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Alzheimer’s Disease (AD) is the most prevalent neurodegenerative disease. The candidate aetiological agent for this disease is the 4kDa amyloid beta (Ab) peptide which is derived from the proteolytic cleavage of the amyloid precursor protein (APP) by the b- and g- secretase, respectively. As cellular prion proteins (PrPc) both regulate the cleavage of APP and mediate Ab induced neurotoxicity, a study was undertaken to establish whether the cellular receptor for PrPc, namely the 37kDa/67kDa laminin receptor (LRP/LR) also played a role in AD pathology. Anti LRP/LR specific antibody (IgG1-iS18) blocking LRP/LR resulted in a significant reduction of both Ab and sAPPb levels (the cleavage products of b-secretase), while APP, b- and g-secretase cell surface levels remained unaltered. LRP/LR was found to co-localise with APP, b- and g-secretase both on the cell surface and intracellularly. Furthermore, FRET demonstrated that an interaction existed between presenilin 1 (PS1) of the g-secretase and LRP/LR, while co-immunoprecipitation confirmed that LRP/LR interacted with both b-secretase and PS1. These results indicate that LRP/LR is implicated in the amyloidogenic processing of APP, through an indirect interaction with the b-secretase and a direct interaction with the g-secretase. These findings also suggest the possibility of utilising IgG1-iS18 as a possible therapeutic for the treatment of AD.

Large-scale neuroimaging data is becoming increasingly available, providing a rich data source with which to study neurological conditions. In this thesis, I demonstrate the utility of large-scale neuroimaging as it applies to Alzheimer’s disease (AD) and normal aging, using univariate parametric mapping, regional analysis, and advanced machine learning. Specifically, this thesis covers: 1) validation and extension of prior studies using large-scale datasets; 2) AD diagnosis and normal aging evaluation empowered by large-scale datasets and advanced deep learning algorithms; 3) enhancement of cerebral blood volume (CBV) fMRI utility with retrospective CBV-fMRI technique. First, I demonstrated the utility of large-scale datasets for validating and extending prior studies using univariate analytics. I presented a study localizing AD-vulnerable regions more reliably and with better anatomical resolution using data from more than 350 subjects. Following a similar approach, I investigated the structural characteristics of healthy APOE ε4 homozygous subjects screened from a large-scale community-based study. To study the neuroimaging signatures of normal aging, we performed a large-scale joint CBV-fMRI and structural MRI study covering age 20-70s, and a structural MRI study of normal aging covering the full age-span, with the elder group screened from a large-scale clinic-based study ensuring no evidence of AD using both longitudinal follow-up and cerebrospinal fluid (CSF) biomarkers evidences. Second, I performed deep learning neuroimaging studies for AD diagnosis and normal aging evaluation, and investigated the regionality associated with each task. I developed an AD diagnosis method using a 3D convolutional neural network model trained and evaluated on ~4,600 structural MRI scans and further investigated a series of novel regionality analyses. I further extensively studied the utility of the structural MRI summary measure derived from the deep learning model in prodromal AD detection. This study constitutes a general analytic framework, which was followed to evaluate normal aging by performing deep learning-based age estimation in cognitively normal population using more than 6,000 scans. The deep learning neuroimaging models classified AD and estimated age with high accuracy, and also revealed regional patterns conforming to neuropathophysiology. The deep learning derived MRI measure demonstrated potential clinical utility, outperforming other AD pathology measures and biomarkers. In addition, I explored the utility of deep learning on positron emission tomography (PET) data for AD diagnosis and regionality analyses, further demonstrating the broad utility and generalizability of the method. Finally, I introduced a technique enabling CBV generation retrospectively from clinical contrast-enhanced scans. The derivation of meaningful functional measures from such clinical scans is only possible through calibration to a reference, which was built from the largest collection of research CBV-fMRI scans from our lab. This method was validated in an epilepsy study and demonstrated the potential to enhance the utility of CBV-fMRI by enriching the CBV-fMRI dataset. This technique is also applicable to AD and normal aging studies, and potentially enables deep learning based analytic approaches applied on CBV-fMRI with similar pipelines used in structural MRI. Collectively, this thesis demonstrates how mechanistic and diagnostic information on brain disorders can be extracted from large-scale neuroimaging data, using both classical statistical methods and advanced machine learning.

