In this study, we evaluated the ameliorative effect and molecular mechanism of red ginseng ( Panax ginseng C.A. Meyer) extract (RGE) on D-galactose (D-gal)-induced premature ovarian failure (POF) using network pharmacology analysis. Ginsenosides are important active ingredients in ginseng, which also contains some sugar and amino acid derivatives. We aimed to determine the key proteins through which RGE regulates POF. In this work, we retrieved and screened for active ingredients in ginseng and the corresponding POF disease targets in multiple databases. A PPI network of genes was constructed in the STRING database and core targets were screened using topological analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were conducted in R software. Finally, molecular docking was conducted to validate the results. Female ICR mice were used to establish a POF mouse model for in vivo experiments. Serum levels of relevant estrogens were determined using ELISA and expression levels of relevant proteins in ovarian tissues were detected using immunofluorescence and western blot analysis. Network pharmacology analysis predicted that PI3K, Akt, Bax, Bcl-2, p16, and other proteins were highly correlated with POF and RGE. The results clearly showed that RGE could increase estradiol (E2) and lower follicle-stimulating hormone (FSH) levels in D-gal-fed mice. RGE restored the expression levels of related proteins by reducing Nrf2-mediated oxidative stress, PI3K/Akt-mediated apoptosis, and senescence signaling pathways. Overall, RGE has the potential to prevent and treat POF and is likely to be a promising natural protector of the ovaries.
Keyphrases
- pi k akt
- signaling pathway
- oxidative stress
- molecular docking
- cell proliferation
- diabetic rats
- gene expression
- mouse model
- genome wide
- epithelial mesenchymal transition
- cell cycle arrest
- risk assessment
- genome wide identification
- insulin resistance
- amino acid
- skeletal muscle
- drug induced
- machine learning
- high glucose
- south africa
- deep learning
- wastewater treatment
- cell death
- endothelial cells
- stress induced
- artificial intelligence
- metabolic syndrome
- data analysis
- big data
- human health
- anti inflammatory