Comparative profiling of cortical gene expression in Alzheimer’s disease patients and mouse models demonstrates a link between amyloidosis and neuroinflammation

Abstract

Alzheimer’s disease (AD) is the most common form of dementia, characterized by accumulation of amyloid β (Aβ) and neurofibrillary tangles. Oxidative stress and inflammation are considered to play an important role in the development and progression of AD. However, the extent to which these events contribute to the Aβ pathologies remains unclear. We performed inter-species comparative gene expression profiling between AD patient brains and the App NL-G-F/NL-G-F and 3xTg-AD-H mouse models. Genes commonly altered in App NL-G-F/NL-G-F and human AD cortices correlated with the inflammatory response or immunological disease. Among them, expression of AD-related genes (C4a/C4b, Cd74, Ctss, Gfap, Nfe2l2, Phyhd1, S100b, Tf, Tgfbr2, and Vim) was increased in the App NL-G-F/NL-G-F cortex as Aβ amyloidosis progressed with exacerbated gliosis, while genes commonly altered in the 3xTg-AD-H and human AD cortices correlated with neurological disease. The App NL-G-F/NL-G-F cortex also had altered expression of genes (Abi3, Apoe, Bin2, Cd33, Ctsc, Dock2, Fcer1g, Frmd6, Hck, Inpp5D, Ly86, Plcg2, Trem2, Tyrobp) defined as risk factors for AD by genome-wide association study or identified as genetic nodes in late-onset AD. These results suggest a strong correlation between cortical Aβ amyloidosis and the neuroinflammatory response and provide a better understanding of the involvement of gender effects in the development of AD.

Concurrent cell type–specific isolation and profiling of mouse brains in inflammation and Alzheimer’s disease

“…Our bulk brain analysis of microglia and astrocyte genes is consistent with the reported APP KI transcriptomic data (53). Upon examination of cell type-specific changes, we noted that most of the observed cellular changes occur in microglia and astrocytes, with little to none occurring in vascular endothelia or oligodendrocytes.…”

“…Consistent with reports using other AD models (51), Apoe is upregulated in both microglia and astrocytes of APP KI mice and such an increase has been proposed to serve as a clearance mechanism (52). Our bulk brain data are also consistent with recently published bulk transcriptomic data in the APP KI model showing increases in genes generally thought to be microglial (including Cst7, Clec7a, Lpl, Hexb, Spp1, Trem2, Tyrobp, and C3ar1) and astrocytic (including Gfap, Serpina3, and Osmr) ( Figure 5, A-C) (53). In agreement with previous bulk brain data from AD mouse models (53-55), we found that genes of the complement pathway were also significantly upregulated (C1qa, C3ar, C4b, Itgam, and Itgb2) at the bulk tissue level.…”

“…In order to investigate how amyloid pathology alters brain biology on a cell type level, we set out to assess the APP NL-G-F KI model of AD, which shares many transcriptional changes seen in human AD (53). Our bulk brain analysis of microglia and astrocyte genes is consistent with the reported APP KI transcriptomic data (53).…”

Neuroinflammation in mouse models of Alzheimer's disease

“…Recently, it was reported that TREM2, as well as CD36 and the receptor for advanced glycation end‐products, work as Aβ sensor molecules and activate microglia . A consistent and elevated expression of TREM2 was described in App NL‐G‐F KI mice . Activated microglia also produce the pro‐inflammatory cytokines CCL3/MIP‐1α and interleukin (IL)‐6, the latter being a key component of the senescence‐associated secretory phenotype, which might provide a pathophysiological connection between cellular/tissue senescence and age‐related chronic diseases in the brain, including AD.…”

“…reported amyloidosis‐dependent transcriptomic profiles in 3xTg AD mice and App NL‐G‐F KI mice . In contrast to 3xTg AD mice, App NL‐G‐F KI mice express genes in common with AD patients, such as neuroinflammation‐related genes ( C4a/C4b , Cd74 , Ctss , Gfap , Nfe212 , Phyhd1 , S100b , Tf , Tgfbr2 and Vim ) and AD risk factor genes ( Abi3 , Apoe , Bin2 , Cd33 , Ctsc , Dock2 , Fcer1g , Frmd6 , Hck , Inpp5D , Ly86 , Plcg2 , Trem2 and Tyrobp ) . Thus, App KI mice might overcome some of the previous limitations associated with APP overexpression‐based mouse models, and could thus serve as a useful research tool for further investigations.…”