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      Large Chromosomal Rearrangements during a Long-Term Evolution Experiment with Escherichia coli

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          ABSTRACT

          Large-scale rearrangements may be important in evolution because they can alter chromosome organization and gene expression in ways not possible through point mutations. In a long-term evolution experiment, twelve Escherichia coli populations have been propagated in a glucose-limited environment for over 25 years. We used whole-genome mapping (optical mapping) combined with genome sequencing and PCR analysis to identify the large-scale chromosomal rearrangements in clones from each population after 40,000 generations. A total of 110 rearrangement events were detected, including 82 deletions, 19 inversions, and 9 duplications, with lineages having between 5 and 20 events. In three populations, successive rearrangements impacted particular regions. In five populations, rearrangements affected over a third of the chromosome. Most rearrangements involved recombination between insertion sequence (IS) elements, illustrating their importance in mediating genome plasticity. Two lines of evidence suggest that at least some of these rearrangements conferred higher fitness. First, parallel changes were observed across the independent populations, with ~65% of the rearrangements affecting the same loci in at least two populations. For example, the ribose-utilization operon and the manB- cpsG region were deleted in 12 and 10 populations, respectively, suggesting positive selection, and this inference was previously confirmed for the former case. Second, optical maps from clones sampled over time from one population showed that most rearrangements occurred early in the experiment, when fitness was increasing most rapidly. However, some rearrangements likely occur at high frequency and may have simply hitchhiked to fixation. In any case, large-scale rearrangements clearly influenced genomic evolution in these populations.

          IMPORTANCE

          Bacterial chromosomes are dynamic structures shaped by long histories of evolution. Among genomic changes, large-scale DNA rearrangements can have important effects on the presence, order, and expression of genes. Whole-genome sequencing that relies on short DNA reads cannot identify all large-scale rearrangements. Therefore, deciphering changes in the overall organization of genomes requires alternative methods, such as optical mapping. We analyzed the longest-running microbial evolution experiment (more than 25 years of evolution in the laboratory) by optical mapping, genome sequencing, and PCR analyses. We found multiple large genome rearrangements in all 12 independently evolving populations. In most cases, it is unclear whether these changes were beneficial themselves or, alternatively, hitchhiked to fixation with other beneficial mutations. In any case, many genome rearrangements accumulated over decades of evolution, providing these populations with genetic plasticity reminiscent of that observed in some pathogenic bacteria.

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          The amphioxus genome and the evolution of the chordate karyotype.

          Lancelets ('amphioxus') are the modern survivors of an ancient chordate lineage, with a fossil record dating back to the Cambrian period. Here we describe the structure and gene content of the highly polymorphic approximately 520-megabase genome of the Florida lancelet Branchiostoma floridae, and analyse it in the context of chordate evolution. Whole-genome comparisons illuminate the murky relationships among the three chordate groups (tunicates, lancelets and vertebrates), and allow not only reconstruction of the gene complement of the last common chordate ancestor but also partial reconstruction of its genomic organization, as well as a description of two genome-wide duplications and subsequent reorganizations in the vertebrate lineage. These genome-scale events shaped the vertebrate genome and provided additional genetic variation for exploitation during vertebrate evolution.
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            Long-Term Experimental Evolution in Escherichia coli. I. Adaptation and Divergence During 2,000 Generations

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              Optimality and evolutionary tuning of the expression level of a protein.

              Different proteins have different expression levels. It is unclear to what extent these expression levels are optimized to their environment. Evolutionary theories suggest that protein expression levels maximize fitness, but the fitness as a function of protein level has seldom been directly measured. To address this, we studied the lac system of Escherichia coli, which allows the cell to use the sugar lactose for growth. We experimentally measured the growth burden due to production and maintenance of the Lac proteins (cost), as well as the growth advantage (benefit) conferred by the Lac proteins when lactose is present. The fitness function, given by the difference between the benefit and the cost, predicts that for each lactose environment there exists an optimal Lac expression level that maximizes growth rate. We then performed serial dilution evolution experiments at different lactose concentrations. In a few hundred generations, cells evolved to reach the predicted optimal expression levels. Thus, protein expression from the lac operon seems to be a solution of a cost-benefit optimization problem, and can be rapidly tuned by evolution to function optimally in new environments.
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                Author and article information

                Journal
                mBio
                MBio
                mbio
                mbio
                mBio
                mBio
                American Society of Microbiology (1752 N St., N.W., Washington, DC )
                2150-7511
                9 September 2014
                Sep-Oct 2014
                : 5
                : 5
                : e01377-14
                Affiliations
                [ a ]Univ. Grenoble Alpes, Laboratoire Adaptation et Pathogénie des Microorganismes (LAPM), Grenoble, France
                [ b ]Centre National de la Recherche Scientifique (CNRS), LAPM, Grenoble, France
                [ c ]Department of Molecular Biosciences, Institute for Cellular and Molecular Biology, Center for Systems and Synthetic Biology, The University of Texas at Austin, Austin, Texas, USA
                [ d ]IAME, UMR 1137, INSERM, Paris, France
                [ e ]IAME, UMR 1137, Université Paris Diderot, Sorbonne Paris Cité, Paris, France
                [ f ]OpGen, Inc., Gaithersburg, Maryland, USA
                [ g ]Direction des Sciences du Vivant, Commissariat à l’energie atomique et aux Energies Alternatives (CEA), Institut de Génomique, Genoscope & CNRS-UMR8030, Évry, France
                [ h ]Laboratoire d’Analyses Bioinformatiques en Génomique et Métabolisme (LABGeM), Évry, France
                [ i ]Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, Michigan, USA
                [ j ]BEACON Center for the Study of Evolution in Action, Michigan State University, East Lansing, Michigan, USA
                Author notes
                Address correspondence to Dominique Schneider, dominique.schneider@ 123456ujf-grenoble.fr .
                [*]

                Present address: Adam M. Briska, DNASTAR, Inc., Madison, Wisconsin, USA; Ryan N. Ptashkin, Department of Computational Medicine & Bioinformatics, University of Michigan Medical School, Ann Arbor, Michigan, USA.

                Invited Editor Søren Molin, Technical University of Denmark Editor Fernando Baquero, Ramón y Cajal University Hospital

                Article
                mBio01377-14
                10.1128/mBio.01377-14
                4173774
                25205090
                f00bee51-c76b-464e-a94e-5470d3bf9091
                Copyright © 2014 Raeside et al.

                This is an open-access article distributed under the terms of the Creative Commons Attribution-Noncommercial-ShareAlike 3.0 Unported license, which permits unrestricted noncommercial use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 22 May 2014
                : 11 August 2014
                Page count
                Pages: 13
                Categories
                Research Article
                Custom metadata
                September/October 2014

                Life sciences
                Life sciences

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