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Inflammatory Bowel Disease (IBD) is a chronic and incurable condition that is increasing inprevalence across the globe. This illness consist of two forms: Crohn’s Disease (CD) andUlcerative Colitis (UC). CD is characterised by a patch inflammation pattern across the gut anda multitude of different factors, such as diet. Contemporary research has found a link betweengut dysbiosis and the development of IBD, suggesting that the microbial flora colonising the guthave a vital part to play in the development of CD.This paper aims to identify taxa associated with CD. This is done through the application ofmachine learning algorithms as standard univariate statistical methods fail to apply in the highlyinterdependent domain of the gut microbiome. The compositionally of the data and externalfactors influencing variance in the data will be taken into account.After applying a Center Log ratio transformation (CLR) to a MetaPhlAn3 taxonomic profile andusing a random forest classifier the following five taxa were identified as the most important inthe association to CD: Ruminococcaceae bacterium, Akkermansia muciniphila, Streptococcusparasanguinis, Flavonifractor plautii and Bifidobacterium bifidum.

Biocrusts and the ecosystem services they provide are becoming more susceptible to fire as exotic annual grass invasions facilitate the spread of desert wildfires. Further, precipitation patterns across the western United States are predicted to change over the next century, and have the potential to dramatically influence fire regimes and the recovery of burned biocrusts. Despite these changes to desert fire and precipitation cycles, our understanding of post-fire biocrust recovery is limited, especially regarding the first two years after fire. To investigate biocrust recovery, we created burn manipulations (i.e., unburned and burned) and tracked crust form and function over two years in one cold and one hot desert ecosystem (UT, USA). We evaluated the entire bacterial community, but focused on Cyanobacteria species that confer soil stability and N fixation capabilities to biocrusts. Specifically, we quantified shifts in biocrust bacterial community composition using target metagenomics of 16S rDNA; monitored biocrust moss and lichen cover; measured N fixation potential; and assessed soil infiltration rates and soil stability. We found little evidence that biocrust form or function recovered from fire within two years. Based on pyrosequencing results, fire altered biocrust community composition in interspace and shrub biocrusts. Cyanobacteria species were almost completely eliminated by fire, constituting 9-21% of unburned plots and less than 0.01% of burned interspace and shrub biocrust communities. Based on cover estimates, no lichen or moss species survived the fire or recovered within two years. N fixation potentials decreased by at least six-fold in burned interspace biocrusts, representing a reduction in soil N inputs into already N-limited desert soils. Soil infiltration rates also drastically declined in burned biocrusts and remained depressed, but only remained depressed for one year. To investigate the interactions between biocrust recovery, fire, and precipitation, we nested precipitation treatments manipulating the amount of monthly rainfall (i.e., ambient, plus 30% and minus 30%) within burn treatments during the second year. Soil NH4+ was the only parameter to be affected by precipitation, and exhibited a positive relationship with precipitation magnitude at the end of one year. Our results demonstrate that fire is a strong destabilizer of the bacterial components of biocrust communities and that the ecosystem services provided by crusts recover at different rates, with N dynamics recovering more slowly than soil ecohydrology.

