ASU Electronic Theses and Dissertations
This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.
In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.
Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.
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Biological and immunological characterization of plant-produced HIV-1 Gag/dgp41 virus-like particles
Description
Anti-retroviral drugs and AIDS prevention programs have helped to decrease the rate of new HIV-1 infections in some communities, however, a prophylactic vaccine is still needed to control the epidemic world-wide. Despite over two decades of research, a vaccine against HIV-1 remains elusive, although recent clinical trials have shown promising results. Recent successes have focused on highly conserved, mucosally-targeted antigens within HIV-1 such as the membrane proximal external region (MPER) of the envelope protein, gp41. MPER has been shown to play critical roles in the viral mucosal transmission, though this peptide is not immunogenic on its own. Gag is a structural protein configuring the enveloped virus particles, and has been suggested to constitute a target of the cellular immunity potentially controlling the viral load. It was hypothesized that HIV-1 enveloped virus-like particles (VLPs) consisting of Gag and a deconstructed form of gp41 comprising the MPER, transmembrane, and cytoplasmic domains (dgp41) could be expressed in plants. Plant-optimized HIV-1 genes were constructed and expressed in Nicotiana benthamiana by stable transformation, or transiently using a tobacco mosaic virus-based expression system or a combination of both. Results of biophysical, biochemical and electron microscopy characterization demonstrated that plant cells could support not only the formation of HIV-1 Gag VLPs, but also the accumulation of VLPs that incorporated dgp41. These particles were purified and utilized in mice immunization experiments. Prime-boost strategies combining systemic and mucosal priming with systemic boosting using two different vaccine candidates (VLPs and CTB-MPR - a fusion of MPER and the B-subunit of cholera toxin) were administered to BALB/c mice. Serum antibody responses against both the Gag and gp41 antigens could be elicited in mice systemically primed with VLPs and these responses could be recalled following systemic boosting with VLPs. In addition, mucosal priming with VLPs allowed for a robust boosting response against Gag and gp41 when boosted with either candidate. Functional assays of these antibodies are in progress to test the antibodies' effectiveness in neutralizing and preventing mucosal transmission of HIV-1. This immunogenicity of plant-based Gag/dgp41 VLPs represents an important milestone on the road towards a broadly-efficacious and inexpensive subunit vaccine against HIV-1.
ContributorsKessans, Sarah (Author) / Mor, Tsafrir S (Thesis advisor) / Matoba, Nobuyuki (Committee member) / Mason, Hugh (Committee member) / Hogue, Brenda (Committee member) / Fromme, Petra (Committee member) / Arizona State University (Publisher)
Created2011
Description
The most advanced social insects, the eusocial insects, form often large societies in which there is reproductive division of labor, queens and workers, have overlapping generations, and cooperative brood care where daughter workers remain in the nest with their queen mother and care for their siblings. The eusocial insects are composed of representative species of bees and wasps, and all species of ants and termites. Much is known about their organizational structure, but remains to be discovered.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
The success of social insects is dependent upon cooperative behavior and adaptive strategies shaped by natural selection that respond to internal or external conditions. The objective of my research was to investigate specific mechanisms that have helped shaped the structure of division of labor observed in social insect colonies, including age polyethism and nutrition, and phenomena known to increase colony survival such as egg cannibalism. I developed various Ordinary Differential Equation (ODE) models in which I applied dynamical, bifurcation, and sensitivity analysis to carefully study and visualize biological outcomes in social organisms to answer questions regarding the conditions under which a colony can survive. First, I investigated how the population and evolutionary dynamics of egg cannibalism and division of labor can promote colony survival. I then introduced a model of social conflict behavior to study the inclusion of different response functions that explore the benefits of cannibalistic behavior and how it contributes to age polyethism, the change in behavior of workers as they age, and its biological relevance. Finally, I introduced a model to investigate the importance of pollen nutritional status in a honeybee colony, how it affects population growth and influences division of labor within the worker caste. My results first reveal that both cannibalism and division of labor are adaptive strategies that increase the size of the worker population, and therefore, the persistence of the colony. I show the importance of food collection, consumption, and processing rates to promote good colony nutrition leading to the coexistence of brood and adult workers. Lastly, I show how taking into account seasonality for pollen collection improves the prediction of long term consequences.