Chronic stress and elevated glucocorticoids (GCs), the major stress hormones, are risk factors for Alzheimer’s disease (AD) and promote AD pathomechanisms in animal models. These include overproduction of synaptotoxic amyloid-β (Aβ) peptides and intraneuronal accumulation of microtubule-associated protein Tau. Tau accumulation is linked to downregulation of the small GTPase Rab35, which mediates Tau degradation via the endolysosomal pathway. Whether Rab35 is also involved in stress/GC-induced Aβ overproduction remains an open question. Here, I find that hippocampal Rab35 levels are decreased not only by stress/GCs, but also by aging, another AD risk factor. Moreover, I show that Rab35 negatively regulates Aβ production by sorting amyloid precursor protein (APP) and β-secretase (BACE1) out of the endosomal network, where they interact to produce Aβ. Interestingly, Rab35 coordinates distinct intracellular trafficking events for BACE1 and APP, mediated by its effectors OCRL and ACAP2, respectively. Additionally, I show that Rab35 overexpression prevents the amyloidogenic trafficking of APP and BACE1 induced by GCs. Finally, I begin to investigate how GCs and/or Rab35 affect the intercellular spread of Aβ and Tau through exosomes. I describe methods for purifying exosomes and measuring their secretion from neurons, astrocytes, and microglial cells in order to determine the effects of stress/GCs and Rab35 on this process. These studies identify Rab35 as a key regulator of Alzheimer’s disease-related protein trafficking, and suggest that its downregulation contributes to stress- and AD-related pathomechanisms.

The aim of this dissertation was 1) to explore early stage aggregation kinetic behavior of Amyloid-β 1-40 (Aβ1-40) by incorporation of unnatural amino acid pcyanophenylalanine as a site-specific fluorescence reporter, 2) to explore the effect of macromolecules on the aggregation of Aβ1-40. Chapter One provides an introduction of Alzheimer’s disease as an amyloidogenic disease, amyloidogenic peptide and amyloid formation. Details were shown about the research progress of Aβ1-40 aggregation and Aβ1-40’s interaction with polyelectrolytes, and how treatments studies were designed. In Chapter two, using Aβ1-23 as a model molecule, the distinct site-specific dynamics was identified, during amyloid formation, and the structural characteristics of amyloid fibrils were defined by using an unnatural amino acid, p-cyanophenylalanine, as a sensitive fluorescent and Raman probe. The results reveal distinct local environmental changes of specific residues during the aggregation of Aβ1-23. The results also suggest that an edge-to-face aromatic interaction between the F4 and F19 residues from the adjacent in-register β-strands plays a key role in the conformational conversion to form and stabilize β-sheet structure. In Chapter Three, p-cyanophenylalanine was incorporated in the full sequence of Aβ1-40. Site-specific information from p-cyanophenylalanine fluorescence was studied and summarized. In Chapter Four, the inhibiting effect of an anionic polyelectrolyte poly(4- styrenesulfonate) (PSS) on the aggregation of Aβ1-40 peptide was reported. The results demonstrate the strong inhibition potential of PSS on the aggregation of Aβ1-40. Additional studies indicate that the presence of both aliphatic backbone as well as aromatic side chain group in PSS is essential for its inhibition activity. In Chapter Five, it was investigated the effect of two polyelectrolytes, chitosan (CHT) and N-trimethyl chitosan chloride (TMC), on the aggregation of Aβ1-40. Results show that both CHT and TMC exhibit a concentration-dependent decrease of amyloid aggregation suggesting their application as amyloid assembly inhibitors. Their binding mechanism was investigated by computational modeling which shows that Aβ1-40 monomer was primarily stabilized by electrostatic interactions with charged amine and quaternary amines of CHT and TMC respectively. Chapter Six, describes all experimental procedures and instrument setup in detail. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2016. / FAU Electronic Theses and Dissertations Collection

Beta-Amyloid (1-40) (Aβ40) is an aggregation prone protein, which undergoes a nucleation-dependent aggregation process causing the pathological neurodegeneration by amyloid plaque formation implicated in Alzheimer’s disease. In this thesis, we investigated the effects of small molecule modulators extracted from the marine invertebrate Pseudopterogorgia elisabethae on the Aβ40 amyloidogenic process using in- vitro ThT fluorescence assay and atomic force microscopy. We also investigated the effects of neutral and anionic phospholipid liposomes on Aβ40 aggregation. Our results show that a marine natural product Pseudopterosin-A and its derivatives can suppress and modulate the Aβ40 aggregation process. Furthermore, our results demonstrate that a neutral phospholipid liposome inhibits Aβ40 fibril formation, whereas the anionic liposomes promote it. / Includes bibliography. / Thesis (M.S.)--Florida Atlantic University, 2015 / FAU Electronic Theses and Dissertations Collection

