Manure is a commonly used soil fertilizer, but there are concerns that this practice could affect the spread of antibiotic resistance genes (ARGs) from farm to fork. A microcosm-scale study evaluated the effect of prior antibiotic use (manure-based soil amendments generated from dairy and beef cattle with or without antibiotic administration), composting, and soil type on the quantity of ARGs and the microbial community composition of dairy and beef manure applied soil. ARGs were analyzed through novel metagenomic techniques and quantitative polymerase chain reaction of sul1, tet(W), and 16S rRNA gene, while the microbial community composition was determined via 16S rRNA amplicon sequencing. The results indicated that while prior antibiotic administration elevated the relative abundance of ARGs and changed the microbial community of raw manure applied soils, composting reduced this effect. However, compost applied soils still had a higher relative abundance of ARGs than the unamended soils and occasionally soil applied with raw manure of untreated cattle. Soil type may be a mediating factor as there were differences observed between the three soil types (sandy loam, silty clay loam, and silty loam) with sandy loam amended soils often having the least attenuation of ARGs. As the relative abundance of ARGs was still elevated and the microbial community composition still significantly different from the unamended soils after 120 days, these results suggest that 120 days is not a long enough waiting period between biological soil amendments and crop harvest for ARG dissipation. / Master of Science

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Antimicrobial resistance (AMR) is a One-Health (human, animal, environment) challenge that requires collaborative, interdisciplinary action. Comparable surveillance data are needed to effectively inform policy interventions aimed at preventing the spread of AMR. Environmental monitoring lags behind that of other One Health sectors and is in need of agreed upon targets and standardized methods. A challenge is that there are numerous microorganisms, antibiotic resistance genes (ARGs), and mobile genetic elements and corresponding methods that have been proposed. In this dissertation, a framework for AMR monitoring of aquatic environments was developed through a combination of literature review and stakeholder input, via surveys and a workshop. Through this process, three targets were selected for standardization: the sulfonamide resistance gene (sul1), the class 1 integron integrase gene (intI1), and cefotaxime-resistant Escherichia coli. Quantitative polymerase chain reaction (qPCR)- and culture-based protocols were developed and pilot tested in two independent laboratories on a set of six water matrices: wastewater, recycled water, and surface water from six different wastewater utilities engaging in water reuse located in five states across the USA. The impact of wastewater treatment and advanced water treatment processes was examined in terms of removal of these targets. Finally, qPCR and culture methods were used to examine the relationship between sul1, intI1, E. coli, and fecal indicators in private household wells across four states in the Southern USA that were identified as susceptible to storm events. The overall findings provide a useful baseline occurrence of the proposed AMR monitoring indicators across a range of water types and protocols that are accessible to water utilities. / Doctor of Philosophy / Life-saving drugs and treatments are failing at an increasing rate because of antimicrobial resistance (AMR). Antimicrobials, such as antibiotics, are a double-edged sword, because they are an effective weapon for killing disease-causing pathogens, but the more they are used the greater the likelihood that microbes that are resistant to them will survive, reproduce, and spread. National action plans for AMR have been created by a majority of countries, emphasizing the importance of antibiotic stewardship and other mitigation strategies. However, numerous data gaps need to be addressed in order to identify strategies that are most likely to be effective and to implement them. Environmental surveillance, including wastewater influent, wastewater effluent, and surface water, could prove an informative means to track AMR trends with time and relate them to human activities and corresponding mitigation efforts. The purpose of this dissertation was to develop a framework for AMR surveillance of aquatic environments and to test it across an array of sample types. We considered an array of possible culture- and DNA-based targets from available scientific literature and engaged experts and stakeholders in narrowing down the list to options that were both informative and feasible. We developed protocols for quantifying an antibiotic resistance gene (sul1), a mobile genetic element that has been implicated in the spread of multi-antibiotic resistance (intI1), and an extended spectrum beta-lactamase (ESBL) producing form of Escherichia coli. We compared the methods between two independent laboratories on untreated wastewater, treated wastewater, recycled water, and surface water collected from six locations across five states. We additionally did a survey of private household well water that was hypothesized to be vulnerable to contamination due to storms and lack of resources for maintenance. The results of this research can help to support environmental monitoring of AMR across the US and globally.

