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Phylogenetic modeling of lateral gene transfer reconstructs the pattern and relative timing of speciations.

ABSTRACT: The timing of the evolution of microbial life has largely remained elusive due to the scarcity of prokaryotic fossil record and the confounding effects of the exchange of genes among possibly distant species. The history of gene transfer events, however, is not a series of individual oddities; it records which lineages were concurrent and thus provides information on the timing of species diversification. Here, we use a probabilistic model of genome evolution that accounts for differences between gene phylogenies and the species tree as series of duplication, transfer, and loss events to reconstruct chronologically ordered species phylogenies. Using simulations we show that we can robustly recover accurate chronologically ordered species phylogenies in the presence of gene tree reconstruction errors and realistic rates of duplication, transfer, and loss. Using genomic data we demonstrate that we can infer rooted species phylogenies using homologous gene families from complete genomes of 10 bacterial and archaeal groups. Focusing on cyanobacteria, distinguished among prokaryotes by a relative abundance of fossils, we infer the maximum likelihood chronologically ordered species phylogeny based on 36 genomes with 8,332 homologous gene families. We find the order of speciation events to be in full agreement with the fossil record and the inferred phylogeny of cyanobacteria to be consistent with the phylogeny recovered from established phylogenomics methods. Our results demonstrate that lateral gene transfers, detected by probabilistic models of genome evolution, can be used as a source of information on the timing of evolution, providing a valuable complement to the limited prokaryotic fossil record.

SUBMITTER: Szollosi GJ

PROVIDER: S-EPMC3491530 | biostudies-literature | 2012 Oct

REPOSITORIES: biostudies-literature

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Phylogenetic modeling of lateral gene transfer reconstructs the pattern and relative timing of speciations.

Szöllosi Gergely J GJ Boussau Bastien B Abby Sophie S SS Tannier Eric E Daubin Vincent V

Proceedings of the National Academy of Sciences of the United States of America 20121004 43

The timing of the evolution of microbial life has largely remained elusive due to the scarcity of prokaryotic fossil record and the confounding effects of the exchange of genes among possibly distant species. The history of gene transfer events, however, is not a series of individual oddities; it records which lineages were concurrent and thus provides information on the timing of species diversification. Here, we use a probabilistic model of genome evolution that accounts for differences betwee ...[more]

PMID: 23043116

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Project description:Lateral gene transfer (LGT) is an important mechanism of natural variation among prokaryotes. Over the full course of evolution, most or all of the genes resident in a given prokaryotic genome have been affected by LGT, yet the frequency of LGT can vary greatly across genes and across prokaryotic groups. The proteobacteria are among the most diverse of prokaryotic taxa. The prevalence of LGT in their genome evolution calls for the application of network-based methods instead of tree-based methods to investigate the relationships among these species. Here, we report networks that capture both vertical and horizontal components of evolutionary history among 1,207,272 proteins distributed across 329 sequenced proteobacterial genomes. The network of shared proteins reveals modularity structure that does not correspond to current classification schemes. On the basis of shared protein-coding genes, the five classes of proteobacteria fall into two main modules, one including the alpha-, delta-, and epsilonproteobacteria and the other including beta- and gammaproteobacteria. The first module is stable over different protein identity thresholds. The second shows more plasticity with regard to the sequence conservation of proteins sampled, with the gammaproteobacteria showing the most chameleon-like evolutionary characteristics within the present sample. Using a minimal lateral network approach, we compared LGT rates at different phylogenetic depths. In general, gene evolution by LGT within proteobacteria is very common. At least one LGT event was inferred to have occurred in at least 75% of the protein families. The average LGT rate at the species and class depth is about one LGT event per protein family, the rate doubling at the phylum level to an average of two LGT events per protein family. Hence, our results indicate that the rate of gene acquisition per protein family is similar at the level of species (by recombination) and at the level of classes (by LGT). The frequency of LGT per genome strongly depends on the species lifestyle, with endosymbionts showing far lower LGT frequencies than free-living species. Moreover, the nature of the transferred genes suggests that gene transfer in proteobacteria is frequently mediated by conjugation.

