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Computational tools to identify correlates of vaccine-induced protection against tuberculosis

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dc.contributor.authorFox, Amy, author
dc.contributor.authorHenao-Tamayo, Marcela, advisor
dc.contributor.authorAnderson, Brooke, advisor
dc.contributor.authorAbdo, Zaid, committee member
dc.contributor.authorFosdick, Bailey, committee member
dc.date.accessioned2022-01-07T11:30:17Z
dc.date.available2023-01-06T11:30:17Z
dc.date.issued2021
dc.description.abstractTuberculosis is a significant threat to human health. While the BCG vaccine exists to protect children from disseminated forms of tuberculosis, it fails to protect against pulmonary tuberculosis. Thus, a better vaccine is needed. However, the immune system in response to tuberculosis and the BCG vaccine is incompletely understood. We sought to develop novel analysis methods to help understand the immune system. This dissertation describes an analysis tool, cyto-feature engineering, that rapidly identifies flow cytometry immune cell populations utilizing experimental controls. The tool was corroborated through testing the pipeline on different types of flow cytometry datasets. Cyto-feature engineering was then utilized to understand the immune response to two immunomodulatory drugs—losartan and propranolol—when used in conjunction with the BCG vaccine. This study identified an increase in T cell responses due to drug administration, but ultimately failed to decrease bacterial burden in the lung and spleen. Other studies employed a new method for identifying immune cells correlated with various metabolites in the context of tuberculosis. The method can be utilized to generate hypotheses from secondary data sources and gain new biological insight. Using this method, we identified a potential correlation between CD45RA and arachidonic acid metabolism which could serve as a potential target for future vaccination studies. The research outlined in this dissertation will hopefully lead to better immunological analyses of data and the development of a better tuberculosis vaccine.
dc.format.mediumborn digital
dc.format.mediumdoctoral dissertations
dc.identifierFox_colostate_0053A_16845.pdf
dc.identifier.urihttps://hdl.handle.net/10217/234253
dc.languageEnglish
dc.language.isoeng
dc.publisherColorado State University. Libraries
dc.relation.ispartof2020-
dc.rightsCopyright and other restrictions may apply. User is responsible for compliance with all applicable laws. For information about copyright law, please see https://libguides.colostate.edu/copyright.
dc.subjectflow cytometry
dc.subjectvaccine
dc.subjecttuberculosis
dc.subjectcomputational tools
dc.titleComputational tools to identify correlates of vaccine-induced protection against tuberculosis
dc.typeText
dcterms.embargo.expires2023-01-06
dcterms.embargo.terms2023-01-06
dcterms.rights.dplaThis Item is protected by copyright and/or related rights (https://rightsstatements.org/vocab/InC/1.0/). You are free to use this Item in any way that is permitted by the copyright and related rights legislation that applies to your use. For other uses you need to obtain permission from the rights-holder(s).
thesis.degree.disciplineMicrobiology, Immunology, and Pathology
thesis.degree.grantorColorado State University
thesis.degree.levelDoctoral
thesis.degree.nameDoctor of Philosophy (Ph.D.)

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