Community-based Analysis for Identifying Populations Relevant to Pollutant Mitigation in Natural and Engineered Processes

Microorganisms are involved in various important environmental processes. While current understanding of these microbial processes is shaped to a large extent by studies of individual populations, increasing efforts have been made to understanding the roles of microbial communities as a whole in environmental processes, which is made possible with the development of high-throughput sequencing technologies. In this dissertation, the microbial communities in anaerobic waste treatment processes and stream waters influenced by anthropogenic activities were investigated as models of engineered and natural systems using microbial community-based analyses. In the anaerobic waste treatment processes, metagenomics analyses revealed the persistence of antibiotic resistant genes (ARGs) and association with specific microbial hosts, providing insight into potential targets for mitigating the spread of ARGs. Further, community-based analysis identified legacy effect as an important mechanism contributing to the assembly of microbial communities, shedding light on potential strategies for the control of important populations underlying waste treatment. In the investigation of stream waters impacted by anthropogenic activities, microbial community-based analyses enabled the successful identification of primary anthropogenic sources contributing to the microbial contamination in stream water, which has long been confounded using traditional indicator-based approaches. Results from this study provide an innovative technique for microbial source tracking not otherwise possible with individual population-based approaches. Community-based analyses, as demonstrated in this dissertation, are capable of identifying interactions between microbial populations which are essential for the survival, persistence, and function of microorganisms in the environment. Furthermore, community-based analyses are capable of utilizing all information embedded in microbial communities, which enables more precise and accurate quantification of microbial community composition and function, paving the way for the development of more effective data analytics techniques for the characterization and modeling of microbial communities.