The slow road to the eukaryotic genome

The eukaryotic genome is a mosaic of eubacterial and archaeal genes in addition to those unique to itself. The mosaic may have arisen as the result of two prokaryotes merging their genomes, or from genes acquired from an endosymbiont of eubacterial origin. A third possibility is that the eukaryotic genome arose from successive events of lateral gene transfer over long periods of time. This theory does not exclude the endosymbiont, but questions whether it is necessary to explain the peculiar set of eukaryotic genes. We use phylogenetic studies and reconstructions of ancestral first appearances of genes on the prokaryotic phylogeny to assess evidence for the lateral gene transfer scenario. We find that phylogenies advanced to support fusion can also arise from a succession of lateral gene transfer events. Our reconstructions of ancestral first appearances of genes reveal that the various genes that make up the eukaryotic mosaic arose at different times and in diverse lineages on the prokaryotic tree, and were not available in a single lineage. Successive events of lateral gene transfer can explain the unusual mosaic structure of the eukaryotic genome, with its content linked to the immediate adaptive value of the genes its acquired. Progress in understanding eukaryotes may come from identifying ancestral features such as the eukaryotic splicesome that could explain why this lineage invaded, or created, the eukaryoticniche.

Detecting the node-density artifact in phylogeny reconstruction

The node-density effect is an artifact of phylogeny reconstruction that can cause branch lengths to be underestimated in areas of the tree with fewer taxa. Webster, Payne, and Pagel (2003, Science 301:478) introduced a statistical procedure (the "delta" test) to detect this artifact, and here we report the results of computer simulations that examine the test's performance. In a sample of 50,000 random data sets, we find that the delta test detects the artifact in 94.4% of cases in which it is present. When the artifact is not present (n = 10,000 simulated data sets) the test showed a type I error rate of approximately 1.69%, incorrectly reporting the artifact in 169 data sets. Three measures of tree shape or "balance" failed to predict the size of the node-density effect. This may reflect the relative homogeneity of our randomly generated topologies, but emphasizes that nearly any topology can suffer from the artifact, the effect not being confined only to highly unevenly sampled or otherwise imbalanced trees. The ability to screen phylogenies for the node-density artifact is important for phylogenetic inference and for researchers using phylogenetic trees to infer evolutionary processes, including their use in molecular clock dating. [Delta test; molecular clock; molecular evolution; node-density effect; phylogenetic reconstruction; speciation; simulation.]

The genome of Rhizobium leguminosarum has recognizable core and accessory components

Background: Rhizobium leguminosarum is an alpha-proteobacterial N-2-fixing symbiont of legumes that has been the subject of more than a thousand publications. Genes for the symbiotic interaction with plants are well studied, but the adaptations that allow survival and growth in the soil environment are poorly understood. We have sequenced the genome of R. leguminosarum biovar viciae strain 3841. Results: The 7.75 Mb genome comprises a circular chromosome and six circular plasmids, with 61% G+C overall. All three rRNA operons and 52 tRNA genes are on the chromosome; essential protein-encoding genes are largely chromosomal, but most functional classes occur on plasmids as well. Of the 7,263 protein-encoding genes, 2,056 had orthologs in each of three related genomes ( Agrobacterium tumefaciens, Sinorhizobium meliloti, and Mesorhizobium loti), and these genes were overrepresented in the chromosome and had above average G+C. Most supported the rRNA-based phylogeny, confirming A. tumefaciens to be the closest among these relatives, but 347 genes were incompatible with this phylogeny; these were scattered throughout the genome but were over-represented on the plasmids. An unexpectedly large number of genes were shared by all three rhizobia but were missing from A. tumefaciens. Conclusion: Overall, the genome can be considered to have two main components: a 'core', which is higher in G+C, is mostly chromosomal, is shared with related organisms, and has a consistent phylogeny; and an 'accessory' component, which is sporadic in distribution, lower in G+C, and located on the plasmids and chromosomal islands. The accessory genome has a different nucleotide composition from the core despite a long history of coexistence.

