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- Member of: ASU Electronic Theses and Dissertations
Description
A synbody is a newly developed protein binding peptide which can be rapidly produced by chemical methods. The advantages of the synbody producing process make it a potential human proteome binding reagent. Most of the synbodies are designed to bind to specific proteins. The peptides incorporated in a synbody are discovered with peptide microarray technology. Nevertheless, the targets for unknown synbodies can also be discovered by searching through a protein mixture. The first part of this thesis mainly focuses on the process of target searching, which was performed with immunoprecipitation assays and mass spectrometry analysis. Proteins are pulled down from the cell lysate by certain synbodies, and then these proteins are identified using mass spectrometry. After excluding non-specific bindings, the interaction between a synbody and its real target(s) can be verified with affinity measurements. As a specific example, the binding between 1-4-KCap synbody and actin was discovered. This result proved the feasibility of the mass spectrometry based method and also suggested that a high throughput synbody discovery platform for the human proteome could be developed. Besides the application of synbody development, the peptide microarray technology can also be used for immunosignatures. The composition of all types of antibodies existing in one's blood is related to an individual's health condition. A method, called immunosignaturing, has been developed for early disease diagnosis based on this principle. CIM10K microarray slides work as a platform for blood antibody detection in immunosignaturing. During the analysis of an immunosignature, the data from these slides needs to be validated by using landing light peptides. The second part of this thesis focuses on the validation of the data. A biotinylated peptide was used as a landing light on the new CIM10K slides. The data was collected in several rounds of tests and indicated that the variation among landing lights was significantly reduced by using the newly prepared biotinylated peptide compared with old peptide mixture. Several suggestions for further landing light improvement are proposed based on the results.
ContributorsSun, Minyao (Author) / Johnston, Stephen Albert (Thesis advisor) / Diehnelt, Chris Wayne (Committee member) / Stafford, Phillip (Committee member) / Arizona State University (Publisher)
Created2011
Description
Immunosignaturing is a new immunodiagnostic technology that uses random-sequence peptide microarrays to profile the humoral immune response. Though the peptides have little sequence homology to any known protein, binding of serum antibodies may be detected, and the pattern correlated to disease states. The aim of my dissertation is to analyze the factors affecting the binding patterns using monoclonal antibodies and determine how much information may be extracted from the sequences. Specifically, I examined the effects of antibody concentration, competition, peptide density, and antibody valence. Peptide binding could be detected at the low concentrations relevant to immunosignaturing, and a monoclonal's signature could even be detected in the presences of 100 fold excess naive IgG. I also found that peptide density was important, but this effect was not due to bivalent binding. Next, I examined in more detail how a polyreactive antibody binds to the random sequence peptides compared to protein sequence derived peptides, and found that it bound to many peptides from both sets, but with low apparent affinity. An in depth look at how the peptide physicochemical properties and sequence complexity revealed that there were some correlations with properties, but they were generally small and varied greatly between antibodies. However, on a limited diversity but larger peptide library, I found that sequence complexity was important for antibody binding. The redundancy on that library did enable the identification of specific sub-sequences recognized by an antibody. The current immunosignaturing platform has little repetition of sub-sequences, so I evaluated several methods to infer antibody epitopes. I found two methods that had modest prediction accuracy, and I developed a software application called GuiTope to facilitate the epitope prediction analysis. None of the methods had sufficient accuracy to identify an unknown antigen from a database. In conclusion, the characteristics of the immunosignaturing platform observed through monoclonal antibody experiments demonstrate its promise as a new diagnostic technology. However, a major limitation is the difficulty in connecting the signature back to the original antigen, though larger peptide libraries could facilitate these predictions.
ContributorsHalperin, Rebecca (Author) / Johnston, Stephen A. (Thesis advisor) / Bordner, Andrew (Committee member) / Taylor, Thomas (Committee member) / Stafford, Phillip (Committee member) / Arizona State University (Publisher)
Created2011
Description
African Swine Fever (ASF), endemic in many African countries, is now spreading to other continents. Though ASF is capable of incurring serious economic losses in affected countries, no vaccine exists to provide immunity to animals. Disease control relies largely on rapid diagnosis and the implementation of movement restrictions and strict eradication programs. Developing a scalable, accurate and low cost diagnostic for ASF will be of great help for the current situation. CIM's 10K random peptide microarray is a new high-throughput platform that allows systematic investigations of immune responses associated with disease and shows promise as a diagnostic tool. In this study, this new technology was applied to characterize the immune responses of ASF virus (ASFV) infections and immunizations. Six sets of sera from ASFV antigen immunized pigs, 6 sera from infected pigs and 20 sera samples from unexposed pigs were tested and analyzed statistically. Results show that both ASFV antigen immunized pigs and ASFV viral infected pigs can be distinguished from unexposed pigs. Since it appears that immune responses to other viral infections are also distinguishable on this platform, it holds the potential of being useful in developing a new ASF diagnostic. The ability of this platform to identify specific ASFV antibody epitopes was also explored. A subtle motif was found to be shared among a set of peptides displaying the highest reactivity for an antigen specific antibody. However, this motif does not seem to match with any antibody epitopes predicted by a linear antibody epitope prediction.
ContributorsXiao, Liang (Author) / Sykes, Kathryn (Thesis advisor) / Zhao, Zhan-Gong (Committee member) / Stafford, Phillip (Committee member) / Arizona State University (Publisher)
Created2011
Description
We propose a novel solution to prevent cancer by developing a prophylactic cancer. Several sources of antigens for cancer vaccines have been published. Among these, antigens that contain a frame-shift (FS) peptide or viral peptide are quite attractive for a variety of reasons. FS sequences, from either mistake in RNA processing or in genomic DNA, may lead to generation of neo-peptides that are foreign to the immune system. Viral peptides presumably would originate from exogenous but integrated viral nucleic acid sequences. Both are non-self, therefore lessen concerns about development of autoimmunity. I have developed a bioinformatical approach to identify these aberrant transcripts in the cancer transcriptome. Their suitability for use in a vaccine is evaluated by establishing their frequencies and predicting possible epitopes along with their population coverage according to the prevalence of major histocompatibility complex (MHC) types. Viral transcripts and transcripts with FS mutations from gene fusion, insertion/deletion at coding microsatellite DNA, and alternative splicing were identified in NCBI Expressed Sequence Tag (EST) database. 48 FS chimeric transcripts were validated in 50 breast cell lines and 68 primary breast tumor samples with their frequencies from 4% to 98% by RT-PCR and sequencing confirmation. These 48 FS peptides, if translated and presented, could be used to protect more than 90% of the population in Northern America based on the prediction of epitopes derived from them. Furthermore, we synthesized 150 peptides that correspond to FS and viral peptides that we predicted would exist in tumor patients and we tested over 200 different cancer patient sera. We found a number of serological reactive peptide sequences in cancer patients that had little to no reactivity in healthy controls; strong support for the strength of our bioinformatic approach. This study describes a process used to identify aberrant transcripts that lead to a new source of antigens that can be tested and used in a prophylactic cancer vaccine. The vast amount of transcriptome data of various cancers from the Cancer Genome Atlas (TCGA) project will enhance our ability to further select better cancer antigen candidates.
ContributorsLee, HoJoon (Author) / Johnston, Stephen A. (Thesis advisor) / Kumar, Sudhir (Committee member) / Miller, Laurence (Committee member) / Stafford, Phillip (Committee member) / Sykes, Kathryn (Committee member) / Arizona State University (Publisher)
Created2012
Description
Immunosignaturing is a technology that allows the humoral immune response to be observed through the binding of antibodies to random sequence peptides. The immunosignaturing microarray is based on complex mixtures of antibodies binding to arrays of random sequence peptides in a multiplexed fashion. There are computational and statistical challenges to the analysis of immunosignaturing data. The overall aim of my dissertation is to develop novel computational and statistical methods for immunosignaturing data to access its potential for diagnostics and drug discovery. Firstly, I discovered that a classification algorithm Naive Bayes which leverages the biological independence of the probes on our array in such a way as to gather more information outperforms other classification algorithms due to speed and accuracy. Secondly, using this classifier, I then tested the specificity and sensitivity of immunosignaturing platform for its ability to resolve four different diseases (pancreatic cancer, pancreatitis, type 2 diabetes and panIN) that target the same organ (pancreas). These diseases were separated with >90% specificity from controls and from each other. Thirdly, I observed that the immunosignature of type 2 diabetes and cardiovascular complications are unique, consistent, and reproducible and can be separated by 100% accuracy from controls. But when these two complications arise in the same person, the resultant immunosignature is quite different in that of individuals with only one disease. I developed a method to trace back from informative random peptides in disease signatures to the potential antigen(s). Hence, I built a decipher system to trace random peptides in type 1 diabetes immunosignature to known antigens. Immunosignaturing, unlike the ELISA, has the ability to not only detect the presence of response but also absence of response during a disease. I observed, not only higher but also lower peptides intensities can be mapped to antigens in type 1 diabetes. To study immunosignaturing potential for population diagnostics, I studied effect of age, gender and geographical location on immunosignaturing data. For its potential to be a health monitoring technology, I proposed a single metric Coefficient of Variation that has shown potential to change significantly when a person enters a disease state.