Antibiotic resistance is a growing human health threat, making infections more difficult to treat and increasing fatalities from and cost of treatment of associated diseases. The rise of multidrug resistant pathogens threatens a return to the pre-antibiotic era where even the most common infections may be impossible to treat. It is estimated that the majority of global antibiotic use, and use in the U.S., is dedicated towards livestock, where they are used to promote growth, treat, or prevent disease. Given that exposure to antibiotics selects for antibiotic resistant bacteria (ARBs) and can stimulate the horizontal transfer of their associated antibiotic resistance genes (ARGs), it is important to examine livestock operations as a reservoir of resistance. Correspondingly, there is growing interest in identifying how agricultural practices can limit the potential for spread of antibiotic resistance through the "farm to fork continuum," starting with antibiotic use practices, manure management and land application and ending with the spread of ARBs and ARGs present onto edible crops and serving as a route of exposure to consumers. This study focused specifically on the effect of composting on the prevalence of ARBs and ARGs in cattle manure. Three composting trials were performed: small-scale, heat-controlled, and large-scale. The small-scale composting trial compared dairy and beef manures, with or without antibiotic treatment (treated beef cattle received chlortetracycline, sulfamethazine, and tylosin while treated dairy cattle received cephapirin and pirlimycin), subject to either static or turned composting. The heat-controlled composting trial examined only dairy manure, with or without antibiotic treatment, subject to static composting, but using external heat tape applied to the composting tumblers to extend the duration of the thermophilic (>55°C) temperature range. The large-scale composting trial examined dairy manure, with or without antibiotic treatment, subject to static composting at a much larger scale that is more realistic to typical farm practices. Samples were analyzed to assess phenotypic resistance using the Kirby Bauer disk diffusion method and by diluting and plating onto antibiotic-supplemented agar. Genetic markers of resistance were also assessed using quantitative polymerase chain reaction (qPCR) to quantify sul1 and tet(W) ARGs; metagenomic DNA sequencing and analysis were also performed to assess and compare total ARG abundance and types across all samples. Results indicate that composting can enrich indicators of phenotypic and genetic resistance traits to certain antibiotics, but that most ARGs are successfully attenuated during composting, as evidenced by the metagenomic sequencing. Maintaining thermophilic composting temperatures for adequate time is necessary for the effective elimination of enteric bacteria. This study suggests that indicator bacteria that survive composting tend to be more resistant than those in the original raw manure; however, extending the thermophilic stage of composting, as was done in the heat-controlled trial, can reduce target indicator bacteria below detection limits. Of the two ARGs specifically quantified via qPCR, prior administration of antibiotics to cattle only had a significant impact on tet(W). There was not an obvious difference in the final antibiotic resistance profiles in the finished beef versus dairy manure composts according to metagenomics analysis. Based on these results, composting is promising as a method of attenuating ARGs, but further research is necessary to examine in depth all of the complex interactions that occur during the composting process to maximize performance. If not applied appropriately, e.g., if time and temperature guidelines are not enforced, then there is potential that composting could exacerbate the spread of certain types of antibiotic resistance. / Master of Science

Anthropogenically generated antibiotic resistance genes (ARGs) are considered emerging contaminants, as they are associated with a critical human health challenge, are persist independent of a bacterial host, are subject to transfer between bacteria, and are present at amplified levels in human-impacted environments. Given the gravity of the problem, there is growing interest in advancing water treatment processes capable of limiting ARG dissemination. This study examined the potential for membrane treatment of microconstituent ARGs, and the effect of wastewater colloids on their removal. Native and spiked extracellular vanA (vancomycin resistance) and blaTEM (β-lactam resistance) ARGs were tracked by quantitative polymerase chain reaction through a cascade of membrane filtration steps. To gain insight into potential associations occurring between ARGs and colloidal material, the wastewater colloids were characterized by scanning electron microscopy, as well as in their protein, polysaccharide, and total organic carbon content. The results suggest that extracellular DNA (eDNA) containing ARGs interacts with wastewater colloids, and can both be protected against degradation and be removed more efficiently in the presence of wastewater colloidal material. Thus, ARG removal may be achievable in sustainable water reuse scenarios using lower cost membranes than would have been selected based on molecular size alone. As membranes are likely to play a vital role in water sustainability, the results of this study enable consideration of ARG removal as part of a comprehensive strategy to manage emerging contaminants and to minimize overall public health risks. / Master of Science

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.

