Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
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Description
Type 1 diabetes (T1D) is the result of an autoimmune attack against the insulin-producing β-cells of the pancreas causing hyperglycemia and requiring the individual to rely on life-long exogenous insulin. With the age of onset typically occurring in childhood, there is increased physical and emotional stress to the child as well as caregivers to maintain appropriate glucose levels. The majority of T1D patients have antibodies to one or more antigens: insulin, IA-2, GAD65, and ZnT8. Although antibodies are detectable years before symptoms occur, the initiating factors and mechanisms of progression towards β-cell destruction are still not known. The search for new autoantibodies to elucidate the autoimmune process in diabetes has been slow, with proteome level screenings on native proteins only finding a few minor antigens. Post-translational modifications (PTM)—chemical changes that occur to the protein after translation is complete—are an unexplored way a self-protein could become immunogenic. This dissertation presents the first large sale screening of autoantibodies in T1D to nitrated proteins. The Contra Capture Protein Array (CCPA) allowed for fresh expression of hundreds of proteins that were captured on a secondary slide by tag-specific ligand and subsequent modification with peroxynitrite. The IgG and IgM humoral response of 48 newly diagnosed T1D subjects and 48 age-matched controls were screened against 1632 proteins highly or specifically expressed in pancreatic cells. Top targets at 95% specificity were confirmed with the same serum samples using rapid antigenic protein in situ display enzyme-linked immunosorbent assay (RAPID ELISA) a modified sandwich ELISA employing the same cell-free expression as the CCPA. For validation, 8 IgG and 5 IgM targets were evaluated with an independent serum sample set of 94 T1D subjects and 94 controls. The two best candidates at 90% specificity were estrogen receptor 1 (ESR1) and phosphatidylinositol 4-kinase type 2 beta (PI4K2B) which had sensitivities of 22% (p=.014) and 25% (p=.045), respectively. Receiver operating characteristic (ROC) analyses found an area under curve (AUC) of 0.6 for ESR1 and 0.58 for PI4K2B. These studies demonstrate the ability and value for high-throughput autoantibody screening to modified antigens and the frequency of Type 1 diabetes.
ContributorsHesterman, Jennifer (Author) / LaBaer, Joshua (Thesis advisor) / Borges, Chad (Committee member) / Sweazea, Karen (Committee member) / Mangone, Marco (Committee member) / Arizona State University (Publisher)
Created2022
Description
Metabolomics focuses on the study of metabolic changes occurring in varioussystems and utilizes quantitative and semi-quantitative measurements of multiple metabolites in parallel. Mass spectrometry (MS) is the most ubiquitous platform in this field, as it provides superior sensitivity regarding measurements of complex metabolic profiles in biological systems. When combined with MS, multivariate statistics and advanced machine learning algorithms provide myriad opportunities for bioinformatics insights beyond simple univariate data comparisons. In this dissertation, the application of MS-based metabolomics is introduced with an emphasis on biomarker discovery for human disease detection. To advance disease diagnosis using MS-based metabolomics, numerous statistical techniques have been implemented in this research including principal component analysis, factor analysis, partial least squares-discriminant analysis (PLS-DA), orthogonal PLS-DA, random forest, receiver operating characteristic analysis, as well as functional pathway/enzyme enrichment analyses. These approaches are highly useful for improving classification sensitivity and specificity related to disease-induced biological variation and can help identify useful biomarkers and potential therapeutic targets. It is also shown that MS-based metabolomics can distinguish between clinical and prodromal disease as well as similar diseases with related symptoms, which may assist in clinical staging and differential diagnosis, respectively. Additionally, MS-based metabolomics is shown to be promising for the early and accurate detection of diseases, thereby improving patient outcomes, and advancing clinical care. Herein, the application of MS methods and chemometric statistics to the diagnosis of breast cancer, coccidioidomycosis (Valley fever), and senile dementia (Alzheimer's disease) are presented and discussed. In addition to presenting original research, previous efforts in biomarker discovery will be synthesized and appraised. A Comment will be offered regarding the state of the science, specifically addressing the inefficient model of repetitive biomarker discovery and the need for increased translational efforts necessary to consolidate metabolomics findings and formalize purported metabolic markers as laboratory developed tests. Various factors impeding the translational throughput of metabolomics findings will be carefully considered with respect to study design, statistical analysis, and regulation of biomedical diagnostics. Importantly, this dissertation will offer critical insights to advance metabolomics from a scientific field to a practical one including targeted detection, enhanced quantitation, and direct-to-consumer considerations.
ContributorsJasbi, Paniz (Author) / Johnston, Carol S (Thesis advisor) / Gu, Haiwei (Thesis advisor) / Lake, Douglas F (Committee member) / Sweazea, Karen (Committee member) / Tasevska, Natasha (Committee member) / Arizona State University (Publisher)
Created2022
Description
Lipolysis or hydrolysis of triglyceride (TG) stored within intracellular lipid droplets (LD), is vital to maintaining metabolic homeostasis in mammals. Regulation of lipolysis and subsequent utilization of liberated fatty acids impacts cellular and organismal functions including body fat accumulation and thermogenesis. Adipose triglyceride lipase (ATGL) is the intracellular rate-limiting enzyme responsible for catalyzing hydrolysis of TG to diacylglycerol (DAG), the initial step of the lipolytic reaction. G0/G1 switch gene-2 (G0S2) and hypoxia-inducible gene-2 (HIG2) are selective inhibitors of ATGL. G0S2 facilitates accumulation of TG in the liver and adipose tissue, while HIG2 functions under hypoxic conditions. Sequence analysis and mutagenesis were used to confirm the presence of conserved domains between these proteins, and that these domains are required for efficient binding and inhibition of ATGL. Further analysis revealed a Positive sequence (Pos-Seq)-LD binding motif in G0S2 but not HIG2. The Pos-Seq mediated ATGL-independent localization to LD and was required for achieving maximal inhibition of ATGL activity by G0S2. Identification and mutational analysis of this motif revealed distinct mechanisms for HIG2 and G0S2 LD association. In addition to molecular characterization of known protein inhibitors of lipolysis, an intracellular member of the apolipoprotein L (ApoL) family, ApoL6, was also identified as a LD and mitochondria associated protein expressed in adipose tissue. Brown adipose tissue uses fatty acids as fuel for increasing its energy output as heat during acute responses to cold exposure. A Comprehensive Lab Animal Monitoring System was used to compare heat production at room temperature (RT) and 4oC in transgenic animals overexpressing ApoL6 in brown adipose tissue. Overexpression of ApoL6 delayed utilization of long-chain fatty acids (LCFAs) as a fuel source while promoting an enhanced thermogenic response during initial cold exposure. ApoL6 mediated inhibition of LCFA utilization results from binding of ApoL6 to Mitochondrial Trifunctional Protein (MTP/TFP), which catalyzes mitochondrial β-oxidation. Indirect calorimetry and fasting acute cold exposure experiments suggest the augmented thermogenic profile of ApoL6 transgenic animals is a result of enhanced utilization of medium-chain fatty acids (MCFAs), glucose, and amino acids as fuel sources. Cumulatively these results indicate multiple mechanisms for regulation lipolysis and fatty acid utilization.
ContributorsCampbell, Latoya E (Author) / Lake, Douglas (Thesis advisor) / Liu, Jun (Committee member) / Folmes, Clifford (Committee member) / Sweazea, Karen (Committee member) / Baluch, Debra (Committee member) / Arizona State University (Publisher)
Created2021