Soil microbial communities are affected extensively by addition of amendments to their environment. Of particular concern is the addition of poultry litter, which contains a substantial C, energy, and nutrient supply, but also antibiotic resistance genes (ARG), antimicrobials, and a multitude of microbial species. This project seeks to primarily assess if there is a change in bacterial community structure in response to poultry litter amendments to pasture land across geographically independent land across northern Georgia. It may be that changes in the relative abundance of bacterial communities also result in alteration in ARGs, and the community resistance to antibiotics (“resistome”) which in turn increases the potential threat of antibiotic resistance genes. While another part of this study will determine changes in integrons and specific ARGs, this project will focus on changes in bacterial communities and the potential functional changes in the community, which in turn have consequences for ARG levels and its horizontal transfer to various members of the soil community. Addition of waste from livestock is a historical method for increasing nutrients needed in the soil for the cultivation of crops, and in turn causes pronounced shifts in soil microbial communities due to the addition of large amounts of carbon, nutrients, foreign microbes, and other material. This study is unique because it utilizes a novel and relatively large landscape-scale to determine if there are discernable and repeatable patterns of bacterial community structure change in response to amendment regardless of exact soil type or source of chicken litter amendment. In the future, these data can also provide insight into the changes in the relative abundance antibiotic related genes associated with community change. / M.S. / Soil is complicated, both in terms of its physical makeup and the organisms that live inside of it. Predicting changes in soil based on the addition of foreign material such as chemicals or biological waste is not an easy process, and whether or not it is even possible to reliably predict those changes is a matter of some dispute. This study is designed to illustrate that such changes can in fact be reliably and consistently predicted even with regard to the addition of complicated materials to the soil. In this study, specifically, the material in question is chicken litter. A mix of the bedding and waste produced by chickens, litter is commonly handled by composting and is added to soil in farms as a fertilizer rich in organic matter. It is possible to point at specific elements of the soil such as the chemistry and bacteria and see how it is changed with the addition of chicken litter, which allows us to determine the nature and extent of the change that chicken litter has on soil. This study is conducted on a larger scale than similar experiments conducted in the past, making it apparent that these relationships exist on a repeated basis. It is the object of this study to pave the way and make it easier for scientists in the future to determine these relationships in other unique contexts.

[ES] El objetivo de la Tesis fue investigar los mecanismos genéticamente determinados involucrados en la deposición de grasa intramuscular (GIM) usando 2 líneas divergentes de GIM (A y B) en el músculo Longissimus thoracis et Lumborum (LTL) de conejos. Consta de cinco estudios que evalúan el rol del huésped y el microbioma usando análisis metabolómicos y metagenómicos. La respuesta a la selección en la 10ma generación fue 0,49g GIM/100g LTL, equivalente a 3,8 desviaciones estándar (DE). Se obtuvo una respuesta correlacionada positiva en la grasa de la canal y cambios en los ácidos grasos (AG) del LTL, con mayor contenido de saturados (A-B= 5,05g/100g GIM) y monoinsaturados (A-B= 5,04g/100g GIM) en la línea A. No hubo diferencia en la grasa del hígado, pero sí en sus AG, como el menor C15:0 (A-B= -0,04g/100g lípidos) y C17:0 (A-B= -0,09g/100g lípidos) en la línea A, que podría deberse a diferente digestión microbiana. El análisis metabolómico del plasma encontró 393 metabolitos diferenciales con 95% precisión clasificatoria y 383 metabolitos con ajuste linear a GIM con 65% capacidad predictiva, de los cuales 322 coincidían con diferencias entre -6,04 y +1,97 DE. Los lípidos fueron mayores en la línea B (ej. triglicéridos, ácidos biliares secundarios (AB-2º), AG) y la carnitina fue menor, sugiriendo mayor absorción intestinal y menor captación y almacenamiento, posiblemente relacionada con menor ß-oxidación de