Integrating bioinformatics, machine learning and molecular biology to elucidate the anti-allergic mechanisms of ginkgo biloba leaves.

Allergic diseases, encompassing allergic asthma, atopic dermatitis, food allergies, have emerged as substantial global public health challenges, with IgE-stimulated mast cell and basophil playing pivotal roles in disease pathogenesis. Our prior research has indicated the potential therapeutic effects of Ginkgo biloba leaf (GBL) extracts in IgE-mediated mast cell degranulation. The present study aimed to identify key genes and pathways associated with IgE-mediated mast cell and basophil responses and explore the mechanisms of GBL components. Initially, DEGs and WGCNA analysis were employed to preliminarily identify candidate genes associated with IgE stimulation in GSE96696 dataset. Subsequently, transcriptomic analysis was conducted on an anti-DNP IgE-induced RBL-2H3 degranulation model to validate the key genes, including Egr1, Tnfaip3, Nfkbid, Il13, Nfkbia, Il3, Il4, Ccl7, Cdkn1a, Csf2, Rel, alongside crucial signaling pathways such as NF-κB, and PI3K-AKT pathways. Machine learning algorithms were then applied to confirm the predictive capacity of key genes. Building upon these findings, molecular biology experiments, molecular docking, and molecular dynamics simulations demonstrated that GBL flavonoids, including quercetin, amentoflavone, ginkgetin, and bilobetin, significantly inhibited the release of cytokines, blocked calcium ion influx, and suppressed degranulation potentially by targeting RELA and AKT1. Collectively, our findings not only identify key genes for IgE-mediated response but also provide preliminary mechanistic insights into GBL.