Gene Content Data Research Articles

Congruence Amidst Discordance between Sequence and Protein-Content Based Phylogenies of Fungi.

Amid the genomic data explosion, phylogenomic analysis has resolved the tree of life of different organisms, including fungi. Genome-wide clustering has also been conducted based on gene content data that can lighten the issue of the unequal evolutionary rate of genes. In this study, using different fungal species as models, we performed phylogenomic and protein-content (PC)-based clustering analysis. The obtained sequence tree reflects the phylogenetic trajectory of examined fungal species. However, 15 PC-based trees constructed from the Pfam matrices of the whole genomes, four protein families, and ten subcellular locations largely failed to resolve the speciation relationship of cross-phylum fungal species. However, lifestyle and taxonomic associations were more or less evident between closely related fungal species from PC-based trees. Pairwise congruence tests indicated that a varied level of congruent or discordant relationships were observed between sequence- and PC-based trees, and among PC-based trees. It was intriguing to find that a few protein family and subcellular PC-based trees were more topologically similar to the phylogenomic tree than was the whole genome PC-based phylogeny. In particular, a most significant level of congruence was observed between sequence- and cell wall PC-based trees. Cophylogenetic analysis conducted in this study may benefit the prediction of the magnitude of evolutionary conservation, interactive associations, or networking between different family or subcellular proteins.

PANINI: Pangenome Neighbour Identification for Bacterial Populations.

The standard workhorse for genomic analysis of the evolution of bacterial populations is phylogenetic modelling of mutations in the core genome. However, a notable amount of information about evolutionary and transmission processes in diverse populations can be lost unless the accessory genome is also taken into consideration. Here, we introduce panini (Pangenome Neighbour Identification for Bacterial Populations), a computationally scalable method for identifying the neighbours for each isolate in a data set using unsupervised machine learning with stochastic neighbour embedding based on the t-SNE (t-distributed stochastic neighbour embedding) algorithm. panini is browser-based and integrates with the Microreact platform for rapid online visualization and exploration of both core and accessory genome evolutionary signals, together with relevant epidemiological, geographical, temporal and other metadata. Several case studies with single- and multi-clone pneumococcal populations are presented to demonstrate the ability to identify biologically important signals from gene content data. panini is available at http://panini.pathogen.watch and code at http://gitlab.com/cgps/panini.

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Aim In the last fifteen years, published reports have described KIR gene-content frequency distributions in more than 120 populations worldwide. However, there have been limited studies examining these data in aggregate to detect overall patterns of variation at regional and global levels. Here, we present KIR gene-content data for 113 worldwide populations collected as part of the 15th and 16th International Histocompatibility and Immunogenetics Workshops. Methods The data were contributed by 31 laboratories during the five year course of this project. All populations were genotyped for KIR gene-content (presence/absence) for all loci. Data are examined for an overview of global patterns of carrier frequency distributions for individual loci, as well as variation in gene-content profiles (genotypes) and estimated haplotypes for the centromeric and telomeric regions and across the entire KIR complex. Forty populations were also genotyped for HLA-B and -C ligands, and the data analyzed for correlation between KIR and HLA ligand frequencies within populations. Results Nearly 100 unique genotypes are present in the populations under study. Nearly half of these genotypes are observed in only a single population, while several are present in nearly all populations. Most KIR gene-content variation can be attributed to a few common geneotypes; six gene-content haplotypes account for the majority of the total observed haplotypic variation in all world regions except Africa. Distinct patterns of variation are observed for the centromeric vs. telomeric KIR loci and haplotypes. In the populations genotyped for HLA ligands, a significant correlation between KIR2DL2 and HLA-C group frequencies is observed. Conclusions This work represents the most comprehensive summary of global KIR gene-content variation to date, and lays a foundation for our understanding of human population diversity in the KIR complex. In addition, it lends further support to the notion of co-evolution of KIR and HLA in human populations.

Assessment of phylogenetic structure in genome size – gene content correlations

Gene content and gene-coding percentage can be predicted from genome size in newly sequenced organisms. Here, we investigate whether these predictions are influenced by phylogenetic relationships between the involved species. Combining a highly resolved phylogenetic tree with a large compilation of gene content data, our results reveal the presence of significant phylogenetic structure in the correlations between genome size and gene content in both bacteria and eukaryotes. The variation in log(gene content) explained by log(genome size) in combination with phylogeny was found to be 97% in bacteria and 55% in eukaryotes. Further, in bacteria, gene-coding percentages are only significantly correlated to genome size if phylogenetic information is taken into account in the analyses. These findings support the usage of phylogenetic correlation models for gene content predictions.