AG. Entre los aminoácidos, destacaron los de cadena ramificada (BCAA) y aromáticos (AAA) indicando menor degradación intestinal de BCAA en la línea A seguida de mayor catabolismo del huésped, y una compleja interacción huésped-microbioma en el metabolismo de los AAA. El análisis metagenómico del ciego confirmó la relevancia del microbioma en la deposición de GIM, con cambios en composición y funcionalidad. El análisis de la composición definió 2 enterotipos con 51 géneros microbianos y 91% precisión clasificatoria: el enterotipo A enriquecido en Hungateiclostridium, Limosilactobacillus, Legionella, Lysinibacillus, Phorphyromonas, Methanosphaera y Desulfovibrio y el enterotipo B en Escherichia, Fonticella, Candidatus Amulumruptor, Methanobrevicater, Exiguobacterium, Flintibacter y Coprococcus. Un balance composicional se propuso como biomarcador para predecir la predisposición genética a la deposición de GIM, compuesto por 26 géneros microbianos con 93% precisión clasificatoria y 69% capacidad predictiva. El análisis de funcionalidad encontró 240 genes microbianos (GM) diferenciales con 95% precisión clasificatoria y 230 GM con ajuste linear a GIM con 79% capacidad predictiva, de los cuales 122 GM coincidían con diferencias entre -0,75 y +0,73 DE. Mayor biosíntesis de lipopolisacáridos y peptidoglicanos, asociado al desarrollo de masa grasa, biosíntesis de AAA, asociado a trastornos relacionados a la deposición grasa, y conversión de propionato a acetato, relacionado con mayor lipogénesis en el hígado y menor síntesis de C15:0 y C17:0, se encontró en la línea A. Además, se encontró mayor degradación de BCAA en la línea B, asociado con menor síntesis de triglicéridos en el hígado. El análisis metabolómico del ciego encontró 142 metabolitos diferenciales con 99% precisión clasificatoria y diferencias entre -1,03 y +1,19 DE; 156 relacionados con GIM en la línea A con 61% capacidad predictiva; y 107 relacionados con GIM en la línea B con 57% capacidad predictiva. Diferencias en el metabolismo de las purinas podrían sugerir mayor eficiencia energética y en la utilización del nitrógeno en la línea B, y diferencias en AB-2º, AAA y BCAA fueron consistentes con los resultados anteriores. Un balance composicional se propuso como biomarcador compuesto por 2 AB-2º y 2 subproductos de las proteínas, con 88% de precisión clasificatoria, sugiriendo que la interacción entre absorción lipídica y metabolismo de proteínas de la dieta influyen en la GIM. De validarse, podría usarse para predecir la predisposición genética a la deposición de GIM. / [CA] L'objectiu de la Tesi va ser investigar els mecanismes genèticament determinats involucrats en la deposició de greix intramuscular (GIM) usant 2 línies divergents de GIM (A i B) en el múscul Longissimus thoracis et Lumborum (LTL) de conills. Consta de cinc estudis que avaluen el rol de l'hoste i el microbioma usant anàlisi metabolòmicos i metagenòmicos. La resposta a la selecció en la 10ma generació va ser 0,49g GIM/100g LTL, equivalent a 3,8 desviacions estàndard (DE) . Es va obtindre una resposta correlacionada positiva en el greix de la canal i canvis en els àcids grassos (AG) del LTL, amb major contingut de saturats (A-B = 5,05g/100g GIM) i monoinsaturats (A-B = 5,04g/100g GIM) en la línia A. No va haver-hi diferència en el greix del fetge, però sí en els seus AG, com el menor C15:0 (A-B = -0,04g/100g lípids) i C17:0 (A-B = -0,09g/100g lípids) en la línia A, que podria deure's a diferent digestió microbiana. L'anàlisi metabolómico del plasma va trobar 393 metabòlits diferencials amb 95% precisió classificatòria i 383 metabòlits amb ajust linear a GIM amb 65% capacitat predictiva, dels quals 322 coincidien amb diferències entre -6,04 i 1,97 DE. Els lípids van ser majors en la línia B (ex. triglicèrids, àcids biliars secundaris (AB- 2º), AG) i la carnitina va ser menor, suggerint major absorció intestinal i menor captació i emmagatzematge, possiblement relacionada amb menor ß-oxidació d'AG. Entre els aminoàcids, van destacar els de cadena ramificada (BCAA) i aromàtics (AAA) indicant menor degradació intestinal de BCAA en la línia A seguida de major catabolisme de l'hoste, i una complexa interacció hoste-microbioma en el metabolisme dels AAA. L'anàlisi metagenòmico del cec va confirmar la rellevància del microbioma en la deposició de GIM, amb canvis en composició i funcionalitat. L'anàlisi de la composició va definir 2 enterotipos amb 51 gèneres microbians i 91% precisió classificatòria: el enterotipo A enriquit en Hungateiclostridium, Limosilactobacillus, Legionel·la, Lysinibacillus, Phorphyromonas, Methanosphaera