Saudi Cultural Missions Theses & Dissertations
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Item Restricted O-GlcNAcylation regulates mitochondrial remodeling and quality control mechanisms in Alzheimer’s disease(University of Kansas Medical Center, 2024-04) Alghusen, Ibtihal M; Slawson, ChadImpaired mitochondria homeostasis is linked to numerous human pathologies including neurodegeneration. In the nervous system, O-GlcNAcylation, a single sugar post-translational modification, is highly enriched and required for neuronal function. O-GlcNAc is regulated by O- GlcNAcase (OGA) and O-GlcNAc transferase (OGT), which respectively remove and add the modification. O-GlcNAc is considered as a therapeutic target against tauopathy for Alzheimer’s disease (AD). However, mitochondrial homeostasis is highly impaired in AD, and our understanding on how O-GlcNAc regulates mitochondrial homeostasis remains limited. We hypothesize that O-GlcNAc regulates mitochondrial homeostasis at multiple levels, mitochondrial remolding, mitochondrial retrograde signaling, and mitochondrial turn over. To examine the effect of the long term OGA inhibition on brain mitochondrial proteome, we used multiplexed quantitative mass spectrometry on mouse treated with OGA inhibitor, Thiamet-G (TMG). We revealed that TMG influences the mitochondrial proteome in a gender specific manner. Mitochondrial electron transport chain (ETC) proteins were significantly decreased in TMG treated male mouse brains compared to saline with almost no change in female. Also, antioxidant proteins and ROS production were significantly altered in both genders treated with TMG in opposite directions, elevation in antioxidant and reduction in ROS were detected in male, while female have reduced antioxidant and elevated ROS. These results indicates that OGA inhibition has a profound effect on cellular energetics, and ROS production in a gender specific manner. We next investigated whether O-GlcNAc regulates of mitochondrial interrogate stress response (ISRmt). Mitochondrial dysfunction triggers ISRmt, which in turns increases the translation of transcription factor Activating Transcription Factor 4 (ATF4). We showed that TMG elevates the expression of A TF4 and its downstream targets upon mitochondrial stress. Importantly, ATF4 occupancy increases at the ATF5 promoter site in brains isolated from TMG treated mice suggesting that O-GlcNAc is regulating ATF4 targeted gene expression. Together, these results indicate that O-GlcNAc regulates the ISRmt through regulating ATF4. Furthermore, we studied whether O-GlcNAc regulates one of the downstream targets of ISRmt, mitophagy, the process of recycling damaged mitochondria. Abnormalities in mitophagy is shown in many studies of AD. We showed that sustained elevation in O-GlcNAcylation via pharmacologically inhibiting OGA increases mitochondrial level of PINK-1 (PTEN-induced kinase-1) and autophagy-related protein light chain 3 (LC3). However, decreasing cellular O- GlcNAcylation by knocking down or knocking out OGT decreases both PINK1 and LC3. Moreover, we detected O-GlcNAc on PINK-1. Collectively, these data demonstrate that O- GlcNAc plays a crucial role in activation of mitophagy. Altogether, these studies provide new evidence supporting the role of O-GlcNAc as a critical regulator of mitochondrial homeostasis. However, we showed that the effect of OGA inhibition on both ISRmt and mitophagy is limited in AD models. Thus, these results indicates that OGA inhibitors might not restore mitochondrial homeostasis in AD. Therefore, administering OGA inhibitors to patients with AD needs further evaluation, especially highlighting gender- specific mitochondrial response to long term OGA inhibition.7 0Item Restricted Investigating Rule Induction Methods in Machine Learning for Improving Medical Dementia Prediction(2023-08-04) Albalawi, Hadeel; Lambrou, TryphonAlzheimer’s Disease (AD) is a neurodegenerative disease related to dementia that predominantly affects the elderly population with symptoms including, but not limited to, cognitive impairment and memory loss. Detecting AD and other conditions like Mild Cognitive Impairment (MCI) can lengthen the lifespan of patients and help them to access the medical services. One approach to achieve a rapid and early diagnosis of AD is using data mining (DM) techniques, which can search various characteristic traits related to Cognitively Normal (CN), AD, and MCI data observations to build classifiers that reveal contributors to the disease. Classifiers developed by DM techniques are used by medical professionals during dementia clinical processes to help make correct diagnosis. In this research, we amend a process based on DM that evaluates characteristics related to dementia conditions, in particular AD. The novelty of the proposed process lies in the classification algorithm that we have named ‘Rules-based Uncertainty Reduction Algorithm’ (RURA). RURA develops classifiers with rules, which strengthen the decisions that can be invoked by the medical professional when evaluating patients with dementia. Empirical evaluation, using several DM algorithms were conducted on biological marker (biomarkers) and behavioral characteristics of data subjects collected from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) data project to analyze the effectiveness of the proposed classification algorithm. The results show that RURA perform over 90% in predicting AD when compared with classifiers developed by other DM algorithms. Furthermore, the results show that delayed word recall and orientation are effective cognitive factors that contribute to the ability to detect dementia early. For the biomarkers, ABETA and neurofibrillary tangles in the neurons showed some associations with AD although fewer than those of the cognitive elements. Moreover, the results obtained show that the classifiers developed by the RURA algorithm from psychological attributes (Subset 2) are higher in accuracy than the other DM algorithms. The results from Subset 2, show that RURA’s classifiers are higher with 11.28%, 1.18%, 1.88%, 3.75%, 1.70%, and 0.27% than PRISM, Nnge, kNN, Naïve Bayes, ORule, and Ridor, separately. Also, RURA developed higher accurate classifiers than the remaining DM algorithms on Subset 12 which includes cognitive attributes and biomarker attributes. However, the performance of RURA did not improve when the general attributes were added to the psychological attributes and the accuracy decreased by 1.34% when mining Subset 2 (psychological attributes and general attributes). More considerably, the results indicate that specific biomarkers in the ADNI-MERGE dataset when used alone by a DM algorithm that we considered for dementia detection often will not result in acceptable predictive systems. For example, when DM algorithms were trained on MRI and PET attributes (Subset 5), the classifiers created had classification accuracies of 42–55% which are relatively low. So, utilizing neuroimaging attributes in the ADNI-MERGE dataset to detect dementia is not ideal using the DM techniques, especially when a baseline visit is used to represent each data subject. Therefore, the biomarkers attributes must be accompanied with psychological elements to improve the detection rate of dementia.12 0