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Antimicrobial resistance (AMR) is a growing global health threat that requires coordinated action across One Health sectors (humans, animals, environment) to stem its spread. Environmental surveillance of AMR is largely behind the curve in current One Health surveillance programs, but recent momentum in the establishment of infrastructure for monitoring of the SARS-CoV-2 virus in sewage provides an impetus for analogous AMR monitoring. Simultaneous advances in research have identified striking trends in various AMR measures in wastewater and other impacted environments across global transects. Methodologies for tracking AMR, including metagenomics, are rapidly advancing, but need to be standardized and made modular for access by LMICs, while also developing systems for sample archiving and data sharing. Such efforts will help optimize effective global AMR policy.

Water reuse has become a promising alternative to alleviate stress on conventional freshwater resources in the face of population growth, sea level rise, source water depletion, eutrophication of water bodies, and climate change. Potable water reuse intentionally looks to purify wastewater effluent to drinking water quality or better through the development and implementation of advanced treatment trains. While membrane-based treatment has become a widely-adopted treatment step to meet this purpose, there is growing interest in implementing treatment trains that harness microorganisms as a more sustainable and less energy-intensive means of removing contaminants of emerging concern (CECs), through biological degradation or transformation. In this dissertation, various aspects of the operation of a microbially-active carbon-based advanced treatment train producing water intended for potable reuse are examined, including fate of dissolved organic carbon, underlying microbial populations, and functional genes are explored. Further, dynamics associated with antibiotic resistance genes (ARGs), identified as a microbially-relevant CECs, are also assessed. Overall, this dissertation advances understanding associated with the interplay between and within treatment processes as they relate to removal of various organic carbon fractions, microbially community dynamics, functional genes, and ARGs. Further, when relevant, these insights are contextualized to operational conditions, process upsets, water quality parameters, and other intended water uses within the water industry with the goal of broadening the application of advanced molecular tools beyond the scope of academic research. Specifically, this dissertation illuminates relationships among organic carbon fractions and molecular markers within an advanced treatment train employing flocculation, coagulation, and sedimentation (FlocSed), ozonation, biologically active carbon (BAC) filtration, granular active carbon (GAC) contacting, and UV disinfection. Biodegradable dissolved organic carbon (BDOC) analysis was adapted specifically as an assay relevant to assessing dissolved organic carbon biodegradability by BAC/GAC-biofilms and applied to profile biodegradable/non-biodegradable organic carbon as wastewater effluent passed through each of these treatment stages. Of particular interest was the role of ozonation in producing bioavailable organic carbon that can be effectively removed by BAC filtration. In addition to understanding the removal of fractionalized organic carbon, next generation DNA sequencing technologies (NGS) were utilized to better understand the microbial dynamics characteristic of complex microbial communities during disinfection and biological treatment. Specifically, this analysis was focused on succession and colonization of taxa, genes related to a wide range of functional interests (e.g. metabolic processes, horizontal gene transfer, DNA repair, and nitrogen cycling), and microbial CECs. Finally, NGS technologies were employed to assess the differences between a wide range of water use categories, including conventional drinking water, potable reuse, and non-potable reuse effluent's microbiomes to identify core and discriminatory taxa associated with intended water usage. The outcomes of this dissertation provide valuable information for optimizing carbon-based treatment trains as an alternative to membrane-based treatment for sustainable water reuse and also advance the application of NGS as a diagnostic tool for assessing the efficacy of various water treatment technologies for achieving treatment goals. Doctor of Philosophy Several factors have led to increased stress on conventional drinking water sources and widespread global water scarcity. Projections indicate that continued population growth, increased water demand, and degradation of current freshwater resources will negatively contribute to water needs and underscore the need to secure new potable (i.e. fit for human consumption) sources. Water reuse is a promising alternative to offset the growing demands on traditional potable sources and ameliorate negative consequences associated with water scarcity. Discharge of treated wastewater to marine environments is especially a lost opportunity, as the water will no longer be of value to freshwater habitats or as a drinking water source. Water reuse challenges the conventional wastewater treatment paradigm by providing advanced treatment of wastewater effluent to produce a valuable resource that can be safely used directly for either non-potable (e.g., irrigation, firefighting) or potable (i.e., drinking water) applications. The means of achieving advanced treatment of wastewater effluents can take many forms, commonly relying on the utilization of membrane filtration. However, membrane filtration is an intensive process and suffers from high initial costs, high operational costs, membrane fouling with time, and the production of a salty and difficult to dispose of waste stream. These drawbacks have motivated the water reuse industry to explore more sustainable approaches to achieving high quality effluents. One such alternative relies on the utilization of microorganisms to provide biological degradation and transformation of contaminants through a process known as biologically active filtration (BAF). Comparatively to membrane systems, BAF is more cost effective and produces significantly fewer byproducts while still producing high quality treated water for reuse. However, the range in quality of the resulting treated water has not yet been fully established, in part due to the lack of understanding of the complex microbial communities responsible for biological treatment. As water and wastewater treatment technologies have evolved over the past century, many biological treatments have remained largely 'black box' due to the lack of effective tools to identify the tens of thousands of species of microbes that inhabit a typical system