ContributorsKukreja, Muskan (Author) / Johnston, Stephen Albert (Thesis advisor) / Stafford, Phillip (Committee member) / Dinu, Valentin (Committee member) / Arizona State University (Publisher)
Created2012
Description
Peptides offer great promise as targeted affinity ligands, but the space of possible peptide sequences is vast, making experimental identification of lead candidates expensive, difficult, and uncertain. Computational modeling can narrow the search by estimating the affinity and specificity of a given peptide in relation to a predetermined protein target. The predictive performance of computational models of interactions of intermediate-length peptides with proteins can be improved by taking into account the stochastic nature of the encounter and binding dynamics. A theoretical case is made for the hypothesis that, because of the flexibility of the peptide and the structural complexity of the target protein, interactions are best characterized by an ensemble of possible bound configurations rather than a single “lock and key” fit. A model incorporating these factors is proposed and evaluated. A comprehensive dataset of 3,924 peptide-protein interface structures was extracted from the Protein Data Bank (PDB) and descriptors were computed characterizing the geometry and energetics of each interface. The characteristics of these interfaces are shown to be generally consistent with the proposed model, and heuristics for design and selection of peptide ligands are derived. The curated and energy-minimized interface structure dataset and a relational database containing the detailed results of analysis and energy modeling are made publicly available via a web repository. A novel analytical technique based on the proposed theoretical model, Virtual Scanning Probe Mapping (VSPM), is implemented in software to analyze the interaction between a target protein of known structure and a peptide of specified sequence, producing a spatial map indicating the most likely peptide binding regions on the protein target. The resulting predictions are shown to be superior to those of two other published methods, and support the validity of the stochastic binding model.
ContributorsEmery, Jack Scott (Author) / Pizziconi, Vincent B (Thesis advisor) / Woodbury, Neal W (Thesis advisor) / Guilbeau, Eric J (Committee member) / Stafford, Phillip (Committee member) / Taylor, Thomas (Committee member) / Towe, Bruce C (Committee member) / Arizona State University (Publisher)
Created2010
Description
Meditation app usage is associated with decreases in stress, anxiety, and depression symptoms. Many meditation app subscribers, however, quickly abandon or reduce their app usage. This dissertation presents three manuscripts which 1) determined the behavioral, demographic, and socioeconomic factors associated with the abandonment of a meditation app, Calm, during the COVID-19 pandemic, 2) determined which participant characteristics predicted meditation app usage in the first eight weeks after subscribing, and 3) determined if changes in stress, anxiety, and depressive symptoms from baseline to Week 8 predicted meditation app usage from Weeks 8-16. In Manuscript 1, a survey was distributed to Calm subscribers in March 2020 that assessed meditation app behavior and meditation habit strength, and demographic information. Cox proportional hazards regression models were estimated to assess time to app abandonment. In Manuscript 2, new Calm subscribers completed a baseline survey on participants’ demographic and baseline mental health information and app usage data were collected over 8 weeks. In Manuscript 3, new Calm subscribers completed a baseline and Week 8 survey on demographic and mental health information. App usage data were collected over 16 weeks. Regression models were used to assess app usage for Manuscripts 2 and 3. Findings from Manuscript 1 suggest meditating after an existing routine decreased risk of app abandonment for pre-pandemic subscribers and for pandemic subscribers. Additionally, meditating “whenever I can” decreased risk of abandonment among pandemic subscribers. No behavioral factors were significant predictors of app abandonment among the long-term subscribers. Findings from Manuscript 2 suggest men had more days of meditation than women. Mental health diagnosis increased average daily meditation minutes. Intrinsic motivation for meditation increased the likelihood of completing any meditation session, more days with meditation sessions, and more average daily meditation minutes. Findings from Manuscript 3 suggest improvements in stress increased average daily meditation minutes. Improvements in depressive symptoms decreased daily meditation minutes. Evidence from this three-manuscript dissertation suggests meditation cue, time of day, motivation, symptom changes, and demographic and socioeconomic variables may be used to predict meditation app usage.
