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UCL Institute of Health Informatics

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Large scale omics data integration for biomarker discovery, drug repositioning and screening for new therapeutic targets for Alzheimer's disease

The problem

Alzheimer’s disease (AD) is the most common form of dementia affecting over 44 million individuals worldwide with numbers expected to triple by 2050. Based on the age of onset, AD can be primarily classified as either early-onset (familial) AD, where AD manifests before the age of 65 and is inherited in a mendelian dominant fashion; or the much more common late-onset (sporadic) AD where AD manifests after the age of 65. Progression of this disease can only be clinically measured and monitored using neuropsychological tests such as Mini Mental State Examination (MMSE) or neuroimaging of amyloid-β (Aβ). However, the high cost of neuroimaging and medical complications prohibit routine global use. A peripheral blood-based biomarker would serve as an easily accessible, relatively cheap and time-effective approach in diagnosing and monitoring AD, however, currently, there is no clinically established diagnostic blood biomarker for AD.

Furthermore, there is no known cure or clinically approved disease modifying therapy; however, drugs are available for temporary symptomatic relief. Drug repositioning may offer an innovative approach to drug discovery and identification of new therapeutic targets for AD.

Our Research

Microarray technologies have the ability to measure genome-wide gene expression providing a comprehensive view of gene activity within biological samples. Using this technology, the detection of a gene or combination of genes that are under or overly expressed in AD, could potentially be exploited as biomarkers for the disease. A vast quantity of gene expression data is increasingly becoming publically available through public repositories such as ArrayExpress, which is a generic database designed to store data from all microarray expression platforms. We are currently exploiting some these databases to integrate transcriptomic data from AD, non-AD neurological and age-related disorders for a large-scale analysis using machine learning techniques to create an AD specific blood diagnostic test with clinical utility in mind.

In addition, these public repositories provide transcriptomic data generated on various human brain tissues, regions where proteins involved in AD manifest. Using a meta-analysis approach and an extensive database containing compound-induced gene expression profiles (connectivity map), we aim to identify potential AD intervening compounds through drug repositioning. Our initial research has incorporated 47 AD brain expression datasets to re-discover a candidate small molecule for possible AD intervention. We now know through mouse trials (Pharmidex), this compound, when administrated intravenously, is also able to cross the Blood Brain Barrier.

Theme 

Discovery Science
Learning Health Systems

Disease

Alzheimer’s disease

People 

Richard Dobson

Stephen Newhouse

Sang Hyuck Lee

Charlie Curtis

Gerome Breen

Claire Troakes

Safa Al-Sarraj