and to track their dynamics with time. Instead, analysis has largely focused on basic water quality indicators. This dissertation takes important steps in advancing the implementation of the study of DNA and biodegradable organic carbon (BDOC) analysis to improve understanding of the mechanisms that drive different water reuse treatment technologies and to identify potential vulnerabilities. Insights gained through application of these tools are contextualized to observed operational conditions, process upsets, and water quality measurements. This helped to advance the use of DNA-based tools to better inform water treatment engineering practice. Specifically, this dissertation dives into the relationships between organic carbon and DNA-based markers within an advanced treatment train employing flocculation, coagulation, and sedimentation (FlocSed), ozonation, biologically active carbon (BAC) filtration, granular active carbon (GAC) contacting, and UV disinfection. Development and application of the BDOC test revealed that the bulk of organic carbon entering the treatment train is dissolved. Further, BDOC analysis served to characterize the impact of specific treatment processes and changes in operational conditions on both biodegradable and non-biodegradable organic carbon fractions. Such information can help to inform continued process optimization. Utilization of DNA-based technologies shed light on the functional capacity of microbial communities present within each stage of treatment and the fate of antibiotic resistance genes (ARGs). ARGs are of concern because, when present in human pathogens, they can result in the failure of antibiotics to cure deadly infections. Other functional genes of interest were also examined using the DNA-based analysis, including genes driving metabolic processes and nitrogen cycling that are critical to water purification during BAF treatment. Also, the DNA-based analyses made it possible to better understand the effects of disinfectants on microbes. Interestingly, some ARG types increased in relative abundance (a measure analogous to percent composition) response to treatments, such as disinfection, and others decreased. Characterization of the microbial communities and their dynamic response to changing operation conditions were also observed. For example, it was possible to characterize how the profiles of microbes changed with time, an ecological process called succession, during BAC filtration and GAC contacting. Generally, this analysis, coupled with the functional analysis, shed light on the important, divergent roles of bacterial communities on organic degradation during both BAC and GAC treatment. Finally, a study was conducted that compared the microbiome (i.e. entire microbial community) between a wide range of conventional drinking water, potable reuse water, and non-potable reuse waters. Here it was found that significant differences existed between the microbial communities of water intended for potable or non-potable usage. This work also looked to expand the application of NGS technologies beyond strictly academic research by developing the application of more advanced DNA-based tools for treatment train assessment and monitoring.

Antimicrobial resistance (AMR) threatens our modern standard of living with the potential return to a pre-antibiotic condition where deadly infections are no longer treatable. Wastewater treatment plants (WWTPs) are vital components in water sanitation infrastructure and are now also being recognized as valuable monitoring points for antibiotics, antibiotic resistant bacteria (ARB), and antibiotic resistance genes (ARGs) disposed of or excreted by human populations. Hospital waste water is of special interest as a potential focused monitoring point and in general research is needed to establish the benefits of both on-site and community-scale wastewater treatment as important barriers to the disseminators of ARGs into the environment. The research aims described herein examine these components of wastewater treatment and how they relate to AMR indicators identified through metagenomic sequencing. Through monitoring of local WWTPs, it was found that AMR indicators shifted over time and in relation to human behavior that changed due to the COVID-19 pandemic. Hospital wastewater did not measurably impact the microbiome during simulated activated sludge wastewater treatment according to broad-scale metagenomic ARG profiling; however, some clinically-relevant ARGs escaped treatment. Lastly, a study of a transect of WWTPs indicated impacts on the abundance of certain ARGs in downstream riverine receiving environments. Nonetheless, there appeared to be a number of other factors at play, and upstream and downstream resistomes tended to remain similar, calling for further research to delineate impacts of various wastewaters and treatments on ARGs in affected aquatic environments. Doctor of Philosophy Antimicrobial resistance (AMR) occurs when bacteria, viruses, and fungi are able to survive in the presence of antibiotics because they carry antibiotic resistance genes (ARGs) encoded in their DNA. AMR is a major public health concern as it makes it so that antibiotics are no longer effective against potentially deadly infections. Wastewater treatment plants (WWTPs) are being discovered as a hub of opportunity for monitoring potential AMR problems in a community. WWTPs receive sewage from homes and various industries. This sewage contains rich information for researchers to examine in terms of which antibiotics, bacteria, and ARGs are circulating in the community. This makes it possible to find out which antibiotics are being consumed in the community and which ARGs might be prevalent. The purpose of this research was to better understand both how WWTPs can be used as monitoring points for AMR and how they can be improved to help reduce ARGs emitted to rivers and streams where treated water is discharged. It was found that the types of ARGs prevalent in wastewater changed over time, especially during the COVID-19 pandemic as people worked from home and changed habits regarding doctors' visits, which impacted antibiotic use. Hospital sewage was studied as a useful indicator of pathogens and ARGs that are challenging a community and also the antibiotics being used. This research explored what happened to ARGs during the treatment of domestic (i.e., from people's homes) wastewater along with hospital wastewater and found that hospital wastewater introduced some ARGs that are typically found in clinical settings, but did not negatively impact the overall wastewater treatment process. Finally, the impact that WWTPs have on rivers to which treated water is discharged was explored. The results indicated that certain ARGs were elevated downstream of the WWTPs. However, when examining all ARGs together, no major shifts due to the treated wastewater were apparent.