On the artefactual parasitic eubacteria clan in conditioned logdet phylogenies: heterotachy and ortholog identification artefacts as explanations

BackgroundPhylogenetic reconstruction methods based on gene content often place all the parasitic and endosymbiotic eubacteria (parasites for short) together in a clan. Many other lines of evidence point to this parasites clan being an artefact. This artefact could be a consequence of the methods used to construct ortholog databases (due to some unknown bias), the methods used to estimate the phylogeny, or both.We test the idea that the parasites clan is an ortholog identification artefact by analyzing three different ortholog databases (COG, TRIBES, and OFAM), which were constructed using different methods, and are thus unlikely to share the same biases. In each case, we estimate a phylogeny using an improved version of the conditioned logdet distance method. If the parasites clan appears in trees from all three databases, it is unlikely to be an ortholog identification artefact.Accelerated loss of a subset of gene families in parasites (a form of heterotachy) may contribute to the difficulty of estimating a phylogeny from gene content data. We test the idea that heterotachy is the underlying reason for the estimation of an artefactual parasites clan by applying two different mixture models (phylogenetic and non-phylogenetic), in combination with conditioned logdet. In these models, there are two categories of gene families, one of which has accelerated loss in parasites. Distances are estimated separately from each category by conditioned logdet. This should reduce the tendency for tree estimation methods to group the parasites together, if heterotachy is the underlying reason for estimation of the parasites clan.ResultsThe parasites clan appears in conditioned logdet trees estimated from all three databases. This makes it less likely to be an artefact of database construction. The non-phylogenetic mixture model gives trees without a parasites clan. However, the phylogenetic mixture model still results in a tree with a parasites clan. Thus, it is not entirely clear whether heterotachy is the underlying reason for the estimation of a parasites clan. Simulation studies suggest that the phylogenetic mixture model approach may be unsuccessful because the model of gene family gain and loss it uses does not adequately describe the real data.ConclusionsThe most successful methods for estimating a reliable phylogenetic tree for parasitic and endosymbiotic eubacteria from gene content data are still ad-hoc approaches such as the SHOT distance method. however, the improved conditioned logdet method we developed here may be useful for non-parasites and can be accessed at http://www.liv.ac.uk/~cgrbios/cond_logdet.html

Determination of Accessory Gene Patterns Predicts the Same Relatedness among Strains of Streptococcus pneumoniae as Sequencing of Housekeeping Genes Does and Represents a Novel Approach in Molecular Epidemiology

Relatedness between isolates of Streptococcus pneumoniae can be determined from sequences of multiple genes belonging to the core genome (multilocus sequence typing [MLST]), but these do not provide information on gene content that may affect the potential of isolates to cause invasive pneumococcal disease. Gene content data, obtained using microarrays, were gathered for 40 clinical isolates of 12 serotypes belonging to 30 multilocus sequence types. We found that sequence variations in housekeeping genes assessed by MLST correlated well with whole-genome microarray analyses identifying the presence/absence of accessory genes/regions. However, isolates belonging to the same clonal complex, as determined by MLST, may not have identical gene contents, potentially affecting virulence. We found fewer intraclonal (same MLST sequence type) differences associated with pneumococcal serotypes of high invasive disease potential, i.e., serotypes rarely found among carriers compared to serotypes frequently found in carriage. Molecular typing of pneumococci based on the presence/absence of 25 genes localized to accessory regions shows the same relatedness among pneumococcal strains as MLST does. We conclude that molecular typing of pneumococci based on variation in the nucleotide sequences of parts of housekeeping genes (MLST) correlates with the presence/absence of genes in the accessory part of the genome. This covariation is likely due to the fact that both sequence variations and gene content variations are created primarily by recombination events in pneumococci.

Phylogenetic analysis, genome evolution and the rate of gene gain in the Herpesviridae

We used complete sequence data from 30 complete Herpesviridae genomes to investigate phylogenetic relationships and patterns of genome evolution. The approach was to identify orthologous gene clusters among taxa and to generate a genomic matrix of gene content. We identified 17 genes with homologs in all 30 taxa and concatenated a subset of 10 of these genes for phylogenetic inference. We also constructed phylogenetic trees on the basis of gene content data. The amino acid and gene content phylogenies were largely concordant, but the amino acid data had much higher internal support. We mapped gene gain events onto the phylogenetic tree by assuming that genes were gained only once during the evolution of herpesviruses. Thirty genes were inferred to be present in the ancestor of all herpesvirus, a number smaller than previously hypothesized. Few genes of recent origin within herpesviruses could be identified as originating from transfer between virus and vertebrate hosts. Inferred rates of gene gain were heterogeneous, with both taxonomic and temporal biases. Nonetheless, the average rate of gene gain was ∼3.5 × 10 −7 genes gained per year, which is an order of magnitude higher than the nucleotide mutation rate for these large DNA viruses.

