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      Dynamics of Genome Rearrangement in Bacterial Populations

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          Genome structure variation has profound impacts on phenotype in organisms ranging from microbes to humans, yet little is known about how natural selection acts on genome arrangement. Pathogenic bacteria such as Yersinia pestis, which causes bubonic and pneumonic plague, often exhibit a high degree of genomic rearrangement. The recent availability of several Yersinia genomes offers an unprecedented opportunity to study the evolution of genome structure and arrangement. We introduce a set of statistical methods to study patterns of rearrangement in circular chromosomes and apply them to the Yersinia. We constructed a multiple alignment of eight Yersinia genomes using Mauve software to identify 78 conserved segments that are internally free from genome rearrangement. Based on the alignment, we applied Bayesian statistical methods to infer the phylogenetic inversion history of Yersinia. The sampling of genome arrangement reconstructions contains seven parsimonious tree topologies, each having different histories of 79 inversions. Topologies with a greater number of inversions also exist, but were sampled less frequently. The inversion phylogenies agree with results suggested by SNP patterns. We then analyzed reconstructed inversion histories to identify patterns of rearrangement. We confirm an over-representation of “symmetric inversions”—inversions with endpoints that are equally distant from the origin of chromosomal replication. Ancestral genome arrangements demonstrate moderate preference for replichore balance in Yersinia. We found that all inversions are shorter than expected under a neutral model, whereas inversions acting within a single replichore are much shorter than expected. We also found evidence for a canonical configuration of the origin and terminus of replication. Finally, breakpoint reuse analysis reveals that inversions with endpoints proximal to the origin of DNA replication are nearly three times more frequent. Our findings represent the first characterization of genome arrangement evolution in a bacterial population evolving outside laboratory conditions. Insight into the process of genomic rearrangement may further the understanding of pathogen population dynamics and selection on the architecture of circular bacterial chromosomes.

          Author Summary

          Whole-genome sequencing has revealed that organisms exhibit extreme variability in chromosome structure. One common type of chromosome structure variation is genome arrangement variation: changes in the ordering of genes on the chromosome. Not only do we find differences in genome arrangement across species, but in some organisms, members of the same species have radically different genome arrangements. We studied the evolution of genome arrangement in pathogenic bacteria from the genus Yersinia. The Yersinia exhibit substantial variation in genome arrangement both within and across species. We reconstructed the history of genome rearrangement by inversion in a group of eight Yersinia, and we statistically quantified the forces shaping their genome arrangement evolution. In particular, we discovered an excess of rearrangement activity near the origin of chromosomal replication and found evidence for a preferred configuration for the relative orientations of the origin and terminus of replication. We also found real inversions to be significantly shorter than expected. Finally, we discovered that no single reconstruction of inversion history is parsimonious with respect to the total number of inversion mutations, but on average, reconstructed genome arrangements favor “balanced” genomes—where the replication origin is positioned opposite the terminus on the circular chromosome.

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          Most cited references 77

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          Application of phylogenetic networks in evolutionary studies.

          The evolutionary history of a set of taxa is usually represented by a phylogenetic tree, and this model has greatly facilitated the discussion and testing of hypotheses. However, it is well known that more complex evolutionary scenarios are poorly described by such models. Further, even when evolution proceeds in a tree-like manner, analysis of the data may not be best served by using methods that enforce a tree structure but rather by a richer visualization of the data to evaluate its properties, at least as an essential first step. Thus, phylogenetic networks should be employed when reticulate events such as hybridization, horizontal gene transfer, recombination, or gene duplication and loss are believed to be involved, and, even in the absence of such events, phylogenetic networks have a useful role to play. This article reviews the terminology used for phylogenetic networks and covers both split networks and reticulate networks, how they are defined, and how they can be interpreted. Additionally, the article outlines the beginnings of a comprehensive statistical framework for applying split network methods. We show how split networks can represent confidence sets of trees and introduce a conservative statistical test for whether the conflicting signal in a network is treelike. Finally, this article describes a new program, SplitsTree4, an interactive and comprehensive tool for inferring different types of phylogenetic networks from sequences, distances, and trees.
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              Mauve: multiple alignment of conserved genomic sequence with rearrangements.

              As genomes evolve, they undergo large-scale evolutionary processes that present a challenge to sequence comparison not posed by short sequences. Recombination causes frequent genome rearrangements, horizontal transfer introduces new sequences into bacterial chromosomes, and deletions remove segments of the genome. Consequently, each genome is a mosaic of unique lineage-specific segments, regions shared with a subset of other genomes and segments conserved among all the genomes under consideration. Furthermore, the linear order of these segments may be shuffled among genomes. We present methods for identification and alignment of conserved genomic DNA in the presence of rearrangements and horizontal transfer. Our methods have been implemented in a software package called Mauve. Mauve has been applied to align nine enterobacterial genomes and to determine global rearrangement structure in three mammalian genomes. We have evaluated the quality of Mauve alignments and drawn comparison to other methods through extensive simulations of genome evolution. Copyright 2004 Cold Spring Harbor Laboratory Press ISSN

                Author and article information

                [1 ]ARC Center of Excellence in Bioinformatics, The University of Queensland, St. Lucia, Queensland, Australia
                [2 ]Institute for Molecular Bioscience, The University of Queensland, St. Lucia, Queensland, Australia
                [3 ]Bioinformatics Group, Alfréd Rényi Institute of Mathematics, Hungarian Academy of Sciences, Budapest, Hungary
                [4 ]eScience Regional Knowledge Centre, Eötvös Loránd University, Budapest, Hungary
                [5 ]Data Mining and Search Research Group, Computer and Automation Institute, Hungarian Academy of Sciences, Budapest, Hungary
                University of Toronto, Canada
                Author notes

                Conceived and designed the experiments: AED. Performed the experiments: AED. Analyzed the data: AED IM. Contributed reagents/materials/analysis tools: AED. Wrote the paper: AED IM MAR.

                Role: Editor
                PLoS Genet
                PLoS Genetics
                Public Library of Science (San Francisco, USA )
                July 2008
                July 2008
                18 July 2008
                : 4
                : 7
                Darling et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
                Pages: 16
                Research Article
                Computational Biology/Evolutionary Modeling
                Evolutionary Biology/Evolutionary and Comparative Genetics
                Evolutionary Biology/Genomics
                Genetics and Genomics/Bioinformatics
                Genetics and Genomics/Chromosome Biology
                Genetics and Genomics/Microbial Evolution and Genomics
                Genetics and Genomics/Population Genetics



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