Genetic variability in response to amyloid beta deposition influences Alzheimer’s disease risk

Salih, Dervis A.; Bayram, Sevinc; Guelfi, Sebastian; Reynolds, Regina H.; Shoai, Maryam; Ryten, Mina; Brenton, Jonathan W.; Zhang, David; Matarin, Mar; Botia, Juan A.; Shah, Runil; Brookes, Keeley J.; Guetta-Baranes, Tamar; Morgan, Kevin; Bellou, Eftychia; Cummings, Damian M.; Escott-Price, Valentina; Hardy, John

Abstract

Genome-wide association studies of late-onset Alzheimer’s disease risk have previously identified genes primarily expressed in microglia that form a transcriptional network. Using transgenic mouse models of amyloid deposition, we previously showed that many of the mouse orthologues of these risk genes are co-expressed and associated with amyloid pathology. In this new study, we generate an improved RNA-seq-derived network that is expressed in amyloid-responsive mouse microglia and we statistically compare this with gene-level variation in previous human Alzheimer’s disease genome-wide association studies to predict at least four new risk genes for the disease (OAS1, LAPTM5, ITGAM/CD11b and LILRB4). Of the mouse orthologues of these genes Oas1a is likely to respond directly to amyloid at the transcriptional level, similarly to established risk gene Trem2, because the increase in Oas1a and Trem2 transcripts in response to amyloid deposition in transgenic mice is significantly higher than both the increase of the average microglial transcript and the increase in microglial number. In contrast, the mouse orthologues of LAPTM5, ITGAM/CD11b and LILRB4 (Laptm5, Itgam/CD11b and Lilra5) show increased transcripts in the presence of amyloid plaques similar in magnitude to the increase of the average microglial transcript and the increase in microglia number, except that Laptm5 and Lilra5 transcripts increase significantly quicker than the average microglial transcript as the plaque load becomes dense. This work suggests that genetic variability in the microglial response to amyloid deposition is a major determinant for Alzheimer’s disease risk, and identification of these genes may help to predict the risk of developing Alzheimer’s disease. These findings also provide further insights into the mechanisms underlying Alzheimer’s disease for potential drug discovery.

• Journal Article Type: Article
• Acceptance Date: Sep 11, 2019
• Online Publication Date: Oct 10, 2019
• Publication Date: Jan 1, 2019
• Deposit Date: Nov 20, 2019
• Publicly Available Date: Nov 20, 2019
• Journal: Brain Communications
• Print ISSN: 2632-1297
• Electronic ISSN: 2632-1297
• Publisher: Oxford University Press
• Peer Reviewed: Peer Reviewed
• Volume: 1
• Issue: 1
• Article Number: fcz022
• DOI: https://doi.org/10.1093/braincomms/fcz022
• Public URL: https://nottingham-repository.worktribe.com/output/3345426
• Publisher URL: https://academic.oup.com/braincomms/article/1/1/fcz022/5585002