Microbial population diversity as a driver of antibiotic resistance evolution

微生物种群多样性是抗生素耐药性进化的驱动力

• 批准号: 2282521
• 金额: --
• 依托单位: University of Manchester
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2282521
• 关键词: Microbial; population; diversity; driver; antibiotic

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.

传统上，微生物进化被认为是通过对有益突变的快速扫描来进行的，这些突变在种群内迅速修复--即所谓的“强选择，弱突变”模式。然而，最近在简单的非生物环境中发现了数千代人的持续多样性(Good等人)。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。2018a)。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特征的变异性，如生长和突变率，如何有助于种群适应新的环境条件的能力。基因组内容对纯培养中进化抗性的能力有相当大的影响(Gifford等人。2018b)，但这是否适用于混合微生物种群尚不清楚。这个问题对于野生微生物种群的进化同样重要(Ramirez，Knight等人。以及已知具有持续多样性的临床感染(Whelan等人)。这个项目将结合湿实验室实验进化和高通量基因组学，以了解细菌种群多样性如何对抗生素耐药性的进化做出贡献。我们将从一组不同的大肠杆菌中构建种群，以研究不同水平的多样性如何对种群的不同进化速度和潜力，或共同的“进化性”做出贡献。由不同水平的多样性组成的种群将在抗生素存在的情况下进行实验进化。讨论的问题将包括：多样性如何影响表型和基因组尺度上的进化的重复性？有没有基因可以让大肠杆菌更好地适应不同的抗生素？在不同的种群中，哪些基因组变化是适应的基础：从头突变、水平基因转移，还是两者兼而有之？(S)可移动的遗传元件，如质粒和噬菌体，在适应中扮演什么角色？实验进化将与高性能计算模拟(Gómez-Llano et使用的那种)相辅相成。这一多学科项目的成功申请者将受到进化生物学基本问题的激励，具有生物学、微生物学、遗传学或进化论的背景。具备分析和定量技能以及微生物学工作经验者优先。将提供培训，使成功的申请者能够与实验室和计算科学家组成的跨学科团队互动。(2018a)环境多效性和人口历史在抗生素选择下的直接适应，遗传学(在线早期)Gifford博士等人。(2018b)《识别和利用加强抗生素耐药性进化的基因》，《自然生态和进化》，2,1033-1039，DOI：10.1038/s41559-018-0547-XMA Gómez-Llano et al。(2016)男性和女性的性印记共同进化。生态与进化，6(19)，7113-7125，DOI：10.1002/ece3.2409BH Good等。(2017年)。60,000代的分子进化动力学。自然，551(7678)，P.45.R Krasovec等人。(2017年)自发突变率是与跨生命领域的人口密度有关的可塑性特征，《公共科学图书馆·生物学》，15(8)，e2002731，doi：10.1371/Joural.pBio.2002731KS Ramirez等人。(2018)通过对全球土壤的独立研究，检测细菌群落中的宏观生态模式。自然微生物学，3(2)，189196，DOI：10.1038/s415640170062xWhelan et al.2017年。囊性纤维化患者肺微生物区系的纵向采样。《公共科学图书馆·综合》，第12(3)页，第0172811页。