Roundup: a multi-genome repository of orthologs and evolutionary distances

We have created a tool for ortholog and phylogenetic profile retrieval called Roundup. Roundup is backed by a massive repository of orthologs and associated evolutionary distances that was built using the reciprocal smallest distance algorithm, an approach that has been shown to improve upon alternative approaches of ortholog detection, such as reciprocal blast. Presently, the Roundup repository contains all possible pair-wise comparisons for over 250 genomes, including 32 Eukaryotes, more than doubling the coverage of any similar resource. The orthologs are accessible through an intuitive web interface that allows searches by genome or gene identifier, presenting results as phylogenetic profiles together with gene and molecular function annotations. Results may be downloaded as phylogenetic matrices for subsequent analysis, including the construction of whole-genome phylogenies based on gene-content data. http://rodeo.med.harvard.edu/tools/roundup.

On conditioned reconstruction, gene content data, and the recovery of fusion genomes

Conditioned reconstruction (CR)1 represents a new phylogenetic method that has been presented as a means of utilizing vast amounts of gene absence/presence data to reconstruct phylogenetic relationships and to directly study the inXuence of genome fusion on evolution (Lake and Rivera, 2004; Rivera and Lake, 2004; Simonson et al., 2005). In the Wrst direct application of CR, the results were stated to unambiguously support the role of an archaeal and eubacterial genome fusion event in the ancestry of the eukaryotic genome (McInerney and Wilkinson, 2005; Rivera and Lake, 2004; Simonson et al., 2005). In the present manuscript, we outline the basic components of CR, discuss the logic behind their application, and subsequently identify concerns with aspects of data interpretation and the current use of conditioning genomes and aligned networks in the study of genome fusion. Conditioned reconstruction begins with the selection of a conditioning genome (CG) that will represent the full set of orthologous genes coded during matrix development (Lake and Rivera, 2004). Strong emphasis has been placed on the strict use of relatively small genomes as conditioning genomes (Lake and Rivera, 2004). This recommendation is based on the observation that the use of relatively large conditioning genomes can induce an artifact referred to as “big genome attraction,” which can mislead phylogenetic inference. All other genomes that will be used in the analysis are scored for the absence or presence of a putative ortholog to each gene in the CG. Orthologs present in other genomes included in the

Selection-driven transcriptome polymorphism inEscherichia coli/Shigellaspecies

To explore the role of transcriptome polymorphism in adaptation of organisms to their environment, we evaluated this parameter for the Escherichia coli/Shigella bacterial species, which is composed of well-characterized phylogenetic groups that exhibit characteristic life styles ranging from commensalism to intracellular pathogenicity. Both the genomic content and the transcriptome of 10 strains representative of the major E. coli/Shigella phylogenetic groups were evaluated using macroarrays displaying the 4290 K12-MG1655 open reading frames (ORFs). Although Shigella and enteroinvasive E. coli (EIEC) are not monophyletic, phylogenetic analysis of the binary coded (presence/absence) gene content data showed that these organisms group together due to similar patterns of undetectable K12-MG1655 genes. The variation in transcript abundance was then analyzed using a core genome of 2880 genes present in all strains, after adjusting RNA hybridization signals for DNA hybridization signals. Nonrandom changes in gene expression during the evolution of the E. coli/Shigella species were evidenced. Phylogenetic analysis of transcriptome data again showed that Shigella and EIEC strains group together in terms of gene expression, and this convergence involved groups of genes displaying biologically coherent patterns of functional divergence. Unlike the other E. coli strains evaluated, Shigella and EIEC are intracellular pathogens, and therefore face similar selective pressures. Thus, within the E. coli/Shigella species, strains exhibiting a particular life style have converged toward a specific gene expression pattern in a subset of genes common to the species, revealing the role of selection in shaping transcriptome polymorphism.

GeneContent: software for whole-genome phylogenetic analysis

GeneContent is a software system to infer the genome phylogeny based on an additive genome distance that can be estimated from the extended gene content data, which contains the genome-wide information (absence of a gene family, presence as single copy or presence as duplicates) across multiple species. GeneContent can also be used to explore the genome-wide evolutionary pattern of gene loss and proliferation. Distribution packages of GeneContent for both Microsoft Windows and Linux operating systems are available at http://xgu.zool.iastate.edu xgu@iastate.edu.