ContributorsRodríguez Messan, Marisabel (Author) / Kang, Yun (Thesis advisor) / Castillo-Chavez, Carlos (Thesis advisor) / Kuang, Yang (Committee member) / Page Jr., Robert E (Committee member) / Gardner, Carl (Committee member) / Arizona State University (Publisher)
Created2018
Description
Efforts to treat prostate cancer have seen an uptick, as the world’s most commoncancer in men continues to have increasing global incidence. Clinically, metastatic
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
prostate cancer is most commonly treated with hormonal therapy. The idea behind
hormonal therapy is to reduce androgen production, which prostate cancer cells
require for growth. Recently, the exploration of the synergistic effects of the drugs
used in hormonal therapy has begun. The aim was to build off of these recent
advancements and further refine the synergistic drug model. The advancements I
implement come by addressing biological shortcomings and improving the model’s
internal mechanistic structure. The drug families being modeled, anti-androgens,
and gonadotropin-releasing hormone analogs, interact with androgen production in a
way that is not completely understood in the scientific community. Thus the models
representing the drugs show progress through their ability to capture their effect
on serum androgen. Prostate-specific antigen is the primary biomarker for prostate
cancer and is generally how population models on the subject are validated. Fitting
the model to clinical data and comparing it to other clinical models through the
ability to fit and forecast prostate-specific antigen and serum androgen is how this
improved model achieves validation. The improved model results further suggest that
the drugs’ dynamics should be considered in adaptive therapy for prostate cancer.
ContributorsReckell, Trevor (Author) / Kostelich, Eric (Thesis advisor) / Kuang, Yang (Committee member) / Mahalov, Alex (Committee member) / Arizona State University (Publisher)
Created2020
Description
Adoptive transfer of T cells engineered to express synthetic antigen-specific T cell receptors (TCRs) has provocative therapeutic applications for treating cancer. However, expressing these synthetic TCRs in a CD4+ T cell line is a challenge. The CD4+ Jurkat T cell line expresses endogenous TCRs that compete for space, accessory proteins, and proliferative signaling, and there is the potential for mixed dimer formation between the α and β chains of the endogenous receptor and that of the synthetic cancer-specific TCRs. To prevent hybridization between the receptors and to ensure the binding affinity measured with flow cytometry analysis is between the tetramer and the TCR construct, a CRISPR-Cas9 gene editing pipeline was developed. The guide RNAs (gRNAs) within the complex were designed to target the constant region of the α and β chains, as they are conserved between TCR clonotypes. To minimize further interference and confer cytotoxic capabilities, gRNAs were designed to target the CD4 coreceptor, and the CD8 coreceptor was delivered in a mammalian expression vector. Further, Golden Gate cloning methods were validated in integrating the gRNAs into a CRISPR-compatible mammalian expression vector. These constructs were transfected via electroporation into CD4+ Jurkat T cells to create a CD8+ knockout TCR Jurkat cell line for broadly applicable uses in T cell immunotherapies.
ContributorsHirneise, Gabrielle Rachel (Author) / Anderson, Karen (Thesis advisor) / Mason, Hugh (Committee member) / Lake, Douglas (Committee member) / Arizona State University (Publisher)
Created2020
Description
The analysis focuses on a two-population, three-dimensional model that attempts to accurately model the growth and diffusion of glioblastoma multiforme (GBM), a highly invasive brain cancer, throughout the brain. Analysis into the sensitivity of the model to
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
changes in the diffusion, growth, and death parameters was performed, in order to find a set of parameter values that accurately model observed tumor growth for a given patient. Additional changes were made to the diffusion parameters to account for the arrangement of nerve tracts in the brain, resulting in varying rates of diffusion. In general, small changes in the growth rates had a large impact on the outcome of the simulations, and for each patient there exists a set of parameters that allow the model to simulate a tumor that matches observed tumor growth in the patient over a period of two or three months. Furthermore, these results are more accurate with anisotropic diffusion, rather than isotropic diffusion. However, these parameters lead to inaccurate results for patients with tumors that undergo no observable growth over the given time interval. While it is possible to simulate long-term tumor growth, the simulation requires multiple comparisons to available MRI scans in order to find a set of parameters that provide an accurate prognosis.
ContributorsTrent, Austin Lee (Author) / Kostelich, Eric (Thesis advisor) / Gumel, Abba (Committee member) / Kuang, Yang (Committee member) / Arizona State University (Publisher)
Created2020