Abstract
While multiple studies have been conducted of gene expression in mouse models of Alzheimer’s disease (AD), their findings have not reached a clear consensus and have not accounted for the potentially confounding effects of changes in cellular composition. To help address this gap, we conducted a re-analysis based meta-analysis of ten independent studies of hippocampal gene expression in mouse models of AD. We used estimates of cellular composition as covariates in statistical models aimed to identify genes differentially expressed at either early or late stages of progression. Our analysis revealed changes in gene expression at early phases shared across studies, including dysregulation of genes involved in cholesterol biosynthesis and the complement system. Expression changes at later stages were dominated by cellular compositional effects. Thus despite the considerable heterogeneity of the mouse models, we identified common patterns that may contribute to our understanding of AD etiology. Our work also highlights the importance of controlling for cellular composition effects in genomics studies of neurodegeneration.
Significance Statement: A molecular understanding of Alzheimer's disease is important to the development of treatments. Because of the difficulty of studying brain tissue in humans, especially at very early stages of progression, many groups have performed transcriptomic studies of rodent models. Our study identified changes in gene expression that are strikingly consistent across multiple such studies, providing substantial insight into molecular changes present at early stages of these disease models and which may be of importance in the human condition. Our study also demonstrates the importance of accounting for the complex effects of neurodegeneration on brain tissue in data analysis.
Footnotes
The authors report no conflict of interest
Supported by the Canadian Institutes of Health Research (Joint Program on Neurodegenerative Disease research), National Institutes of Health grant MH111099, the Natural Sciences and Engineering Research Council of Canada and the University of British Columbia Graduate Program in Bioinformatics.
This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license, which permits unrestricted use, distribution and reproduction in any medium provided that the original work is properly attributed.
Jump to comment: