抗生素剂量和营养供应以不同方式驱动抗生素耐药性和持久性的进化

Etthel M Windels, Lloyd Cool, Eline Persy, Janne Swinnen, Paul Matthay, Bram Van den Bergh, Tom Wenseleers, Jan Michiels
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引用次数: 0

摘要

由于出现了耐受抗生素的细菌变种,有效治疗细菌感染变得越来越具有挑战性。抗生素耐药性和持久性已被确定为细菌的两大生存机制,多项研究表明,在反复用药治疗的情况下,耐药性或持久性突变体的选择迅速而强烈。然而,人们对环境条件对耐药性和持久性进化的影响以及这两种表型之间潜在的相互作用知之甚少。根据抗药性突变体和持久性突变体不同的生长和存活特征,我们假设抗生素剂量和治疗过程中的营养供应可能在抗生素胁迫的进化适应过程中起到关键作用。为了验证这一假设,我们将高通量实验进化与间歇性抗生素暴露下细菌进化的数学模型相结合。我们的研究表明,抗生素治疗期间的高营养水平会促进高水平抗药性的选择,但当抗生素浓度足够低时,抗药性的出现主要与持久性无关。在较高剂量下,抗药性的进化得益于之前或同时进行的持久性突变体的选择,这确保了种群在恶劣条件下的生存。总之,我们的实验数据和数学模型阐明了在不同抗生素治疗方案下提高细菌存活率的进化途径,这是设计有效抗生素疗法的关键。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Antibiotic dose and nutrient availability differentially drive the evolution of antibiotic resistance and persistence
Effective treatment of bacterial infections proves increasingly challenging due to the emergence of bacterial variants that endure antibiotic exposure. Antibiotic resistance and persistence have been identified as two major bacterial survival mechanisms, and several studies have shown a rapid and strong selection of resistance or persistence mutants under repeated drug treatment. Yet, little is known about the impact of the environmental conditions on resistance and persistence evolution, and the potential interplay between both phenotypes. Based on the distinct growth and survival characteristics of resistance and persistence mutants, we hypothesized that the antibiotic dose and availability of nutrients during treatment might play a key role in the evolutionary adaptation to antibiotic stress. To test this hypothesis, we combined high-throughput experimental evolution with a mathematical model of bacterial evolution under intermittent antibiotic exposure. We show that high nutrient levels during antibiotic treatment promote selection of high-level resistance, but that resistance mainly emerges independently of persistence when the antibiotic concentration is sufficiently low. At higher doses, resistance evolution is facilitated by the preceding or concurrent selection of persistence mutants, which ensures survival of populations in harsh conditions. Collectively, our experimental data and mathematical model elucidate the evolutionary routes towards increased bacterial survival under different antibiotic treatment schedules, which is key to designing effective antibiotic therapies.
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