Phylogenetic mixture models can reduce node-density artifacts

We investigate the performance of phylogenetic mixture models in reducing a well-known and pervasive artifact of phylogenetic inference known as the node-density effect, comparing them to partitioned analyses of the same data. The node-density effect refers to the tendency for the amount of evolutionary change in longer branches of phylogenies to be underestimated compared to that in regions of the tree where there are more nodes and thus branches are typically shorter. Mixture models allow more than one model of sequence evolution to describe the sites in an alignment without prior knowledge of the evolutionary processes that characterize the data or how they correspond to different sites. If multiple evolutionary patterns are common in sequence evolution, mixture models may be capable of reducing node-density effects by characterizing the evolutionary processes more accurately. In gene-sequence alignments simulated to have heterogeneous patterns of evolution, we find that mixture models can reduce node-density effects to negligible levels or remove them altogether, performing as well as partitioned analyses based on the known simulated patterns. The mixture models achieve this without knowledge of the patterns that generated the data and even in some cases without specifying the full or true model of sequence evolution known to underlie the data. The latter result is especially important in real applications, as the true model of evolution is seldom known. We find the same patterns of results for two real data sets with evidence of complex patterns of sequence evolution: mixture models substantially reduced node-density effects and returned better likelihoods compared to partitioning models specifically fitted to these data. We suggest that the presence of more than one pattern of evolution in the data is a common source of error in phylogenetic inference and that mixture models can often detect these patterns even without prior knowledge of their presence in the data. Routine use of mixture models alongside other approaches to phylogenetic inference may often reveal hidden or unexpected patterns of sequence evolution and can improve phylogenetic inference.

Phylogenetic analysis of the pPT23A plasmid family of Pseudomonas syringae

The pPT23A plasmid family of Pseudomonas syringae contains members that contribute to the ecological and pathogenic fitness of their P. syringae hosts. In an effort to understand the evolution of these plasmids and their hosts, we undertook a comparative analysis of the phylogeny of plasmid genes and that of conserved chromosomal genes from P. syringae. In total, comparative sequence and phylogenetic analyses were done utilizing 47 pPT23A family plasmids (PFPs) from 16 pathovars belonging to six genomospecies. Our results showed that the plasmid replication gene (repA), the only gene currently known to be distributed among all the PFPs, had a phylogeny that was distinct from that of the P. syringae hosts of these plasmids and from those of other individual genes on PFPs. The phylogenies of two housekeeping chromosomal genes, those for DNA gyrase B subunit (gyrB) and primary sigma factor (rpoD), however, were strongly associated with genomospecies of P. syringae. Based on the results from this study, we conclude that the pPT23A plasmid family represents a dynamic genome that is mobile among P. syringae pathovars.

Assembly rules for protein networks derived from phylogenetic-statistical analysis of whole genomes

Background: We report an analysis of a protein network of functionally linked proteins, identified from a phylogenetic statistical analysis of complete eukaryotic genomes. Phylogenetic methods identify pairs of proteins that co-evolve on a phylogenetic tree, and have been shown to have a high probability of correctly identifying known functional links. Results: The eukaryotic correlated evolution network we derive displays the familiar power law scaling of connectivity. We introduce the use of explicit phylogenetic methods to reconstruct the ancestral presence or absence of proteins at the interior nodes of a phylogeny of eukaryote species. We find that the connectivity distribution of proteins at the point they arise on the tree and join the network follows a power law, as does the connectivity distribution of proteins at the time they are lost from the network. Proteins resident in the network acquire connections over time, but we find no evidence that 'preferential attachment' - the phenomenon of newly acquired connections in the network being more likely to be made to proteins with large numbers of connections - influences the network structure. We derive a 'variable rate of attachment' model in which proteins vary in their propensity to form network interactions independently of how many connections they have or of the total number of connections in the network, and show how this model can produce apparent power-law scaling without preferential attachment. Conclusion: A few simple rules can explain the topological structure and evolutionary changes to protein-interaction networks: most change is concentrated in satellite proteins of low connectivity and small phenotypic effect, and proteins differ in their propensity to form attachments. Given these rules of assembly, power law scaled networks naturally emerge from simple principles of selection, yielding protein interaction networks that retain a high-degree of robustness on short time scales and evolvability on longer evolutionary time scales.

