Complex Interplay of Physiology and Selection in the Emergence of Antibiotic Resistance.
Complex Interplay of Physiology and Selection in the Emergence of Antibiotic Resistance.
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DOI：
10.1016/j.cub.2016.04.015
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发表时间：
2016-06-06
期刊：
影响因子：
--
通讯作者：
Kussell E
中科院分区：
文献类型：
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作者：
Lin WH;Kussell E
抗生素耐药性的出现是一个对人类健康至关重要的进化过程[1]，通常发生在抗生素水平变化的情况下。选择性扫描，其中抗性细胞在种群中占主导地位，是这一过程的关键步骤[2]。虽然在实验室实验中已经研究了抗性的出现[3-8]，但在波动胁迫下选择性扫描的完整进程，从单细胞中的随机事件到群体中的固定，尚未被表征。在这里，我们研究波动选择使用工程化的随机开关控制四环素耐药性的大肠杆菌种群。使用微流体和活细胞成像，我们用相同总量的四环素治疗多个大肠杆菌种群，但以不同的时间模式给药。我们发现，暴露于短或长抗生素脉冲的人群可能会通过选择性扫描产生耐药性，而中间脉冲允许更高的增长率，但抑制选择性扫描。单细胞测量和动态增长模型的基础上，我们确定了人口增长的主要决定因素，并表明，生理记忆和环境的持续时间可以强烈调节电阻的出现。我们在模型合成系统中的详细定量提供了关于单细胞生理学和选择之间相互作用的关键教训，这应该为治疗方案的设计提供信息[9-12]，并分析适应波动选择的表型多样性群体[13-17]。
Emergence of antibiotic resistance, an evolutionary process of major importance for human health [1], often occurs under changing levels of antibiotics. Selective sweeps, in which resistant cells become dominant in the population, are a critical step in this process [2]. While resistance emergence has been studied in laboratory experiments [3–8], the full progression of selective sweeps under fluctuating stress, from stochastic events in single cells to fixation in populations, has not been characterized. Here, we study fluctuating selection usingEscherichia colipopulations engineered with a stochastic switch controlling tetracycline resistance. Using microfluidics and live-cell imaging, we treat multipleE. colipopulations with the same total amount of tetracycline but administered in different temporal patterns. We find that populations exposed to either short or long antibiotic pulses are likely to develop resistance through selective sweeps, whereas intermediate pulses allow higher growth rates but suppress selective sweeps. On the basis of single-cell measurements and a dynamic growth model, we identify the major determinants of population growth and show that both physiological memory and environmental durations can strongly modulate the emergence of resistance. Our detailed quantification in a model synthetic system provides key lessons on the interaction between single-cell physiology and selection that should inform the design of treatment regimens [9–12] and the analysis of phenotypically diverse populations adapting under fluctuating selection [13–17].