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      Variation in Mutant Prevention Concentrations

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          Abstract

          Objectives:Understanding how phenotypic traits vary has been a longstanding goal of evolutionary biologists. When examining antibiotic-resistance in bacteria, it is generally understood that the minimum inhibitory concentration (MIC) has minimal variation specific to each bacterial strain-antibiotic combination. However, there is a less studied resistance trait, the mutant prevention concentration (MPC), which measures the MIC of the most resistant sub-population. Whether and how MPC varies has been poorly understood. Here, we ask a simple, yet important question: How much does the MPC vary, within a single strain-antibiotic association? Using a Staphylococcus species and five antibiotics from five different antibiotic classes—ciprofloxacin, doxycycline, gentamicin, nitrofurantoin, and oxacillin—we examined the frequency of resistance for a wide range of concentrations per antibiotic, and measured the repeatability of the MPC, the lowest amount of antibiotic that would ensure no surviving cells in a 10 10 population of bacteria.

          Results: We found a wide variation within the MPC and distributions that were rarely normal. When antibiotic resistance evolved, the distribution of the MPC changed, with all distributions becoming wider and some multi-modal.

          Conclusion: Unlike the MIC, there is high variability in the MPC for a given bacterial strain-antibiotic combination.

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          Most cited references52

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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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            Mutation frequencies and antibiotic resistance.

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              Determining mutation rates in bacterial populations.

              When properly determined, spontaneous mutation rates are a more accurate and biologically meaningful reflection of underlying mutagenic mechanisms than are mutant frequencies. Because bacteria grow exponentially and mutations arise stochastically, methods to estimate mutation rates depend on theoretical models that describe the distribution of mutant numbers among parallel cultures, as in the original Luria-Delbr]uck fluctuation analysis. An accurate determination of mutation rate depends on understanding the strengths and limitations of these methods, and how to design fluctuation assays to optimize a given method. In this paper we describe a number of methods to estimate mutation rates, give brief accounts of their derivations, and discuss how they behave under various experimental conditions. Copyright 2000 Academic Press.
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                Author and article information

                Contributors
                Journal
                Front Microbiol
                Front Microbiol
                Front. Microbiol.
                Frontiers in Microbiology
                Frontiers Media S.A.
                1664-302X
                31 January 2019
                2019
                : 10
                : 42
                Affiliations
                [1] 1Department of Ecology and Evolutionary Biology, University of California, Los Angeles , Los Angeles, CA, United States
                [2] 2Santa Fe Institute , Santa Fe, NM, United States
                Author notes

                Edited by: José Luis Capelo, Universidade Nova de Lisboa, Portugal

                Reviewed by: Jozsef Soki, University of Szeged, Hungary; Min Yue, Zhejiang University, China

                *Correspondence: Pamela J. Yeh, pamelayeh@ 123456ucla.edu

                These authors have contributed equally to this work

                This article was submitted to Antimicrobials, Resistance and Chemotherapy, a section of the journal Frontiers in Microbiology

                Article
                10.3389/fmicb.2019.00042
                6365975
                30766517
                aab5d73e-413a-474f-b5ef-4248cf84a8c3
                Copyright © 2019 Gianvecchio, Lozano, Henderson, Kalhori, Bullivant, Valencia, Su, Bello, Wong, Cook, Fuller, Neal and Yeh.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 31 October 2018
                : 11 January 2019
                Page count
                Figures: 3, Tables: 1, Equations: 0, References: 63, Pages: 9, Words: 0
                Funding
                Funded by: Hellman Foundation 10.13039/100010336
                Funded by: National Center for Advancing Translational Sciences 10.13039/100006108
                Award ID: UL1TR001881
                Funded by: National Institutes of Health 10.13039/100000002
                Award ID: T32-GM008185
                Categories
                Microbiology
                Original Research

                Microbiology & Virology
                antibiotic resistance,selection,staphylococcus epidermidis,repeatability,replication

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