Mapping the landscape of the lymphocytic choriomeningitis virus stable signal peptide reveals novel functional domains

The stable signal peptide (SSP) of the lymphocytic choriomeningitis virus surface glycoprotein precursor has several unique characteristics. The SSP is unusually long, at 58 amino acids, and contains two hydrophobic domains, and its sequence is highly conserved among both Old and New World arenaviruses. To better understand the functions of the SSP, a panel of point and deletion mutants was created by in vitro mutagenesis to target the highly conserved elements within the SSP. We were also able to confirm critical residues required for separate SSP functions by trans-complementation. Using these approaches, it was possible to resolve functional domains of the SSP. In characterizing our SSP mutants, we discovered that the SSP is involved in several distinct functions within the viral life cycle, beyond translocation of the viral surface glycoprotein precursor into the endoplasmic reticulum lumen. The SSP is required for efficient glycoprotein expression, posttranslational maturation cleavage of GP1 and GP2 by SKI-1/S1P protease, glycoprotein transport to the cell surface plasma membrane, formation of infectious virus particles, and acid pH-dependent glycoprotein-mediated cell fusion.

Predicting functional gene links from phylogenetic-statistical analyses of whole genomes

An important element of the developing field of proteomics is to understand protein-protein interactions and other functional links amongst genes. Across-species correlation methods for detecting functional links work on the premise that functionally linked proteins will tend to show a common pattern of presence and absence across a range of genomes. We describe a maximum likelihood statistical model for predicting functional gene linkages. The method detects independent instances of the correlated gain or loss of pairs of proteins on phylogenetic trees, reducing the high rates of false positives observed in conventional across-species methods that do not explicitly incorporate a phylogeny. We show, in a dataset of 10,551 protein pairs, that the phylogenetic method improves by up to 35% on across-species analyses at identifying known functionally linked proteins. The method shows that protein pairs with at least two to three correlated events of gain or loss are almost certainly functionally linked. Contingent evolution, in which one gene's presence or absence depends upon the presence of another, can also be detected phylogenetically, and may identify genes whose functional significance depends upon its interaction with other genes. Incorporating phylogenetic information improves the prediction of functional linkages. The improvement derives from having a lower rate of false positives and from detecting trends that across-species analyses miss. Phylogenetic methods can easily be incorporated into the screening of large-scale bioinformatics datasets to identify sets of protein links and to characterise gene networks.

A phylogenetic reappraisal of the Peltophorum group (Caesalpinieae: Leguminosae) based on the chloroplast trnL-F, rbcL and rps16 sequence data

The monophyly of the Peltophorum group, one of nine informal groups recognized by Polhill in the Caesalpinieae, was tested using sequence data from the trnL-F, rbcL, and rps16 regions of the chloroplast genome. Exemplars were included from all 16 genera of the Peltophorum group, and from 15 genera representing seven of the other eight informal groups in the tribe. The data were analyzed separately and in combined analyses using parsimony and Bayesian methods. The analysis method had little effect on the topology of well-supported relationships. The molecular data recovered a generally well-supported phylogeny with many intergeneric relationships resolved. Results show that the Peltophorum group as currently delimited is polyphyletic, but that eight genera plus one undescribed genus form a core Peltophorum group, which is referred to here as the Peltophorum group sensu stricto. These genera are Bussea, Conzattia, Colvillea, Delonix, Heteroflorum (inedit.), Lemuropisum, Parkinsonia, Peltophorum, and Schizolobium. The remaining eight genera of the Peltophorum group s.l. are distributed across the Caesalpinieae. Morphological support for the redelimited Peltophorum group and the other recovered clades was assessed, and no unique synapomorphy was found for the Peltophorum group s.s. A proposal for the reclassification of the Peltophorum group s.l. is presented.

LVB: parsimony and simulated annealing in the search for phylogenetic trees

Summary: The program LVB seeks parsimonious phylogenies from nucleotide alignments, using the simulated annealing heuristic. LVB runs fast and gives high quality results.

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.

Cross-species amplification and phylogenetic reconstruction using fragaria microsatellite primers

Twenty-eight microsatellite primer pairs developed from Fragaria vesca ‘Rügen’ were applied to sixteen accessions representing eight diploid Fragaria species. The number of alleles generated, the power of discrimination and the percentage of accessions where no PCR product could be amplified were calculated for each locus for the thirteen non-F. vesca accessions. A phylogeny was then generated for the species accessions sampled, using the presence or absence of alleles at the polymorphic loci as character states. Despite the problems inherent in phylogeny reconstruction from microsatellite data, the phylogeny showed some congruence with a previously published phylogeny of Fragaria, based on nucleotide sequence data. However, relationships inferred from microsatellite allele data were relatively unresolved and poorly supported. The genetic basis of allelic polymorphisms at specific loci was investigated through direct sequencing of the PCR products amplified by three primer pairs. The potential utility of sequence data generated from microsatellite loci in evolutionary studies of closely related species groups is briefly explored.

