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- Creators: Arizona State University
- Creators: Wang, Xiao
Description
Alzheimer's Disease (AD) is a debilitating neurodegenerative disease. The disease leads to dementia and loss of cognitive functions and affects about 4.5 million people in the United States. It is the 7th leading cause of death and is a huge financial burden on the healthcare industry. There are no means of diagnosing the disease before neurodegeneration is significant and sadly there is no cure that controls its progression. The protein beta-amyloid or Aâ plays an important role in the progression of the disease. It is formed from the cleavage of the Amyloid Precursor Protein by two enzymes - â and ã-secretases and is found in the plaques that are deposits found in Alzheimer brains. This work describes the generation of therapeutics based on inhibition of the cleavage by â-secretase. Using in-vitro recombinant antibody display libraries to screen for single chain variable fragment (scFv) antibodies; this work describes the isolation and characterization of scFv that target the â-secretase cleavage site on APP. This approach is especially relevant since non-specific inhibition of the enzyme may have undesirable effects since the enzyme has been shown to have other important substrates. The scFv iBSEC1 successfully recognized APP, reduced â-secretase cleavage of APP and reduced Aâ levels in a cell model of Alzheimer's Disease. This work then describes the first application of bispecific antibody therapeutics to Alzheimer's Disease. iBSEC1 scFv was combined with a proteolytic scFv that enhances the "good" pathway (á-secretase cleavage) that results in alternative cleavage of APP to generate the bispecific tandem scFv - DIA10D. DIA10D reduced APP cleavage by â-secretase and steered it towards the "good" pathway thus increasing the generation of the fragment sAPPá which is neuroprotective. Finally, treatment with iBSEC1 is evaluated for reduced oxidative stress, which is observed in cells over expressing APP when they are exposed to stress. Recombinant antibody based therapeutics like scFv have several advantages since they retain the high specificity of the antibodies but are safer since they lack the constant region and are smaller, potentially facilitating easier delivery to the brain
ContributorsBoddapati, Shanta (Author) / Sierks, Michael (Thesis advisor) / Arizona State University (Publisher)
Created2011
Description
Patients with Alzheimer's disease (AD) exhibit a significantly higher incidence of unprovoked seizures compared to age-matched non-AD controls, and animal models of AD (i.e., transgenic human amyloid precursor protein, hAPP mice) display neural hyper-excitation and epileptic seizures. Hyperexcitation is particularly important because it contributes to the high incidence of epilepsy in AD patients as well as AD-related synaptic deficits and neurodegeneration. Given that there is significant amyloid-β (Aβ) accumulation and deposition in AD brain, Aβ exposure ultimately may be responsible for neural hyper-excitation in both AD patients and animal models. Emerging evidence indicates that α7 nicotinic acetylcholine receptors (α7-nAChR) are involved in AD pathology, because synaptic impairment and learning and memory deficits in a hAPPα7-/- mouse model are decreased by nAChR α7 subunit gene deletion. Given that Aβ potently modulates α7-nAChR function, that α7-nAChR expression is significantly enhanced in both AD patients and animal models, and that α7-nAChR play an important role in regulating neuronal excitability, it is reasonable that α7-nAChRs may contribute to Aβ-induced neural hyperexcitation. We hypothesize that increased α7-nAChR expression and function as a consequence of Aβ exposure is important in Aβ-induced neural hyperexcitation. In this project, we found that exposure of Aβ aggregates at a nanomolar range induces neuronal hyperexcitation and toxicity via an upregulation of α7-nAChR in cultured hippocampus pyramidal neurons. Aβ up-regulates α7-nAChRs function and expression through a post translational mechanism. α7-nAChR up-regulation occurs prior to Aβ-induced neuronal hyperexcitation and toxicity. Moreover, inhibition of α7-nAChR or deletion of α7-nAChR prevented Aβ induced neuronal hyperexcitation and toxicity, which suggests that α7-nAChRs are required for Aβ induced neuronal hyperexcitation and toxicity. These results reveal a profound role for α7-nAChR in mediating Aβ-induced neuronal hyperexcitation and toxicity and predict that Aβ-induced up-regulation of α7-nAChR could be an early and critical event in AD etiopathogenesis. Drugs targeting α7-nAChR or seizure activity could be viable therapies for AD treatment.
