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

North Dakota State University Fargo, Fargo ND

Investigators

Abstract

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.