Antibiotic resistance (AR) is a critical and looming threat to human health that requires action across the One Health continuum (humans, animals, environment). Coordinated surveillance within the environmental sector is largely underdeveloped in current National Action Plans to combat the spread of AR, and a lack of effective study approaches and standard analytical methods have led to a dearth of impactful environmental monitoring data on the prevalence and risk of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) in aquatic environments. In this dissertation, integrated surveillance approaches of surface water and wastewater systems are demonstrated, and efforts are made towards standardizing both metagenomic- and culture-based techniques for globally comparable environmental monitoring. A field study of differentially-impacted watersheds on the island of Puerto Rico post-Hurricane Maria demonstrated the effectiveness of metagenomics in defining direct impact of anthropogenic stress and human fecal contamination on the proliferation of ARGs in riverine systems. The contribution of treated wastewater effluents to the dissemination of highly mobile and clinically-relevant ARGs and their connection to local clinical settings was also revealed. At the international scale, a transect of conventional activated sludge wastewater treatment plants (WWTPs), representing both US/European and Asian regions, were found to significantly attenuate ARG abundance through the removal of total bacterial load and human fecal indicators, regardless of influent ARG compositions. Strong structural symmetry between microbiome and ARG compositions through successional treatment stages suggested that horizontal gene transfer plays a relatively minor role in actively shaping resistomes during treatment. Risk assessment models, however, indicated high-priority plasmid-borne ARGs in final treated effluents discharged around the world, indicating potentially increased transmission risks in downstream environments. Advancements were also made toward standardizing methods for the generation of globally representative and comparable metagenomic- and culture-based AR monitoring data via two comprehensive and critical literature reviews. The first review provides guidance in next-generation sequencing (NGS) studies of environmental AR, proposing a framework for experimental controls, adequate sequencing depths, appropriate use of public databases, and the derivation of datatypes that are conducive for risk assessment. The second review focuses on antibiotic-resistant Enterococcus spp. as robust monitoring targets and an attractive alternative to more widely adopted Gram-negative organisms, while proposing workflows that generate universally equivalent datatypes. Finally, quantitative metagenomic (qMeta) techniques were benchmarked using internal reference standards for high-throughput quantification of ARGs with statistical reproducibility. Doctor of Philosophy Antimicrobials have contributed to the reduction of infectious diseases in humans and animals since the early 20th century, increasing productivity and saving countless lives. However, their industrial-scale application across human, animal, and agricultural sectors over the last several decades, especially the use of antibiotics, have engendered the proliferation of antibiotic resistance (AR). AR occurs when changes in bacteria cause the drugs used to treat infections to become less effective and has become one of the leading public health threats of the 21st century. The global spread of AR through the transmission and evolution of antibiotic resistant bacteria (ARB; known colloquially as "superbugs") and antibiotic resistance genes (ARGs) across the One Health continuum (i.e., humans, animals, and the environment) is resulting in increased hospitalization, length of hospital stays, suffering, death, and overall health-care associated costs globally. This dissertation demonstrates the use of metagenomics, the sequencing of all genetic material (e.g., DNA) recovered from a microbial community, for the comprehensive monitoring of ARB and ARGs in aquatic environments, a key pathway for the dissemination of AR into and out of human populations. In order to impede the proliferation of AR, surveillance systems are currently in place to track the spread and evolution of ARB and ARGs in humans and livestock, as well as agri-food sectors. However, the surveillance in natural and built environments (i.e., rivers and domestic sewage) has significantly lagged due to the lack of standard monitoring targets and methodologies. It is also a goal of this dissertation to suggest guidance for the collection of metagenomic- and culture-based AR monitoring data to generate universally comparable results that can be included in centralized databases. Riverine systems are ideal models for tracking input of antibiotic resistance to the natural environment by human activity. After Hurricane-Maria, many of Puerto Rico's wastewater treatment plants (WWTPs) went offline, discharging raw sewage to local surface waters. In a cross-sectional study of watersheds impacted by WWTPs, the abundance of ARGs was directly correlated to increases in local population density. Also, highly mobile and clinically-relevant ARGs were found directly downstream of WWTPs across the island. We found that many of these ARGs corresponded well to forms AR endemic to the region. WWTPs are the primary engineering controls put in place to curb the spread of human and animal waste streams and can help to reduce AR. An international transect of conventional activated sludge WWTPs representing US/Europe and Asia were sampled to garner a mechanistic understanding of the fate or ARGs through treatment. Although WWTPs remove total bacteria, human fecal indicators, and much of the abundance of ARGs, mobile and clinically-relevant ARGs are discharged around the world in large quantities. Consideration is needed in certain regions of iv the world where the managing of human waste streams is the first line of defense against the dissemination of resistance to local communities. Two comprehensive critical literature reviews were conducted to evaluate the various methodologies for generating and analyzing metagenomic- and culture-based AR monitoring data. These reviews address the need for experimental rigor and disclosure of extensive metadata for inclusion in future, centralized databases. The articles further provide guidance with respect to universally comparable datatypes and efficient workflows that will aid in the scale-up of the collection of environmental monitoring data within a global surveillance framework. Finally, a study was conducted to benchmark the use of internal DNA reference standards for the absolute quantification of ARGs (i.e., on a ARG copy per volume of sample basis). The statistical framework for ARG detection and its implications for wastewater-based surveillance systems of AR are also discussed.

Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a “One Health” approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a "One Health" approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. Doctor of Philosophy Antibiotic resistance is ability of bacteria to withstand an antibiotic to which they were once sensitive. Antibiotic resistance is a global threat that can pose a serious threat to public health. In order to curb the spread of antibiotic resistance, it is imperative that efforts commensurate with the "One Health" approach. Since ecosystem networks can act as channels for the spread and spread of antibiotic resistance, there is growing recognition that a robust global environmental monitoring framework is required to promote a true one-health approach. The ideal goal would be to develop approaches that can inform the global spread of antibiotic resistance, help prioritize monitoring objectives and present robust data analysis frameworks for resistance profiling, and ultimately help develop strategies to contain the spread of antibiotic resistance. The objective of the work described in this thesis was to evaluate and develop different data analysis paradigms and their applications in the study and characterization of antibiotic resistance in different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. The Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. The results of Chapters 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes.

As climate change exacerbates water scarcity and alters available water and fertilizer resources, it is vital that take appropriate measures to ensure sustainable treatment of water, wastewater, and other waste streams that are protective of public health and support recovery and reuse of water and nutrients. The overarching theme of this dissertation is the advancement of next-generation DNA sequencing (NGS) and computational tools for achieving these goals. A suite of relevant fecal and environmental opportunistic pathogens are examined using both culture-based and NGS-based methods. Of particular concern to this research was not only the attenuation and inactivation of pathogens, but also ensuring that optimal treatment approaches reduce antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs). Key systems that were the focus of this effort included nutrient reuse (wastewater-derived biosolids and cattle-derived manure), water reuse, and drinking water systems disrupted by a major hurricane. A field study was carried out to survey a suite of pathogens from source-to tap in six small drinking water systems in Puerto Rico six months after Hurricane Maria. The study revealed that pathogenic Leptospira DNA was detected in all systems that were reliant on surface water. On the other hand, Salmonella spp. was detected in surface and groundwater sources and some distribution system waters both by culture and PCR. The study provided comparison of molecular-, microscopic-, and culture-based analysis for pathogen detection and highlighted the need for disaster preparedness for small water systems, including back-up power supply and access to chlorination as soon as possible after a natural disaster. A second field-study examined wastewater derived solids across an international transect of wastewater treatment plants in order to gain insight into the range of ARG concentrations encountered. It was found that, while total ARGs did not vary between treatment or continent of origin, clinically-relevant ARGs (i.e., ARGs encoding resistance to important classes of antibiotics used in humans) were significantly higher in solids derived from Asian wastewater treatment plants. Estimated loading rates of ARGs to soil under a scenario of land application were determined, highlighting in all cases that they are orders of magnitude higher than in the aqueous effluent. Livestock manure, derived from control cattle and cattle undergoing typical antibiotic treatment, and corresponding composts were also evaluated as common soil amendments in a separate study. In this study, the amendments were applied to two soil types in a greenhouse setting, in order to compare the resulting carriage of ARGs on a root (radish) versus leafy (lettuce) vegetable. Remarkably, radishes were found to harbor the highest relative abundance of total ARGs enumerated by metagenomics, even higher than corresponding soils or manures. Although the total microbial load will be lower on a harvested vegetable, the results suggest that the vegetable surface environment can differentially favor the survival of ARBs. The role of wastewater and water reuse treatment processes in reducing ARB and ARGs was also investigated at field-scale. Two independent wastewater treatment plants both substantially reduced total ARG relative and absolute abundance through biological treatment and settling according to metagenomic analysis. The subsequent water reuse treatment train of one system produced water for non- potable purposes and found further reduction in ARGs after chlorination, but a five hundred percent increase in the relative abundance of ARGs in the subsequent distribution system. In the second plant, which employed a membrane-free ozone-biologically-activated carbon-granular activated carbon treatment train for indirect potable reuse, there were notable increases in total ARG relative abundance following ozonation and chlorination. However, these numbers attenuated below background aquifer levels before recharge. Culture-based analysis of these systems targeting resistant ESKAPE pathogens (Escherichia coil, Staphylococcus aureus, Klebsiella spp., Acinetobacter baumannii, Pseudomonas aeruginosa, and Enterococcus spp.) indicated similar trends as the metagenomic ARG analysis for both systems, but was challenged by sub-optimal media for wastewater samples and low confirmation rates, limiting