ContributorsLiu, Qiang (Author) / Wu, Jie (Thesis advisor) / Lukas, Ronald J (Committee member) / Chang, Yongchang (Committee member) / Sierks, Michael (Committee member) / Smith, Brian (Committee member) / Vu, Eric (Committee member) / Arizona State University (Publisher)
Created2011
Description
Sparse learning is a technique in machine learning for feature selection and dimensionality reduction, to find a sparse set of the most relevant features. In any machine learning problem, there is a considerable amount of irrelevant information, and separating relevant information from the irrelevant information has been a topic of focus. In supervised learning like regression, the data consists of many features and only a subset of the features may be responsible for the result. Also, the features might require special structural requirements, which introduces additional complexity for feature selection. The sparse learning package, provides a set of algorithms for learning a sparse set of the most relevant features for both regression and classification problems. Structural dependencies among features which introduce additional requirements are also provided as part of the package. The features may be grouped together, and there may exist hierarchies and over- lapping groups among these, and there may be requirements for selecting the most relevant groups among them. In spite of getting sparse solutions, the solutions are not guaranteed to be robust. For the selection to be robust, there are certain techniques which provide theoretical justification of why certain features are selected. The stability selection, is a method for feature selection which allows the use of existing sparse learning methods to select the stable set of features for a given training sample. This is done by assigning probabilities for the features: by sub-sampling the training data and using a specific sparse learning technique to learn the relevant features, and repeating this a large number of times, and counting the probability as the number of times a feature is selected. Cross-validation which is used to determine the best parameter value over a range of values, further allows to select the best parameter value. This is done by selecting the parameter value which gives the maximum accuracy score. With such a combination of algorithms, with good convergence guarantees, stable feature selection properties and the inclusion of various structural dependencies among features, the sparse learning package will be a powerful tool for machine learning research. Modular structure, C implementation, ATLAS integration for fast linear algebraic subroutines, make it one of the best tool for a large sparse setting. The varied collection of algorithms, support for group sparsity, batch algorithms, are a few of the notable functionality of the SLEP package, and these features can be used in a variety of fields to infer relevant elements. The Alzheimer Disease(AD) is a neurodegenerative disease, which gradually leads to dementia. The SLEP package is used for feature selection for getting the most relevant biomarkers from the available AD dataset, and the results show that, indeed, only a subset of the features are required to gain valuable insights.
ContributorsThulasiram, Ramesh (Author) / Ye, Jieping (Thesis advisor) / Xue, Guoliang (Committee member) / Sen, Arunabha (Committee member) / Arizona State University (Publisher)
Created2011
Description
Alzheimer's Disease (AD) is the sixth leading cause of death in the United States and the most common form of dementia. Its cause remains unknown, but it is known to involve two hallmark pathologies: Amyloid Beta plaques and neurofibrillary tangles (NFTs). Several proteins have been implicated in the formation of neurofibrillary tangles, including Tau and S100B. S100B is a dimeric protein that is typically found bound to Ca(II) or Zn(II). These experiments relate to the involvement of S100B in Alzheimer's Disease-related processes and the results suggest that future research of S100B is warranted. Zn(II)-S100B was found to increase the rate at which tau assembled into paired helical filaments, as well as affect the rate at which tubulin polymerized into microtubules and the morphology of SH-SY5Y neuroblastoma cells after 72 hours of incubation. Zn(II)-S100B also increased the firing rate of hippocampal neurons after 36 hours of incubation. Together, these results suggest several possibilities: Zn(II)-S100B may be a key part of the formation of paired helical filaments (PHFs) that subsequently form NFTs. Zn(II)-S100B may also be competing with tau to bind tubulin, which could lead to an instability of microtubules and subsequent cell death. This finding aligns with the neurodegeneration that is commonly seen in AD and which could be a result of this microtubule instability. Ultimately, these results suggest that S100B is likely involved in several AD-related processes, and if the goal is to find an efficient and effective therapeutic target for AD, the relationship between S100B, particularly Zn(II)-S100B, and tau needs to be further studied.
ContributorsNaegele, Hayley (Author) / Mcgregor, Wade C (Thesis advisor) / Baluch, Debra (Committee member) / Francisco, Wilson (Committee member) / Arizona State University (Publisher)
Created2013
Description
Cognitive function declines with normal age and disease states, such as Alzheimer's disease (AD). Loss of ovarian hormones at menopause has been shown to exacerbate age-related memory decline and may be related to the increased risk of AD in women versus men. Some studies show that hormone therapy (HT) can have beneficial effects on cognition in normal aging and AD, but increasing evidence suggests that the most commonly used HT formulation is not ideal. Work in this dissertation used the surgically menopausal rat to evaluate the cognitive effects and mechanisms of progestogens proscribed to women. I also translated these questions to the clinic, evaluating whether history of HT use impacts hippocampal and entorhinal cortex volumes assessed via imaging, and cognition, in menopausal women. Further, this dissertation investigates how sex impacts responsiveness to dietary interventions in a mouse model of AD. Results indicate that the most commonly used progestogen component of HT, medroxyprogesterone acetate (MPA), impairs cognition in the middle-aged and aged surgically menopausal rat. Further, MPA is the sole hormone component of the contraceptive Depo Provera, and my research indicates that MPA administered to young-adult rats leads to long lasting cognitive impairments, evident at middle age. Natural progesterone has been gaining increasing popularity as an alternate option to MPA for HT; however, my findings suggest that progesterone also impairs cognition in the middle-aged and aged surgically menopausal rat, and that the mechanism may be through increased GABAergic activation. This dissertation identified two less commonly used progestogens, norethindrone acetate and levonorgestrel, as potential HTs that could improve cognition in the surgically menopausal rat. Parameters guiding divergent effects on cognition were discovered. In women, prior HT use was associated with larger hippocampal and entorhinal cortex volumes, as well as a modest verbal memory enhancement. Finally, in a model of AD, sex impacts responsiveness to a dietary cognitive intervention, with benefits seen in male, but not female, transgenic mice. These findings have clinical implications, especially since women are at higher risk for AD diagnosis. Together, it is my hope that this information adds to the overarching goal of optimizing cognitive aging in women.
