CAREER: Differentiating mechanisms of ecological divergence in sympatric microbial populations using an integrated population genomics approach

职业：使用综合群体基因组学方法区分同域微生物种群的生态分歧机制

• 批准号: 1453397
• 负责人: Peter Bergholz
• 金额: $ 81.05万
• 依托单位: North Dakota State University Fargo
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-05-01 至 2020-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1453397&HistoricalAwards=false
• 关键词: CAREER; Differentiating; mechanisms; ecological; divergence

A centerpiece of evolutionary ecology research is to understand how new adaptations arise and spread in populations leading to speciation. The adaptation of microbes is important to understand, because microbes are: a) the primary drivers of global biogeochemical cycles, b) the means of production for many applications in biotechnology, and c) the causes of most infectious diseases. Microbes are frequently moved around by the action of animals, plants, wind and water, and may be deposited in unfavorable habitats. As a consequence, microbes adapt to new environments through to a combination of fast mutation rates and acquisition of new genes directly from their environment. There is a pressing need to better understand these processes by which microbes generate and maintain adaptive genetic variation, because such information is needed to forecast the responses of microbial species to rapid changes in land management or climate. This project studies how adaptive genetic variation permits a common bacteria important in biotechnology and the food supply, Escherichia coli, to survive after deposition in unfavorable habitats. Data on this process can tell us about how adaptive variation in populations contributes to the formation of new species. This project is designed to use the process of passive dispersal to diverse habitats as a natural experiment in order to differentiate between three models of ecological divergence in microbial populations. Studies of the landscape level structure in microbial populations will be complemented with genome-wide association studies. These experiments will link genomic polymorphisms to variation in phenotype, gene expression regulation, and source environment characteristics. These analyses will be coupled with laboratory challenges to quantify the adaptive value of genomic polymorphisms. Machine learning analytical techniques will be used to determine which of three models of ecological divergence best explains the data. An education program will teach undergraduate students about the ecological processes contributing to microevolution and speciation in microbes. The program will combine an agent-based model and data from experiments to teach students to interpret genomic adaptations in light of ecological processes acting on microbes in natural environments.

进化生态学研究的核心是了解新的适应性是如何在种群中产生和传播的，从而导致物种形成。 了解微生物的适应性很重要，因为微生物是：a）全球地球化学循环的主要驱动力，B）生物技术中许多应用的生产手段，以及c）大多数传染病的原因。微生物经常在动物、植物、风和水的作用下四处移动，并可能沉积在不利的栖息地。因此，微生物通过快速突变率和直接从环境中获得新基因的组合来适应新环境。迫切需要更好地了解微生物产生和维持适应性遗传变异的这些过程，因为需要这些信息来预测微生物物种对土地管理或气候快速变化的反应。该项目研究适应性遗传变异如何允许生物技术和食品供应中重要的常见细菌大肠杆菌在不利的栖息地沉积后生存。关于这一过程的数据可以告诉我们种群中的适应性变异如何有助于新物种的形成。该项目旨在利用被动扩散到不同生境的过程作为一个自然实验，以区分微生物种群生态分歧的三种模式。微生物种群景观水平结构的研究将与全基因组关联研究相补充。这些实验将把基因组多态性与表型、基因表达调控和源环境特征的变化联系起来。 这些分析将与实验室挑战相结合，以量化基因组多态性的适应价值。机器学习分析技术将用于确定三种生态分歧模型中哪一种最能解释数据。一个教育项目将教授本科生有关微生物微进化和物种形成的生态过程。该计划将结合联合收割机基于代理的模型和实验数据，教学生解释基因组适应在自然环境中的微生物作用的生态过程。