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Comparative Genomics of Plant-Associated Pseudomonas spp.: Insights into Diversity and Inheritance of Traits Involved in Multitrophic Interactions

1 , 2 , * , 3 , 4 , 1 , 3 , 1 , 3 , 2 , 2 , 3 , 1 , 3 , 2 , 1 , 3 , 5 , 5 , 1 , 6 , 6 , 6 , 6 , 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 5 , 15 , 15 , 16 , 3

PLoS Genetics

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      We provide here a comparative genome analysis of ten strains within the Pseudomonas fluorescens group including seven new genomic sequences. These strains exhibit a diverse spectrum of traits involved in biological control and other multitrophic interactions with plants, microbes, and insects. Multilocus sequence analysis placed the strains in three sub-clades, which was reinforced by high levels of synteny, size of core genomes, and relatedness of orthologous genes between strains within a sub-clade. The heterogeneity of the P. fluorescens group was reflected in the large size of its pan-genome, which makes up approximately 54% of the pan-genome of the genus as a whole, and a core genome representing only 45–52% of the genome of any individual strain. We discovered genes for traits that were not known previously in the strains, including genes for the biosynthesis of the siderophores achromobactin and pseudomonine and the antibiotic 2-hexyl-5-propyl-alkylresorcinol; novel bacteriocins; type II, III, and VI secretion systems; and insect toxins. Certain gene clusters, such as those for two type III secretion systems, are present only in specific sub-clades, suggesting vertical inheritance. Almost all of the genes associated with multitrophic interactions map to genomic regions present in only a subset of the strains or unique to a specific strain. To explore the evolutionary origin of these genes, we mapped their distributions relative to the locations of mobile genetic elements and repetitive extragenic palindromic (REP) elements in each genome. The mobile genetic elements and many strain-specific genes fall into regions devoid of REP elements (i.e., REP deserts) and regions displaying atypical tri-nucleotide composition, possibly indicating relatively recent acquisition of these loci. Collectively, the results of this study highlight the enormous heterogeneity of the P. fluorescens group and the importance of the variable genome in tailoring individual strains to their specific lifestyles and functional repertoire.

      Author Summary

      We sequenced the genomes of seven strains of the Pseudomonas fluorescens group that colonize plant surfaces and function as biological control agents, protecting plants from disease. In this study, we demonstrated the genomic diversity of the group by comparing these strains to each other and to three other strains that were sequenced previously. Only about half of the genes in each strain are present in all of the other strains, and each strain has hundreds of unique genes that are not present in the other genomes. We mapped the genes that contribute to biological control in each genome and found that most of the biological control genes are in the variable regions of the genome, which are not shared by all of the other strains. This finding is consistent with our knowledge of the distinctive biology of each strain. Finally, we looked for new genes that are likely to confer antimicrobial traits needed to suppress plant pathogens, but have not been identified previously. In each genome, we discovered many of these new genes, which provide avenues for future discovery of new traits with the potential to manage plant diseases in agriculture or natural ecosystems.

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      Most cited references 190

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      MEGA5: molecular evolutionary genetics analysis using maximum likelihood, evolutionary distance, and maximum parsimony methods.

      Comparative analysis of molecular sequence data is essential for reconstructing the evolutionary histories of species and inferring the nature and extent of selective forces shaping the evolution of genes and species. Here, we announce the release of Molecular Evolutionary Genetics Analysis version 5 (MEGA5), which is a user-friendly software for mining online databases, building sequence alignments and phylogenetic trees, and using methods of evolutionary bioinformatics in basic biology, biomedicine, and evolution. The newest addition in MEGA5 is a collection of maximum likelihood (ML) analyses for inferring evolutionary trees, selecting best-fit substitution models (nucleotide or amino acid), inferring ancestral states and sequences (along with probabilities), and estimating evolutionary rates site-by-site. In computer simulation analyses, ML tree inference algorithms in MEGA5 compared favorably with other software packages in terms of computational efficiency and the accuracy of the estimates of phylogenetic trees, substitution parameters, and rate variation among sites. The MEGA user interface has now been enhanced to be activity driven to make it easier for the use of both beginners and experienced scientists. This version of MEGA is intended for the Windows platform, and it has been configured for effective use on Mac OS X and Linux desktops. It is available free of charge from
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        MrBayes 3: Bayesian phylogenetic inference under mixed models.

