通过环境转换抑制抗生素耐药性的进化

IF 1.2 4区 环境科学与生态学 Q4 ECOLOGY
Bryce Morsky, Dervis Can Vural
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引用次数: 1

摘要

变化环境下的生态学和进化在生物学的许多子领域都很重要,对医学也有影响。在这里,我们探讨一个例子:波动的环境对抗生素耐药性出现的后果,这是一个巨大的和日益严重的问题。通常,使用高剂量的抗生素来迅速消除感染,并尽量减少可能出现耐药性的时间。然而,这种策略可能不是最优的。由于竞争会降低适应能力,而抵抗通常会产生繁殖成本,因此抵抗突变体的适应能力取决于它们所处的环境。在这里,我们展示了可以利用环境变化适应度来防止抗性出现的条件。我们开发了一个具有竞争表型的微生物系统的随机Lotka-Volterra模型:一个对抗生素敏感的野生菌株和一个耐药的突变菌株。我们研究了各种脉冲应用抗生素对种群抑制的影响。利用竞争,我们展示了一种环境转换策略如何抑制感染,同时避免耐药突变。我们讨论了该程序的局限性,取决于微生物和药效学和方法来改善他们。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Suppressing evolution of antibiotic resistance through environmental switching

Ecology and evolution under changing environments are important in many subfields of biology with implications for medicine. Here, we explore an example: the consequences of fluctuating environments on the emergence of antibiotic resistance, which is an immense and growing problem. Typically, high doses of antibiotics are employed to eliminate the infection quickly and minimize the time under which resistance may emerge. However, this strategy may not be optimal. Since competition can reduce fitness and resistance typically has a reproductive cost, resistant mutants’ fitness can depend on their environment. Here we show conditions under which environmental varying fitness can be exploited to prevent the emergence of resistance. We develop a stochastic Lotka-Volterra model of a microbial system with competing phenotypes: a wild strain susceptible to the antibiotic, and a mutant strain that is resistant. We investigate the impact of various pulsed applications of antibiotics on population suppression. Leveraging competition, we show how a strategy of environmental switching can suppress the infection while avoiding resistant mutants. We discuss limitations of the procedure depending on the microbe and pharmacodynamics and methods to ameliorate them.

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来源期刊
Theoretical Ecology
Theoretical Ecology 环境科学-生态学
CiteScore
3.30
自引率
6.20%
发文量
23
审稿时长
>12 weeks
期刊介绍: Theoretical Ecology publishes innovative research in theoretical ecology, broadly defined. Papers should use theoretical approaches to answer questions of ecological interest and appeal to and be readable by a broad audience of ecologists. Work that uses mathematical, statistical, computational, or conceptual approaches is all welcomed, provided that the goal is to increase ecological understanding. Papers that only use existing approaches to analyze data, or are only mathematical analyses that do not further ecological understanding, are not appropriate. Work that bridges disciplinary boundaries, such as the intersection between quantitative social sciences and ecology, or physical influences on ecological processes, will also be particularly welcome. All areas of theoretical ecology, including ecophysiology, population ecology, behavioral ecology, evolutionary ecology, ecosystem ecology, community ecology, and ecosystem and landscape ecology are all appropriate. Theoretical papers that focus on applied ecological questions are also of particular interest.
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