Transcriptomic and functional analysis of Ab1-42 oligomer-stimulated human monocyte-derived microglia-like cells. Transcriptomic and functional analysis of Ab1-42 oligomer-stimulated human monocyte-derived microglia-like cells

Dysregulation of microglial function contributes to Alzheimer’s disease (AD) pathogenesis. Several genetic and transcriptome studies have revealed microglia specific genetic risk factors, and changes in microglia expression profiles in AD pathogenesis, viz. the human-Alzheimer’s microglia/myeloid (HAM) profile in AD patients and the disease-associated microglia profile (DAM) in AD mouse models. The transcriptional changes involve genes in immune and inflammatory pathways, and in pathways associated with Aβ clearance. Aβ oligomers have been suggested to be the initial trigger of microglia activation in AD. To study the direct response to Aβ oligomers exposure, we assessed changes in gene expression in an in vitro model for microglia, the human monocyte-derived microglial-like (MDMi) cells. We confirmed the initiation of an inflammatory profile following LPS stimulation, based on increased expression of IL1B, IL6, and TNFα. In contrast, the Ab1-42 oligomers did not induce an inflammatory profile or a classical HAM or DAM profile. Interestingly, we observed a specific increase in the expression of metallothioneins in the Aβ1-42 oligomer treated MDMi cells. Metallothioneins are involved in metal ion regulation, protection against reactive oxygen species, and have anti-inflammatory properties. In conclusion, our data suggests that Aβ1-42 oligomers may trigger a protective response both in vitro and in vivo. Overall design: Bulk RNAseq analysis on MDMi cells stimulated with AB1-42 oligomers, LPS, PBS and vehicle. Libraries were de-multiplexed and raw reads were aligned to the hg19 human RefSeq transcriptome with Burrows-Wheeler Aligner (BWA) (Li and Durbin, 2010). Duplicate reads and reads that mapped equally well to multiple locations were discarded. The quality control, normalization, and identification of differentially expressed genes were done with DESeq2, an R (version 3.6.3) based package (Love MI et al 2014).