Matching Items (160)
Description
Structural Magnetic Resonance Imaging analysis is a vital component in the study of Alzheimer’s Disease pathology and several techniques exist as part of the existing research conducted. In particular, volumetric approaches in this field are known to be beneficial due to the increased capability to express morphological characteristics when compared to manifold methods. To aid in the improvement of the field, this paper aims to propose an intrinsic volumetric conic system that can be applied to bounded volumetric meshes to enable a more effective study of subjects. The computation of the metric involves the use of heat kernel theory and conformal parameterization on genus-0 surfaces extended to a volumetric domain. Additionally, this paper also explores the use of the ’TetCNN’ architecture on the classification of hippocampal tetrahedral meshes to detect features that correspond to Alzheimer’s indicators. The model tested was able to achieve remarkable results with a measured classification accuracy of above 90% in the task of differentiating between subjects diagnosed with Alzheimer’s and normal control subjects.
ContributorsGeorge, John Varghese (Author) / Wang, Yalin (Thesis advisor) / Hansford, Dianne (Committee member) / Gupta, Vikash (Committee member) / Arizona State University (Publisher)
Created2023
Description
Transient Receptor Potential Vanilloid-1 (TRPV1) is an integral membrane polymodal cation channel involved in various essential biological functions, including thermosensing, thermoregulation, and nociception. Discrete TRPV1 activation modes such as ligand, heat, and proton have been challenging to disentangle. However, dissecting the polymodal nature of TRPV1 is essential for therapeutic development. The human TRPV1 (hTRPV1) voltage-sensing like domain (VSLD; transmembrane helices S1-S4) contains the canonical vanilloid ligand binding site and significantly contributes to thermosensing. Nuclear magnetic resonance (NMR)-detected studies probe the role of the hTRPV1-VSLD in TRPV1 polymodal function. The hTRPV1-VSLD is identified as an allosteric hub for all three primary TRPV1 activation modes and demonstrates plasticity in chemical ligand modulation. The presented results underscore molecular features in the VSLD that dictate TRPV1 function, highlighting important considerations for future therapeutic design.
ContributorsOwens, Aerial M. (Author) / Van Horn, Wade D. (Thesis advisor) / Levitus, Marcia (Committee member) / LaBaer, Joshua (Committee member) / Arizona State University (Publisher)
Created2023
Description
The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) pandemic, declared in March 2020 resulted in an unprecedented scientific effort that led to the deployment in less than a year of several vaccines to prevent severe disease, hospitalizations, and death from coronavirus disease 2019 (COVID-19). Most vaccine models focus on the production of neutralizing antibodies against the spike (S) to prevent infection. As the virus evolves, new variants emerge that evade neutralizing antibodies produced by natural infection and vaccination, while memory T cell responses are long-lasting and resilient to most of the changes found in variants of concern (VOC). Several lines of evidence support the study of T cell-mediated immunity in SARS-CoV-2 infections. First, T cell reactivity against SARS-CoV-2 is found in both (cluster of differentiation) CD4+ and CD8+ T cell compartments in asymptomatic, mild, and severe recovered COVID-19 patients. Second, an early and stronger CD8+ T cell response correlates with less severe COVID-19 disease [1-4]. Third, both CD4+ and CD8+ T cells that are reactive to SARS-CoV-2 viral antigens are found in healthy unexposed individuals suggesting that cross-reactive and conserved epitopes may be protective against infection.
The current study is focused on the T cell-mediated response, with special attention to conserved, non-spike-cross-reactive epitopes that may be protective against SARS-CoV-2. The first chapter reviews the importance of epitope prediction in understanding the T cell-mediated responses to a pathogen. The second chapter centers on the validation of SARS-CoV-2 CD8+ T cell predicted peptides to find conserved, immunodominant, and immunoprevalent epitopes that can be incorporated into the next generation of vaccines against severe COVID-19 disease. The third chapter explores pre-existing immunity to SARS-CoV-2 in a pre-pandemic cohort and finds two highly immunogenic epitopes that are conserved among human common cold coronaviruses (HCoVs). To end, the fourth chapter explores the concept of T cell receptor (TCR) cross-reactivity by isolating SARS-CoV-2-reactive TCRs to elucidate the mechanisms of cross-reactivity to SARS-CoV-2 and other human coronaviruses (HCoVs).
