Evolutionary Tradeoffs in Antibiotic Resistance

当细菌中的突变导致先前抑制浓度的抗生素耐药性出现时， 使其变得可生存。通过突变的积累， 耐药性，已经有许多容易治疗的感染已经变得几乎无法治愈，并正在部分蔓延， 从遗传学角度来说。抗性进化的经典模型，即抗性突变体具有适应性优势， 抗生素使用的存在，因此在人群中传播到接近固定，抓住了抗生素的兴起 但是仔细观察，它无法解释耐药性的几个关键特征。第一，抗生素 在临床人群中，耐药性很少达到固定;更重要的是，敏感性高于耐药性。 种群遗传模型可以预测。其次，抗生素耐药性存在，而且可能很常见， 在人类开始使用抗生素之前，临床感染就已经开始了。第三，尽管普遍存在 环境中的产寄生虫细菌，这些相同的细菌仍然被敏感的 邻居由于这些原因，我们假设现有的抗性进化模型是不完整的， 特别是在环境中存在着一些进化因素， 对人类使用抗生素产生类似或更大程度的耐药性演变的影响。在这里，我们将 联合收割机在模型系统中的进化实验与计算建模和数据库挖掘相结合， 序列数据来研究对抗性演化的制约，重点关注两个关键方面：空间作用 结构在抗性进化中的作用，以及自私的遗传因素，包括寄生和寄生 质粒。抗性为微生物进化的研究提供了一个几乎理想的模型系统;适合度可以 明确规定的、强加的选择性压力可以很容易地调整，并且可以自发地或通过 水平基因转移我们希望能够揭示这种疾病出现、传播和 抗生素耐药性的局限性。

Antibiotic resistance emerges when a mutation in a bacterium causes a previously inhibitory concentration of a compound to become survivable. Through the accumulation of mutations conferring varying increases in resistance, already many easy-to-treat infections have become nearly incurable, and are spreading in part anthropogenically. The classical model of resistance evolution, that a resistant mutant has a fitness advantage in the presence of antibiotic use, and so spreads in the population to near-fixation, captures the rise of antibiotic resistance, but on closer inspection fails to explain several critical features of resistance. First, antibiotic resistance rarely reaches fixation in clinical populations; more importantly, sensitivity is higher than the population-genetic models would predict. Second, antibiotic resistance was present, and likely common, in clinical infections before the human use of antibiotics even began. Third, despite the widespread prevalence of antibiotic-producing bacteria in the environment, these same bacteria remain surrounded by sensitive neighbors. For these reasons, we hypothesize that the existing model of resistance evolution is incomplete, and in particular that there exist evolutionary factors in the environment which have a potentially countervailing effect on resistance evolution of similar or greater magnitude to the human use of antibiotics. Here, we will combine evolution experiments in model systems with computational modeling and database mining of sequence data to study the constraints on the evolution of resistance, focusing on two key areas: the role of spatial structure in the evolution of resistance, and the role of selfish genetic elements including phages and parasitic plasmids. Resistance provides an almost ideal model system for the study of microbial evolution; fitness can be well defined, imposed selective pressures can be readily tuned, and can emerge either spontaneously or by horizontal gene transfer. We expect to uncover the evolutionary mechanisms behind the emergence, spread, and limitation of antibiotic resistance.