Parkinson's disease (PD) is a typical neurodegenerative disease. α‐Lipoic acid (α‐LA) can reduce the incidence of neuropathy. The present study explored the role and mechanism of α‐LA in 1‐methyl‐4‐phenylpyridinium (MPP+)‐induced cell model of PD. The PD model was induced via treating PC12 cells with MPP+ at different concentrations. MPP+ and α‐LA effects on PC12 cells were assessed from cell viability and ferroptosis. Cell viability was detected using the cell counting kit‐8 assay. Malondialdehyde (MDA), 4‐hydroxynonenal (4‐HNE), iron, reactive xygen species (ROS), and glutathione (GSH) concentrations, and ferroptosis‐related protein SLC7A11 and GPx4 expressions were used for ferroptosis evaluation. p‐PI3K, p‐Akt, and nuclear factor erythroid 2‐related factor 2 (Nrf2) protein levels were detected. The PI3K/Akt/Nrf2 pathway inhibitors were applied to verify the role of the PI3K/Akt/Nrf2 pathway in α‐LA protection against MPP+‐induced decreased cell viability and ferroptosis. MPP+‐reduced cell viability and induced ferroptosis as presented by increased MDA, 4‐HNE, iron, and ROS concentrations, and reduced levels of GSH and ferroptosis marker proteins (SLC7A11 and GPx4). α‐LA attenuated MPP+‐induced cell viability decline and ferroptosis. The PI3K/Akt/Nrf2 pathway was activated after α‐LA treatment. Inhibiting the PI3K/Akt/Nrf2 pathway weakened the protection of α‐LA against MPP+ treatment. We highlighted that α‐LA alleviated MPP+‐induced cell viability decrease and ferroptosis in PC12 cells via activating the PI3K/Akt/Nrf2 pathway.
<b><i>Background:</i></b> Alzheimer’s disease (AD) is a chronic neurodegenerative disease. In this study, potential diagnostic biomarkers were identified for AD. <b><i>Methods:</i></b> All AD samples and healthy samples were collected from 2 datasets in the GEO database, in which differentially expressed genes (DEGs) were analyzed by using the limma package of R language. GO and KEGG pathway enrichment was conducted basing on the DEGs via the clusterProfiler package of R. And, the PPI network construction and gene prediction were performed using the STRING database and Cytoscape. Then, a logistic regression model was constructed to predict the sample type. <b><i>Results:</i></b> Bioinformatic analysis of GEO datasets revealed 2,063 and 108 DEGs in GSE5281 and GSE4226 datasets, separately, and 15 overlapping DEGs were found. GO and KEGG enrichment analysis revealed terms associated with neurodevelopment. Then, we built a logistic regression model based on the hub genes from the PPI network and optimized the model to 3 genes (ALDOA, ENC1, and NFKBIA). The values of area under the curve of the training set GSE5281 and testing set GSE4226 were 0.9647 and 0.7857, respectively, which implied the efficacy of this model. <b><i>Conclusion:</i></b> The comprehensive bioinformatic analysis of gene expression in AD patients and the effective logistic regression model built in our study may provide promising research value for diagnostic methods of AD.
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