i Desulfovibrio i el enterotipo B en Escherichia, Fonticella, Candidatus Amulumruptor, Methanobrevicater, Exiguobacterium, Flintibacter i Coprococcus. Un balanç composicional es va proposar com biomarcador per a predir la predisposició genètica a la deposició de GIM, compost per 26 gèneres microbians amb 93% precisió classificatòria i 69% capacitat predictiva. L'anàlisi de funcionalitat va trobar 240 gens microbians (GM) diferencials amb 95% precisió classificatòria i 230 GM amb ajust linear a GIM amb 79% capacitat predictiva, dels quals 122 GM coincidien amb diferències entre -0,75 i 0,73 DE. Major biosíntesi de lipopolisacàrids i peptidoglicans, associat al desenvolupament de massa grassa, biosíntesi de AAA, associat a trastorns relacionats a la deposició grassa, i conversió de propionat a acetat, relacionat amb major lipogènesis en el fetge i menor síntesi de C15:0 i C17:0, es va trobar en la línia A. A més, es va trobar major degradació de BCAA en la línia B, associat amb menor síntesi de triglicèrids en el fetge. L'anàlisi metabolòmico del cec va trobar 142 metabòlits diferencials amb 99% precisió classificatòria i diferències entre -1,03 i 1,19 DE; 156 relacionats amb GIM en la línia A amb 61% capacitat predictiva; i 107 relacionats amb GIM en la línia B amb 57% capacitat predictiva. Diferències en el metabolisme de les purins podrien suggerir major eficiència energètica i en la utilització del nitrogen en la línia B, i diferències en AB-2º, AAA i BCAA van ser consistents amb els resultats anteriors. Un balanç composicional es va proposar com biomarcador compost per 2 AB-2º i 2 subproductes de les proteïnes, amb 88% de precisió classificatòria, suggerint que la interacció entre absorció lipídica i metabolisme de proteïnes de la dieta influeixen en la GIM. De validar-se, podria usar-se per a predir la predisposició genètica a la deposició de GIM. / [EN] The Thesis aimed to study the genetically determined mechanisms involved in intramuscular fat (IMF) deposition, using two lines divergently selected for IMF in Longissimus thoracis et Lumborum (LTL) muscle of rabbits (H and L lines). It comprises five studies focused on studying the host and microbiome roles using metabolomics and metagenomics approaches. The response to selection in the 10th generation was 0.49g IMF/100g LTL, equivalent to 3.8 standard deviations (SD). Selection led to a positive correlated response in carcass adiposity, and to changes in the fatty acids (FA) of LTL, showing greater saturated (H-L= 5.05g/100g IMF) and monounsaturated FA (H-L= 5.04g/100g IMF) in the H line. No differences were found in liver fat, but they were found in its FA profile, being the most notorious the lower C15:0 (H-L= -0.04g/100g lipids) and C17:0 (H-L= -0.09g/100g lipids) in the H line, which could be due to different microbial digestion. The plasma metabolomics analysis identified 393 differential metabolites with 95% classification accuracy, and 383 metabolites with linear adjustment to IMF and 65% prediction ability, from which 322 overlapped with differences ranging from -6.04 to +1.97 SD. Lipids were greater in the L line (e.g., triglycerides, secondary bile acids, FA) while carnitine was lower, suggesting greater intestinal lipids absorption in the L line, followed by their lower uptake and storage, possibly related to lower FA ß oxidation. Among amino acids, branched-chain (BCAA) and aromatic (AAA) stood out, indicating lower BCAA gut degradation in the H line followed by greater host catabolism, and a complex host-microbiome AAA metabolism. The caecum metagenomics analysis confirmed the microbial relevance in IMF development, identifying changes in its composition and functionality. The microbial composition analysis defined two enterotypes with 51 microbial genera and 91% classification accuracy. The H-enterotype was enriched in Hungateiclostridium, Limosilactobacillus, Legionella, Lysinibacillus, Phorphyromonas, Methanosphaera and Desulfovibrio, and the L-enterotype in Escherichia, Fonticella, Candidatus Amulumruptor, Methanobrevicater, Exiguobacterium, Flintibacter and Coprococcus. A compositional balance was proposed as biomarker to predict the genetic predisposition to IMF deposition, composed of 26 microbial genera, with 93% classification accuracy and 69% prediction ability. The microbial functionality analysis identified 240 differential microbial genes (MG) with 95% classification accuracy, and 230 MG with linear adjustment to IMF and 79% prediction ability, from which 122 overlapped with