Project description:BackgroundWith the advent of high-throughput targeted metabolic profiling techniques, the question of how to interpret and analyze the resulting vast amount of data becomes more and more important. In this work we address the reconstruction of metabolic reactions from cross-sectional metabolomics data, that is without the requirement for time-resolved measurements or specific system perturbations. Previous studies in this area mainly focused on Pearson correlation coefficients, which however are generally incapable of distinguishing between direct and indirect metabolic interactions.ResultsIn our new approach we propose the application of a Gaussian graphical model (GGM), an undirected probabilistic graphical model estimating the conditional dependence between variables. GGMs are based on partial correlation coefficients, that is pairwise Pearson correlation coefficients conditioned against the correlation with all other metabolites. We first demonstrate the general validity of the method and its advantages over regular correlation networks with computer-simulated reaction systems. Then we estimate a GGM on data from a large human population cohort, covering 1020 fasting blood serum samples with 151 quantified metabolites. The GGM is much sparser than the correlation network, shows a modular structure with respect to metabolite classes, and is stable to the choice of samples in the data set. On the example of human fatty acid metabolism, we demonstrate for the first time that high partial correlation coefficients generally correspond to known metabolic reactions. This feature is evaluated both manually by investigating specific pairs of high-scoring metabolites, and then systematically on a literature-curated model of fatty acid synthesis and degradation. Our method detects many known reactions along with possibly novel pathway interactions, representing candidates for further experimental examination.ConclusionsIn summary, we demonstrate strong signatures of intracellular pathways in blood serum data, and provide a valuable tool for the unbiased reconstruction of metabolic reactions from large-scale metabolomics data sets.

Project description:MOTIVATION: Recently, a shift was made from using Gene Ontology (GO) to evaluate molecular network data to using these data to construct and evaluate GO. Dutkowski et al. provide the first evidence that a large part of GO can be reconstructed solely from topologies of molecular networks. Motivated by this work, we develop a novel data integration framework that integrates multiple types of molecular network data to reconstruct and update GO. We ask how much of GO can be recovered by integrating various molecular interaction data. RESULTS: We introduce a computational framework for integration of various biological networks using penalized non-negative matrix tri-factorization (PNMTF). It takes all network data in a matrix form and performs simultaneous clustering of genes and GO terms, inducing new relations between genes and GO terms (annotations) and between GO terms themselves. To improve the accuracy of our predicted relations, we extend the integration methodology to include additional topological information represented as the similarity in wiring around non-interacting genes. Surprisingly, by integrating topologies of bakers' yeasts protein-protein interaction, genetic interaction (GI) and co-expression networks, our method reports as related 96% of GO terms that are directly related in GO. The inclusion of the wiring similarity of non-interacting genes contributes 6% to this large GO term association capture. Furthermore, we use our method to infer new relationships between GO terms solely from the topologies of these networks and validate 44% of our predictions in the literature. In addition, our integration method reproduces 48% of cellular component, 41% of molecular function and 41% of biological process GO terms, outperforming the previous method in the former two domains of GO. Finally, we predict new GO annotations of yeast genes and validate our predictions through GIs profiling. AVAILABILITY AND IMPLEMENTATION: Supplementary Tables of new GO term associations and predicted gene annotations are available at http://bio-nets.doc.ic.ac.uk/GO-Reconstruction/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

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Project description:Gardnerella spp. are pathognomonic for bacterial vaginosis, which increases the risk of preterm birth and the transmission of sexually transmitted infections. Gardnerella spp. are genetically diverse, comprising what have recently been defined as distinct species with differing functional capacities. Disease associations with Gardnerella spp. are not straightforward: patients with BV are usually infected with multiple species, and Gardnerella spp. are also found in the vaginal microbiome of healthy women. Genome comparisons of Gardnerella spp. show evidence of lateral gene transfer (LGT), but patterns of LGT have not been characterized in detail. Here we sought to define the role of LGT in shaping the genetic structure of Gardnerella spp. We analyzed whole genome sequencing data for 106 Gardnerella strains and used these data for pan genome analysis and to characterize LGT in the core and accessory genomes, over recent and remote timescales. In our diverse sample of Gardnerella strains, we found that both the core and accessory genomes are clearly differentiated in accordance with newly defined species designations. We identified putative competence and pilus assembly genes across most species; we also found them to be differentiated between species. Competence machinery has diverged in parallel with the core genome, with selection against deleterious mutations as a predominant influence on their evolution. By contrast, the virulence factor vaginolysin, which encodes a toxin, appears to be readily exchanged among species. We identified five distinct prophage clusters in Gardnerella genomes, two of which appear to be exchanged between Gardnerella species. Differences among species are apparent in their patterns of LGT, including their exchange with diverse gene pools. Despite frequent LGT and co-localization in the same niche, our results show that Gardnerella spp. are clearly genetically differentiated and yet capable of exchanging specific genetic material. This likely reflects complex interactions within bacterial communities associated with the vaginal microbiome. Our results provide insight into how such interactions evolve and are maintained, allowing these multi-species communities to colonize and invade human tissues and adapt to antibiotics and other stressors.

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Phylogenetic modeling of lateral gene transfer reconstructs the pattern and relative timing of speciations.

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Phylogenetic modeling of lateral gene transfer reconstructs the pattern and relative timing of speciations.

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