ContributorsSullivan, Mariah (Author) / Stecher, Chad (Thesis advisor) / Huberty, Jennifer (Committee member) / Buman, Matthew (Committee member) / Larkey, Linda (Committee member) / Chung, Yunro (Committee member) / Arizona State University (Publisher)
Created2022
Description
Young adult collegiate women, particularly students with adverse childhood experiences (ACEs) and who have experienced intimate partner violence (IPV) victimization, report a myriad of adverse mental health and academic difficulties. Practicing yoga has demonstrated promising findings among adults as a healing modality in the aftermath of interpersonal violence victimization and traumatization. Less known are the associations between collegiate women’s yoga participation and their mental health, body connection, and academic well-being examined through a yoga feminist- trauma conceptual framework. Among young adult collegiate women, this study examined (1) associations amongst socio-demographics, mental health service use, IPV types, and yoga participation (2) the strength and direction of associations on measures of ACEs, mental health, body connection, and academic well-being, (3) whether yoga participation predicted students’ mental health, body connection, and academic well-being after controlling for confounding variables, including ACEs and IPV victimization, and (4) whether socio-demographics, mental health service use, ACEs, and IPV types predicted yoga participation. This study was observational, cross-sectional, and gathered self-report quantitative data. Eligible participants were current collegiate women enrolled at an urban, public university in the southwestern United States who were 18 to 24 years of age. The main sub-sample (n = 93) included students who were ever in an intimate relationship and practiced yoga within the past year. IRB approval was obtained. Findings demonstrated that yoga participation was not a significant predictor of students’ mental health, body connection, or academic well-being. Socio-demographics, mental health service use, ACEs, and IPV did not predict yoga participation. However, women with greater ACEs fared worse on measures of mental health (i.e., depression and post-traumatic stress disorder symptoms), and women with experiences of IPV harassment reported greater post-traumatic stress disorder symptoms. Further, employed women reported fewer depression symptoms and were less likely to experience emotional IPV. Lastly, students with greater body connection (more awareness) fared better academically. This research supports prior literature on the adverse mental health outcomes among young adult collegiate women with histories of interpersonal violence. Further examination is warranted into employment and body connection, particularly related to yoga, as protective factors of students' health, safety, and academic well-being.
ContributorsKappas Mazzio, Andrea Alexa (Author) / Messing, Jill T (Thesis advisor) / Mendoza, Natasha (Committee member) / Huberty, Jennifer (Committee member) / Arizona State University (Publisher)
Created2022
Description
This dissertation describes a series of four studies on cognitive aging, working memory, and cognitive flexibility in dogs (Canis lupus familiaris) and their wild relatives. In Chapters 2 and 3, I designed assessments for age-related cognitive deficits in pet dogs which can be deployed rapidly using inexpensive and accessible materials. These novel tests can be easily implemented by owners, veterinarians, and clinicians and therefore, may improve care for elderly dogs by aiding in the diagnosis of dementia. In addition, these widely deployable tests may facilitate the use of dementia in pet dogs as a naturally occurring model of Alzheimer’s Disease in humans.In Chapters 4 and 5, I modified one of these tests to demonstrate for the first time that coyotes (Canis latrans) and wolves (Canis lupus lupus) develop age-related deficits in cognitive flexibility. This was an important first step towards differentiating between the genetic and environmental components of dementia in dogs and in turn, humans. Unexpectedly, I also detected cognitive deficits in young, adult dogs and wolves but not coyotes. These finding add to a recent shift in understanding cognitive development in dogs which may improve cognitive aging tests as well as training, care, and use of working and pet dogs. These findings also suggest that the ecology of coyotes may select for flexibility earlier in development.