Identifying adaptive genetic divergence among populations from genome scans

The identification of signatures of natural selection in genomic surveys has become an area of intense research, stimulated by the increasing ease with which genetic markers can be typed. Loci identified as subject to selection may be functionally important, and hence (weak) candidates for involvement in disease causation. They can also be useful in determining the adaptive differentiation of populations, and exploring hypotheses about speciation. Adaptive differentiation has traditionally been identified from differences in allele frequencies among different populations, summarised by an estimate of F-ST. Low outliers relative to an appropriate neutral population-genetics model indicate loci subject to balancing selection, whereas high outliers suggest adaptive (directional) selection. However, the problem of identifying statistically significant departures from neutrality is complicated by confounding effects on the distribution of F-ST estimates, and current methods have not yet been tested in large-scale simulation experiments. Here, we simulate data from a structured population at many unlinked, diallelic loci that are predominantly neutral but with some loci subject to adaptive or balancing selection. We develop a hierarchical-Bayesian method, implemented via Markov chain Monte Carlo (MCMC), and assess its performance in distinguishing the loci simulated under selection from the neutral loci. We also compare this performance with that of a frequentist method, based on moment-based estimates of F-ST. We find that both methods can identify loci subject to adaptive selection when the selection coefficient is at least five times the migration rate. Neither method could reliably distinguish loci under balancing selection in our simulations, even when the selection coefficient is twenty times the migration rate.

A phylogenetic mixture model for detecting pattern-heterogeneity in gene sequence or character-state data

We describe a general likelihood-based 'mixture model' for inferring phylogenetic trees from gene-sequence or other character-state data. The model accommodates cases in which different sites in the alignment evolve in qualitatively distinct ways, but does not require prior knowledge of these patterns or partitioning of the data. We call this qualitative variability in the pattern of evolution across sites "pattern-heterogeneity" to distinguish it from both a homogenous process of evolution and from one characterized principally by differences in rates of evolution. We present studies to show that the model correctly retrieves the signals of pattern-heterogeneity from simulated gene-sequence data, and we apply the method to protein-coding genes and to a ribosomal 12S data set. The mixture model outperforms conventional partitioning in both these data sets. We implement the mixture model such that it can simultaneously detect rate- and pattern-heterogeneity. The model simplifies to a homogeneous model or a rate- variability model as special cases, and therefore always performs at least as well as these two approaches, and often considerably improves upon them. We make the model available within a Bayesian Markov-chain Monte Carlo framework for phylogenetic inference, as an easy-to-use computer program.

Phylogenomics of eukaryotes: Impact of missing data on large alignments

Resolving the relationships between Metazoa and other eukaryotic groups as well as between metazoan phyla is central to the understanding of the origin and evolution of animals. The current view is based on limited data sets, either a single gene with many species (e.g., ribosomal RNA) or many genes but with only a few species. Because a reliable phylogenetic inference simultaneously requires numerous genes and numerous species, we assembled a very large data set containing 129 orthologous proteins (similar to30,000 aligned amino acid positions) for 36 eukaryotic species. Included in the alignments are data from the choanoflagellate Monosiga ovata, obtained through the sequencing of about 1,000 cDNAs. We provide conclusive support for choanoflagellates as the closest relative of animals and for fungi as the second closest. The monophyly of Plantae and chromalveolates was recovered but without strong statistical support. Within animals, in contrast to the monophyly of Coelomata observed in several recent large-scale analyses, we recovered a paraphyletic Coelamata, with nematodes and platyhelminths nested within. To include a diverse sample of organisms, data from EST projects were used for several species, resulting in a large amount of missing data in our alignment (about 25%). By using different approaches, we verify that the inferred phylogeny is not sensitive to these missing data. Therefore, this large data set provides a reliable phylogenetic framework for studying eukaryotic and animal evolution and will be easily extendable when large amounts of sequence information become available from a broader taxonomic range.

