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Generalized Psychophysiological Interaction (PPI) Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
Published on: November 14, 2017
Identification of potential drug targets for Alzheimer's disease from genetic insights: A Mendelian randomization
Yan-Hong Zhao1, Jing-Hua Ning1, Xin Zhang1
1College of Basic Medical Sciences, Dali University, Dali, Yunnan, China.
Abstract:
Alzheimer disease (AD) is a primary neurodegenerative disorder of the brain with an unknown cause and complex pathogenesis. It is the most common form of dementia and poses a significant threat to the health of the aging population worldwide. However, effective pharmacological treatments remain limited. This study employed publicly available genome-wide association study summary statistics, which included 4907 plasma proteins as exposures and AD as the outcome. To explore the causal relationship between plasma proteins and AD, 5 Mendelian randomization (MR) analyses were applied. Heterogeneity in the results was assessed using the Cochrane Q test. Horizontal pleiotropy was evaluated through the MR-Egger intercept test. Sensitivity analysis was conducted using a leave-one-out approach. Plasma proteins exhibiting significant associations with AD were subjected to Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses to elucidate their biological functions and pathways. The protein-protein interaction network was constructed via the STRING database, and hub genes were identified based on node degree and visualized with Cytoscape. Potential drug candidates targeting these hub genes were predicted via the Drug Signatures Database. The binding affinities of the candidate drugs to the hub gene-encoded proteins were subsequently validated through molecular docking via the CB-Dock2 platform. Finally, the expression patterns of the hub genes across various cell types were explored via single-cell sequencing analysis, and an external GEO validation dataset was established for verification. MR analysis revealed that 39 plasma proteins were significantly associated with AD. Functional and pathway enrichment analyses revealed that these proteins were predominantly enriched in the nuclear factor-κB signaling pathway. Further screening identified 10 hub genes: APOE, CSF3, TNFAIP3, PHGDH, PEBP1, MICB, LGMN, TGM1, CD55, and CCL21. The Drug Signatures Database predicted 5 potential drug candidates. Molecular docking analysis demonstrated strong binding affinities between these drug candidates and the hub genes. Single-cell sequencing analysis revealed that most hub genes presented elevated expression levels in oligodendrocytes. The results of the MR analysis were consistent with those of the external validation set, underscoring the reliability of this study. Through MR analysis, this study systematically identified 10 hub genes associated with AD and predicted 5 potential drug candidates. These findings offer novel insights into the molecular mechanisms underlying AD and may contribute to improved strategies for clinical diagnosis and targeted therapy.
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