A common haplotype lowers PU.1 expression in myeloid cells and delays onset of Alzheimer’s disease

Abstract

A genome-wide survival analysis of 14,406 Alzheimer’s disease (AD) cases and 25,849 controls identified eight previously reported AD risk loci and fourteen novel loci associated with age at onset. LD score regression of 220 cell types implicated regulation of myeloid gene expression in AD risk. In particular, the minor allele of rs1057233 (G), within the previously reported CELF1 AD risk locus, showed association with delayed AD onset and lower expression of SPI1 in monocytes and macrophages. SPI1 encodes PU.1, a transcription factor critical for myeloid cell development and function. AD heritability is enriched within the PU.1 cistrome, implicating a myeloid PU.1 target gene network in AD. Finally, experimentally altered PU.1 levels affect the expression of mouse orthologs of many AD risk genes and the phagocytic activity of mouse microglial cells. Our results suggest that lower SPI1 expression reduces AD risk by regulating myeloid gene expression and cell function.

Inflammatory mechanisms in neurodegeneration

“…; Efthymiou and Goate ; Huang et al . ). These studies have intensified the interest in studying the microglial contribution to AD pathogenesis.…”

Modeling Alzheimer’s disease with iPSC-derived brain cells

“…Despite a less aggressive progression, SAD shares the major characteristics of FAD, including Aβ plaque deposition and NFT pathology, and accounts for the vast majority (>95%) of all AD cases [28]. While no SAD-causative mutations have been found, a considerable number of genetic loci that increase or decrease the risk for developing SAD have now been reported [29][30][31][32][33][34]. The first identified was the APOE locus, encoding apolipoprotein E (APOE) [35].…”

“…APOE is most studied as a lipid carrier secreted from astrocytes that facilitates Aβ clearance from the brain, however, recent studies have revealed potentially detrimental roles of APOE4 also in neurons and microglial cells [38,39]. Genome-wide association studies (GWAS) in the last decade have identified numerous additional SAD risk genes, many of which are expressed primarily in non-neuronal cells of the brain [29][30][31][32][33][34] (Table 1). Combined with the persistent failure of AD clinical trials, largely aimed at reducing Aβ production by neurons, these recent genetic findings have begun to shift the focus of AD research toward better understanding the roles and functions of non-neuronal cells during neurodegeneration in AD.…”

“…Their involvement in neurodegenerative processes has long been appreciated, but until recently microglia were generally thought to respond to disease-associated pathologies rather than to play a causative role [133]. Improved sequencing technologies in the past 10 years have allowed GWAS to identify a surprising number of microglial genes as risk factors for late onset AD, including TREM2, CD33, HLA-DRB1, INPP5D, MS4A6A, CASS4, and SPI1 [29][30][31][32]. These findings clearly implicate microglial dysfunction as a driver of neurodegeneration under certain circumstances.…”

Genome-Scale Transcriptional Regulatory Network Models of Psychiatric and Neurodegenerative Disorders

“…Disease-related changes in transcriptional networks could occur either at the genetic level (i.e., disease-associated variation in the DNA sequence) or at the transcriptional level (i.e., changes in gene expression), or by both mechanisms. To date, several TFs and cofactors have been implicated in SCZ (Whitton et al, 2018;Wright et al, 2016;Xia et al, 2018), ASD (Darnell et al, 2011;Sugathan et al, 2014), MDD (Wray et al, 2018), or AD (Huang et al, 2017), based on convergent evidence that the gene encoding a TF or cofactor is associated with risk for a disease, and its binding sites or target genes are also associated with risk for that disease or are differentially expressed in the brains of disease cases versus controls. Previous studies have also used gene coexpression to identify disease-perturbed networks in the brain (Gandal et al, 2018;Torkamani et al, 2010;Voineagu et al, 2011;Zhang et al, 2013) and have observed that a subset of differentially expressed networks are enriched for genes that are also associated with genetic risk for the same disease (Voineagu et al, 2011;Zhang et al, 2013).…”