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Theses and Dissertations

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Browsing Theses and Dissertations by Author "Abdo, Zaid, advisor"

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    Microbes in the mucosa: impacts of the mucosal immune system and oral vaccination with Lactobacillus acidophilus on the gut microbiome
    (Colorado State University. Libraries, 2021) Fox, Bridget E., author; Dean, Gregg, advisor; Abdo, Zaid, advisor; Tobet, Stuart, committee member; Ryan, Elizabeth, committee member
    The mucosal immune system is constantly balancing between the clearance of pathogens, tolerance of self-antigen and food, and maintenance of homeostasis within the microbiota. Vaccination via mucosal routes is advantageous because it provides protection at local mucosal sites and systemically. However, induction of efficacious responses are often difficult due to the inherent barriers of the mucosal tissues. We have developed a probiotic-based mucosal vaccination platform that utilizes recombinant Lactobacillus acidophilus (rLA) to overcome these obstacles presented in oral vaccination. Here, we sought to determine whether repeated administration of rLA alters the intestinal microbiome as a result of L. acidophilus probiotic activity (direct competition and selective exclusion) or from the host's mucosal immune response against the rLA vaccine. To address the latter, IgA-seq was employed to characterize shifts in IgA-bound bacterial populations. Additionally, we determined whether using rice bran as a prebiotic would influence the immunogenicity of the vaccine and/or IgA bound bacterial populations. Our results show that the prebiotic influenced the kinetics of rLA antibody induction, and that the rLA platform does not cause lasting disturbances to the microbiome. Nucleotide-binding oligomerization domain containing 2 (NOD2) has presented itself as an essential regulator of immune responses within the gastrointestinal tract. This innate immune receptor is expressed by several cell types, including both hematopoietic and nonhematopoietic cells within the gastrointestinal tract. Mice harboring knockouts of NOD2 only in CD11c+ cells were used to better characterize NOD2 signaling during mucosal vaccination with rLA. We show that NOD2 signaling in CD11c+ cells is critical for mounting a humoral immune response against rLA. Additionally, disruption of NOD2 signaling in CD11c+ cells results in an altered bacterial microbiome profile in both vaccinated and unvaccinated mice.
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    Modern considerations for the use of naive Bayes in the supervised classification of genetic sequence data
    (Colorado State University. Libraries, 2021) Lakin, Steven M., author; Abdo, Zaid, advisor; Rajopadhye, Sanjay, committee member; Stenglein, Mark, committee member; Stewart, Jane, committee member
    Genetic sequence classification is the task of assigning a known genetic label to an unknown genetic sequence. Often, this is the first step in genetic sequence analysis and is critical to understanding data produced by molecular techniques like high throughput sequencing. Here, we explore an algorithm called naive Bayes that was historically successful in classifying 16S ribosomal gene sequences for microbiome analysis. We extend the naive Bayes classifier to perform the task of general sequence classification by leveraging advancements in computational parallelism and the statistical distributions that underlie naive Bayes. In Chapter 2, we show that our implementation of naive Bayes, called WarpNL, performs within a margin of error of modern classifiers like Kraken2 and local alignment. We discuss five crucial aspects of genetic sequence classification and show how these areas affect classifier performance: the query data, the reference sequence database, the feature encoding method, the classification algorithm, and access to computational resources. In Chapter 3, we cover the critical computational advancements introduced in WarpNL that make it efficient in a modern computing framework. This includes efficient feature encoding, introduction of a log-odds ratio for comparison of naive Bayes posterior estimates, description of schema for parallel and distributed naive Bayes architectures, and use of machine learning classifiers to perform outgroup sequence classification. Finally in Chapter 4, we explore a variant of the Dirichlet multinomial distribution that underlies the naive Bayes likelihood, called the beta-Liouville multinomial. We show that the beta-Liouville multinomial can be used to enhance classifier performance, and we provide mathematical proofs regarding its convergence during maximum likelihood estimation. Overall, this work explores the naive Bayes algorithm in a modern context and shows that it is competitive for genetic sequence classification.