Many antibiotic resistance genes (ARGs) conferring resistance to a broad range of antibiotics have often been detected in aquatic environments such as untreated and treated wastewater, river and surface water. ARG proliferation in the aquatic environment could depend upon various factors such as geospatial variations, the type of aquatic body, and the type of wastewater (untreated or treated) discharged into these aquatic environments. Likewise, the strong interconnectivity of aquatic systems may accelerate the spread of ARGs through them. Hence a comparative and a holistic study of different aquatic environments is required to appropriately comprehend the problem of antibiotic resistance. Many studies approach this issue using molecular techniques such as metagenomic sequencing and metagenomic data analysis. Such analyses compare the broad spectrum of ARGs in water and wastewater samples, but these studies use comparisons which are limited to similarity/dissimilarity analyses. However, in such analyses, the discriminatory ARGs (associated ARGs driving such similarity/ dissimilarity measures) may not be identified. Consequentially, the reason which drives the dissimilarities among the samples would not be identified and the reason for antibiotic resistance proliferation may not be clearly understood. In this study, an effective methodology, using Extremely Randomized Trees (ET) Algorithm, was formulated and demonstrated to capture such ARG variations and identify discriminatory ARGs among environmentally derived metagenomes. In this study, data were grouped by: geographic location (to understand the spread of ARGs globally), untreated vs. treated wastewater (to see the effectiveness of WWTPs in removing ARGs), and different aquatic habitats (to understand the impact and spread within aquatic habitats). It was observed that there were certain ARGs which were specific to wastewater samples from certain locations suggesting that site-specific factors can have a certain effect in shaping ARG profiles. Comparing untreated and treated wastewater samples from different WWTPs revealed that biological treatments have a definite impact on shaping the ARG profile. While there were several ARGs which got removed after the treatment, there were some ARGs which showed an increase in relative abundance irrespective of location and treatment plant specific variables. On comparing different aquatic environments, the algorithm identified ARGs which were specific to certain environments. The algorithm captured certain ARGs which were specific to hospital discharges when compared with other aquatic environments. It was determined that the proposed method was efficient in identifying the discriminatory ARGs which could classify the samples according to their groups. Further, it was also effective in capturing low-level variations which generally get over-shadowed in the analysis due to highly abundant genes. The results of this study suggest that the proposed method is an effective method for comprehensive analyses and can provide valuable information to better understand antibiotic resistance. / MS

Bacterial resistance to antibiotics has become a worldwide health problem, resulting in untreatable infections and escalating healthcare costs. Wastewater treatment plants are a critical point of control between anthropogenic sources of pathogens, antibiotic resistant bacteria (ARBs), antibiotic resistance genes (ARGs), and the environment through discharge of treated effluent and land application of biosolids. Recent studies observing an apparent resuscitation of pathogens and pathogen indicators and the widening realization of the importance of addressing environmental reservoirs of ARGs all lead toward the need for improved understanding of ARG fate and pathogen inactivation kinetics and mechanisms in sludge stabilization technologies. This research has investigated the fate of two pathogens, methicillin-resistant Staphylococcus aureus (MRSA) and Escherichia coli, and various ARGs under pasteurization, anaerobic digestion, biosolids storage, and land application conditions. Pathogen die-off occurs at a rate specific to each pathogen and matrix in ambient and mesophilic temperature environments. Viable but nonculturable (VBNC) states are initiated by thermal treatments, such as thermophilic digestion and possibly pasteurization, and allow the persistence of pathogen cells and any ARGs contained therein through treatment and into the receiving environment where resuscitation or transformation could occur. Raw sludge ARG content does affect digester effluent quality, although the predominant mechanisms of ARG persistence may be different in mesophilic versus thermophilic digestion. In both thermophilic and mesophilic digestion, a correlation was observed between raw sludge and digester ARGs associated with Class 1 integrons, possibly as a result of horizontal gene transfer. ARB survival was shown to contribute to ARG content in mesophilic digestion, but not thermophilic digestion. Thermophilic digestion may achieve a higher ARG reduction because of reduced microbial diversity compared to mesophilic digestion. However, it is evident that horizontal gene transfer still does occur, particularly with highly mobile integrons, so that complete reduction of all ARGs would not be possible with thermophilic digestion alone. Surprisingly, the experiments that introduced various concentrations of antibiotic sulfamethoxazole and antimicrobial nanosilver did not induce enhanced rates of horizontal gene transfer. Finally, ARG concentrations in biosolids increased during cold temperature storage suggesting that there is a stress induction of horizontal gene transfer of integron-associated ARGs. / Ph. D.

