Matching Items (21)
Filtering by
- Genre: Doctoral Dissertation
Description
In an effort to begin validating the large number of discovered candidate biomarkers, proteomics is beginning to shift from shotgun proteomic experiments towards targeted proteomic approaches that provide solutions to automation and economic concerns. Such approaches to validate biomarkers necessitate the mass spectrometric analysis of hundreds to thousands of human samples. As this takes place, a serendipitous opportunity has become evident. By the virtue that as one narrows the focus towards "single" protein targets (instead of entire proteomes) using pan-antibody-based enrichment techniques, a discovery science has emerged, so to speak. This is due to the largely unknown context in which "single" proteins exist in blood (i.e. polymorphisms, transcript variants, and posttranslational modifications) and hence, targeted proteomics has applications for established biomarkers. Furthermore, besides protein heterogeneity accounting for interferences with conventional immunometric platforms, it is becoming evident that this formerly hidden dimension of structural information also contains rich-pathobiological information. Consequently, targeted proteomics studies that aim to ascertain a protein's genuine presentation within disease- stratified populations and serve as a stepping-stone within a biomarker translational pipeline are of clinical interest. Roughly 128 million Americans are pre-diabetic, diabetic, and/or have kidney disease and public and private spending for treating these diseases is in the hundreds of billions of dollars. In an effort to create new solutions for the early detection and management of these conditions, described herein is the design, development, and translation of mass spectrometric immunoassays targeted towards diabetes and kidney disease. Population proteomics experiments were performed for the following clinically relevant proteins: insulin, C-peptide, RANTES, and parathyroid hormone. At least thirty-eight protein isoforms were detected. Besides the numerous disease correlations confronted within the disease-stratified cohorts, certain isoforms also appeared to be causally related to the underlying pathophysiology and/or have therapeutic implications. Technical advancements include multiplexed isoform quantification as well a "dual- extraction" methodology for eliminating non-specific proteins while simultaneously validating isoforms. Industrial efforts towards widespread clinical adoption are also described. Consequently, this work lays a foundation for the translation of mass spectrometric immunoassays into the clinical arena and simultaneously presents the most recent advancements concerning the mass spectrometric immunoassay approach.
ContributorsOran, Paul (Author) / Nelson, Randall (Thesis advisor) / Hayes, Mark (Thesis advisor) / Ros, Alexandra (Committee member) / Williams, Peter (Committee member) / Arizona State University (Publisher)
Created2011
Description
Signal processing techniques have been used extensively in many engineering problems and in recent years its application has extended to non-traditional research fields such as biological systems. Many of these applications require extraction of a signal or parameter of interest from degraded measurements. One such application is mass spectrometry immunoassay (MSIA) which has been one of the primary methods of biomarker discovery techniques. MSIA analyzes protein molecules as potential biomarkers using time of flight mass spectrometry (TOF-MS). Peak detection in TOF-MS is important for biomarker analysis and many other MS related application. Though many peak detection algorithms exist, most of them are based on heuristics models. One of the ways of detecting signal peaks is by deploying stochastic models of the signal and noise observations. Likelihood ratio test (LRT) detector, based on the Neyman-Pearson (NP) lemma, is an uniformly most powerful test to decision making in the form of a hypothesis test. The primary goal of this dissertation is to develop signal and noise models for the electrospray ionization (ESI) TOF-MS data. A new method is proposed for developing the signal model by employing first principles calculations based on device physics and molecular properties. The noise model is developed by analyzing MS data from careful experiments in the ESI mass spectrometer. A non-flat baseline in MS data is common. The reasons behind the formation of this baseline has not been fully comprehended. A new signal model explaining the presence of baseline is proposed, though detailed experiments are needed to further substantiate the model assumptions. Signal detection schemes based on these signal and noise models are proposed. A maximum likelihood (ML) method is introduced for estimating the signal peak amplitudes. The performance of the detection methods and ML estimation are evaluated with Monte Carlo simulation which shows promising results. An application of these methods is proposed for fractional abundance calculation for biomarker analysis, which is mathematically robust and fundamentally different than the current algorithms. Biomarker panels for type 2 diabetes and cardiovascular disease are analyzed using existing MS analysis algorithms. Finally, a support vector machine based multi-classification algorithm is developed for evaluating the biomarkers' effectiveness in discriminating type 2 diabetes and cardiovascular diseases and is shown to perform better than a linear discriminant analysis based classifier.
