Alzheimer's disease

Alzheimer's disease (AD) is projected to triple by 2050, highlighting the urgent need for disease-modifying treatment strategies. Our gene therapy approach tackles three critical challenges: a) delivering drugs effectively to the brain and brain bioavailability of those delivered drugs, b) intervening early in the disease process to prevent progression into nonreversible stages, and c) managing the behavioral and psychological symptoms of dementia (BPSD) that significantly impact patients and caregivers. Our non-invasive ocular delivery system effectively delivered therapies to CNS as indicated by the localization of those transcribed genes and translated protein products in different brain regions, including the hippocampus, cortex, dorsal lateral geniculate nucleus, red nucleus, and pontine nucleus This approach could overcome the limitations of traditional drug delivery methods for neurological diseases. In a 3xTg AD mouse model of AD, we evaluated the efficacy of Choline Acetyltransferase (ChAT) gene therapy on early disease progression. A single treatment improved impaired memory functions such as cognitive flexibility, memory extinction and working memory, reduced amyloid beta oligomers and phosphorylated tau protein levels, and enhanced mitochondrial dynamics through the regulation of fusion, fission and mitophagy. Additionally, ChAT gene therapy modulated apoptosis, inflammation and the activity of microglia and astrocytes in parts through the activation of AKT. These findings suggest ChAT gene therapy's potential to slow or prevent AD
progression if administered early in the disease course.

Alzheimer’s disease (AD) is a common neurodegenerative disorder. The most recognized disease pathology is the Amyloid-β (Aβ) cascade hypothesis which states that the accumulation of Aβ plaques might be the cause of AD. In the AD brain, Aβ plaques stockpile a variety of molecular components including metals, lipids, nucleic acids, carbohydrates, and peptides, indicating Aβ aggregation might be influenced by these modulators. In this dissertation, we investigated the effects of Zn2+ and carnosine, phospholipids, and β-hairpins on Aβ aggregation to dissect their mechanistic roles in the amyloidogenesis of Aβ. We first systematically studied the kinetic impact of Zn2+ on the aggregation of Aβ40 and Aβ40-M. Our results show that the presence of Zn2+ transforms the Aβ40 aggregation kinetics from a single sigmoidal to a biphasic process, while the aggregation of Aβ40-M is significantly suppressed by Zn2+. We also found that a nature dipeptide, carnosine, remarkably decreases the activity of Zn2+ on modulating Aβ aggregation, although it has a weak direct effect on the peptide aggregation kinetics. Second, we investigated the activities of Aβ40 and Aβ42 in inducing membrane damage and the effects of lipid membranes on the aggregation of these peptides using liposome models containing mitochondrial-specific phospholipid–cardiolipin (CL).

The amyloid beta (Aβ) peptide has been linked to Alzheimer’s Disease (AD) since the early 1990s. Since then, many studies have characterized the peptide and examined its aggregation process. Aβ is a 40 or 42-residue peptide, composed of a charged N-terminal and hydrophobic C-terminal, that aggregates into characteristic β-sheets forming insoluble plaques in the brains of (AD) patients. In recent years an intermediate oligomeric species has been shown to interact with lipid membranes, largely resulting in the etiology of AD. In this study, two fragments are used, the 23-residue N-terminal fragment, Aβ23 and the 30-residue C-terminal fragment, Aβ11-40, to better understand the role of the N and C-terminus in the aggregation of Aβ peptide. Aβ11-40 has also been found in the brains of AD patients, playing a biological role in the disease. This study used analytical and biophysical techniques to systematically synthesize, purify, characterize, and study these fragments' aggregation in different conditions. We investigated the effects of lipid membranes on the aggregation of Aβ23 and Aβ11-40 and the activities of these peptides in inducing membrane damage. The results show that the aggregation of Aβ23 was increased in the presence of lipid membranes, likely due to favorable electrostatic interactions. However, the aggregation of Aβ11-40 was not influenced by lipid membranes. A dye leakage study was carried out to study the membrane damage occurring as a result of fragments' interaction with lipid membranes. The results showed that neither fragment had a profound effect on membrane destruction, although the charge of the lipid head seemed to play a role. This work's second study focused on the effect of three specific polysaccharides, heparin, chitosan (CHT), and trimethyl chitosan (TMC), on the aggregation of Aβ23 and Aβ11-40. The results showed that for Aβ23, heparin increased aggregation, while both CHT and TMC decreased aggregation. However, for Aβ11-40, both heparin and CHT did not affect aggregation, while TMC decreased aggregation.

In the U.S., an estimated 16 million persons provide unpaid care for family and friends with Alzheimer’s disease and related dementias (ADRD). These caregivers are experiencing challenges, such as lack social interaction, which further impacts their own health. Social isolation for caregivers is now considered to be another challenge due to living in a global pandemic.
The purpose of this study was to address the gap in understanding rural informal caregiver by examining social connectedness through the use of story-guided dialogues among rural caregivers of PWD during a global pandemic. Story Theory guides intentional dialogue, to bring forward connecting with self-in-relation through use of story path, noting low, high, and turning points.

Alzheimer’s Disease (AD) is a complex brain disorder that affects at least one in every ten persons aged 65 and above worldwide. The pathogenesis of this disorder remains elusive. In this work, we utilized a rich set of publicly available gene expression data to elucidate the genes and molecular processes that may underlie its pathogenesis. We developed a new ranking score to prioritize molecular pathways enriched in differentially expressed genes during AD. After applying our new ranking score, GO categories such as cotranslational protein targeting to membrane, SRP-dependent cotranslational protein targeting to membrane, and spliceosomal snRNP assembly were found to be significantly associated with AD. We also confirm the protein-protein interaction between APP, NPAS4 and ARNT2 and explain that this interaction could be implicated in AD. This interaction could serve as a theoretical framework for further analyses into the role of NPAS4 and other immediate-early genes in AD pathogenesis.