In Chapter 5, I piloted the use of the same cognitive flexibility test for red and gray foxes so that future studies may test for lifespan changes in the cognition of small-bodied captive canids. More broadly, this paradigm may accommodate physical and behavioral differences between diverse pet and captive animals. In Chapters 4 and 5, I examined which ecological traits drive the evolution of behavioral flexibility and in turn, species resilience. I found that wolves displayed less flexibility than dogs and coyotes suggesting that species which do not rely heavily on unstable resources may be ill-equipped to cope with human habitat modification. Ultimately, this comparative work may help conservation practitioners to identify and protect species that cannot cope with rapid and unnatural environmental change.
ContributorsVan Bourg, Joshua (Author) / Wynne, Clive D (Thesis advisor) / Aktipis, C. Athena (Committee member) / Gilby, Ian C (Committee member) / Young, Julie K (Committee member) / Arizona State University (Publisher)
Created2022
Description
College students experience a considerable amount of stress. Unmanaged stress is associated with poor academic performance, health risk behaviors (i.e., inadequate sleep and physical activity, alcohol consumption, poor dietary behaviors), and poor mental health. Coping with stress has become a priority among universities. The most tested stress-related programs to date have been mindfulness-based and face-to-face. These programs demonstrated significant improvements in stress, mindfulness, and self-compassion among college students. However, they may be burdensome to students as studies report low attendance and low compliance due to class conflicts or not enough time. Few interventions have used more advanced technologies (i.e., mobile apps) as a mode of delivery. The purpose of this study is to report adherence to a consumer-based mindfulness meditation mobile application (i.e., Calm) and test its effects on stress, mindfulness, and self-compassion in college students. We will also explore what the relationship is between mindfulness and health behaviors.
College students were recruited using fliers on college campus and social media. Eligible participants were randomized to one of two groups: (1) Intervention - meditate using Calm, 10 min/day for eight weeks and (2) Control – no participation in mindfulness practices (received the Calm application after 12-weeks). Stress, mindfulness, and self-compassion and health behaviors (i.e., sleep disturbance, alcohol consumption, physical activity, fruit and vegetable consumption) were measured using self-report. Outcomes were measured at baseline and week eight.
Of the 109 students that enrolled in the study, 41 intervention and 47 control participants were included in analysis. Weekly meditation participation averaged 38 minutes with 54% of participants completing at least half (i.e., 30 minutes) of meditations. Significant changes between groups were found in stress, mindfulness, and self-compassion (all P<0.001) in favor of the intervention group. A significant negative association (p<.001) was found between total mindfulness and sleep disturbance.
An eight-week consumer-based mindfulness meditation mobile application (i.e., Calm) was effective in reducing stress, improving mindfulness and self-compassion among undergraduate college students. Mobile applications may be a feasible, effective, and less burdensome way to reduce stress in college students.
College students were recruited using fliers on college campus and social media. Eligible participants were randomized to one of two groups: (1) Intervention - meditate using Calm, 10 min/day for eight weeks and (2) Control – no participation in mindfulness practices (received the Calm application after 12-weeks). Stress, mindfulness, and self-compassion and health behaviors (i.e., sleep disturbance, alcohol consumption, physical activity, fruit and vegetable consumption) were measured using self-report. Outcomes were measured at baseline and week eight.
Of the 109 students that enrolled in the study, 41 intervention and 47 control participants were included in analysis. Weekly meditation participation averaged 38 minutes with 54% of participants completing at least half (i.e., 30 minutes) of meditations. Significant changes between groups were found in stress, mindfulness, and self-compassion (all P<0.001) in favor of the intervention group. A significant negative association (p<.001) was found between total mindfulness and sleep disturbance.
An eight-week consumer-based mindfulness meditation mobile application (i.e., Calm) was effective in reducing stress, improving mindfulness and self-compassion among undergraduate college students. Mobile applications may be a feasible, effective, and less burdensome way to reduce stress in college students.
ContributorsGlissmann, Christine (Author) / Huberty, Jennifer (Thesis advisor) / Sebren, Ann (Committee member) / Larkey, Linda (Committee member) / Lee, Chong (Committee member) / Arizona State University (Publisher)
Created2018