statistical analysis. In order to advance the application of NGS, molecular, and associated bioinformatic tools for monitoring pathogens and antibiotic resistance in environmental systems, newly emerging methods and field standards for antibiotic resistance assessment were also evaluated. Hybrid assembly, the assembly for both short and long metagenomic sequencing reads, were assessed with an in silico framework in order to determine which available assemblers produced the most accurate and long contigs. Hybrid assembly was found to produce longer and more accurate assemblies at all coverages by reducing error as compared to short read assembly, though the outputs differed in composition from long read assembly. Where it is possible, it is beneficial to sequence using both long- and short-read NGS technologies and employ hybrid assembly, but further validation is recommended. Genome resolved metagenomics has also emerged as a strategy to recover individual bacterial genomes from the mixed metagenomic samples though this is often not well validated. In order to address this, genomes were assembled from reclaimed water systems and were compared against whole-genome sequences of antibiotic resistant E.coli isolates. Metagenome-derived genomes were found to produce similar profiles in wastewater treatment plant influents. A final theme to this dissertation addresses the need to standardize targets, methodologies, and reporting of antibiotic resistance in the environment. A systematic literature review was conducted on assays for the enumeration of key ARGs across aquatic environments and recommendations are summarized for the production of comparable data. In sum, this dissertation advances knowledge about the occurrence of pathogens, ARB, and ARGs across aquatic and agricultural systems and across several countries. Advances are made in the application of NGS tools for environmental monitoring of antibiotic resistance and other targets and a path forward is recommended for continued improvement as both DNA sequencing technologies and computational methodologies continue to rapidly advance. Doctor of Philosophy Understanding bacteria in our engineered systems is critical to ensuring drinking water, recycled water, and manure-derived soil amendments are safe for downstream applications. As novel approaches for assessing bacteria are developed, standardized methods and evaluations much be developed to ensure that sound conclusions are made that can appropriately inform policy and practice for the protection of public health. This dissertation focuses on combining bacterial culture and DNA sequencing methods for the study of pathogens (i.e., disease-causing organisms) and antibiotic resistance (i.e., ability of some bacteria to survive antibiotic treatments) in agricultural manure management, water reuse, and drinking water systems. Additionally, this work sought to advance emergent metagenomic analysis tools, which provides a new and potentially powerful pathogen and antibiotic resistance monitoring approach through direct extraction and sequencing of DNA from environmental samples. Antibiotic resistance is a global health challenge and it has been widely recognized that wastewater and agriculture are key control points. When antibiotics are ingested by people or livestock, they select for resistant bacteria in the gut. Mitigation efforts are needed, particularly at wastewater treatment plants and on farms, to ensure that excreted antibiotics and resistant bacteria do not further propagate and pose a risk. However, additional challenges such as climate change have spurred the need for more efficient use of our water and nutrient resources. In this work I examined how nutrient and water reuse treatment methods affect antibiotic resistant bacteria and antibiotic resistance genes using DNA sequencing as well as culture-based methods. In order to assess agricultural practices, a systems approach was conducted at the greenhouse scale to identify key control points to stem the spread of antibiotic resistance when vegetables are grown in soils amended with cattle-derived manure fertilizers. Along the food production chain, vegetables (i.e., radish and lettuce) were found to harbor higher proportions of bacteria carrying antibiotic resistance genes, although the estimated numbers of these bacteria were lower. Solids from an international transect of wastewater treatment plants (Sweden, Switzerland, USA, India, Hong Kong, Phillippenes) were examined because they are also foten used as soil amendments. DNA sequencing of these solids revealed that total measured antibiotic resistance genes did not vary between treatment or continent of origin. Calculations were made to determine the range of total hypothetical outputs of ARGs if the biosolids are land applied. Wastewater reuse systems were also examined using culture and metagenomic DNA analysis so that living pathogens could be compared alongside the total (dead and alive) antibiotic resistance genes. While standard wastewater and subsequent water reuse treatments were found to reduce the absolute numbers of antibiotic resistance genes and bacteria in a treatment plant producing water for non-potable reuse (i.e., irrigation), increases in culturable resistant pathogens and antibiotic resistance genes were apparent in the distribution system (i.e., in the pipes conveying treated water to the point of use). Similar reductions in antibiotic resistant bacteria and resistance genes were also seen in a plant using more advanced treatment (ozonation paired with biofiltration) to produce water suitable for indirect potable reuse via aquifer recharge, but there were indications that ozone and chlorine can increase the proportion of antibiotic resistant bacteria. Finally, genomes were recovered from the metagenomic sequencing analysis and were compared to sequenced culture