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Evolutionary Trajectories to Antibiotic Resistance

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Annual Review of Microbiology

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      Abstract

      The ability to predict the evolutionary trajectories of antibiotic resistance would be of great value in tailoring dosing regimens of antibiotics so as to maximize the duration of their usefulness. Useful prediction of resistance evolution requires information about (a) the mutation supply rate, (b) the level of resistance conferred by the resistance mechanism, (c) the fitness of the antibiotic-resistant mutant bacteria as a function of drug concentration, and (d) the strength of selective pressures. In addition, processes including epistatic interactions and compensatory evolution, coselection of drug resistances, and population bottlenecks and clonal interference can strongly influence resistance evolution and thereby complicate attempts at prediction. Currently, the very limited quantitative data on most of these parameters severely limit attempts to accurately predict trajectories of resistance evolution.

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      Most cited references 115

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      Darwinian evolution can follow only very few mutational paths to fitter proteins.

      Five point mutations in a particular beta-lactamase allele jointly increase bacterial resistance to a clinically important antibiotic by a factor of approximately 100,000. In principle, evolution to this high-resistance beta-lactamase might follow any of the 120 mutational trajectories linking these alleles. However, we demonstrate that 102 trajectories are inaccessible to Darwinian selection and that many of the remaining trajectories have negligible probabilities of realization, because four of these five mutations fail to increase drug resistance in some combinations. Pervasive biophysical pleiotropy within the beta-lactamase seems to be responsible, and because such pleiotropy appears to be a general property of missense mutations, we conclude that much protein evolution will be similarly constrained. This implies that the protein tape of life may be largely reproducible and even predictable.
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        Antibiotic resistance and its cost: is it possible to reverse resistance?

        Most antibiotic resistance mechanisms are associated with a fitness cost that is typically observed as a reduced bacterial growth rate. The magnitude of this cost is the main biological parameter that influences the rate of development of resistance, the stability of the resistance and the rate at which the resistance might decrease if antibiotic use were reduced. These findings suggest that the fitness costs of resistance will allow susceptible bacteria to outcompete resistant bacteria if the selective pressure from antibiotics is reduced. Unfortunately, the available data suggest that the rate of reversibility will be slow at the community level. Here, we review the factors that influence the fitness costs of antibiotic resistance, the ways by which bacteria can reduce these costs and the possibility of exploiting them.
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          Evolution of the mutation rate.

           Michael Lynch (2010)
          Understanding the mechanisms of evolution requires information on the rate of appearance of new mutations and their effects at the molecular and phenotypic levels. Although procuring such data has been technically challenging, high-throughput genome sequencing is rapidly expanding knowledge in this area. With information on spontaneous mutations now available in a variety of organisms, general patterns have emerged for the scaling of mutation rate with genome size and for the likely mechanisms that drive this pattern. Support is presented for the hypothesis that natural selection pushes mutation rates down to a lower limit set by the power of random genetic drift rather than by intrinsic physiological limitations, and that this has resulted in reduced levels of replication, transcription, and translation fidelity in eukaryotes relative to prokaryotes. Copyright 2010 Elsevier Ltd. All rights reserved.
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            Author and article information

            Affiliations
            [1 ]Department of Medical Biochemistry and Microbiology, Uppsala University, 751 23 Uppsala, Sweden;
            Journal
            Annual Review of Microbiology
            Annu. Rev. Microbiol.
            Annual Reviews
            0066-4227
            1545-3251
            September 08 2017
            September 08 2017
            : 71
            : 1
            : 579-596
            10.1146/annurev-micro-090816-093813
            © 2017

            http://www.annualreviews.org/licenses/tdm

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