Use of 16S rRNA-targeted oligonucleotide probes to investigate function and phylogeny of sulphate-reducing bacteria and methanogenic archaea in a UK estuary

Sulphate-reducing bacteria (SRB) and methanogenic archaea (MA) are important anaerobic terminal oxidisers of organic matter. However, we have little knowledge about the distribution and types of SRB and MA in the environment or the functional role they play in situ. Here we have utilised sediment slurry microcosms amended with ecologically significant substrates, including acetate and hydrogen, and specific functional inhibitors, to identify the important SRB and MA groups in two contrasting sites on a UK estuary. Substrate and inhibitor additions had significant effects on methane production and on acetate and sulphate consumption in the slurries. By using specific 16S-targeted oligonucleotide probes we were able to link specific SRB and MA groups to the use of the added substrates. Acetate consumption in the freshwater-dominated sediments was mediated by Methanosarcinales under low-sulphate conditions and Desulfobacter under the high-sulphate conditions that simulated a tidal incursion. In the marine-dominated sediments, acetate consumption was linked to Desulfobacter. Addition of trimethylamine, a non-competitive substrate for methanogenesis, led to a large increase in Methanosarcinales signal in marine slurries. Desulfobulbus was linked to non-sulphate-dependent H-2 consumption in the freshwater sediments. The addition of sulphate to freshwater sediments inhibited methane production and reduced signal from probes targeted to Methanosarcinales and Methanomicrobiales, while the addition of molybdate to marine sediments inhibited Desulfobulbus and Desulfobacterium. These data complement our understanding of the ecophysiology of the organisms detected and make a firm connection between the capabilities of species, as observed in the laboratory, to their roles in the environment. (C) 2003 Federation of European Microbiological Societies. Published by Elsevier Science B.V. All rights reserved.

Space partitioning for scalable K-means

K-Means is a popular clustering algorithm which adopts an iterative refinement procedure to determine data partitions and to compute their associated centres of mass, called centroids. The straightforward implementation of the algorithm is often referred to as `brute force' since it computes a proximity measure from each data point to each centroid at every iteration of the K-Means process. Efficient implementations of the K-Means algorithm have been predominantly based on multi-dimensional binary search trees (KD-Trees). A combination of an efficient data structure and geometrical constraints allow to reduce the number of distance computations required at each iteration. In this work we present a general space partitioning approach for improving the efficiency and the scalability of the K-Means algorithm. We propose to adopt approximate hierarchical clustering methods to generate binary space partitioning trees in contrast to KD-Trees. In the experimental analysis, we have tested the performance of the proposed Binary Space Partitioning K-Means (BSP-KM) when a divisive clustering algorithm is used. We have carried out extensive experimental tests to compare the proposed approach to the one based on KD-Trees (KD-KM) in a wide range of the parameters space. BSP-KM is more scalable than KDKM, while keeping the deterministic nature of the `brute force' algorithm. In particular, the proposed space partitioning approach has shown to overcome the well-known limitation of KD-Trees in high-dimensional spaces and can also be adopted to improve the efficiency of other algorithms in which KD-Trees have been used.

Peroxy radical partitioning during the AMMA radical intercomparison exercise

Peroxy radicals were measured onboard two scientific aircrafts during the AMMA (African Monsoon Multidisciplinary Analysis) campaign in summer 2006. This paper reports results from the flight on 16 August 2006 during which measurements of HO2 by laser induced fluorescence spectroscopy at low pressure (LIF-FAGE) and total peroxy radicals (RO2* = HO2+ΣRO2, R = organic chain) by two similar instruments based on the peroxy radical chemical amplification (PeRCA) technique were subject of a blind intercomparison. The German DLR-Falcon and the British FAAM-BAe-146 flew wing tip to wing tip for about 30 min making concurrent measurements on 2 horizontal level runs at 697 and 485 hPa over the same geographical area in Burkina Faso. A full set of supporting measurements comprising photolysis frequencies, and relevant trace gases like CO, NO, NO2, NOy, O3 and a wider range of VOCs were collected simultaneously. Results are discussed on the basis of the characteristics and limitations of the different instruments used. Generally, no data bias are identified and the RO2* data available agree quite reasonably within the instrumental errors. The [RO2*]/[HO2] ratios, which vary between 1:1 and 3:1, as well as the peroxy radical variability, concur with variations in photolysis rates and in other potential radical precursors. Model results provide additional information about dominant radical formation and loss processes.

