Computational tools to identify correlates of vaccine-induced protection against tuberculosis
Date
2021
Authors
Fox, Amy, author
Henao-Tamayo, Marcela, advisor
Anderson, Brooke, advisor
Abdo, Zaid, committee member
Fosdick, Bailey, committee member
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Abstract
Tuberculosis 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.
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Subject
flow cytometry
vaccine
tuberculosis
computational tools