A Meta-Analysis of Alzheimer's Disease Brain Transcriptomic Data
23 April 2019
Patel H, Dobson RJB, Newhouse SJ
Journal of Alzheimer’s disease : JAD (2019) 68(4):1635-1656
BACKGROUND: Microarray technologies have identified imbalances in the expression of specific genes and biological pathways in Alzheimer’s disease (AD) brains. However, there is a lack of reproducibility across individual AD studies, and many related neurodegenerative and mental health disorders exhibit similar perturbations.
OBJECTIVE: Meta-analyze publicly available transcriptomic data from multiple brain-related disorders to identify robust transcriptomic changes specific to AD brains.
METHODS: Twenty-two AD, eight schizophrenia, five bipolar disorder, four Huntington’s disease, two major depressive disorder, and one Parkinson’s disease dataset totalling 2,667 samples and mapping to four different brain regions (temporal lobe, frontal lobe, parietal lobe, and cerebellum) were analyzed. Differential expression analysis was performed independently in each dataset, followed by meta-analysis using a combining p-value method known as Adaptively Weighted with One-sided Correction.
RESULTS: Meta-analysis identified 323, 435, 1,023, and 828 differentially expressed genes specific to the AD temporal lobe, frontal lobe, parietal lobe, and cerebellum brain regions, respectively. Seven of these genes were consistently perturbed across all AD brain regions with SPCS1 gene expression pattern replicating in RNA-Seq data. A further nineteen genes were perturbed specifically in AD brain regions affected by both plaques and tangles, suggesting possible involvement in AD neuropathology. In addition, biological pathways involved in the “metabolism of proteins” and viral components were significantly enriched across AD brains.
CONCLUSION: This study identified transcriptomic changes specific to AD brains, which could make a significant contribution toward the understanding of AD disease mechanisms and may also provide new therapeutic targets.
Health data research
Health Data Science is a discipline that combines maths, statistics and technology to study different types of health problems using data. It provides the tools to manage and analyse very large...
Health Data Research UK researchers develop innovative tools and technologies needed to unlock knowledge from complex and diverse health data, to address some of the biggest health challenges that...
Professor of Biostats and Health Informatics, King's College London and Institute of Health Informatics at University College London (UCL)
Richard Dobson is Professor of Biomedical and Health Informatics at the Institute of Health Informatics, UCL, as well as Professor of Medical Informatics at King’s College London and lead for...