ContributorsBuddi, Sai (Author) / Taylor, Thomas (Thesis advisor) / Cochran, Douglas (Thesis advisor) / Nelson, Randall (Committee member) / Duman, Tolga (Committee member) / Arizona State University (Publisher)
Created2012
Description
Cancer is one of the most serious global diseases. We have focused on cancer immunoprevention. My thesis projects include developing a prophylactic primary and metastatic cancer vaccines, early cancer detection and investigation of genes involved in tumor development. These studies were focused on frame-shift (FS) antigens. The FS antigens are generated by genomic mutations or abnormal RNA processing, which cause a portion of a normal protein to be translated out of frame. The concept of the prophylactic cancer vaccine is to develop a general cancer vaccine that could prevent healthy people from developing different types of cancer. We have discovered a set of cancer specific FS antigens. One of the FS candidates, structural maintenance of chromosomes protein 1A (SMC1A) FS, could start to accumulate at early stages of tumor and be specifically exposed to the immune system by tumor cells. Prophylactic immunization with SMC1A-FS could significantly inhibit primary tumor development in different murine tumor models and also has the potential to inhibit tumor metastasis. The SMC1A-FS transcript was detected in the plasma of the 4T1/BALB/c mouse tumor model. The tumor size was correlated with the transcript ratio of the SMC1A-FS verses the WT in plasma, which could be measured by regular RT-PCR. This unique cancer biomarker has a practical potential for a large population cancer screen, as well as clinical tumor monitoring. With a set of mimotope peptides, antibodies against SMC1A-FS peptide were detected in different cancer patients, including breast cancer, pancreas cancer and lung cancer with a 53.8%, 56.5% and 12.5% positive rate respectively. This suggested that the FS antibody could be a biomarker for early cancer detection. The characterization of SMC1A suggested that: First, the deficiency of the SMC1A is common in different tumors and able to promote tumor initiation and development; second, the FS truncated protein may have nucleolus function in normal cells. Mis-control of this protein may promote tumor development. In summary, we developed a systematic general cancer prevention strategy through the variety immunological and molecular methods. The results gathered suggest the SMC1A-FS may be useful for the detection and prevention of cancer.
ContributorsShen, Luhui (Author) / Johnston, Stephen Albert (Thesis advisor) / Chang, Yung (Committee member) / Miller, Laurence (Committee member) / Sykes, Kathryn (Committee member) / Jacobs, Bertram (Committee member) / Arizona State University (Publisher)
Created2012
Description
Ebola hemorrhagic fever (EHF) is a severe and often fatal disease in human and nonhuman primates, caused by the Ebola virus. Approximately 30 years after the first epidemic, there is no vaccine or therapeutic medication approved to counter the Ebola virus. In this dissertation, a geminiviral replicon system was used to produce Ebola immune complex (EIC) in plant leaves and tested it as an Ebola vaccine. The EIC was produced in Nicotiana benthamiana leaves by fusing Ebola virus glycoprotein (GP1) to the C-terminus of heavy chain of 6D8 monoclonal antibody (mAb), which is specific to the 6D8 epitope of GP1, and co-expressing the fusion with the light chain of 6D8 mAb. EIC was purified by ammonium sulfate precipitation and protein A or protein G affinity chromatography. EIC was shown to be immunogenic in mice, but the level of antibody against Ebola virus was not sufficient to protect the mice from lethal the Ebola challenge. Hence, different adjuvants were tested in order to improve the immunogenicity of the EIC. Among several adjuvants that we used, Poly(I:C), which is a synthetic analog of double-stranded ribonucleic acid that can interact with a Toll-like receptor 3, strongly increased the efficacy of our Ebola vaccine. The mice immunized with EIC co-administered with Poly(I:C) produced high levels of neutralizing anti-Ebola IgG, and 80% of the mice were protected from the lethal Ebola virus challenge. Moreover, the EIC induced a predominant T-helper type 1 (Th1) response, whereas Poly(I:C) co-delivered with the EIC stimulated a mixed Th1/Th2 response. This result suggests that the protection against lethal Ebola challenge requires both Th1 and Th2 responses. In conclusion, this study demonstrated that the plant-produced EIC co-delivered with Poly(I:C) induced strong and protective immune responses to the Ebola virus in mice. These results support plant-produced EIC as a good vaccine candidate against the Ebola virus. It should be pursued further in primate studies, and eventually in clinical trials.