isolates to validate the capabilities of metagenomic analysis to re-assemble genomes at the strain level, which is often required for pathogen confirmation. Pathogens were also assessed in disrupted drinking water systems in Puerto Rico after Hurricane Maria. Small scale systems that were disrupted by the storm were sampled to identify if pathogens were measurable six months after the hurricane. This work revealed that genes attributed to pathogenic Leptospira were detected in all surface water reliant systems while Salmonella spp. were detected by culture and DNA methods, but only in the source surface and groundwaters, not in the distribution systems delivering water to from the treatment site to the tap. This research also contributed to the advancement of big data analysis pipelines as well as to the standardization of methods to ensure that data produced across studies are comparable. Hybrid assembly, an emergent method that combines both short and long metagenomic DNA sequences generated by different technologies to more accurately recover genomes, was found to improve reliability and accuracy of algorithms aimed at reassembling DNA fragments. Antibiotic resistance is a global challenge, but without standardized methodologies for environmental monitoring, it will be difficult to compare measurements across countries and treatment processes in order to identify effective mitigation strategies. A critical literature review was conducted on assays for the enumeration of key antibiotic resistance genes across aquatic environments so that comparable data can be generated. This will be critical to tap into the tremendous volumes of antibiotic resistance monitoring data being generated around the globe to help identify trends and inform solutions. Collectively, this dissertation advances knowledge about the occurrence of pathogens, antibiotic resistant bacteria and antibiotic resistance genes across aquatic and agricultural systems while also critically evaluating emerging methods for the detection of antibiotic resistance in the environment.

Wastewater treatment plants (WWTPs) receive a confluence of sewage containing antibiotics, antibiotic resistant bacteria, antibiotic resistance genes (ARGs), and pathogens, thus serving as key point of interest for the surveillance of antibiotic resistance (AR) dissemination. This thesis advances knowledge about the fate of AR indicators throughout treatment and reuse. The field study informs approaches for monitoring AR at a WWTP by characterizing the resistome (i.e., full profile of ARGs) and microbiome across eight sampling events via metagenomic sequencing, complemented by antibiotic data. The WWTP significantly reduced the total load of ARGs and antibiotics, although correlations between ARGs and antibiotics were generally weak. Quantitative polymerase chain reaction was applied to validate the quantitative capacity of metagenomics, whereby we found strong correlations. The influent and effluent to the WWTP were remarkably stable with time, providing further insight into the sampling frequency necessary for adequate surveillance. The laboratory study examined the effects of commonly applied disinfection processes (chlorination, chloramination, and ultraviolet irradiation [UV]) on the inactivation of antibiotic resistant pathogens and corresponding susceptible pathogens in recycled and potable water. Further, we evaluated their regrowth following disinfection by simulating distribution. Acinetobacter baumannii, an environmental opportunistic pathogen, regrew especially well following UV disinfection, although not when a disinfectant residual was present. Enterococcus faecium, a fecal pathogen, did not regrow following any disinfection process. There were no significant differences between water types. The findings of this study emphasize a need to move beyond the framework of assessing treatment efficacy based on the attenuation of fecal pathogens. Master of Science Wastewater treatment plants (WWTPs) have traditionally been designed and further enhanced to minimize environmental contamination caused by solid waste, fecal pathogens, nutrients (e.g., nitrogen), and organic matter. However, treatment has not been optimized to remove the contaminants of emerging concern (CECs) investigated in this thesis: antibiotic resistant bacteria (ARB), antibiotic resistance genes (ARGs), and antibiotics. WWTPs are key point of interest for local and global surveillance of antibiotic resistance as they can receive the aforementioned CECs (via human excretion or improper disposal) from various sources (e.g., residences, hospitals). Antibiotic resistant bacteria have caused 2.8 million infections and subsequently 35,000 deaths in the United States each year. Considering treated wastewater can serve as a route of exposure for humans, potential spread of antibiotic resistance by WWTPs is of high priority to mitigate from a public health perspective. In the first study utilizing a technology to assess the full complement of ARGs in a given sample, we observed that the total load of ARGs was removed by approximately 50% across wastewater treatment, on average; total antibiotic load exhibited a similar reduction. The second study demonstrated that antibiotic resistant environmental opportunistic pathogen (i.e., pathogens which take advantage of the "opportunity" to infect an immunocompromised host, especially thriving in low nutrient engineered systems), Acinetobacter baumannii, possesses the ability to regrow following disinfection in the absence of a disinfectant residual. In contrast, antibiotic resistant Enterococcus faecium, an opportunistic pathogen of fecal origin, was successfully inactivated and unable to regrow. The findings of this study emphasize a need to move beyond the framework of assessing treatment efficacy based on the attenuation of fecal pathogens.