differences ranging from -0.75 to +0.73 SD and related to numerous metabolisms. In the H line, greater lipopolysaccharides and peptidoglycans biosynthesis, related to fat-mass development, AAA biosynthesis, related to associated disorders of increased fat deposition, and propionate to acetate conversion, related to greater liver lipogenesis and lower C15:0 and C17:0 synthesis, were found. Additionally, greater BCAA degradation was found in the L line, related to lower triglycerides synthesis in the liver. The caecum metabolomics analysis identified 142 differential metabolites with 99% classification accuracy and differences ranging from -1.03 to +1.19 SD; 156 related to IMF in the H line with 61% prediction ability; and 107 related to IMF in the L line with 57% prediction ability. Differences found in purine metabolism could suggest greater energy and nitrogen utilization efficiencies in the L line, while those in secondary bile acids, AAA and BCAA were consistent with the previous results. A compositional balance composed of two secondary bile acids and two proteins by-products with 88% classification accuracy was proposed as biomarker, suggesting that the interaction between lipids absorption and dietary proteins metabolism influences IMF. If validated, it could be used to predict the genetic predisposition to IMF deposition. / Zubiri Gaitán, A. (2024). Intramuscular Fat Deposition in Rabbits: Insights into Host-Microbiome Biological Mechanisms [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/202875

Heterotrophic protists, consisting largely of the Cercozoa, Amoebozoa, Ciliophora, Discoba and some Stramenopiles, are a poorly characterized component of life on Earth. They play an important ecological role in soil communities and provide key insights into the nature of one of life’s most enigmatic evolutionary transitions: the development of the complex unicell. Soil ecosystems are crucial to the functioning of global biogeochemical cycles (e.g. carbon and nitrogen) but are at risk of drastic change from anthropogenic climate change. Heterotrophic protists are the primary regulators of bacterial diversity in soils and as such play integral roles in biogeochemical cycling, nutrient mobilization, and trophic cascades in food webs under stress. Understanding the nature of these changes requires examining the rates, diversity, and resiliency of interactions that occur between soil organisms. However, soils are the most taxonomically diverse ecosystems on Earth and disentangling the complexities of dynamic and varied biotic interactions in them requires a unique model system. The McMurdo Dry Valleys of Antarctica, one of the harshest terrestrial environments on Earth, serve as a model soil ecosystem owing to their highly reduced biological diversity. Exploring the functioning of heterotrophic protists in these valleys provides a way to test the applicability of this model system to other soil food webs. However, very little is known about their taxonomic diversity, which is a strong predictor of function. Therefore, I reviewed the Antarctic literature to compile a checklist of all known terrestrial heterotrophic protists in Antarctica. I found significant geographical, methodological, and taxonomic biases and outlined how to address these in future research programs. I also conducted a molecular survey of whole soil communities using 18 shotgun metagenomes representing major landscape features of the McMurdo Dry Valleys. The results revealed the dominance of Cercozoa and point to an Antarctic heterotrophic protist soil community that is taxonomically diverse and reflects the structure and composition of communities at lower latitudes. To investigate whether biotic interactions or abiotic factors were a larger driver for Antarctic heterotrophic protists, I conducted variation partitioning using environmental data (e.g. moisture, pH and electrical conductivity). Biotic variables were more significant and accounted for more of the variation than environmental variables. Taken together, it is clear that heterotrophic protists play key ecological roles in this ecosystem. Deeper insights into the ecology of these organisms in the McMurdo Dry Valleys also have implications for the search for complex unicellular life in our universe. I discuss the theoretical underpinnings of searching for these forms of life outside of Earth, conclude that they are likely to occur, and postulate how future missions could practically search for complex unicells.

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.