Indiana University-Purdue University Indianapolis (IUPUI) / Alzheimer’s disease (AD) is a neurological disorder defined by the presence of plaques comprised mostly of amyloid-β (Aβ), and neurofibrillary tangles consisting of hyperphosphorylated microtubule associated protein tau (MAPT). AD is also characterized by widespread synapse loss and degeneration followed by death of neurons in the brain. Inflammatory processes, such as glial activation, are also implicated. In order to study mechanisms of neurodegeneration and evaluate potential therapeutic agents that could slow or reverse this process, a tissue culture system was developed based on primary embryonic cerebrocortical neurons. This culture system was observed to exhibit time-dependent neurodegeneration, glial proliferation, and synaptic marker loss consistent with AD-affected brains. The regulatory promoter regions of several genes implicated in AD, including the Aβ precursor protein (APP), β-amyloid cleaving enzyme (BACE1), and MAPT, were studied in this culture model. The MAPT gene promoter activity followed the pattern of neuronal maturation and degeneration quite closely, increasing in the initial phase of the tissue culture, then reducing markedly during neurodegeneration while APP and BACE1 gene promoters remained active. Deletion series of these promoters were tested to give an initial indication of the active regions of the gene promoter regions. Furthermore, the effects of exogenous Aβ and overexpression of p25, which are two possible pathogenic mechanisms of gene regulation in AD, were studied. Response to Aβ varied between the promoters and by length of the Aβ fragment used. Overexpression of p25 increased MAPT, but not APP or BACE1, promoter activity. This neurodegeneration model was also used to study the putative neuroprotective action of the NMDA receptor antagonist memantine. Treatment with memantine prevented loss of synaptic markers and preserved neuronal morphology, while having no apparent effect on glial activation. The protective action on synaptic markers was also observed with two other structurally distinct NMDA receptor antagonists, suggesting that the effects of memantine are produced by its action on the NMDA receptor. It is concluded that this tissue culture model will be useful for the study of gene regulation and therapeutic agents for neurodegeneration, and that the efficacy of memantine may result from preservation of synaptic connections in the brain.

Rural West Virginia has a very high percentage of older adults. The age-related disease of Alzheimer’s threatens the health of older Appalachians, yet research on Alzheimer’s disease (AD) in this population is scarce. In order to improve screening rates for cognitive impairment, Appalachians need to understand their vulnerability. The first step would be to assess their knowledge about AD but a suitable AD knowledge test has not been developed. The purpose of this study was to test the reliability and validity of a new measure of knowledge about AD that is culturally congruent, and to examine factors that may predict AD knowledge in this rural population. A correlational descriptive study was conducted with 240 participants from four samples of older adults in south central rural Appalachian West Virginia using surveys and face-to-face interviews. Results from tests for stability, reliability including Rasch modeling, discrimination and point biserial indices, and concurrent, divergent, and construct validity were favorable. Findings were that although more diversity in test item difficulty is needed, the test discriminated well between persons with higher and lower levels of education [F(2, 226) = 170.51, p = .001]. Using multiple regression, the predictors of AD knowledge included caregiver status, miles from a healthcare provider, gender, and education; (R2=.05, F(4,187) = 2.65, p =. 04). Only years of education accounted for a significant proportion of unique variance in predicting the total BKAD score (t = 2.14, p =. 03). Implications include the need for further tool refinement, testing for health literacy, coordination with recent statewide efforts to educate the public regarding AD, and community based participatory research in designing culturally effective education programs that will ultimately increase screening and detection of Alzheimer’s disease in rural populations. / Includes bibliography. / Dissertation (Ph.D.)--Florida Atlantic University, 2013.

It was hypothesized that 192 IgG-saporin lesions of the basal forebrain cholinergic system (BFCS) would disrupt differential reinforcement of low rate (DRL) learning in an uncued DRL task, but would not impair acquisition and performance in the cued version of the task. Results suggest that BFCS lesions impair vigilance to the external cues despite continued practice in the cued DRL, whereas continuous attention to internally produced cues recovers with extended practice in the uncued DRL.

Indiana University-Purdue University Indianapolis (IUPUI) / Alzheimer's disease (AD) is a neurodegenerative disorder characterized by gradual loss of brain functions, usually preceded by memory impairments. It has been widely affecting aging Americans over 65 old and listed as 6th leading cause of death. More importantly, unlike other diseases, loss of brain function in AD progression usually leads to the significant decline in self-care abilities. And this will undoubtedly exert a lot of pressure on family members, friends, communities and the whole society due to the time-consuming daily care and high health care expenditures. In the past decade, while deaths attributed to the number one cause, heart disease, has decreased 16 percent, deaths attributed to AD has increased 68 percent. And all of these situations will continue to deteriorate as the population ages during the next several decades. To prevent such health care crisis, substantial efforts have been made to help cure, slow or stop the progression of the disease. The massive data generated through these efforts, like multimodal neuroimaging scans as well as next generation sequences, provides unprecedented opportunities for researchers to look into the deep side of the disease, with more confidence and precision. While plenty of efforts have been made to pull in those existing machine learning and statistical models, the correlated structure and high dimensionality of imaging and genetics data are generally ignored or avoided through targeted analysis. Therefore their performances on imaging genetics study are quite limited and still have plenty to be improved. The primary contribution of this work lies in the development of novel prior knowledge-guided regression and association models, and their applications in various neurobiological problems, such as identification of cognitive performance related imaging biomarkers and imaging genetics associations. In summary, this work has achieved the following research goals: (1) Explore the multimodal imaging biomarkers toward various cognitive functions using group-guided learning algorithms, (2) Development and application of novel network structure guided sparse regression model, (3) Development and application of novel network structure guided sparse multivariate association model, and (4) Promotion of the computation efficiency through parallelization strategies.