DPP-4 x cyclin-A x p38α

In this study, Cryptococcus neoformans BNCC225501 was used as research objects to explore the antifungal activity of magnolol. The molecular mechanisms underlying magnolol against Cryptococcus neoformans BNCC225501 were explored using metabolomics and transcriptomics. The results offer ideas and directions for developing new antifungal drugs based on magnolol.

The minimum inhibitory concentration (MIC) and the method of drawing the growth curve were used to determine whether magnolol had fungistatic and fungicidal effects on Cryptococcus neoformans. Cryptococcus neoformans was cultured on urea basal medium and Niger seed medium, and different concentrations of magnolol were added to these media to detect the effects of magnolol on the urease and melanin of Cryptococcus neoformans. Network pharmacology was used to screen for potential target genes of magnolol and fungal infection, obtaining common targets. Further molecular docking was performed between magnolol and the proteins corresponding to the common targets.

The MIC of the individual tested agents against Cryptococcus neoformans strain was 8 μg/mL for magnolol. The combination of magnolol and fluconazole revealed good synergistic effects against Cryptococcus neoformans (FIC ≤ 0.5). At a magnolol concentration of 8 μg/mL, the expression of urease was inhibited. Compared to the control group, in the experimental group treated with magnolol, among the differential metabolites, lipids and lipid‐like molecules accounted for the highest percentage of differential metabolites, except for unclassified metabolites. A total of 30% of the metabolites belonged to lipids and lipid‐like molecules, followed by amino acids, peptides, and analogues (22.63%), organoheterocyclic compounds (7.91%), alkaloids (6.17%), sugars (6.01%), nucleosides, nucleotides and analogues (3.80%), organic oxygen compounds (2.22%), aromatic compounds (1.90%), organic acids (1.90%), and other categories (16.93%). KEGG pathway enrichment analysis results showed that the metabolites were mainly enriched in glyoxylate and dicarboxylate metabolism, arginine biosynthesis, valine, leucine, and isoleucine degradation; arginine and proline metabolism; cysteine and methionine metabolism; pentose and glucuronate interconversions; sphingolipid metabolism; histidine metabolism; and the citrate cycle. Compared to the control group, in the experimental group treated with magnolol, a total of 928 genes were detected to be altered. The GO enrichment results showed that the altered genes were enriched in the component of membrane and oxidation‐reduction process. KEGG enrichment analysis showed that the differential genes were mainly enriched in the DNA replication, NOD‐like receptor signaling pathway, and fatty acid biosynthesis. Combined analysis of metabolomics and transcriptomics demonstrated that the glycerophospholipid metabolism pathway and glutathione metabolism pathway were significantly changed after treatment with magnolol. Screening identified 31 target genes for magnolol and 969 fungal genes, with three intersecting targets, DPP4, MAPK14, and CCNA2. Molecular docking studies showed that magnolol and the target protein had a good binding ability, a docking binding energy of −7.3 kcal/mol.

Magnolol has fungistatic and fungicidal ability for Cryptococcus neoformans and can inhibit the urease production capacity of Cryptococcus neoformans. The antifungal mechanism of magnolol may be that it disrupts redox homeostasis and inhibits detoxification by affecting Glutathione metabolism.

Aging is a main risk factor for a number of debilitating diseases and contributes to an increase in mortality. Previous studies have shown that Ginkgo biloba extract (EGb) can prevent and treat aging‐related diseases, but its pharmacological effects need to be further clarified. This study aimed to propose a network pharmacology‐based method to identify the therapeutic pathways of EGb for aging. The active components of EGb and targets of sample chemicals were obtained from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP) database. Information on aging‐related genes was obtained from the Human Ageing Genomic Resources database and JenAge Ageing Factor Database. Subsequently, a network containing the interactions between the putative targets of EGb and known therapeutic targets of aging was established, which was used to investigate the pharmacological mechanisms of EGb for aging. A total of 24 active components, 154 targets of active components of EGb, and 308 targets of aging were obtained. Network construction and pathway enrichment were conducted after data integration. The study found that flavonoids (quercetin, luteolin, and kaempferol) and beta‐sitosterol may be the main active components of EGb. The top eight candidate targets, namely, PTGS2, PPARG, DPP4, GSK3B, CCNA2, AR, MAPK14, and ESR1, were selected as the main therapeutic targets of EGb. Pathway enrichment results in various pathways were associated with inhibition of oxidative stress, inhibition of inflammation, amelioration of insulin resistance, and regulation of cellular biological processes. Molecular docking results showed that PPARG had better binding capacity with beta‐sitosterol, and PTGS2 had better binding capacity with kaempferol and quercetin. The main components of EGb may act on multiple targets, such as PTGS2, PPARG, DPP4, and GSK3B, to regulate multiple pathways, and play an antiaging role by inhibiting oxidative stress, inhibiting inflammation, and ameliorating insulin resistance.