ContributorsCarmona, Jacqueline (Author) / Anderson, Karen S (Thesis advisor) / Lake, Douglas (Thesis advisor) / Maley, Carlo (Committee member) / Mangone, Marco (Committee member) / LaBaer, Joshua (Committee member) / Arizona State University (Publisher)
Created2023
Description
Redox homeostasis is described as the net physiologic balance between inter-convertible oxidized and reduced equivalents within subcellular compartments that remain in a dynamic equilibrium. This equilibrium is impacted by reactive oxygen species (ROS), which are natural by-products of normal cellular activity. Studies have shown that cancer cells have high ROS levels and altered redox homeostasis due to increased basal metabolic activity, mitochondrial dysfunction, peroxisome activity, as well as the enhanced activity of NADPH oxidase, cyclooxygenases, and lipoxygenases. Glioblastoma (GBM) is the most prevalent primary brain tumor in adults with a median survival of 15 months. GBM is characterized by its extreme resistance to therapeutic interventions as well as an elevated metabolic rate that results in the exacerbated production of ROS. Therefore, many agents with either antioxidant or pro-oxidant mechanisms of action have been rigorously employed in preclinical as well as clinical settings for treating GBM by inducing oxidative stress within the tumor. Among those agents are well-known antioxidant vitamin C and small molecular weight SOD mimic BMX-001, both of which are presently in clinical trials on GBM patients. Despite the wealth of investigations, limited data is available on the response of normal brain vs glioblastoma tissue to these therapeutic interventions. Currently, a sensitive and rapid liquid chromatography tandem mass spectrometry (LC-MS/MS) method was established for the quantification of a panel of oxidative stress biomarkers: glutathione (GSH), cysteine (Cys), glutathione disulfide (GSSG), and cysteine disulfide in human-derived brain tumor and mouse brain samples; this method will be enriched with additional oxidative stress biomarkers homocysteine (Hcy), methionine (Met), and cystathionine (Cyst). Using this enriched method, we propose to evaluate the thiol homeostasis and the redox state of both normal brain and GBM in mice after exposure with redox-active therapeutics. Our results showed that, compared to normal brain (in intact mice), GBM tissue has significantly lower GSH/GSSG and Cys/CySS ratios indicating much higher oxidative stress levels. Contralateral “normal” brain tissue collected from the mice with intracranial GBM were also under significant oxidative stress compared to normal brains collected from the intact mice. Importantly, normal brain tissue in both studies retained the ability to restore redox homeostasis after treatment with a redox-active therapeutic within 24 hours while glioblastoma tissue does not. Ultimately, elucidating the differential redox response of normal vs tumor tissue will allow for the development of more redox-active agents with therapeutic benefit.
ContributorsShaik, Kamal (Author) / LaBaer, Joshua (Thesis director) / Tovmasyan, Artak (Committee member) / Barrett, The Honors College (Contributor) / School of Life Sciences (Contributor) / Historical, Philosophical & Religious Studies, Sch (Contributor) / Dean, W.P. Carey School of Business (Contributor)
Created2022-12
Description
DNA methylation (DNAm) is an epigenetic mark with a critical role in regulating gene expression. Altered clinical states, including toxin exposure and viral infections, can cause aberrant DNA methylation in cells, which may persist during cell division. Current methods to study genome-wide methylome profiles of the cells require a long processing time and are expensive. Here, a novel technique called Multiplexed Methylated DNA Immunoprecipitation Sequencing (Mx-MeDIP-Seq), which is amenable to automation. Up to 15 different samples can be combined into the same run of Mx-MeDIP-Seq, using only 25 ng of DNA per sample. Mx-MeDIP-Seq was used to study DNAm profiles of peripheral blood mononuclear cells (PBMCs) in two biologically distinct RNA viral infections with different modes of transmission, symptoms, and interaction with the host immune system: human immunodeficiency virus1 (HIV-1) and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Analysis of 90 hospitalized patients with SARS-CoV-2 and 57 healthy controls revealed that SARS-CoV-2 infection led to alterations in 920 methylated regions in PBMCs, resulting in a change in transcription that affects host immune response and cell survival. Analysis of publicly available RNA-Sequencing data in COVID-19 correlated with DNAm in several key pathways. These findings provide a mechanistic view toward further understanding of viral infections. Genome-wide DNAm changes post HIV-1-infection from 37 chronically ill patients compared to 17 controls revealed dysregulation of the actin cytoskeleton, which could contribute to the establishment of latency in HIV-1 infections. Longitudinal DNAm analysis identified several potentially protective and harmful genes that could contribute to disease suppression or progression.
