Theses and Dissertations from UMD
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New submissions to the thesis/dissertation collections are added automatically as they are received from the Graduate School. Currently, the Graduate School deposits all theses and dissertations from a given semester after the official graduation date. This means that there may be up to a 4 month delay in the appearance of a give thesis/dissertation in DRUM
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Item AUGMENTING SEQUENCING TECHNOLOGY FOR BETTER INFERENCE IN SOIL MICROBIOME ANALYSIS(2023) Epp Schmidt, Dietrich; Yarwood, Stephanie A; Environmental Science and Technology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)The advent of DNA sequencing revolutionized the field of microbiome research. Many organisms, by virtue of their codependence and/or growth rate, are either impossible or extremely challenging to get into pure culture. Sequencing allows important taxonomic and phylogenetic information to be obtained independent of culturing. Development of the sequencing technology itself has allowed for high throughput workflow that has allowed low cost and extensive sampling of microbiomes across environments. The co-development of reference datasets for taxonomy and functional assignments, along with open-source bioinformatics pipelines has further empowered scientists to explore microbiomes in many environments. However, there are limitations to sequence data that have constrained the ecological inferences in microbiome research. One such limitation, the compositional nature of sequence data, has impeded our ability to make accurate inferences about the environmental drivers of taxon abundance and covariance across conditions. In this dissertation I explore the use of quantitative PCR in combination with sequencing techniques to generate “Quantitative Sequencing” data (QSeq) that mitigates the limitations of compositionality on inferences relating to taxon abundance and covariance across environmental gradients. In chapter 1, I reviewed key characteristics of the soil environment and sequencing as a mechanism for sampling. In chapter 2, I leveraged modeling, synthesis, and literature review methods to establish the questions and data characteristics that demand QSeq methodology. I show that even small amounts of variation in total abundance make determining the effects of environment (biotic and abiotic factors) on any given taxon unreliable without QSeq. In Chapter 3, I extend the logic of quantitative sequencing to improve metagenome prediction from PICRUSt2. Using data synthesis methods, accounting for 16S gene abundance consistently improved the accuracy of predicted functional genes. This was confirmed by high correlations between predicted and measured gene abundance (QPCR). There was however a large variation in prediction accuracy, likely due in part to database biases and in part to decoupling of bacterial function from taxonomy. In Chapter 4, I applied QSeq in the context of an experimental, long-term farming system that has large gradients in total abundance with depth, and I used QSeq to identify taxa that changed in abundance due to different farming system management and soil depth. Finally in Chapter 5, I used QSeq to identify putative N-fixing taxa that responded to glyphosate in four experimental farming systems. I show that the abundance of these taxa were decoupled from other effects of glyphosate on N-fixation in soybean across farming systems.Item Viromics and biogeography of estuarine virioplankton(2021) Sun, Mengqi; Chen, Feng; Marine-Estuarine-Environmental Sciences; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Viruses are the most abundant biological entity in the ocean, and they can influence microbial mortality, evolution and biogeochemical cycles in marine ecosystems. Virioplankton communities in oceans have been studied extensively using viral metagenomics (viromics), but the estuarine viromes remain relatively unexplored. Estuaries are a complex and dynamic ecosystem. My dissertation is dedicated to understanding the composition and distribution of the virioplankton community in the Delaware Bay and Chesapeake Bay by investigating 16 viromes collected from these two bays. A total of 26,487 viral populations (contigs > 5kb) were identified in the two bays, establishing a high quality viromic dataset. The vast majority of the dominant viral populations are unclassified viruses. Viral sequences obtained from marine single cell genomes or long read single molecule sequencing comprised 13 of the top 20 most abundant viral populations, suggesting that we are still far from understanding the diversity of viruses in estuaries. Abundant viral populations (top 5,000) are significantly different between the Delaware Bay and Chesapeake Bay, indicating a strong niche adaptation of the viral community to each estuary. Surprisingly, no clear spatiotemporal patterns were observed for the viral community based on water temperature and salinity. The composition of known viruses (i.e. phages infecting Acinetobacter, Puniceispirillum, Pelagibacter, Synechococcus, Prochlorococcus, etc.) appeared to be relatively consistent across a wide range of salinity gradients and different seasons. Overall, the estuarine viral community is distinct from that in the ocean according to the composition of known viruses. N4-like viruses belong to a newly established viral family and have been isolated from diverse bacterial groups. Marine N4-like viruses were first found in the Chesapeake Bay, but little is known about their biogeographic pattern in the estuarine environment. N4-like viruses were confirmed to be rare in the estuary, and relatively more abundant in the samples from lower water temperature. Viruses which infect SAR11 bacteria (pelagiphage) are one of most abundant viral groups in the open ocean. We found that the abundance and community profile of pelagiphage in the estuaries is similar to that in the open ocean, and has no correlation with environmental factors.Item Investigating the Distribution of CRISPR Adaptive Immune Systems Among Prokaryotes(2019) Weissman, Jake; Johnson, Philip L.F.; Biology; Digital Repository at the University of Maryland; University of Maryland (College Park, Md.)Just as larger organisms face the constant threat of infection by pathogens, so too do bacteria and archaea. In response, prokaryotes employ a diverse set of strategies to simultaneously cope with their viral and physical environments. Here I explore the ecology and evolution of the CRISPR adaptive immune system, a powerful form of protection against viruses that is the only known example of adaptive immunity in prokaryotes. CRISPR systems are widespread across diverse bacterial and archaeal lineages, suggesting that CRISPR effectively defends against viruses in a broad array of environments. Nevertheless, this defense system is nearly absent in many bacterial groups, and in many environments. I focus on understanding these patterns in CRISPR incidence and the ecological drivers behind them. First, I identify the ecological conditions that favor the adoption of a CRISPR-based defense strategy. I develop a phylogenetically-conscious machine learning approach to build a predictive model of CRISPR incidence using data on over 100 phenotypic traits across over 2600 species and discovered a strong but hitherto-unknown negative interaction between CRISPR and aerobicity. I then consider the multiplicity of CRISPR arrays on a genome, testing whether or not selection favors redundancy in immunity. I use a comparative genomics approach, looking across all prokaryotes to demonstrate that on average, organisms are under selection to maintain more than one CRISPR array. I then explain this surprising result with a theoretical model demonstrating that a trade-off between memory span and learning speed could select for paired “long-term memory” and “short-term memory” CRISPR arrays. Finally, I provide a theoretical examination of the phenomenon of immune loss, specifically in the context of CRISPR immunity. In doing so, I propose an additional mechanism to answer the perennial question: “How do bacteria and bacteriophage coexist stably over long time-spans?” I show that the regular loss of immunity by the bacterial host can produce host-phage coexistence more reliably than other mechanisms, pairing a general model of immunity with an experimental and theoretical case study of CRISPR-based immunity.