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- All Subjects: Immunology
- All Subjects: Rheumatoid Arthritis
Description
In this dissertation, new data-driven techniques are developed to solve three problems related to generating predictive models of the immune system. These problems and their solutions are summarized as follows. The first problem is that, while cellular characteristics can be measured using flow cytometry, immune system cells are often analyzed only after they are sorted into groups by those characteristics. In Chapter 3 a method of analyzing the cellular characteristics of the immune system cells by generating Probability Density Functions (PDFs) to model the flow cytometry data is proposed. To generate a PDF to model the distribution of immune cell characteristics a new class of random variable called Sliced-Distributions (SDs) is developed. It is shown that the SDs can outperform other state-of-the-art methods on a set of benchmarks and can be used to differentiate between immune cells taken from healthy patients and those with Rheumatoid Arthritis. The second problem is that while immune system cells can be broken into different subpopulations, it is unclear which subpopulations are most significant. In Chapter 4 a new machine learning algorithm is formulated and used to identify subpopulations that can best predict disease severity or the populations of other immune cells. The proposed machine learning algorithm performs well when compared to other state-of-the-art methods and is applied to an immunological dataset to identify disease-relevant subpopulations of immune cells denoted immune states. Finally, while immunotherapies have been effectively used to treat cancer, selecting an optimal drug dose and period of treatment administration is still an open problem. In Chapter 5 a method to estimate Lyapunov functions of a system with unknown dynamics is proposed. This method is applied to generate a semialgebraic set containing immunotherapy doses and period of treatment that is predicted to eliminate a patient's tumor. The problem of selecting an optimal pulsed immunotherapy treatment from this semialgebraic set is formulated as a Global Polynomial Optimization (GPO) problem. In Chapter 6 a new method to solve GPO problems is proposed and optimal pulsed immunotherapy treatments are identified for this system.
ContributorsColbert, Brendon (Author) / Peet, Matthew M (Thesis advisor) / Acharya, Abhinav P (Committee member) / Berman, Spring M (Committee member) / Crespo, Luis G (Committee member) / Yong, Sze Z (Committee member) / Arizona State University (Publisher)
Created2021
Description
Autoimmunity develops when the immune system targets self-antigens within the body. Rheumatoid arthritis (RA) is a common autoimmune disease, and its progression is characterized by pro-inflammatory immune cells rapidly proliferating, migrating, and infiltrating joint tissue to provoke inflammation. In order to fulfill this taxing autoreactive response, an increase in energy metabolism is required by immune cells, such as dendritic cells (DCs). Therefore, a shift in DC energy reliance from the Krebs cycle toward glycolysis occurs. This metabolic shift phenotypically transitions DCs from anti-inflammatory properties toward an aggressive pro-inflammatory phenotype, in turn activating pro-inflammatory T cells and promoting RA pathogenesis. If the disease persists uncontrollably, further complications and eventual joint dysfunction can occur. Although, clinically approved drugs can prevent RA progression, they require frequent administration for temporary symptom relief. Furthermore, current approved biological products for RA are not known to have a direct modulatory effect on immunometabolism. Given that cellular metabolism controls immune cell function, this work aims to harness perturbations within RA immune cell energy metabolism and utilizes it as a therapeutic target by reprogramming immune cell metabolism via the delivery of metabolite-based particles. The two-time delivery of these particles reduced RA inflammation in a RA collagen-induced arthritis (CIA) mouse model and generated desired responses with long-term effects. Specifically, this work was achieved by:
Aim 1 – developing and delivering metabolite-based polymeric microparticles synthesized from the Krebs cycle metabolite, alpha-ketoglutarate (aKG; termed paKG MPs) to DCs to modulate their energy metabolism and promote anti-inflammatory properties (in context of RA).
Aim 2 – exploiting the encapsulation ability of paKG MPs to inhibit DC glycolysis in the presence of the CIA self-antigen (collagen type II (bc2)) for the treatment of RA in CIA mice. Herein, paKG MPs encapsulating a glycolytic inhibitor and bc2 induce an anti-inflammatory DC phenotype in vitro and generate suppressive bc2-specific T cell responses and reduce paw inflammation in CIA mice.
ContributorsMangal, Joslyn Lata (Author) / Acharya, Abhinav P (Thesis advisor) / Florsheim, Esther B (Committee member) / Wu, Hsin-Jung Joyce (Committee member) / Anderson, Karen (Committee member) / Arizona State University (Publisher)
Created2022