Amyloid pathology changes hippocampal GFAP‐positive astrocytes phenotype: Molecular and cell biology/APP/Abeta/amyloid. (7th December 2020) - Zimmer, Eduardo R.; Bellaver, Bruna; Ferreira, Pamela C.L.; de Souza, Débora Guerini; De Bastiani, Marco Antônio
Abstract: Background: Astrocytes are physically intercalated with neurons and a single astrocyte simultaneously exchanges information with multiple neurons. In Alzheimer's disease (AD), they seem to respond to pathology by becoming reactive, i.e. changing morphology and overexpressing specific proteins, such as the glial fibrillary acidic protein (GFAP). Astrocyte reactivity is one of the earliest brain changes in AD and may serve as a potential target for early diagnosis and treatment. Still, few astrocyte biomarkers have been investigated in AD and, though promising, further development is needed. Therefore, a better understanding of AD‐reactive astrocyte phenotypes is needed. Here, we aimed at evaluating hippocampal astrocytes phenotype in the presence of amyloid pathology in a transgenic AD‐like mice model. We hypothesize that hippocampal astrocytes will react to amyloid‐pathology by assuming a different phenotype when compared to their wild‐type (WT) littermates. Methods: Hippocampal GFAP‐positive astrocytes data from 10 months‐old male and female PS2APP [Tg(Thy1‐APPSwe, Prnp‐PSEN2*N141I)152HLaoz]mice, which express human APP K670N/M671L and human presenilin 2 N141I mutations, and WT littermates (n = 5 per group) were obtained from GEO dataset(GSE129770) and used to evaluate differentially expressed genes (DEGs) and altered pathways/biological processes. In brief, raw fastq data reads were submitted to quality control assessment. Afterwards, we used the Salmon algorithm on high‐quality RNAseq samples. Finally, DEGs were evaluated using the DESeq2 method. Further data exploration included Gene Ontology (GO) functional evaluation. All RNAseq data analysis were performed using R/Bioconductor. Results: Principal component analyses (PCA) clearly differentiated gene expression from P2APP and WT astrocytes (Figure 1A). We further identified 1140 up‐regulated and 646 down‐regulated genes in P2APP astrocytes compared to WT (Figure 1B‐C). Interestingly, cell‐to‐cell communication pathways were amongst the top 10 altered GO pathways (Figure 1D). Differences were considered statistically significant at P<0.05. Conclusion: We identified a striking gene expression signature in hippocampal astrocytes of a mice model presenting heavy load of amyloid‐β. Therefore, changes in astrocyte phenotype, which can impact astrocyte morphology, protein expression and function, may hold the key for the development of novel biomarkers and for advancing our understanding of AD pathophysiology.