Phylogenetic relationships within the moss family Bryaceae based on chloroplast DNA evidence

The Bryaceae are a large cosmopolitan family of mosses containing genera of considerable taxonomic difficulty. Phylogenetic relationships within the family were inferred using data from chloroplast DNA sequences (rps4 and trnL-trnF region). Parsimony and maximum likelihood optimality criteria, and Bayesian phylogenetic inference procedures were employed to reconstruct relationships. The genera Bryum and Brachymenium are not monophyletic groups. A clade comprising Plagiobryum, Acidodontium, Mielichhoferia macrocarpa, Bryum sects. Bryum, Apalodictyon, Limbata, Leucodontium, Caespiticia, Capillaria (in part: sect. Capillaria), and Brachymenium sect. Dicranobryum, is well supported in all analyses and represents a major lineage within the family. Section Dicranobryum of Brachymenium is more closely related to section Bryum than to the other sections of Brachymenium, as are Mielichhoferia macrocarpa and M. himalayana. Species of Acidodontium form a clade with Anomobryum julaceum. The grouping of species with a rosulate gametophytic growth form suggests the presence of a 'rosulate' clade similar in circumscription to the genus Rosulabryum. Mielichhoferia macrocarpa and M. himalayana are transferred to Bryum as B. porsildii and B. caucasicum, respectively.

Phylogenetic relationships of the Fox (Forkhead) gene family in the Bilateria

The Forkhead or Fox gene family encodes putative transcription factors. There are at least four Fox genes in yeast, 16 in Drosophila melanogaster (Dm) and 42 in humans. Recently, vertebrate Fox genes have been classified into 17 groups named FoxA to FoxQ [Genes Dev. 14 (2000) 142]. Here, we extend this analysis to invertebrates, using available sequences from D. melanogaster, Anopheles gambiae (Ag), Caenorhabditis elegans (Ce), the sea squirt Ciona intestinalis (Ci) and amphioxus Branchiostoma floridae (Bf), from which we also cloned several Fox genes. Phylogenetic analyses lend support to the previous overall subclassification of vertebrate genes, but suggest that four subclasses (FoxJ, L, N and Q) could be further subdivided to reflect their relationships to invertebrate genes. We were unable to identify orthologs of Fox subclasses E, H, I, J, M and Q1 in D. melanogaster, A. gambiae or C. elegans, suggesting either considerable loss in ecdysozoans or the evolution of these subclasses in the deuterostome lineage. Our analyses suggest that the common ancestor of protostomes and deuterostomes had a minimum complement of 14 Fox genes. (C) 2003 Elsevier B.V. All rights reserved.

Cloacibacterium normanense gen. nov., sp nov., a novel bacterium in the family Flavobacteriaceae isolated from municipal wastewater

Phenotypic and phylogenetic studies were performed on three isolates of an unknown Gram-negative, facultatively anaerobic, non-motile, yellow-pigmented, rod-shaped organism isolated from raw sewage. 16S rRNA gene sequence analysis indicated that these strains were members of the Bergeyella-Chryseobacterium-Riemerella branch of the family Flavobacteriaceae. The unknown bacterium was readily distinguished from reference strains by 16S rRNA gene sequencing and biochemical tests. The organism contained menaquinone MK-6 as the predominant respiratory quinone and had a DNA G + C content of 31 mol%. A most probable number-PCR approach was developed to detect, and estimate the numbers of, this organism. Untreated wastewater from one plant yielded an estimated count of 1.4 x 10(5) cells ml(-1), and untreated wastewater from a second plant yielded an estimated count of 1.4 x 10(4) cells ml(-1). Signal was not detected from treated effluent or from human stool specimens. On the basis of the results of the study presented, it is proposed that the unknown bacterium be classified in a novel genus Cloacibacterium, as Cloacibacterium normanense gen. nov., sp. nov., which is also the type species. The type strain of Cloacibacterium normanense is strain NRS1(T) (=CCUG 46293(T)=CIP 108613(T) =ATCC BAA-825(T) = DSM 15886(T)).