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      Experimental Evolution as a High-Throughput Screen for Genetic Adaptations

      research-article
      a , b ,
      mSphere
      American Society for Microbiology
      evolutionary biology, genomics, population genetics

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          Abstract

          Experimental evolution is a method in which populations of organisms, often microbes, are founded by one or more ancestors of known genotype and then propagated under controlled conditions to study the evolutionary process. These evolving populations are influenced by all population genetic forces, including selection, mutation, drift, and recombination, and the relative contributions of these forces may be seen as mysterious.

          ABSTRACT

          Experimental evolution is a method in which populations of organisms, often microbes, are founded by one or more ancestors of known genotype and then propagated under controlled conditions to study the evolutionary process. These evolving populations are influenced by all population genetic forces, including selection, mutation, drift, and recombination, and the relative contributions of these forces may be seen as mysterious. Here, I describe why the outcomes of experimental evolution should be viewed with greater certainty because the force of selection typically dominates. Importantly, any mutant rising rapidly to high frequency in large populations must have acquired adaptive traits in the selective environment. Sequencing the genomes of these mutants can identify genes or pathways that contribute to an adaptation. I review the logic and simple mathematics why this evolve-and-resequence approach is a powerful way to find the mutations or mutation combinations that best increase fitness in any new environment.

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          Most cited references40

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          Long-Term Experimental Evolution in Escherichia coli. I. Adaptation and Divergence During 2,000 Generations

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            Rate and molecular spectrum of spontaneous mutations in the bacterium Escherichia coli as determined by whole-genome sequencing.

            Knowledge of the rate and nature of spontaneous mutation is fundamental to understanding evolutionary and molecular processes. In this report, we analyze spontaneous mutations accumulated over thousands of generations by wild-type Escherichia coli and a derivative defective in mismatch repair (MMR), the primary pathway for correcting replication errors. The major conclusions are (i) the mutation rate of a wild-type E. coli strain is ~1 × 10(-3) per genome per generation; (ii) mutations in the wild-type strain have the expected mutational bias for G:C > A:T mutations, but the bias changes to A:T > G:C mutations in the absence of MMR; (iii) during replication, A:T > G:C transitions preferentially occur with A templating the lagging strand and T templating the leading strand, whereas G:C > A:T transitions preferentially occur with C templating the lagging strand and G templating the leading strand; (iv) there is a strong bias for transition mutations to occur at 5'ApC3'/3'TpG5' sites (where bases 5'A and 3'T are mutated) and, to a lesser extent, at 5'GpC3'/3'CpG5' sites (where bases 5'G and 3'C are mutated); (v) although the rate of small (≤4 nt) insertions and deletions is high at repeat sequences, these events occur at only 1/10th the genomic rate of base-pair substitutions. MMR activity is genetically regulated, and bacteria isolated from nature often lack MMR capacity, suggesting that modulation of MMR can be adaptive. Thus, comparing results from the wild-type and MMR-defective strains may lead to a deeper understanding of factors that determine mutation rates and spectra, how these factors may differ among organisms, and how they may be shaped by environmental conditions.
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              Evolutionary paths to antibiotic resistance under dynamically sustained drug selection.

              Antibiotic resistance can evolve through the sequential accumulation of multiple mutations. To study such gradual evolution, we developed a selection device, the 'morbidostat', that continuously monitors bacterial growth and dynamically regulates drug concentrations, such that the evolving population is constantly challenged. We analyzed the evolution of resistance in Escherichia coli under selection with single drugs, including chloramphenicol, doxycycline and trimethoprim. Over a period of ∼20 days, resistance levels increased dramatically, with parallel populations showing similar phenotypic trajectories. Whole-genome sequencing of the evolved strains identified mutations both specific to resistance to a particular drug and shared in resistance to multiple drugs. Chloramphenicol and doxycycline resistance evolved smoothly through diverse combinations of mutations in genes involved in translation, transcription and transport. In contrast, trimethoprim resistance evolved in a stepwise manner, through mutations restricted to the gene encoding the enzyme dihydrofolate reductase (DHFR). Sequencing of DHFR over the time course of the experiment showed that parallel populations evolved similar mutations and acquired them in a similar order.
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                Author and article information

                Contributors
                Role: Editor
                Journal
                mSphere
                mSphere
                msph
                msph
                mSphere
                mSphere
                American Society for Microbiology (1752 N St., N.W., Washington, DC )
                2379-5042
                9 May 2018
                May-Jun 2018
                : 3
                : 3
                : e00121-18
                Affiliations
                [a ]Department of Microbiology and Molecular Genetics, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
                [b ]Pittsburgh Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
                Escola Paulista de Medicina, Universidade Federal de São Paulo
                Author notes
                Address correspondence to vaughn.cooper@ 123456pitt.edu .

                Citation Cooper VS. 2018. Experimental evolution as a high-throughput screen for genetic adaptations. mSphere 3:e00121-18. https://doi.org/10.1128/mSphere.00121-18.

                Author information
                https://orcid.org/0000-0001-7726-0765
                Article
                mSphere00121-18
                10.1128/mSphere.00121-18
                5956144
                29743200
                d9dba1c8-0f69-400d-8212-c1a48b9c3a05
                Copyright © 2018 Cooper.

                This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license.

                History
                Page count
                Figures: 2, Tables: 1, Equations: 3, References: 44, Pages: 7, Words: 4669
                Funding
                Funded by: HHS | NIH | National Institute of Allergy and Infectious Diseases (NIAID), https://doi.org/10.13039/100000060;
                Award ID: U01 AI124302-01
                Award Recipient :
                Funded by: HHS | NIH | National Institute of General Medical Sciences (NIGMS), https://doi.org/10.13039/100000057;
                Award ID: R01GM110444
                Award Recipient :
                Funded by: National Aeronautics and Space Administration (NASA), https://doi.org/10.13039/100000104;
                Award ID: NAI CAN-7 NNA15BB04A
                Award Recipient :
                Categories
                Opinion/Hypothesis
                Ecological and Evolutionary Science
                Custom metadata
                May/June 2018

                evolutionary biology,genomics,population genetics
                evolutionary biology, genomics, population genetics

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