Microbial population diversity as a driver of antibiotic resistance evolution

传统上，微生物进化被认为是通过快速清除有益的突变而在种群中快速固定的，即所谓的“强选择，弱突变”模型。然而，最近在简单的非生物环境中发现了数千代的持续多样性（Good et al. 2017）挑战了这一观点。多样性的程度可以显著影响种群是否能够适应新的条件（Gifford等人，2018 a）。鉴于这些发现，有相当大的需要了解基因组和表型多样性如何有助于种群的进化。特别是，我们需要了解基因组内容和表型性状的变异性，如生长和突变率，如何有助于种群适应新环境条件的能力。基因组含量对纯培养物中进化抗性的能力具有相当大的影响（Gifford等人，2018 b），但对于混合微生物种群是否如此尚不清楚。这个问题对于野生微生物种群（Ramirez，Knight et al. 2018）和临床感染中的进化同样重要，已知这些微生物种群具有持续的多样性（Whelan et al. 2017）。该项目将联合收割机湿实验室实验进化和高通量基因组学相结合，以了解细菌种群多样性如何有助于抗生素耐药性的进化。我们将从一组不同的大肠杆菌构建人群，以研究不同水平的多样性如何有助于不同的进化速率和潜力，或集体，“进化”的人群。在抗生素存在的情况下，将通过实验进化出由不同多样性水平组成的种群。所讨论的问题包括：多样性如何影响表型和基因组尺度上进化的可重复性？是否有基因允许E.大肠杆菌能更好地适应多种抗生素吗？什么样的基因组变化是不同种群适应的基础：新生突变、水平基因转移，还是两者的混合？移动的遗传因素，如质粒和噬菌体，在适应中起什么作用？实验进化将与高性能计算模拟（Gómez-Llano et. al 2016），在那里可以实现对性状分布的精确控制。成功的申请人到这个多学科的项目将被进化生物学的基本问题所激励，具有生物学，微生物学，遗传学或进化的背景。分析和定量技能和微生物学经验将是一个优势。将提供培训，使成功的申请人能够与基于实验室和计算科学家的跨学科团队进行互动。DR Gifford等人（2018 a）抗生素选择下的环境多效性和人口统计学史直接适应，遗传（早期在线）DR Gifford et al.（2018 b）Identifying and exploiting genes that potentiate the evolution of antibiotic resistance，Nature Ecology and Evolution，2，1033-1039，doi：10.1038/s41559-018-0547-xMA Gómez-Llano et al.（2016）The coevolution of sexual imprinting by male and females. Ecology and Evolution，6（19），7113-7125，doi：10.1002/ece3.2409BH Good，et al.（2017）.分子进化超过六万代的动力学。自然，551（7678），p.45.R Krasnovic et al.（2017）自发突变率是一种与生命领域人口密度相关的可塑性性状，PLoS Biology，15（8），e2002731，doi：10.1371/journal.pbio.2002731KS Ramirez et al.（2018）在全球土壤的独立研究中检测细菌群落的宏观生态模式。Nature Microbiology，3（2），189-196，doi：10.1038/s41564-017-0062-xWhelan et al. 2017.囊性纤维化患者肺部微生物群的纵向采样PLoS One，12（3），p.e0172811.

Classically, microbial evolution has been thought to progress via rapid sweeps of beneficial mutations that quickly fix within populations-the so-called 'strong selection, weak mutation' model. However, recent discovery of sustained diversity over thousands of generations in simple abiotic environments (Good et al. 2017) challenges this view. The extent of diversity can significantly influence whether populations are able to adapt to novel conditions (Gifford et al. 2018a). In light of these findings, there is considerable need to understand how genomic and phenotypic diversity contributes to the evolution of populations. Particularly, we need to understand how variability in genomic content and phenotypic traits, such as growth and mutation rates, contribute to a population's ability to adapt to new environmental conditions. Genomic content has considerable impact on the ability to evolve resistance in pure cultures (Gifford et al. 2018b), but whether this is true for mixed microbial populations is unknown. This question is equally important for evolution in wild microbial populations (Ramirez, Knight et al. 2018) and in clinical infections, which are known to harbour sustained diversity (Whelan et al. 2017).This project will combine wet-lab experimental evolution and high-throughput genomics to understand how bacterial population diversity contributes to the evolution of antibiotic resistance. We will construct populations from a diverse set of Escherichia coli to investigate how different levels of diversity contribute to different evolutionary rates and potential, or collectively, 'evolvability' of populations. Populations consisting of different levels of diversity will be experimentally evolved in the presence of antibiotics. Questions addressed will include: how does diversity affect the repeatability of evolution on phenotypic and genomic scales? Are there genes that allow E. coli to adapt better to a diverse set of antibiotics? What genomic changes underlie adaptation in diverse populations: de novo mutations, horizontal gene transfer, or a mixture of both? What role(s) do mobile genetic elements, such as plasmids and bacteriophages, play in adaptation? Experimental evolution will be complemented with high performance computing simulations (of the sort used by Gómez-Llano et. al 2016) where precise control over the distributions of traits can be achieved.The successful applicant to this multi-disciplinary project will be motivated by fundamental questions in evolutionary biology, with a background in biology, microbiology, genetics or evolution. Analytical and quantitative skills and microbiology experience would be an advantage. Training will be provided to enable the successful applicant to interact with a cross-disciplinary team of laboratory-based and computational scientists.DR Gifford et al. (2018a) Environmental pleiotropy and demographic history direct adaptation under antibiotic selection, Heredity (online early)DR Gifford et al. (2018b) Identifying and exploiting genes that potentiate the evolution of antibiotic resistance, Nature Ecology and Evolution, 2, 1033-1039, doi:10.1038/s41559-018-0547-xMA Gómez-Llano et al. (2016) The coevolution of sexual imprinting by males and females. Ecology and Evolution, 6(19), 7113-7125, doi:10.1002/ece3.2409BH Good, et al. (2017). The dynamics of molecular evolution over 60,000 generations. Nature, 551(7678), p.45.R Krasovec et al. (2017) Spontaneous mutation rate is a plastic trait associated with population density across domains of life, PLoS Biology, 15(8), e2002731, doi:10.1371/journal.pbio.2002731KS Ramirez et al. (2018) Detecting macroecological patterns in bacterial communities across independent studies of global soils. Nature Microbiology, 3(2), 189-196, doi:10.1038/s41564-017-0062-xWhelan et al. 2017. Longitudinal sampling of the lung microbiota in individuals with cystic fibrosis. PLoS One, 12(3), p.e0172811.