ContributorsBraden, Brittany Blair (Author) / Bimonte-Nelson, Heather A. (Thesis advisor) / Neisewander, Janet L (Committee member) / Conrad, Cheryl D. (Committee member) / Baxter, Leslie C (Committee member) / Arizona State University (Publisher)
Created2012
Description
Alzheimer's Disease (AD) is a progressive neurodegenerative disease accounting for 50-80% of dementia cases in the country. This disease is characterized by the deposition of extracellular plaques occurring in regions of the brain important for cognitive function. A primary component of these plaques is the amyloid-beta protein. While a natively unfolded protein, amyloid-beta can misfold and aggregate generating a variety of different species including numerous different soluble oligomeric species some of which are precursors to the neurofibrillary plaques. Various of the soluble amyloid-beta oligomeric species have been shown to be toxic to cells and their presence may correlate with progression of AD. Current treatment options target the dementia symptoms, but there is no effective cure or alternative to delay the progression of the disease once it occurs. Amyloid-beta aggregates show up many years before symptoms develop, so detection of various amyloid-beta aggregate species has great promise as an early biomarker for AD. Therefore reagents that can selectively identify key early oligomeric amyloid-beta species have value both as potential diagnostics for early detection of AD and as well as therapeutics that selectively target only the toxic amyloid-beta aggregate species. Earlier work in the lab includes development of several different single chain antibody fragments (scFvs) against different oligomeric amyloid-beta species. This includes isolation of C6 scFv against human AD brain derived oligomeric amyloid-beta (Kasturirangan et al., 2013). This thesis furthers research in this direction by improving the yields and investigating the specificity of modified C6 scFv as a diagnostic for AD. It is motivated by experiments reporting low yields of the C6 scFv. We also used the C6T scFv to characterize the variation in concentration of this particular oligomeric amyloid-beta species with age in a triple transgenic AD mouse model. We also show that C6T can be used to differentiate between post-mortem human AD, Parkinson's disease (PD) and healthy human brain samples. These results indicate that C6T has potential value as a diagnostic tool for early detection of AD.
ContributorsVenkataraman, Lalitha (Author) / Sierks, Michael (Thesis advisor) / Rege, Kaushal (Committee member) / Pauken, Christine (Committee member) / Arizona State University (Publisher)
Created2013
Description
The goal of this thesis is to test whether Alzheimer's disease (AD) is associated with distinctive humoral immune changes that can be detected in plasma and tracked across time. This is relevant because AD is the principal cause of dementia, and yet, no specific diagnostic tests are universally employed in clinical practice to predict, diagnose or monitor disease progression. In particular, I describe herein a proteomic platform developed at the Center for Innovations in Medicine (CIM) consisting of a slide with 10.000 random-sequence peptides printed on its surface, which is used as the solid phase of an immunoassay where antibodies of interest are allowed to react and subsequently detected with a labeled secondary antibody. The pattern of antibody binding to the microarray is unique for each individual animal or person. This thesis will evaluate the versatility of the microarray platform and how it can be used to detect and characterize the binding patterns of antibodies relevant to the pathophysiology of AD as well as the plasma samples of animal models of AD and elderly humans with or without dementia. My specific aims were to evaluate the emergence and stability of immunosignature in mice with cerebral amyloidosis, and characterize the immunosignature of humans with AD. Plasma samples from APPswe/PSEN1-dE9 transgenic mice were evaluated longitudinally from 2 to 15 months of age to compare the evolving immunosignature with non-transgenic control mice. Immunological variation across different time-points was assessed, with particular emphasis on time of emergence of a characteristic pattern. In addition, plasma samples from AD patients and age-matched individuals without dementia were assayed on the peptide microarray and binding patterns were compared. It is hoped that these experiments will be the basis for a larger study of the diagnostic merits of the microarray-based immunoassay in dementia clinics.
