Abstract

Introduction: Mulberry leaf (ML) is known for its antibacterial and anti-inflammatory properties, historically documented in "Shen Nong's Materia Medica". This study aimed to investigate the effects of ML on enterovirus 71 (EV71) using network pharmacology, molecular docking, and in vitro experiments. Methods: We successfully pinpointed shared targets between mulberry leaves (ML) and the EV71 virus by leveraging online databases. Our investigation delved into the interaction among these identified targets, leading to the identification of pivotal components within ML that possess potent anti-EV71 properties. The ability of these components to bind to the targets was verified by molecular docking. Moreover, bioinformatics predictions were used to identify the signaling pathways involved. Finally, the mechanism behind its anti-EV71 action was confirmed through in vitro experiments. Results: Our investigation uncovered 25 active components in ML that targeted 231 specific genes. Of these genes, 29 correlated with the targets of EV71. Quercetin, a major ingredient in ML, was associated with 25 of these genes. According to the molecular docking results, Quercetin has a high binding affinity to the targets of ML and EV71. According to the KEGG pathway analysis, the antiviral effect of Quercetin against EV71 was found to be closely related to the NF-κB signaling pathway. The results of immunofluorescence and Western blotting showed that Quercetin significantly reduced the expression levels of VP1, TNF-α, and IL-1β in EV71-infected human rhabdomyosarcoma cells. The phosphorylation level of NF-κB p65 was reduced, and the activation of NF-κB signaling pathway was suppressed by Quercetin. Furthermore, our results showed that Quercetin downregulated the expression of JNK, ERK, and p38 and their phosphorylation levels due to EV71 infection. Conclusion: With these findings in mind, we can conclude that inhibiting the NF-κB signaling pathway is a critical mechanism through which Quercetin exerts its anti-EV71 effectiveness.

Full Text
Published version (Free)

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call