Biomass partitioning and growth efficiency in four naturally regenerated forest tree species

Current forest growth models and yield tables are almost exclusively based on data from mature trees, reducing their applicability to young and developing stands. To address this gap, young European beech, sessile oak, Scots pine and Norway spruce trees approximately 0 to 10 years old were destructively sampled in a range of naturally regenerated forest stands in Central Europe. Diameter at base and height were first measured in situ for up to 175 individuals per species. Subsequently, the trees were excavated and dry biomass of foliage, branches, stems and roots was measured. Allometric relations were then used to calculate biomass allocation coefficients (BAC) and growth efficiency (GE) patterns in young trees. We found large differences in BAC and GE between broadleaves and conifers, but also between species within these categories. Both BAC and GE are strongly age-specific in young trees, their rapidly changing values reflecting different growth strategies in the earliest stages of growth. We show that linear relationships describing biomass allocation in older trees are not applicable in young trees. To accurately predict forest biomass and carbon stocks, forest growth models need to include species and age specific parameters of biomass allocation patterns.

Genome sequences of the human body louse and its primary endosymbiont provide insights into the permanent parasitic lifestyle

As an obligatory parasite of humans, the body louse (Pediculus humanus humanus) is an important vector for human diseases, including epidemic typhus, relapsing fever, and trench fever. Here, we present genome sequences of the body louse and its primary bacterial endosymbiont Candidatus Riesia pediculicola. The body louse has the smallest known insect genome, spanning 108 Mb. Despite its status as an obligate parasite, it retains a remarkably complete basal insect repertoire of 10,773 protein-coding genes and 57 microRNAs. Representing hemimetabolous insects, the genome of the body louse thus provides a reference for studies of holometabolous insects. Compared with other insect genomes, the body louse genome contains significantly fewer genes associated with environmental sensing and response, including odorant and gustatory receptors and detoxifying enzymes. The unique architecture of the 18 minicircular mitochondrial chromosomes of the body louse may be linked to the loss of the gene encoding the mitochondrial single-stranded DNA binding protein. The genome of the obligatory louse endosymbiont Candidatus Riesia pediculicola encodes less than 600 genes on a short, linear chromosome and a circular plasmid. The plasmid harbors a unique arrangement of genes required for the synthesis of pantothenate, an essential vitamin deficient in the louse diet. The human body louse, its primary endosymbiont, and the bacterial pathogens that it vectors all possess genomes reduced in size compared with their free-living close relatives. Thus, the body louse genome project offers unique information and tools to use in advancing understanding of coevolution among vectors, symbionts, and pathogens.

Phylogeny and metabolic scaling in mammals

The scaling of metabolic rates to body size is widely considered to be of great biological and ecological importance, and much attention has been devoted to determining its theoretical and empirical value. Most debate centers on whether the underlying power law describing metabolic rates is 2/3 (as predicted by scaling of surface area/volume relationships) or 3/4 ("Kleiber's law"). Although recent evidence suggests that empirically derived exponents vary among clades with radically different metabolic strategies, such as ectotherms and endotherms, models, such as the metabolic theory of ecology, depend on the assumption that there is at least a predominant, if not universal, metabolic scaling exponent. Most analyses claimed to support the predictions of general models, however, failed to control for phylogeny. We used phylogenetic generalized least-squares models to estimate allometric slopes for both basal metabolic rate (BMR) and field metabolic rate (FMR) in mammals. Metabolic rate scaling conformed to no single theoretical prediction, but varied significantly among phylogenetic lineages. In some lineages we found a 3/4 exponent, in others a 2/3 exponent, and in yet others exponents differed significantly from both theoretical values. Analysis of the phylogenetic signal in the data indicated that the assumptions of neither species-level analysis nor independent contrasts were met. Analyses that assumed no phylogenetic signal in the data (species-level analysis) or a strong phylogenetic signal (independent contrasts), therefore, returned estimates of allometric slopes that were erroneous in 30% and 50% of cases, respectively. Hence, quantitative estimation of the phylogenetic signal is essential for determining scaling exponents. The lack of evidence for a predominant scaling exponent in these analyses suggests that general models of metabolic scaling, and macro-ecological theories that depend on them, have little explanatory power.

