Data-driven strategies for vaccine design
Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, February 2018.
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| Format: | Thesis |
| Language: | eng |
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Massachusetts Institute of Technology
2018
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| Online Access: | http://hdl.handle.net/1721.1/117327 |
| _version_ | 1826190011103444992 |
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| author | Kaczorowski, Kevin J |
| author2 | Arup K. Chakraborty. |
| author_facet | Arup K. Chakraborty. Kaczorowski, Kevin J |
| author_sort | Kaczorowski, Kevin J |
| collection | MIT |
| description | Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, February 2018. |
| first_indexed | 2024-09-23T08:33:39Z |
| format | Thesis |
| id | mit-1721.1/117327 |
| institution | Massachusetts Institute of Technology |
| language | eng |
| last_indexed | 2024-09-23T08:33:39Z |
| publishDate | 2018 |
| publisher | Massachusetts Institute of Technology |
| record_format | dspace |
| spelling | mit-1721.1/1173272019-04-09T18:29:17Z Data-driven strategies for vaccine design Kaczorowski, Kevin J Arup K. Chakraborty. Massachusetts Institute of Technology. Department of Chemical Engineering. Massachusetts Institute of Technology. Department of Chemical Engineering. Chemical Engineering. Thesis: Ph. D., Massachusetts Institute of Technology, Department of Chemical Engineering, February 2018. Cataloged from PDF version of thesis. Includes bibliographical references. Vaccination is one of the greatest achievements in immunology and in general medicine, and has virtually eradicated many infectious diseases that plagued humans in the past. Vaccination involves injecting an individual with some version of the pathogen in order to allow the individual to develop a memory immune response that will protect them from future challenge with the same pathogen. Until recently, vaccine development has largely followed empirical paradigms that have proven successful against many diseases. However, many pathogens have now evolved that defy success using the traditional approaches. Rational design of vaccines against such pathogens will likely require interdisciplinary approaches spanning engineering, immunology, and the physical sciences. In this thesis, we combine theoretical approaches with protein sequence and clinical data to address two contemporary problems in vaccinology: 1. Developing an antibody vaccine against HIV, an example of a highly mutable pathogen; and 2. Understanding how the many immune components work collectively to effect a systemic immune response, such as to vaccines. In HIV-infected individuals, antibodies produced by the immune system bind to specific parts of an HIV protein called Envelope (Env). However, the virus evades the immune response due to its high mutability, thus making effective vaccine design a huge challenge. To predict the mutational vulnerabilities of the virus, we developed a model (a fitness landscape) to translate sequence data into knowledge of viral fitness, a measure of the ability of the virus to replicate and thrive. The landscape accounts explicitly for coupling interactions between mutations at different positions within the protein, which often dictate how the virus evades the immune response. We developed new computational approaches that enabled us to tackle the large size and mutational variability of Env, since previous approaches have been unsuccessful in this case. A small fraction of HIV-infected individuals produce a class of antibodies called broadly neutralizing antibodies (bnAbs), which neutralize a diverse number of HIV strains and can thus tolerate many mutations in Env. To investigate the mechanisms underlying breadth of these bnAbs, we combined our landscape with 3D protein structures to gain insight into the spatial distribution of binding interactions between bnAbs and Env. Based on this, we designed an optimal set of immunogens (i.e. Env sequences), with mutations at key residues, that are potentially likely to lead to the elicitation of bnAbs via vaccination. We hope that these antigens will soon be tested in animal models. Even when the right immunogens are included in a vaccine, a potent immune response is not always induced. For example, some individuals do not respond to protective influenza vaccines as desired. The human immune system consists of many different immune cells that coordinate their actions to fight infections and respond to vaccines. The balance between these cell populations is determined by direct interactions and soluble factors such as cytokines, which serve as messengers between cells. A mechanistic understanding of how the various immune components cooperate to bring about the immune response can guide strategies to improve vaccine efficacy. To investigate whether differences in immune response could be explained by variation in immune cell compositions across individuals, we analyzed experimental measurements of various immune cell population frequencies in a cohort of healthy humans. We demonstrated that human immune variation in these parameters is continuous rather than discrete. Furthermore, we showed that key combinations of these immune parameters can be used to predict immune response to diverse stimulations, namely cytokine stimulation and vaccination. Thus, we defined the concept of an individual's "immunotype" as their location within the space of these key combinations of parameters. This result highlights a previously unappreciated connection between immune cell composition and systemic immune responses, and can guide future development of therapies that aim to collectively, rather than independently, manipulate immune cell frequencies. by Kevin J. Kaczorowski. Ph. D. 2018-08-08T19:49:42Z 2018-08-08T19:49:42Z 2017 2018 Thesis http://hdl.handle.net/1721.1/117327 1046678187 eng MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission. http://dspace.mit.edu/handle/1721.1/7582 146 pages application/pdf Massachusetts Institute of Technology |
| spellingShingle | Chemical Engineering. Kaczorowski, Kevin J Data-driven strategies for vaccine design |
| title | Data-driven strategies for vaccine design |
| title_full | Data-driven strategies for vaccine design |
| title_fullStr | Data-driven strategies for vaccine design |
| title_full_unstemmed | Data-driven strategies for vaccine design |
| title_short | Data-driven strategies for vaccine design |
| title_sort | data driven strategies for vaccine design |
| topic | Chemical Engineering. |
| url | http://hdl.handle.net/1721.1/117327 |
| work_keys_str_mv | AT kaczorowskikevinj datadrivenstrategiesforvaccinedesign |