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      Microbial Experimental Evolution – a proving ground for evolutionary theory and a tool for discovery

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          Abstract

          Microbial experimental evolution uses controlled laboratory populations to study the mechanisms of evolution. The molecular analysis of evolved populations enables empirical tests that can confirm the predictions of evolutionary theory, but can also lead to surprising discoveries. As with other fields in the life sciences, microbial experimental evolution has become a tool, deployed as part of the suite of techniques available to the molecular biologist. Here, I provide a review of the general findings of microbial experimental evolution, especially those relevant to molecular microbiologists that are new to the field. I also relate these results to design considerations for an evolution experiment and suggest future directions for those working at the intersection of experimental evolution and molecular biology.

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          Most cited references114

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          Identification of mutations in laboratory-evolved microbes from next-generation sequencing data using breseq.

          Next-generation DNA sequencing (NGS) can be used to reconstruct eco-evolutionary population dynamics and to identify the genetic basis of adaptation in laboratory evolution experiments. Here, we describe how to run the open-source breseq computational pipeline to identify and annotate genetic differences found in whole-genome and whole-population NGS data from haploid microbes where a high-quality reference genome is available. These methods can also be used to analyze mutants isolated in genetic screens and to detect unintended mutations that may occur during strain construction and genome editing.
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            Long-Term Experimental Evolution in Escherichia coli. I. Adaptation and Divergence During 2,000 Generations

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              Tn-seq; high-throughput parallel sequencing for fitness and genetic interaction studies in microorganisms

              Biological pathways are structured in complex networks of interacting genes. Solving the architecture of such networks may provide valuable information, such as how microorganisms cause disease. Here we present a method (Tn-seq) for accurately determining quantitative genetic interactions on a genome-wide scale in microorganisms. Tn-seq is based on the assembly of a saturated Mariner transposon insertion library. After library selection, changes in frequency of each insertion mutant are determined by sequencing of the flanking regions en masse. These changes are used to calculate each mutant’s fitness. Fitness was determined for each gene of the gram-positive bacterium Streptococcus pneumoniae, a causative agent of pneumonia and meningitis. A genome-wide screen for genetic interactions identified both alleviating and aggravating interactions that could be further divided into seven distinct categories. Due to the wide activity of the Mariner transposon, Tn-seq has the potential to contribute to the exploration of complex pathways across many different species.
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                Author and article information

                Contributors
                mike.mcdonald@monash.edu
                Journal
                EMBO Rep
                EMBO Rep
                10.1002/(ISSN)1469-3178
                EMBR
                embor
                EMBO Reports
                John Wiley and Sons Inc. (Hoboken )
                1469-221X
                1469-3178
                24 July 2019
                August 2019
                24 July 2019
                : 20
                : 8 ( doiID: 10.1002/embr.v20.8 )
                : e46992
                Affiliations
                [ 1 ] School of Biological Sciences Monash University Melbourne Vic. Australia
                Author notes
                [*] [* ]Corresponding author. Tel: +61 3 9905 1697; E‐mail: mike.mcdonald@ 123456monash.edu
                Author information
                https://orcid.org/0000-0002-5735-960X
                Article
                EMBR201846992
                10.15252/embr.201846992
                6680118
                31338963
                fc3f2ae3-1eeb-4e09-91ea-c7a6ae725a27
                © 2019 The Author. Published under the terms of the CC BY 4.0 license

                This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

                History
                : 01 September 2018
                : 23 March 2019
                : 28 June 2019
                Page count
                Figures: 3, Tables: 0, Pages: 14, Words: 12305
                Funding
                Funded by: Department of Education and Training|Australian Research Council (ARC)
                Award ID: FT170100441
                Award ID: DP180102161
                Categories
                Review
                Review
                Custom metadata
                2.0
                embr201846992
                August 2019
                Converter:WILEY_ML3GV2_TO_NLMPMC version:5.6.7 mode:remove_FC converted:05.08.2019

                Molecular biology
                adaptation,directed evolution,experimental evolution,microbiome evolution,selection,evolution,microbiology, virology & host pathogen interaction

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