Comparisons of host mitochondrial, nuclear and endosymbiont bacterial genes reveal cryptic fig wasp species and the effects of Wolbachia on host mtDNA evolution and diversity

Background Figs and fig-pollinating wasp species usually display a highly specific one-to-one association. However, more and more studies have revealed that the "one-to-one" rule has been broken. Co-pollinators have been reported, but we do not yet know how they evolve. They may evolve from insect speciation induced or facilitated by Wolbachia which can manipulate host reproduction and induce reproductive isolation. In addition, Wolbachia can affect host mitochondrial DNA evolution, because of the linkage between Wolbachia and associated mitochondrial haplotypes, and thus confound host phylogeny based on mtDNA. Previous research has shown that fig wasps have the highest incidence of Wolbachia infection in all insect taxa, and Wolbachia may have great influence on fig wasp biology. Therefore, we look forward to understanding the influence of Wolbachia on mitochondrial DNA evolution and speciation in fig wasps. Results We surveyed 76 pollinator wasp specimens from nine Ficus microcarpa trees each growing at a different location in Hainan and Fujian Provinces, China. We found that all wasps were morphologically identified as Eupristina verticillata, but diverged into three clades with 4.22-5.28% mtDNA divergence and 2.29-20.72% nuclear gene divergence. We also found very strong concordance between E. verticillata clades and Wolbachia infection status, and the predicted effects of Wolbachia on both mtDNA diversity and evolution by decreasing mitochondrial haplotypes. Conclusions Our study reveals that the pollinating wasp E. verticillata on F. microcarpa has diverged into three cryptic species, and Wolbachia may have a role in this divergence. The results also indicate that Wolbachia strains infecting E. verticillata have likely resulted in selective sweeps on host mitochondrial DNA.

A genetic linkage map of an apple rootstock progeny anchored to the Malus genome sequence

An apple rootstock progeny raised from the cross between the very dwarfing ‘M.27’ and the more vigorous ‘M.116’ (‘M.M.106’ × ‘M.27’) was used for the construction of a linkage map comprising a total of 324 loci: 252 previously mapped SSRs, 71 newly characterised or previously unmapped SSR loci (including 36 amplified by 33 out of the 35 novel markers reported here), and the self-incompatibility locus. The map spanned the 17 linkage groups (LG) expected for apple covering a genetic distance of 1,229.5 cM, an estimated 91% of the Malus genome. Linkage groups were well populated and, although marker density ranged from 2.3 to 6.2 cM/SSR, just 15 gaps of more than 15 cM were observed. Moreover, only 17.5% of markers displayed segregation distortion and, unsurprisingly in a semi-compatible backcross, distortion was particularly pronounced surrounding the self-incompatibility locus (S) at the bottom of LG17. DNA sequences of 273 SSR markers and the S locus, representing a total of 314 loci in this investigation, were used to anchor to the ‘Golden Delicious’ genome sequence. More than 260 of these loci were located on the expected pseudo-chromosome on the ‘Golden Delicious’ genome or on its homeologous pseudo-chromosome. In total, 282.4 Mbp of sequence from 142 genome sequence scaffolds of the Malus genome were anchored to the ‘M.27’ × ‘M.116’ map, providing an interface between the marker data and the underlying genome sequence. This will be exploited for the identification of genes responsible for traits of agronomic importance such as dwarfing and water use efficiency.

Genome scale reconstruction of a Salmonella metabolic model comparison of similarity and differences with a commensal Escherichia coli strain

Salmonella are closely related to commensal Escherichia coli but have gained virulence factors enabling them to behave as enteric pathogens. Less well studied are the similarities and differences that exist between the metabolic properties of these organisms that may contribute toward niche adaptation of Salmonella pathogens. To address this, we have constructed a genome scale Salmonella metabolic model (iMA945). The model comprises 945 open reading frames or genes, 1964 reactions, and 1036 metabolites. There was significant overlap with genes present in E. coli MG1655 model iAF1260. In silico growth predictions were simulated using the model on different carbon, nitrogen, phosphorous, and sulfur sources. These were compared with substrate utilization data gathered from high throughput phenotyping microarrays revealing good agreement. Of the compounds tested, the majority were utilizable by both Salmonella and E. coli. Nevertheless a number of differences were identified both between Salmonella and E. coli and also within the Salmonella strains included. These differences provide valuable insight into differences between a commensal and a closely related pathogen and within different pathogenic strains opening new avenues for future explorations.