        MrBayes 3 performs Bayesian phylogenetic analysis combining information from different data partitions or subsets evolving under different stochastic evolutionary models. This allows the user to analyze heterogeneous data sets consisting of different data types-e.g. morphological, nucleotide, and protein-and to explore a wide variety of structured models mixing partition-unique and shared parameters. The program employs MPI to parallelize Metropolis coupling on Macintosh or UNIX clusters.
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          progressiveMauve: Multiple Genome Alignment with Gene Gain, Loss and Rearrangement

          Background Multiple genome alignment remains a challenging problem. Effects of recombination including rearrangement, segmental duplication, gain, and loss can create a mosaic pattern of homology even among closely related organisms. Methodology/Principal Findings We describe a new method to align two or more genomes that have undergone rearrangements due to recombination and substantial amounts of segmental gain and loss (flux). We demonstrate that the new method can accurately align regions conserved in some, but not all, of the genomes, an important case not handled by our previous work. The method uses a novel alignment objective score called a sum-of-pairs breakpoint score, which facilitates accurate detection of rearrangement breakpoints when genomes have unequal gene content. We also apply a probabilistic alignment filtering method to remove erroneous alignments of unrelated sequences, which are commonly observed in other genome alignment methods. We describe new metrics for quantifying genome alignment accuracy which measure the quality of rearrangement breakpoint predictions and indel predictions. The new genome alignment algorithm demonstrates high accuracy in situations where genomes have undergone biologically feasible amounts of genome rearrangement, segmental gain and loss. We apply the new algorithm to a set of 23 genomes from the genera Escherichia, Shigella, and Salmonella. Analysis of whole-genome multiple alignments allows us to extend the previously defined concepts of core- and pan-genomes to include not only annotated genes, but also non-coding regions with potential regulatory roles. The 23 enterobacteria have an estimated core-genome of 2.46Mbp conserved among all taxa and a pan-genome of 15.2Mbp. We document substantial population-level variability among these organisms driven by segmental gain and loss. Interestingly, much variability lies in intergenic regions, suggesting that the Enterobacteriacae may exhibit regulatory divergence. Conclusions The multiple genome alignments generated by our software provide a platform for comparative genomic and population genomic studies. Free, open-source software implementing the described genome alignment approach is available from

            Author and article information

            [1 ]Agricultural Research Service, U.S. Department of Agriculture, Corvallis, Oregon, United States of America
            [2 ]Department of Botany and Plant Pathology, Oregon State University, Corvallis, Oregon, United States of America
            [3 ]Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia
            [4 ]Department of Plant Pathology, Washington State University, Pullman, Washington, United States of America
            [5 ]Laboratory of Phytopathology, Wageningen University, Wageningen, The Netherlands
            [6 ]The J. Craig Venter Institute, Rockville, Maryland, United States of America
            [7 ]Agricultural Research Service, U.S. Department of Agriculture, Davis, California, United States of America
            [8 ]Agricultural Research Service, U.S. Department of Agriculture, Charleston, South Carolina, United States of America
            [9 ]Department of Biology, Utah State University, Logan, Utah, United States of America
            [10 ]Institute of Environmentally-Friendly Agriculture, Chonnam National University, Gwangju, Korea
            [11 ]Department of Plant Pathology and Microbiology, Texas A&M University, College Station, Texas, United States of America
            [12 ]Department of Horticultural Sciences, Texas A&M University, College Station, Texas, United States of America
            [13 ]Department of Plant and Microbial Biology, University of California Berkeley, Berkeley, California, United States of America
            [14 ]Department of Plant Biology and Pathology, Rutgers, The State University of New Jersey, New Brunswick, New Jersey, United States of America
            [15 ]Agricultural Research Service, U.S. Department of Agriculture, Pullman, Washington, United States of America
            [16 ]The J. Craig Venter Institute, San Diego, California, United States of America
            University of Toronto, Canada
            Author notes

            Conceived and designed the experiments: JEL KAH DVM EWD BTS VOS MDH TAK EAP LSP JMR LST AEA ITP DYK. Performed the experiments: KAH DVM EWD BTS VOS MDH TAK KB LIR JEvdM RB. Analyzed the data: JEL KAH DVM EWD CKL LDHE SLH SGT NLW DR JBH LMB ASD CKL BTS VOS MDH CS JEvdM JMR. Contributed reagents/materials/analysis tools: DAK WPW AJA YCK LSP SEL DMW DYK. Wrote the paper: JEL KAH DVM EWD LST ITP.

            Role: Editor
            PLoS Genet
            PLoS Genet
            PLoS Genetics
            Public Library of Science (San Francisco, USA )
            July 2012
            July 2012
            5 July 2012
            : 8
            : 7
            This is an open-access article, free of all copyright, and may be freely reproduced, distributed, transmitted, modified, built upon, or otherwise used by anyone for any lawful purpose. The work is made available under the Creative Commons CC0 public domain dedication.
            Pages: 27
            Research Article
            Gene Function
            Genome-Wide Association Studies
            Comparative Genomics
            Genome Sequencing
            Bacterial Taxonomy
            Bacterial Evolution
            Microbial Ecology
            Microbial Metabolism
            Plant Microbiology
            Plant Science
            Plant Microbiology
            Plant Pathology
            Chemical Biology



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