Abstract

AbstractBackgroundMicroglia, the resident macrophages of the brain, are implicated in Alzheimer’s disease (AD). The genetic risk for AD is significantly enriched near microglial genes and enhancers. In the human brain, APOE is the strongest genetic risk factor for late onset AD and is primarily expressed in microglia and astrocytes. The “disease associated microglia” (DAM) state, in which inflammatory pathways are upregulated in amyloid plaque‐associated microglia, is a characteristic pathological response in AD.MethodHere, we studied how amyloid pathology interacts with genetic risk for AD to regulate the DAM state.We profiled gene regulation in human iPS‐derived microglia. We xenotransplanted the microglia into the APPNL‐G‐F mouse model of AD using isogenic APOE2/3/4 and APOE knockout (KO) microglia (n = 5 animals per microglial APOE genotype). We characterized dissociated iPS‐derived microglia from each animal by RNA‐seq, CUT&Tag for H3K27ac, and ATAC‐seq. We performed quantitative analyses of the epigenetic landscapes of microglial populations across heterogeneous activation states. This allowed us to infer the regulatory pathways that are responsible for the differential microglia response to amyloid aggregation, depending on APOE.ResultWe observed widespread differences in gene regulation across microglia related to APOE genotype. While we observed upregulation of chemokine and interferon response signalling in AD‐risk variant e4, we observed corresponding downregulation in the AD‐protective variant e2. Additional analyses identified increased chromatin accessibility at vitamin D receptor binding motifs in e2 through motif analysis. Finally, we identified gene expression networks and modules underlying differential microglial activation states.ConclusionOur findings are important for understanding microglia and their activation in AD and neurodegenerative diseases. Here, we profiled comprehensive epigenetic landscapes in a human‐mouse chimera in the context of APOE genotype. This enabled us to leverage the realistic biological environment for the microglia offered by this model, and dissect genetic effects underlying microglial responses for the first time.

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