Alzheimer’s disease is typically detected using a combination of cognitive-behavioral assessment exams and interviews of both the patient and a family member or caregiver, both administered and interpreted by a trained physician. This procedure, while standard in medical practice, can be time consuming and expensive for both the patient and the diagnostician especially because proper training is required to interpret the collected information and determine an appropriate diagnosis. The use of machine learning techniques to augment diagnostic procedures has been previously examined in limited capacity but to date no research examines real-world medical applications of predictive analytics for health records and cognitive exam scores. This dissertation seeks to examine the efficacy of detecting cognitive impairment due to Alzheimer’s disease using machine learning, including multi-modal neural network architectures, with a real-world clinical dataset used to determine the accuracy and applicability of the generated models. An in-depth analysis of each type of data (e.g. cognitive exams, questionnaires, demographics) as well as the cognitive domains examined (e.g. memory, attention, language) is performed to identify the most useful targets, with cognitive exams and questionnaires being found to be the most useful features and short-term memory, attention, and language found to be the most important cognitive domains. In an effort to reduce medical costs and streamline procedures, optimally predictive and efficient groups of features were identified and selected, with the best performing and economical group containing only three questions and one cognitive exam component, producing an accuracy of 85%. The most effective diagnostic scoring procedure was examined, with simple threshold counting based on medical documentation being identified as the most useful. Overall predictive analysis found that Alzheimer’s disease can be detected most accurately using a bimodal multi-input neural network model using separated cognitive domains and questionnaires, with a detection accuracy of 88% using the real-world testing set, and that the technique of analyzing domains separately serves to significantly improve model efficacy compared to models that combine them.

Alzheimer’s disease (AD) is a deleterious neurodegenerative disease caused in major part by the aberrant processing and accumulation of amyloid beta peptides. In this dissertation, we systematically investigated the role of N-terminal region (NTR) residues of amyloid (1-40) (Aβ40) peptide in amyloidogenesis, lipid bilayer membrane interaction and damage, as well as neurotoxicity. Herein, we investigated the role of NTR residues on the aggregation and amyloid fibril formation process, to gain understanding on the electrostatic and hydrophobic constituents of the mechanism. This was achieved by substituting specific charged residues located in the NTR of Aβ40 and investigating their effects through a variety of techniques. We also investigated the role of NTR charged residues in their interaction with supported phospholipid bilayer membranes through the use of Quartz Crystal Microbalance with Dissipation (QCM-D) monitoring to gain insight on the mechanistic details of the interaction. To further understand the implications of substituting charged NTR residues on membrane interaction, pore formation and damage, we utilized a carboxyfluorescein dye leakage assay to quantify the membrane damage caused by Aβ40 and the NTR mutants. We also performed neurotoxicity assay with SH-SY5Y neuroblastoma cells to shed light on the effects of NTR substitutions on cellular toxicity. Finally, we synthesized a polymer, trimethyl chitosan (TMC), and utilized it as a polyelectrolyte monitor of electrostatic interactions occurring between TMC and the NTR of Aβ40. Our results demonstrate that the NTR charged residues of Aβ40 contribute significantly to the aggregation process, amyloidogenesis, and phospholipid membrane interaction and perturbation by means of electrostatic, thermodynamic and hydrophobic forces.

Protein aggregation, oligomer and fibril formation is one of the dominant
characteristics in the pathogenesis of a number of neurodegenerative diseases, such as
Alzheimer’s disease (AD). Inhibition of toxic oligomer and fibril formation is one of
the approaches to find potential drug candidates for AD. Additionally, early diagnosis
of these amyloid species can provide mechanistic understanding of protein aggregation
and thus can pave the way for preventing the onset of AD. The aim of this dissertation
was 1) to explore the effects of charged cholesterol derivatives on the aggregation
kinetic behavior of Amyloid-β40 (Aβ40), 2) to probe Aβ40 oligomer and amyloid
formation in vitro using gold nanoparticles (AuNPs), and 3) to monitor the kinetic
effect of various natural product molecules on Aβ40 aggregation in vitro. In the first
chapter, a general introduction about AD as an amyloidogenic disease, amyloid cascade
hypothesis, and the manipulation of Aβ peptides aggregation kinetics using different
approaches was presented. In the second chapter, we studied the effects of oppositely charged cholesterol derivatives on the aggregation kinetics of Aβ. In the third chapter,
we developed a gold nanoparticles (AuNPs) assay to probe Aβ40 oligomers and
amyloid formation. In chapter IV, we monitored the effects of various small molecules
on the aggregation kinetics of Aβ40. In chapter V, we discussed the methods and
experimental details.

Two skills necessary for the execution of proficient calculation, retrieving arithmetic facts from memory and accessing number magnitude information, were studied in a group of patients diagnosed with Alzheimer's disease (AD), mild cognitive impairment (MCI), and healthy controls to try to elucidate the locus of impairment in AD-related calculation deficits. This was achieved through the use of an arithmetic production task and a number-matching task as measures of explicit and implicit retrieval of arithmetic facts, and a numerical Stroop task that assesses automatic access to number magnitude representation. AD patients, but not MCI patients, showed high response latencies and a high number of errors when performing multiplications in the production task, and reduced automatic retrieval of arithmetic task in the number-matching task. All participants showed the classic problem-size effect often reported in the mathematical cognition literature. Performance on the numerical Stroop task suggests that access to number magnitude information is relatively resistant to cognitive impairment. ... Results for the AD group are consistent with a pattern of preserved and impaired cognitive processes that might mediate the reported calculation deficits in AD.