Cortical lipid metabolic pathway alteration of early Alzheimer’s disease and candidate drugs screen

Abstract

Abstract Background Lipid metabolism changes occur in early Alzheimer's disease (AD) patients. Yet little is known about metabolic gene changes in early AD cortex. Methods The lipid metabolic genes selected from two datasets (GSE39420 and GSE118553) were analyzed with enrichment analysis. Protein–protein interaction network construction and correlation analyses were used to screen core genes. Literature analysis and molecular docking were applied to explore potential therapeutic drugs. Results 60 lipid metabolic genes differentially expressed in early AD patients’ cortex were screened. Bioinformatics analyses revealed that up-regulated genes were mainly focused on mitochondrial fatty acid oxidation and mediating the activation of long-chain fatty acids, phosphoproteins, and cholesterol metabolism. Down-regulated genes were mainly focused on lipid transport, carboxylic acid metabolic process, and neuron apoptotic process. Literature reviews and molecular docking results indicated that ACSL1, ACSBG2, ACAA2, FABP3, ALDH5A1, and FFAR4 were core targets for lipid metabolism disorder and had a high binding affinity with compounds including adenosine phosphate, oxidized Photinus luciferin, BMS-488043, and candidate therapeutic drugs especially bisphenol A, benzo(a)pyrene, ethinyl estradiol. Conclusions AD cortical lipid metabolism disorder was associated with the dysregulation of the PPAR signaling pathway, glycerophospholipid metabolism, adipocytokine signaling pathway, fatty acid biosynthesis, fatty acid degradation, ferroptosis, biosynthesis of unsaturated fatty acids, and fatty acid elongation. Candidate drugs including bisphenol A, benzo(a)pyrene, ethinyl estradiol, and active compounds including adenosine phosphate, oxidized Photinus luciferin, and BMS-488043 have potential therapeutic effects on cortical lipid metabolism disorder of early AD.

Keywords

• Alzheimer’s disease
• Early-onset
• Cortex
• Lipid metabolism disorder
• Bioinformatics
• Candidate compounds