ContributorsPhoolcharoen, Waranyoo (Author) / Mason, Hugh S (Thesis advisor) / Chen, Qiang (Thesis advisor) / Arntzen, Charles J. (Committee member) / Change, Yung (Committee member) / Ma, Julian (Committee member) / Arizona State University (Publisher)
Created2010
Description
Vaccines are one of the most effective ways of combating infectious diseases and developing vaccine platforms that can be used to produce vaccines can greatly assist in combating global public health threats. This dissertation focuses on the development and pre-clinical testing of vaccine platforms that are highly immunogenic, easily modifiable, economically viable to produce, and stable. These criteria are met by the recombinant immune complex (RIC) universal vaccine platform when produced in plants. The RIC platform is modeled after naturally occurring immune complexes that form when an antibody, a component of the immune system that recognizes protein structures or sequences, binds to its specific antigen, a molecule that causes an immune response. In the RIC platform, a well-characterized antibody is linked via its heavy chain, to an antigen tagged with the antibody-specific epitope. The RIC antibody binds to the epitope tags on other RIC molecules and forms highly immunogenic complexes. My research has primarily focused on the optimization of the RIC platform. First, I altered the RIC platform to enable an N-terminal antigenic fusion instead of the previous C-terminal fusion strategy. This allowed the platform to be used with antigens that require an accessible N-terminus. A mouse immunization study with a model antigen showed that the fusion location, either N-terminal or C-terminal, did not impact the immune response. Next, I studied a synergistic response that was seen upon co-delivery of RIC with virus-like particles (VLP) and showed that the synergistic response could be produced with either N-terminal or C-terminal RIC co-delivered with VLP. Since RICs are inherently insoluble due to their ability to form complexes, I also examined ways to increase RIC solubility by characterizing a panel of modified RICs and antibody-fusions. The outcome was the identification of a modified RIC that had increased solubility while retaining high immunogenicity. Finally, I modified the RIC platform to contain multiple antigenic insertion sites and explored the use of bioinformatic tools to guide the design of a broadly protective vaccine.
ContributorsPardhe, Mary (Author) / Mason, Hugh S (Thesis advisor) / Chen, Qiang (Committee member) / Mor, Tsafrir (Committee member) / Wilson, Melissa (Committee member) / Arizona State University (Publisher)
Created2021
Description
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is the causative agent of coronavirus disease 2019 (COVID-19) that emerged from a zoonotic host at the end of 2019 and caused a public health crisis. In this collection of studies, Nicotiana benthamiana plants are used to set the foundation for producing monoclonal antibodies (mAbs) with homogeneous glycosylation to neutralize SARS-CoV-2 and potentially address the immunopathology often observed with severe COVID-19. Specifically, a mAb against the human interleukin (IL)-6 receptor (sarilumab) was generated and evaluated in vitro for its potential to reduce IL-6 signaling that has been shown to be associated with more severe cases of COVID-19. Furthermore, multiple mAbs that bind to the receptor-binding domain (RBD) of SARS-CoV-2 and efficiently neutralize the virus were developed using plant-based expression. Several of these mAbs are from different classes of RBD-binding mAbs that have distinct binding sites from one another. Several mAbs from different classes showed synergy in neutralizing the ancestral strain of SARS-CoV-2 and a smaller subset showed synergy when tested against the highly mutated Omicron (B.1.1.529) variant. Of interest, a novel RBD-binding mAb, termed 11D7, that was raised against the ancestral strain and derived from a hybridoma, appears to have an epitope on the RBD that contributes more synergy to a mAb combination that efficiently neutralizes the B.1.1.529 variant of SARS-CoV-2. This epitope was partially mapped by competitive binding and shows that it overlaps with another known antibody that binds a cryptic, distal epitope, away from the receptor binding site, giving insight into the potential mechanism by which 11D7 neutralizes SARS-CoV-2, as well as potentially allowing it to resist SARS-CoV-2 immune evasion more efficiently. Furthermore, this mAb carries a highly homogeneous glycan pattern when expressed in N. benthamiana, that may contribute to enhanced effector function and provides a tool to elucidate the precise role of crystallizable fragment (Fc)-mediated protection in SARS-CoV-2 infection. Ultimately, these studies provide evidence of the utility of plant-made mAbs to be used as cocktail members, giving clarity to the use of less potent mAbs as valuable cocktail components which will spur further investigations into how mAbs with unique epitopes work together to efficiently neutralize SARS-CoV-2.