ContributorsRestrepo Jimenez, Lucas (Author) / Johnston, Stephen A. (Thesis advisor) / Chang, Yung (Committee member) / Reiman, Eric (Committee member) / Sierks, Michael (Committee member) / Arizona State University (Publisher)
Created2011
Description
Alzheimer's Disease (AD) is the most common form of dementia observed in elderly patients and has significant social-economic impact. There are many initiatives which aim to capture leading causes of AD. Several genetic, imaging, and biochemical markers are being explored to monitor progression of AD and explore treatment and detection options. The primary focus of this thesis is to identify key biomarkers to understand the pathogenesis and prognosis of Alzheimer's Disease. Feature selection is the process of finding a subset of relevant features to develop efficient and robust learning models. It is an active research topic in diverse areas such as computer vision, bioinformatics, information retrieval, chemical informatics, and computational finance. In this work, state of the art feature selection algorithms, such as Student's t-test, Relief-F, Information Gain, Gini Index, Chi-Square, Fisher Kernel Score, Kruskal-Wallis, Minimum Redundancy Maximum Relevance, and Sparse Logistic regression with Stability Selection have been extensively exploited to identify informative features for AD using data from Alzheimer's Disease Neuroimaging Initiative (ADNI). An integrative approach which uses blood plasma protein, Magnetic Resonance Imaging, and psychometric assessment scores biomarkers has been explored. This work also analyzes the techniques to handle unbalanced data and evaluate the efficacy of sampling techniques. Performance of feature selection algorithm is evaluated using the relevance of derived features and the predictive power of the algorithm using Random Forest and Support Vector Machine classifiers. Performance metrics such as Accuracy, Sensitivity and Specificity, and area under the Receiver Operating Characteristic curve (AUC) have been used for evaluation. The feature selection algorithms best suited to analyze AD proteomics data have been proposed. The key biomarkers distinguishing healthy and AD patients, Mild Cognitive Impairment (MCI) converters and non-converters, and healthy and MCI patients have been identified.
ContributorsDubey, Rashmi (Author) / Ye, Jieping (Thesis advisor) / Wang, Yalin (Committee member) / Wu, Tong (Committee member) / Arizona State University (Publisher)
Created2012
Description
Rapid advance in sensor and information technology has resulted in both spatially and temporally data-rich environment, which creates a pressing need for us to develop novel statistical methods and the associated computational tools to extract intelligent knowledge and informative patterns from these massive datasets. The statistical challenges for addressing these massive datasets lay in their complex structures, such as high-dimensionality, hierarchy, multi-modality, heterogeneity and data uncertainty. Besides the statistical challenges, the associated computational approaches are also considered essential in achieving efficiency, effectiveness, as well as the numerical stability in practice. On the other hand, some recent developments in statistics and machine learning, such as sparse learning, transfer learning, and some traditional methodologies which still hold potential, such as multi-level models, all shed lights on addressing these complex datasets in a statistically powerful and computationally efficient way. In this dissertation, we identify four kinds of general complex datasets, including "high-dimensional datasets", "hierarchically-structured datasets", "multimodality datasets" and "data uncertainties", which are ubiquitous in many domains, such as biology, medicine, neuroscience, health care delivery, manufacturing, etc. We depict the development of novel statistical models to analyze complex datasets which fall under these four categories, and we show how these models can be applied to some real-world applications, such as Alzheimer's disease research, nursing care process, and manufacturing.
ContributorsHuang, Shuai (Author) / Li, Jing (Thesis advisor) / Askin, Ronald (Committee member) / Ye, Jieping (Committee member) / Runger, George C. (Committee member) / Arizona State University (Publisher)
Created2012
Description
The purpose of this project is to present research within three main categories of treatment and care such as exercise, socialization and alternative therapies (art, pet, and reminiscent therapies) for Alzheimer’s Disease (AD). These categories will be examined in the following countries: United Kingdom, United States, Brazil, and China. Then, the synthesized material will be analyzed and placed into a comparison and contrast model showcasing what each country is currently using and the success of the particular resource within a heat map. According to the research found on the following categories of exercise, socialization and alternative therapies, I will conclude that a combination of aerobic and resistance training, routine support groups and art/pet therapies are the most effective treatment options against Alzheimer’s Disease.
ContributorsLew, Arianna Freedom (Author) / Lupone, Kathleen (Thesis director) / Holzapfel, Simon (Committee member) / School of Life Sciences (Contributor) / Watts College of Public Service & Community Solut (Contributor) / Barrett, The Honors College (Contributor)
Created2019-05