The emergence of antibiotic resistance in riverine systems has become a growing issue worldwide. The use of watershed-scale models is popular with many other water quality issues but not in the case of antibiotic resistance. In this study, we introduce a watershed-scale bacteria fate and transport model to simulate antibiotic resistance in E. coli. This model was developed through amendments to an existing watershed-scale physically based hydrological model (SWAT), and the newly modified model was called SWAT-ARB. The SWAT-ARB model was employed in the receiving environment of a WWTP in the Adyar River basin, India. The SWAT-ARB model simulations of resistant fractions (resistant E. coli concentration/E. coli concentration) in stream water were analyzed by the flow levels with the application of a range of parameter values. It is concluded that the model can be used to test prevailing hypotheses and evaluate the current state of knowledge. For instance, model simulations suggest that the influx of ARB can be a primary driver of antibiotic resistance in rivers compared to ambient antibiotic concentrations. We used the SWAT-ARB model to quantify the impact of climate change on antibiotic resistance. Six climate models were used to obtain the future climates in two distinct scenarios. The model was applied to three watersheds as Adyar basin- India, Crab Creek basin- USA, and upper Viskan basin- Sweden. It was concluded that temperature increase may greatly affect the colder climates (Crab Creek and Viskan) with higher simulated resistant fractions. In case of Adyar basin, resistant fractions are alleviated in high flow conditions, while aggravated in low flow conditions. Doctor of Philosophy The antibiotic resistance occurs when bacteria no longer responds to antibiotics. Hence, the diseases that caused by resistant bacteria are harder to treat. These antibiotic resistant bacteria end up in our rivers because of our heavy use of antibiotics in human and animal treatments. Thus, the spread of antibiotic resistance has become a water quality issue in the rivers worldwide. Scientists generally use computer models to understand water quality issues in rivers. These computer models are important because of high cost of monitoring and their use in finding how environment works. Up to the date of this publication, there is no sophisticated enough model to simulate antibiotic resistance in rivers. Hence, we created a river basin scale model to simulate antibiotic resistance. We found that the influx of ARB can be a primary driver of antibiotic resistance in rivers compared to ambient antibiotic concentrations. The model was applied to three watersheds as Adyar basin- India, Crab Creek basin- USA, and upper Viskan basin- Sweden. It was concluded that temperature increase may greatly affect the colder climates (Crab Creek and Viskan) with higher antibiotic resistant bacteria compared to susceptible bacteria.

As the population in water stressed areas increases, it is critical that wastewater treatment plants (WWTPs) continue to replenish depleted water supplies, and serve as an alternative water source. WWTPs depend on microorganisms in activated sludge to remove pollutants from wastewater and therefore an understanding of how these microorganisms are affected by various conditions and pollutants is needed. Also, as consumer products and industrial processes evolve, so do the pollutants they discharge to wastewater. In order to keep pace with these changes, understanding the effects of emerging contaminants to WWTP processes is essential. The research herein assesses microbial community dynamics of the response of nitrifying microorganisms in activated sludge to variation in ammonia concentration and evaluates the impact of engineered nanoparticles on activated sludge microbial communities and other emerging pollutants, such as antibiotic resistance genes and disinfection by-products. In order to assess microbial community dynamics of the response of nitrifying microorganisms to removal of ammonia in the feed, nitrifying activated sludge reactors were operated at various relevant temperatures and the nitrifying microbial community was characterized using activity assays and bio-molecular techniques. We found that Nitrospira spp. were the dominant nitrifying microorganisms, exhibiting stable relative abundance across multiple trials and over a range of temperatures. These results indicate the possibility of comammox bacteria in the system and highlight the complexity of nitrifying microbial communities in activated sludge relative to past understanding. Both microbial and chemical impacts of engineered nanoparticles on WWTP processes were also investigated. Metagenomic analysis of DNA extracted from activated sludge sequencing batch reactors dosed with gold nanoparticles with varied surface coating and morphology indicated that nanoparticle morphology impacted the microbial community and antibiotic resistance gene content more than surface coating. However, nanoparticle fate was controlled by surface coating more than morphology. Disinfection by-product formation in the presence of nanoparticles during WWTP disinfection was assessed using silver, titanium dioxide, ceria, and zero valent iron nanoparticles. Silver nanoparticles were found to enhance trihalomethane formation, which was attributed to the citrate coating of the nanoparticles. These studies both raise concern over the relationship between engineered nanoparticles and other emerging concerns in WWTPs, and take a step towards informing nanoparticle design in a manner that limits their associated environmental impact. Ph. D.

