Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.806764
Title: Analysis for large scale omics data integration, biomarker discovery, drug repositioning and screening for new therapeutic targets for Alzheimer's Disease
Author: Patel, Hamel
Awarding Body: King's College London
Current Institution: King's College London (University of London)
Date of Award: 2020
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Abstract:
The increase in life expectancy has profoundly increased the ageing population, which, unfortunately, is also accompanied by a rise in age-related disorders, including Alzheimer’s Disease (AD). The most common form of dementia is AD, which was first described over a century ago, however, to date, there still exists a lack of understanding the molecular changes specific to the disease, a clinically established robust blood-based biomarker for accurate disease diagnosis and a lack of treatments. This thesis begins by investigating microarray gene expression profiling from Asymptomatic AD (AsymAD) human brains, who were clinically free from dementia; however, upon autopsy, they were observed to consist of hallmark AD pathology. A significant increase of transcriptomic activity in the frontal cortex (FC) brain region of AsymAD subjects was detected, suggesting fundamental changes in AD may initially begin within the FC brain region prior to symptoms of AD. In addition, overactivation of the brain “glutamate-glutamine cycle” and disruption to the brain energy pathways in both AsymAD and AD subjects were identified, suggesting these may be the earliest biological pathways disrupted in the disease, providing potential targets for early disease intervention. Secondly, existing and novel microarray gene expression studies of human AD brains were integrated into the largest known AD meta-analysis to date and is the first to incorporate numerous non-AD neurological disorders to identify AD-specific molecular changes. Seven genes were observed to be specifically and consistently perturbed across AD brains, with SPCS1 gene expression pattern found to replicate in RNA-Seq data. The cerebellum brain region is often regarded to be free from hallmark AD pathology and was incorporated into the analysis as a secondary control to identify an additional nineteen genes that may be involved with hallmark AD pathology. Furthermore, biological processes often reported as disrupted in AD were observed to be tissue-specific, and viral components were found to be specifically enriched across AD brains. Thirdly, an automated transcriptomic based drug repositioning pipeline was developed to query the reprocessed Connectivity Map to identify candidate compounds for disease intervention. Drug repositioning the AsymAD gene expression profile identified several candidate compounds that are already FDA approved for the treatment of AD and cognitive impairment, suggesting these compounds may be effective in the early stage of the disease. Drug repositioning the AD gene expression profile identified an anti-biotic compound for disease intervention. Finally, a machine learning approach was used to identify a blood-based 28 gene expression profile, which is enriched for “herpes simplex infection”, and can distinguish AD from Parkinson’s Disease, Multiple Sclerosis, Amyotrophic Lateral Sclerosis, Bipolar Disorder, Schizophrenia, Coronary Artery Disease, Rheumatoid Arthritis, Chronic Obstructive Pulmonary Disease, and cognitively healthy subjects with 66.3% PPV and 90.6% NPV. Overall, the work undertaken in this thesis provides new insight into the molecular changes occurring in both the asymptomatic and symptomatic phase of the disease, demonstrates a framework for a possible blood-based transcriptomic diagnosis test, provides new potential therapeutic targets, identifies candidate compounds that require further investigation for disease intervention and provides new avenues for future AD-related research.
Supervisor: Dobson, Richard James Butler ; Newhouse, Stephen Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.806764  DOI: Not available
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