ContributorsJugler, Collin (Author) / Chen, Qiang (Thesis advisor) / Lake, Douglas (Committee member) / Steele, Kelly (Committee member) / Mason, Hugh (Committee member) / Arizona State University (Publisher)
Created2022
Description
Infrastructure systems are facing non-stationary challenges that stem from climate change and the increasingly complex interactions between the social, ecological, and technological systems (SETSs). It is crucial for transportation infrastructures—which enable residents to access opportunities and foster prosperity, quality of life, and social connections—to be resilient under these non-stationary challenges. Vulnerability assessment (VA) examines the potential consequences a system is likely to experience due to exposure to perturbation or stressors and lack of the capacity to adapt. Post-fire debris flow and heat represent particularly challenging problems for infrastructure and users in the arid U.S. West. Post-fire debris flow, which is manifested with heat and drought, produces powerful runoff threatening physical transportation infrastructures. And heat waves have devastating health effects on transportation infrastructure users, including increased mortality rates. VA anticipates the potential consequences of these perturbations and enables infrastructure stakeholders to improve the system's resilience. The current transportation climate VA—which only considers a single direct climate stressor on the infrastructure—falls short of addressing the wildfire and heat challenges. This work proposes advanced transportation climate VA methods to address the complex and multiple climate stressors and the vulnerability of infrastructure users. Two specific regions were chosen to carry out the progressive transportation climate VA: 1) the California transportation networks’ vulnerability to post-fire debris flows, and 2) the transportation infrastructure user’s vulnerability to heat exposure in Phoenix.
ContributorsLi, Rui (Author) / Chester, Mikhail V. (Thesis advisor) / Middel, Ariane (Committee member) / Hondula, David M. (Committee member) / Pendyala, Ram (Committee member) / Arizona State University (Publisher)
Created2022
Description
Scientists are entrusted with developing novel molecular strategies for effective prophylactic and therapeutic interventions. Antivirals are indispensable tools that can be targeted at viral domains directly or at cellular domains indirectly to obstruct viral infections and reduce pathogenicity. Despite their transformative potential in healthcare, to date, antivirals have been clinically approved to treat only 10 out of the greater than 200 known pathogenic human viruses. Additionally, as obligate intracellular parasites, many virus functions are intimately coupled with host cellular processes. As such, the development of a clinically relevant antiviral is challenged by the limited number of clear targets per virus and necessitates an extensive insight into these molecular processes. Compounding this challenge, many viral pathogens have evolved to evade effective antivirals. Therefore, a means to develop virus- or strain-specific antivirals without detailed insight into each idiosyncratic biochemical mechanism may aid in the development of antivirals against a larger swath of pathogens. Such an approach will tremendously benefit from having the specific molecular recognition of viral species as the lowest barrier. Here, I modify a nanobody (anti-green fluorescent protein) that specifically recognizes non-essential epitopes (glycoprotein M-pHluorin chimera) presented on the extra virion surface of a virus (Pseudorabies virus strain 486). The nanobody switches from having no inhibitory properties (tested up to 50 μM) to ∼3 nM IC50 in in vitro infectivity assays using porcine kidney (PK15) cells. The nanobody modifications use highly reliable bioconjugation to a three-dimensional wireframe deoxyribonucleic acid (DNA) origami scaffold. Mechanistic studies suggest that inhibition is mediated by the DNA origami scaffold bound to the virus particle, which obstructs the internalization of the viruses into cells, and that inhibition is enhanced by avidity resulting from multivalent virus and scaffold interactions. The assembled nanostructures demonstrate negligible cytotoxicity (<10 nM) and sufficient stability, further supporting their therapeutic potential. If translatable to other viral species and epitopes, this approach may open a new strategy that leverages existing infrastructures – monoclonal antibody development, phage display, and in vitro evolution - for rapidly developing novel antivirals in vivo.
