Bioinformatic Analysis of Wastewater Metagenomes Reveals Microbial Ecological and Evolutionary Phenomena Underlying Associations of Antibiotic Resistance with Antibiotic Use

Antibiotic resistance (AR) is a pervasive crisis that is intricately woven into social and environmental systems. Its escalation is fueled by factors such overuse, poverty, climate change, and the heightened interconnectedness characteristic of our era of globalization. In this dissertation, the impact of antibiotic usage is addressed from the perspective of wastewater-based surveillance (WBS) at the wastewater treatment plant (WWTP) and microbial ecology. Antibiotic usage and contamination was found to influence the prevalence of antibiotic resistance genes (ARGs) and resistant bacteria in both lab-scale and full-scale wastewater treatment settings. Through application of novel bioinformatic approaches developed herein, metagenomics revealed associations between sewage-associated microbes and community antibiotic use that were in part mediated by microbial ecological processes and horizontal gene transfer (HGT). In sum, this dissertation increases the arsenal of bioinformatic tools for AR surveillance in wastewater environments and advances knowledge with respect to the contribution of antibiotic use to the spread of antibiotic resistance at the community-scale. Three studies served to evaluate and/or develop bioinformatic resources for molecular characterization of AR in wastewater. Hybrid assembly combining emerging long read DNA sequencing and short read sequencing was evaluated and found to improve accuracy relative to assembly of long or short reads alone. A novel database of mobile genetic element (MGE) marker genes, mobileOG-db, was compiled in order to address short-comings with pre-existing resources. A pipeline for detecting HGT in metagenomes, Kairos, was created in order to facilitate the detection of HGT in metagenome assemblies which greatly amplified coverage of ARGs. In Chapter 5, a lab-scale study of WWTP bioreactors revealed that elevated antibiotic contamination was correlated with increased prevalence of corresponding ARGs. In addition, multiple in situ HGT events of ARGs encoding resistance to the elevated antibiotics were predicted, including one HGT event likely mediated by a novel bacteriophage. In Chapter 6, influent and effluent from a full-scale municipal WWTP were collected twice-weekly for one year and subjected to deep shotgun metagenomic sequencing. In parallel, collaboration with clinicians enabled statistical modeling of antibiotic usage and resistance, revealing associations between antibiotic prescriptions patterns in the region and resistance at the WWTP. Finally, Chapter 7 details bioinformatic recovery of diverse extended spectrum beta-lactamase gene recovery from the influent and effluent metagenomes, shedding light on the dynamics of circulating resistance genes. In sum, this dissertation identifies bioinformatic evidence for the selection of AR in wastewater environments as a result of antibiotic use in the community and advances hypotheses for explaining the mechanisms of the observed phenomena.