Bioinformatics analysis and experimental validation of genes related to the pathogenesis of Alzheimer's disease

doi:10.12307/2023.855

Abstract

Abstract: BACKGROUND: The mechanism mining of Alzheimer’s disease is very important and bioinformatics is an effective prediction technology for the potential targets of Alzheimer’s disease.
OBJECTIVE: To screen the genes related to the pathogenesis of Alzheimer’s disease and verify them at the animal level by bioinformatics analysis.
METHODS: Differentially expressed genes were screened through the GEO online database. GO/KEGG enrichment analysis was performed using the DAVID online database. A protein-protein interaction network was constructed using the STRING database. Target protein distribution in human was identified using the HPA database. Finally, protein expression was analyzed using immunofluorescence and western blot techniques.
RESULTS AND CONCLUSION: Gene chips for Alzheimer’s disease and healthy populations were obtained using GSE48350, a dataset within the GEO online database, and the GEO2R online analysis platform was used to analyze the differentially expressed genes between the two populations, including 42 up-regulated genes and 131 down-regulated genes. Results of the GO enrichment analysis suggested that the differentially expressed genes were mainly located in presynaptic, transport vesicles and extracellular vesicles, to mediate biological processes such as neurotransmitter release and synaptic vesicle function. KEGG pathway analysis results showed that differentially expressed genes were mainly enriched in synaptic function-related signaling pathways such as neuroactive ligand-receptor interactions, γ-aminobutyric acid synapses and retrograde endogenous cannabinoid signaling. Based on the protein interaction network, five up-regulated hub genes (CLU, GFAP, CD44, FOS, ANXA1) and five down-regulated hub genes (GABRG2, SYT1, SYN2, KCNC1, SLC32A1) were identified. It was confirmed that the expression of GABRG2, KCNC1, and SLC32A1 in the hippocampal tissue in a mouse model of Alzheimer’s disease were significantly downregulated. To conclude, these three genes can be used as potential genes for the treatment and diagnosis of Alzheimer’s disease and provide important clues for the treatment of Alzheimer’s disease. GABRG2, KCNC1 and SLC32A1 are also co-therapeutic targets for Alzheimer’s disease and epilepsy.

Key words: GEO database, Alzheimer’s disease, epilepsy, bioinformatics, GABRG2, KCNC1, SLC32A1

CLC Number:

Li Xinru, Chai Shifan, Li Weiran, Cai Hongyan, Ye Yucai, Li Shuo, Hou Meng, Wang Zhaojun. Bioinformatics analysis and experimental validation of genes related to the pathogenesis of Alzheimer's disease [J]. Chinese Journal of Tissue Engineering Research, 2023, 27(35): 5653-5658.

Figures/Tables 9

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