ContributorsPradhan, Swechchha (Author) / Hariadi, Rizal (Thesis advisor) / Hogue, Ian (Committee member) / Varsani, Arvind (Committee member) / Chen, Qiang (Committee member) / Arizona State University (Publisher)
Created2022
Description
Cities globally are experiencing substantial warming due to ongoing urbanization and climate change. However, existing efforts to mitigate urban heat focus mainly on new technologies, exacerbate social injustices, and ignore the need for a sustainability lens that considers environmental, social, and economic perspectives. Heat in urban areas is amplified and urgently needs to be considered as a critical sustainability issue that crosses disciplinary and sectoral (traditional) boundaries. The missing urgency is concerning because urban overheating is a multi-faceted threat to the well-being and performance of individuals as well as the energy efficiency and economy of cities. Urban heat consequences require transformation in ways of thinking by involving the best available knowledge engaging scientists, policymakers, and communities. To do so, effective heat mitigation planning requires a considerable amount of diverse knowledge sources, yet urban planners face multiple barriers to effective heat mitigation, including a lack of usable, policy-relevant science and governance structures. To address these issues, transdisciplinary approaches, such as co-production via partnerships and the creation of usable, policy-relevant science, are necessary to allow for sustainable and equitable heat mitigation that allow cities to work toward multiple Sustainable Development Goals (SDGs) using a systems approach. This dissertation presents three studies that contribute to a sustainability lens on urban heat, improve the holistic and multi-perspective understanding of heat mitigation strategies, provide contextual guidance for reflective pavement as a heat mitigation strategy, and evaluate a multilateral, sustainability-oriented, co-production partnership to foster heat resilience equitably in cities. Results show that science and city practice communicate differently about heat mitigation strategies while both avoid to communicate disservices and trade-offs. Additionally, performance evaluation of heat mitigation strategies for decision-making needs to consider multiple heat metrics, people, and background climate. Lastly, the partnership between science, city practice, and community needs to be evaluated to be accountable and provide a pathway of growth for all partners. The outcomes of this dissertation advance research and awareness of urban heat for science, practice, and community, and provide guidance to improve holistic and sustainable decision-making in cities and partnerships to address SDGs around urban heat.
ContributorsSchneider, Florian Arwed (Author) / Middel, Ariane (Thesis advisor) / Vanos, Jennifer K (Committee member) / Withycombe Keeler, Lauren (Committee member) / Arizona State University (Publisher)
Created2023
Description
Extreme heat and its human impacts are significant public health challenges that disproportionately affect certain populations. Often, people with the least resources to cope with the heat also live in the hottest regions of cities. Previous heat vulnerability research has predominantly been conducted at a coarse geographic scale, yet translating relationships measured at aggregated scales to the individual level can result in ecological fallacy. Prior work has also primarily studied the most severe health outcomes: hospitalization/emergency care and mortality. It is likely that magnitudes more people are experiencing negative health impacts from heat that do not necessarily result in medical care. Such less severe impacts are under-researched in the literature.This dissertation addresses these knowledge gaps by identifying how social characteristics and physical measurements of heat at the individual and household level act independently and in concert to influence human heat-related outcomes, especially less severe outcomes. In the first paper, meta-analysis was used to quantify the summary effects of vulnerability indicators on incidence of heat-related illness. More proximal vulnerability indicators (e.g., residential air conditioning use, indoor heat exposure, etc.) tended to have the strongest impact on odds of experiencing heat-related illness than more distal indicators. In the next paper, indoor air temperature observations were related to the social characteristics of the residents. The strongest predictor of indoor air temperature was the residents’ ideal thermally comfortable temperature, despite affordability. In the final paper, fine scale biometeorological observations of the outdoor thermal environment near residents’ homes were linked to their experience with heat-related illness. The outdoor thermal environment appeared to have a stronger, more consistent impact on heat-related illness among households in a lower income neighborhood compared to a higher income one. These findings affirm the value of employing residential heat mitigation solutions at the individual and household scale, indoors and outdoors. Across all chapters, the indoor thermal environment, and the ability to modify it, had a clear impact on residents’ comfort and health. Solutions that target the most proximal causal factors of heat-related illness will likely have the greatest impact on reducing the burden of heat on human health and well-being.
ContributorsWright, Mary K (Author) / Hondula, David M (Thesis advisor) / Larson, Kelli L (Committee member) / Middel, Ariane (Committee member) / Arizona State University (Publisher)
Created2023