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.

Rising global antibiotic resistance has caused concerns over sources and pathways for the spread of contributing factors. Majority of the antimicrobials used in the U.S. are involved in veterinary medicine, primarily with livestock rearing. Animal manure land application integrates livestock farming and agroecosystems. This manure contains antibiotic resistant elements (AREs) (resistant bacteria, resistance genes, and veterinary antibiotics) that contribute towards antimicrobial resistance. Altering manure application techniques can reduce surface runoff of other contaminants such as excess N and P, pesticides, and hormones, that can impact water quality. Conventional tillage practices in the U.S. has reduced or stopped, making subsurface injection of manure a promising option when compared to surface application. Our research compared manure application methods, manure application seasons, cropping system, and manure-rainfall time gaps to gauge the impact on AREs in the environment. Two field-scale rainfall simulation studies were conducted along with one laboratory study. Using the injection method lowered concentrations of manure associated AREs entering surface runoff. When manure was surface applied and rainfall occurred 7 d after application, 9-30 times less resistant fecal coliform bacteria (FCB) entered surface runoff when compared to 1 d time gap for that broadcast method. Within a day of manure application, antibiotic resistance gene (ARG) profiles in soil began to differ from each other based on manure application and soil ARG richness in all manure-amended soil increased compared to the background. Runoff from injection plots contained 52 ARGs with higher abundance compared to runoff from surface applied plots. ARGs in the former were more correlated to soil and more correlated to manure in the latter. The highest antibiotic concentrations were in the injection slit soil of those plots. Antibiotic concentrations in samples corresponded positively to concentrations of resistant FCB and ARGs, and there was a positive correlation between resistant FCB and their associated ARGs (Spearman's ρ = 0.43-0.63). A CRIISPR-Cas12a assay for quantification of ARGs in environmental samples was just as precise as conventional methods. There is also potential for in-situ detection. These combined results can hopefully help farmers improve manure management practices that mitigate spread of AREs to surrounding water, crops, and soil. / Doctor of Philosophy / Rising global antibiotic resistance cause concerns over sources and pathways for the spread of contributing factors. Most of the antimicrobials used in the U.S. are involved in veterinary medicine, especially with livestock rearing. Overuse of antibiotics that are medically important to human medicine compromises the effectiveness of our medicines. Animal manure contains antibiotic resistant elements (AREs) such as resistant bacteria, resistance genes, and antibiotics) that contribute towards resistance issues. Once these AREs enter the environment, they can be taken up by crops, runoff into surface water or leached into ground water, or even reside within the animal products we consume. Altering manure application techniques is beneficial for nutrient conservation but also potentially for reducing ARE spread. With our research, we compared manure application methods, manure application seasons, cropping systems, and manure-rainfall time gaps to find ways to balance the need for manure application and the spread of resistance. We used two field-scale rainfall simulation studies along with one laboratory study. Overall, using the injection method resulted in significantly lower concentrations of manure associated AREs entering surface runoff. When manure was surface applied and rainfall occurred 7 d after application, less resistant fecal coliform bacteria (FCB) entered surface runoff when compared to the 1 d time gap for broadcast methods. Within a day of manure application, antibiotic resistance gene (ARG) profiles in soil began to differ from each other and soil ARG totals in all manure applied soil increased compared to the background. Runoff from injection plots contained more soil ARGs and runoff from surface applied plots containing more manure associated ARGs. The subsurface injection method also caused highest antibiotic concentrations in the injection slit soil of those plots. High antibiotic concentrations in samples generally meant high concentrations of resistant FCB and ARGs, and resistant FCB were also found with their associated ARGs as well. A CRISPR-Cas12a assay for quantification of ARGs in environmental samples was just as precise as conventional methods. There is also potential for onsite detection. These combined results can hopefully help farmers improve manure management practices that mitigate spread of AREs to surrounding water, crops, and soil.