ContributorsRidha, Inam (Author) / LaBaer, Joshua (Thesis advisor) / Murugan, Vel (Thesis advisor) / Plaisier, Christopher (Committee member) / Nikkhah, Mehdi (Committee member) / Vernon, Brent (Committee member) / Arizona State University (Publisher)
Created2022
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
Antibodies are the immunoglobulins which are secreted by the B cells after a microbial invasion. They are stable and stays in the serum for a long time which makes them an excellent biomarker for disease diagnosis. Inflammatory bowel disease is a type of autoimmune disease where the immune system mistakenly attacks the commensal bacteria and leads to inflammation. We studied antibody response of 100 Crohn’s disease (CD), 100 ulcerative colitis (UC) and 100 healthy controls against 1,173 bacterial and 397 viral proteins. We found some anti-bacterial antibodies higher in CD compared to controls while some antibodies lower in UC compared to controls. We were able to build biomarker panels with AUCs of 0.81, 0.87, and 0.82 distinguishing CD vs. control, UC vs. control, and CD vs. UC, respectively. Subgroup analysis based on the Montreal classification revealed that penetrating CD behavior (B3), colonic CD location (L2), and extensive UC (E3) exhibited highest antibody reactivity among all patients. We also wanted to study the reason for the presence of autoantibodies in the sera of healthy individuals. A meta-analysis of 9 independent biomarker study was performed to find 77 common autoantibodies shared by healthy individuals. There was no gender bias; however, the number of autoantibodies increased with age, plateauing around adolescence. Molecular mimicry likely contributed to the elicitation of a subset of these common autoantibodies as 21 common autoantigens had 7 or more ungapped amino acid matches with viral proteins. Intrinsic properties of protein like hydrophilicity, basicity, aromaticity, and flexibility were enriched for common autoantigens. Subcellular localization and tissue expression analysis indicated the sequestration of some autoantigens from circulating autoantibodies can explain the absence of autoimmunity in these healthy individuals.
ContributorsShome, Mahasish (Author) / LaBaer, Joshua (Thesis advisor) / Borges, Chad (Committee member) / Stephanopoulos, Nicholas (Committee member) / Arizona State University (Publisher)
Created2021
Description
Exposure of liquid biospecimens like plasma and serum (P/S) to improper handling and storage can impact the integrity of biomolecules, potentially leading to apparent quantitative changes of important clinical proteins. An accurate and quick estimate of the quality of biospecimens employed in biomarker discovery and validation studies is essential to facilitating accurate conclusions. ΔS-Cys-Albumin is a marker of blood P/S exposure to thawed conditions that can quantitatively track the exposure of P/S to temperatures greater than their freezing point of -30 C. Reported here are studies carried out to evaluate the potential of ΔS-Cys-Albumin to track the stability of clinically important analytes present in P/S upon their exposure to thawed conditions. P/S samples obtained from both cancer-free donors and cancer patients were exposed to 23 C (room temperature), 4 C and -20 C degrees, and the degree to which the apparent concentrations of clinically relevant biomolecules present in P/S were impacted during the time it took ΔS-Cys-Albumin to reach zero was measured. Analyte concentrations measured by molecular interaction-based assays were significantly impacted when samples were exposed to the point where average ΔS-Cys-Albumin fell below 12% at each temperature. Furthermore, the percentage of proteins that became unstable with time under thawed conditions exhibited a strong inverse linear relationship to ΔS-Cys-Albumin, indicating that ΔS-Cys-Albumin can serve as an effective surrogate marker to track the stability of other clinically relevant proteins in plasma as well as to estimate the fraction of proteins that have been destabilized by exposure to thawed conditions, regardless of what the exposure temperature(s) may have been. These results indicated that P/S exposure to thawed conditions disrupts epitopes required for clinical protein quantification via molecular interaction-based assays. In continuation of this theme, a spurious binding event between two clinically important proteins, Apolipoprotein E (ApoE) and Interferon- (IFN) present in human plasma under in vitro experimental conditions is also reported. The interaction was confirmed to be evident only when ApoE was expressed in vitro with a Glutathione-S-Transferase (GST) fusion tag. Future steps required to find the exact manner in which the GST fusion tag facilitated the association between ApoE and IFNγ are discussed with emphasis on the possible pitfalls associated with using fusion proteins for studying novel protein-protein interactions.
