeneity varies between data sets. Furthermore, the improvements in model fit observed by adding complexity to the model suggest that the THMMs used in this study do not capture all the evolutionary heterogeneity occurring in the data. These observations all indicate that current tests may consistently underestimate the degree of temporal heterogeneity occurring in data. Finally, there is a weak link between the amount of heterogeneity detected and the level of divergence between the sequences, suggesting that variability in the evolutionary process will be a particular problem for deep phylogeny.

Modelling the evolution of protein coding sequences sampled from Measurably Evolving Populations.

Models of nucleotide or amino acid sequence evolution that implement homogeneous and stationary Markov processes of substitutions are mathematically convenient but are unlikely to represent the true complexity of evolution. With the large amounts of data that next generation sequencing promises, appropriate models of evolution are important, particularly when data are collected from ancient and sub-fossil remains, where changes in evolutionary parameters are the norm and not the exception. In this paper, we describe a new codon-based model of evolution that applies to Measurably Evolving Populations (MEPs). A MEP is defined as a population from which it is possible to detect a statistically significant accumulation of substitutions when sequences are obtained at different times. The new model of codon evolution permits changes to the substitution process, including changes to the intensity of selection and the proportions of sites undergoing different selective pressures. In our serial model of codon evolution, changes in the selective regime occur simultaneously across all lineages. Different regions of the protein may also evolve under distinct selective patterns. We illustrate the application of the new model to a dataset of HIV-1 sequences obtained from an infected individual before and after the commencement of antiretroviral therapy.

Evolution of nucleotide sequences and expression patterns of hydrophobin genes in the ectomycorrhizal fungusPaxillus involutus

Hydrophobins are small, secreted proteins that play important roles in the development of pathogenic and symbiotic fungi. Evolutionary mechanisms generating sequence and expression divergence among members in hydrophobin gene families are largely unknown. Seven hydrophobin (hyd) genes and one hyd pseudogene were isolated from strains of the ectomycorrhizal fungus Paxillus involutus. Sequences were analysed using phylogenetic methods. Expression profiles were inferred from microarray experiments. The hyd genes included both young (recently diverged) and old duplicates. Some young hyd genes exhibited an initial phase of enhanced sequence evolution owing to relaxed or positive selection. There was no significant association between sequence divergence and variation in expression levels. However, three hyd genes displayed a shift in the expression levels or an altered tissue specificity following duplication. The Paxillus hyd genes evolve according to the so-called birth-and-death model in which some duplicates are maintained for a long time, whereas others are inactivated through mutations. The role of subfunctionalization and/or neofunctionalization for preserving the hyd duplicates in the genome is discussed.

In silico sequence evolution with site-specific interactions along phylogenetic trees

A biological sequence usually has many sites whose evolution depends on other positions of the sequence, but this is not accounted for by commonly used models of sequence evolution. Here we introduce a Markov model of nucleotide sequence evolution in which the instantaneous substitution rate at a site depends on the states of other sites. Based on the concept of neighbourhood systems, our model represents a universal description of arbitrarily complex dependencies among sites. We show how to define complex models for some illustrative examples and demonstrate that our method provides a versatile resource for simulations of sequence evolution with site-specific interactions along a tree. For example, we are able to simulate the evolution of RNA taking into account both secondary structure as well as pseudoknots and other tertiary interactions. To this end, we have developed a program Simulating Site-Specific Interactions (SISSI) that simulates evolution of a nucleotide sequence along a phylogenetic tree incorporating user defined site-specific interactions. Furthermore, our method allows to simulate more complex interactions among nucleotide and other character based sequences. We implemented our method in an ANSI C program SISSI which runs on UNIX/Linux, Windows and Mac OS systems, including Mac OS X. SISSI is available at http://www.bi.uni-duesseldorf.de/software/sissi/