Outputs of human-derived chemicals and constituents to the environment, and shifts in these outputs, can result in unintended consequences to human and ecological health. One such shift is the advent of the modern antibiotic era, in which mass production and outputs of antibiotics, which are mostly naturally-derived microbial defense compounds and include a few synthetic antimicrobials, has profound implications for contributing to the spread of antibiotic resistance. Antibiotic resistance arises from mutations and/or sharing of antibiotic resistance genes (ARGs) among bacteria via horizontal gene transfer, with carriage of ARGs by pathogenic bacteria of particular concern to human health. While most attention to stopping the spread of antibiotic resistance has been devoted to the clinic, it is critical to consider the environmental origin, ecology and pathways by which antibiotic resistance spreads in order to develop comprehensive strategies to combat antibiotic resistance. In particular, wastewater treatment plants (WWTPs) represent a potentially key critical control point given that they receive antibiotic resistant bacteria (ARB) and ARGs from the population, which are then routed to activated sludge biological treatment, consisting of high density, highly active microbial populations. The research projects described in this dissertation aimed to explore the occurrence of ARGs in WWTPs, particularly WWTPs in developing countries representing the extremes of what is expected to be encountered in terms of potential to spread antibiotic resistance, and to improve and apply novel technologies for monitoring key markers of antibiotic resistance in WWTPs and affected environments. The pathogen Staphylococcus aureus and a corresponding ARG (methicillin resistance mecA gene) were chosen as model biocontaminants of concern due to their environmental and public health relevance. The results reported in Chapters 3-5 advance the knowledge of bio(nano)sensing techniques and highlight areas of promise and challenge. The results reported in Chapter 2 provided insight into the baseline levels of ARGs expected in a highly impacted WWTP in India, thereby highlighting the magnitude and global scale of the problem of antibiotic resistance as well as the need for innovative solutions. Ph. D.

Natural microbial communities are the ultimate drivers of change in any ecosystem. Through chemical contamination of natural environments, these communities are exposed to many different types of chemical stressors; however, research on whole genome responses to this contaminant stress is limited. This research examined the stress response of a common soil bacterium, Pseudomonas aeruginosa, to a common environmental pollutant, pentachlorophenol (PCP). In the first part of the research, it was revealed that nutrient-limited P. aeruginosa is able to respond to PCP with minimal physiological damage due to the upregulation of multidrug efflux pumps. Further study of this PCP-mediated induction of efflux pumps revealed a simultaneous increase in antibiotic resistance. It was discovered that the resistance nodulation-cell division (RND) efflux pump, MexAB-OprM, in particular is responsible for the PCP-induced increase in antibiotic resistance. Both whole cell physiological indicators and whole genome analysis were used to examine the stress response of P. aeruginosa to PCP. Cells were grown in a chemostat at a low growth rate to simulate nutrient-limiting growth in the natural environment. Whole cell acetate uptake rates (WAUR) and viable cell counts as colony forming units (CFU) were determined as cells were exposed to increasing concentration of PCP. At the same time, changes in gene expression were examined by Affymetrix microarray technology. Results showed little change in whole-cell physiology, with no difference in WAUR and only a slight reduction in CFU. However, the microarrays revealed that over 100 genes either increased or decreased expression greater than two-fold due to the PCP exposure. In particular, multiple multidrug efflux genes were upregulated in response to the PCP. The results were validated by real time reverse transcription polymerase chain reaction (RT-PCR) for one of these genes. Further analysis of the effects of MexAB-OprM showed that this particular efflux pump is essential for the response of P. aeruginosa to the toxin PCP. Induction of multidrug efflux pumps is responsible for the development of antibiotic resistance in strains of P. aeruginosa. Therefore, it was investigated whether PCP might induce resistance to a variety of antibiotics. The research was further extended to examine the effect of a variety of organic contaminants on MexAB-OprM efflux and antibiotic resistance development. PCP, 2,4-dinitrophenol, benzoate and Roundup® all induced antibiotic resistance. However, although MexAB-OprM is required for optimal growth in the presence of all chemicals, this particular efflux pump is only involved in increased resistance with PCP. This was confirmed using RT-PCR as mexB expression was induced by PCP, but not by the other three chemicals. A long term generational study on the effects of PCP did not result in a stable antibiotic-resistant phenotype; however, RT-PCR showed that mexB induction is a direct result of PCP exposure and can be reversed by removal of PCP. Together, these results demonstrate the necessity to understand functional responses to contaminant stress. Discovery of direct induction of multidrug efflux pumps and the resulting increase in antibiotic resistance has significant implications for environmental microbiology and public health. This research suggests that organic contamination may result in antibiotic resistance and that antibiotic resistant strains may have a survival advantage in contaminated environments. Ph. D.