Widespread use of antibiotics in livestock production can result in the dissemination of bacteria carrying antibiotic resistance genes (ARGs) to the broader environment. Within agroecosystems, ARGs can pose a risk to livestock handlers, farmers, and ultimately consumers. The overall goals of this dissertation are to examine the presence of resistance (antibiotic, metal) in agricultural soils and evaluate the most critical potential points of best management control of antibiotic resistance spread along the agricultural production chain. The relative impacts of agricultural practices, manure management, native soil microbiota, and type of crop grown and harvested on the agricultural resistome are multi-dimensional and cannot be captured via a single analytical technique or by focusing on one specific point in the agricultural process. Culture-, molecular "indicator"-, and next-generation sequencing- based methods were employed to characterize antibiotic resistance via taxonomic and functional profiles on the broader manure, soil, and vegetable surface microbial communities through 16S rRNA amplicon sequencing and shotgun metagenomics. Although antibiotic concentrations dissipated in the soil after 28 days after amendment application, antibiotic resistance presence was recoverable throughout the entire 120d growing season in the compost and manure amendments, the amended soil, and on vegetable surfaces. The addition of organic fertilizers increased antibiotic resistance presence compared to background levels. Further, metals and metal resistance were also measured in the amended soils and were found to be at greater levels in the inorganically fertilized soils compared to the manures and compost amended soils. Analysis of the widespread agroecosystem microbial community composition and broader metagenome has characterized varying genera profiles in the soil and on the vegetable surfaces and specific ARG and mobile genetic element (plasmid) co-occurrences. These co-occurrences highlight which ARGs may be most critical for future antibiotic resistance dissemination research. It is imperative to employ multiple methods when measuring agricultural resistance, as one method alone may miss significant patterns and lead to different best management recommendations. Linking the livestock manure, soil, and vegetable surface-associated ARBs, ARGs, resistomes, and microbiomes will help identify critical control points for mitigation of agricultural dissemination of antibiotic resistance to the environment and food production. / Doctor of Philosophy / By 2050, it is estimated that antibiotic resistant infections will be the leading cause of death worldwide. It is important to consider human, animal, and environmental health when researching antibiotic resistance, which is known as a "One Health" approach. In this dissertation work, I focus on the environmental side of antibiotic resistance in our agricultural systems. Agriculture is a known source of antibiotic resistance due to its use of antibiotics in livestock as a treatment for illness, and in some instances, as a growth promoter. Over one growing season, I measured antibiotic resistance in an agricultural setting using many techniques. First, I analyzed the effects of inorganic (chemical) versus organic (manure and compost) fertilization on antibiotic resistance in the soil. I measured antibiotic resistance by growing antibiotic resistant bacteria, quantifying specific antibiotic resistant genes (ARGs) using DNA amplification, and quantifying all the ARGs in the soil using a next-generation sequencing (NGS) technique called shotgun metagenomics. I found that adding manure to the soil increases ARGs compared to background soil levels, and that composting in an effective management strategy in decreasing ARGs in the soil over time. Second, I analyzed the same effects of fertilization on metal resistance in the soil. I was able to use the same NGS dataset to measure metal resistance genes (MRGs). I found that adding inorganic chemical fertilizer increases MRGs in the agricultural soils compared to the organic (manure or compost) fertilizer. Additionally, I studied the microbes that live in the agricultural soils using another kind of NGS data specific for microbial identification. I found that although there were small differences between the microbial populations in the soil when fertilizers were added, they returned to similar composition over the growing season. Lastly, I measured antibiotic resistance and microbes throughout the entire agricultural system. I picked the point of fertilization (manure management), soil, and the lettuce surface to evaluate if antibiotic resistance spreads from the farm to the vegetable that ends up on a consumer's plate. I found that at each point antibiotic resistance is present, but at different levels. Composting reduces ARGs compared to raw manure. Agricultural soils may act as a natural buffer to antibiotic resistance. Lettuce plants grown in compost fertilized soils have less ARGs than lettuce plants grown in manure. There are many agricultural management practices that effectively reduce antibiotic resistance and using all of them plus many measurement methods will ultimately help farmers and consumers reduce antibiotic resistance in our agricultural systems.