ContributorsKapuruge, Erandi Prasadini (Author) / Borges, Chad R (Thesis advisor) / LaBaer, Joshua (Committee member) / Van Horn, Wade (Committee member) / Arizona State University (Publisher)
Created2021
Description
Communicating with computers through thought has been a remarkable achievement in recent years. This was made possible by the use of Electroencephalography (EEG). Brain-computer interface (BCI) relies heavily on Electroencephalography (EEG) signals for communication between humans and computers. With the advent ofdeep learning, many studies recently applied these techniques to EEG data to perform various tasks like emotion recognition, motor imagery classification, sleep analysis, and many more.
Despite the rise of interest in EEG signal classification, very few studies have explored the MindBigData dataset, which collects EEG signals recorded at the stimulus of seeing a digit and thinking about it. This dataset takes us closer to realizing the idea of mind-reading or communication via thought. Thus classifying these signals into the respective digit that the user thinks about is a challenging task. This serves as a motivation to study this dataset and apply existing deep learning techniques to study it.
Given the recent success of transformer architecture in different domains like Computer Vision and Natural language processing, this thesis studies transformer architecture for EEG signal classification. Also, it explores other deep learning
techniques for the same. As a result, the proposed classification pipeline achieves comparable performance with the existing methods.
ContributorsMuglikar, Omkar Dushyant (Author) / Wang, Yalin (Thesis advisor) / Liang, Jianming (Committee member) / Venkateswara, Hemanth (Committee member) / Arizona State University (Publisher)
Created2021
Description
Statistical Shape Modeling is widely used to study the morphometrics of deformable objects in computer vision and biomedical studies. There are mainly two viewpoints to understand the shapes. On one hand, the outer surface of the shape can be taken as a two-dimensional embedding in space. On the other hand, the outer surface along with its enclosed internal volume can be taken as a three-dimensional embedding of interests. Most studies focus on the surface-based perspective by leveraging the intrinsic features on the tangent plane. But a two-dimensional model may fail to fully represent the realistic properties of shapes with both intrinsic and extrinsic properties. In this thesis, severalStochastic Partial Differential Equations (SPDEs) are thoroughly investigated and several methods are originated from these SPDEs to try to solve the problem of both two-dimensional and three-dimensional shape analyses. The unique physical meanings of these SPDEs inspired the findings of features, shape descriptors, metrics, and kernels in this series of works. Initially, the data generation of high-dimensional shapes, here, the tetrahedral meshes, is introduced. The cerebral cortex is taken as the study target and an automatic pipeline of generating the gray matter tetrahedral mesh is introduced. Then, a discretized Laplace-Beltrami operator (LBO) and a Hamiltonian operator (HO) in tetrahedral domain with Finite Element Method (FEM) are derived. Two high-dimensional shape descriptors are defined based on the solution of the heat equation and Schrödinger’s equation. Considering the fact that high-dimensional shape models usually contain massive redundancies, and the demands on effective landmarks in many applications, a Gaussian process landmarking on tetrahedral meshes is further studied. A SIWKS-based metric space is used to define a geometry-aware Gaussian process. The study of the periodic potential diffusion process further inspired the idea of a new kernel call the geometry-aware convolutional kernel. A series of Bayesian learning methods are then introduced to tackle the problem of shape retrieval and classification. Experiments of every single item are demonstrated. From the popular SPDE such as the heat equation and Schrödinger’s equation to the general potential diffusion equation and the specific periodic potential diffusion equation, it clearly shows that classical SPDEs play an important role in discovering new features, metrics, shape descriptors and kernels. I hope this thesis could be an example of using interdisciplinary knowledge to solve problems.
ContributorsFan, Yonghui (Author) / Wang, Yalin (Thesis advisor) / Lepore, Natasha (Committee member) / Turaga, Pavan (Committee member) / Yang, Yezhou (Committee member) / Arizona State University (Publisher)
Created2021