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. / Doctor of Philosophy / Antibiotics are key lifesaving drugs that have dramatically improved life expectancy throughout the 20th and 21st centuries. However, there has been an increased incidence of resistance among many important bacterial pathogens in recent decades. The more antibiotics are used, the more chance that resistant bacteria can evolve, survive, and spread. Outpatient care accounts for the vast majority of therapeutic antibiotic use, with more than 200 million prescriptions written for antibiotics in 2021 in the United States. While performing a vital function in combatting disease, oral antibiotics can inadvertently harm the resident microbes of the intestinal tract (i.e., the gut microbiome) by decreasing the diversity of the microbes present and increasing the number of resistant bacteria. At a community level, antibiotic usage also has the potential to induce increased prevalence of antibiotics and antibiotic resistant bacteria in the environment as well, primarily via human excreta (urine and feces). Wastewater represents a key interface between human-derived contaminants and the environment. In regions with centralized wastewater management, antibiotics- and resistant bacteria-containing excreta are typically transported via sewage conveyance systems to a wastewater treatment plant (WWTP). At the WWTP, diverse microbes interact with and degrade various organic contaminants in a series of processes combining physical, chemical, and biological treatments. Due to the intermingling of environmental microbes, antibiotics, and antibiotic resistant bacteria, wastewater is increasingly being recognized as an important venue for antibiotic resistance surveillance and for potential interventions. Awareness of wastewater-based surveillance and epidemiology has surged as a result of the COVID-19 pandemic and such efforts are enshrined in the National COVID-19 Preparedness Plan. However, such a task is fundamentally more challenging for antibiotic resistance than for SARS-CoV-2, as it comprises multiple bacterial strains, antibiotic resistance genes, and resistance mechanisms. In this respect, DNA sequencing of wastewater, i.e., "metagenomics," holds promise as a broad monitoring tool with an unprecedented degree of biological granularity. In this dissertation, we address the impact of antibiotic usage at the WWTP from the perspective of wastewater-based surveillance. We evaluate antibiotic usage at the community-scale as a selective force among bacteria inhabiting WWTPs and identify microbial interactions that influence the escape of resistant bacteria in the effluent. A field-study of wastewater entering the WWTP and cleaned effluent water discharged by the WWTP revealed certain antibiotics and corresponding forms of antibiotic resistance were particularly prone to proliferation in the WWTP. Novel bioinformatic tools were developed and applied to the study of wastewater to reveal these associations. In sum, this dissertation advances knowledge of wastewater as both a mediator of environmental health and as a reflection of community-health in the form of antibiotic resistance.

Dans le contexte des changements globaux, un des enjeux majeurs en génomique écologique est d'estimer le potentiel adaptatif des populations naturelles. Répondre à cet enjeu nécessite 3 étapes: identification des agents sélectifs et de leurs échelles spatiales de variation, identification des bases génétiques de l'adaptation et étude de la dynamique adaptative sur une courte échelle de temps. Durant ma thèse, je me suis intéressé à étudier le potentiel adaptatif de la plante modèle Arabidopsis thaliana. A partir de 168 populations naturelles d'A. thaliana caractérisées pour 24 traits phénotypiques et 60 facteurs abiotiques (climat, sol) et biotiques (communautés végétales et microbiote), j'ai pu mettre en évidence que les communautés végétales étaient les principaux agents sélectifs associés à la fitness. Après avoir séquencé le génome de ces 168 populations (~ 4.8 millions de SNPs), j'ai effectué des analyses de type 'association génome-environnement' couplé à des scans génomiques de différenciation génétique spatiale. Ces analyses ont confirmé l'importance de considérer les interactions plante-plante dans l'étude de l'adaptation chez A. thaliana. Afin d'étudier le potentiel adaptatif d'A. thaliana sur le court terme dans le contexte d'un réchauffement climatique, j'ai combiné une étude de résurrection in situ avec une étude de Genome Wide Association mapping, à partir de 195 accessions locales caractérisées pour 29 traits phénotypiques et pour environ 1.9 million de SNPs. J'ai identifié une architecture originale de l'adaptation vers un nouvel optimum phénotypique combinant (i) de rares QTLs avec des degrés de pléiotropie intermédiaires fortement sélectionnés et (ii) de très nombreux QTLs spécifiques d'un micro-habitat et faiblement sélectionnés. A travers les différents projets abordés pendant ma thèse, j'ai pu suggérer qu'une architecture génétique flexible pouvait permettre à A. thaliana de s'adapter rapidement aux changements globaux, tout en maintenant de la diversité génétique au sein des populations naturelles d'A. thaliana. / In the context of global changes, one of the challenges in ecological genomics is to estimate the adaptive potential of natural populations. Three steps are requested to address this challenge: identification of the selective agents and their associated spatial grains, identification of the genetic bases of adaptation and monitoring the adaptive dynamics of natural population over a short time period. Here, I aimed at studying the adaptive potential of the model plant Arabidopsis thaliana. Based on 168 natural populations of A. thaliana characterized for 24 phenotypic traits and 60 abiotic (climate, soil) and biotic (plant communities and microbiota) factors, plant communities were found to be the main selective agents. Based on 4.8 million SNPs, I combined Genome Environment Association analysis with genome scans for signatures of selection. I confirmed the importance to consider plant-plant interactions when studying adaptation in A. thaliana. To monitor the adaptive dynamics of a natural population in the context of global warming, I combined an in situ resurrection study with an approach of GWA mapping based on 195 local accessions characterized for 29 phenotypic traits and 1.9 million SNPs. Adaptive evolutionary changes were largely driven by rare QTLs with intermediate degrees of pleiotropy under strong selection. In addition to these rare pleiotropic QTLs, weak selection was detected for frequent small micro-habitat-specific QTLs that shape single traits. Overall, I suggest that a rapid adaptive phenotypic evolution can be rapidly achieved in A. thaliana, while still maintaining genetic variation in natural populations.