A network-based pharmacology study of active compounds and targets of Fritillaria thunbergii against influenza. (December 2020) - Kim, Minjee; Kim, Young Bong
Graphical abstract: Highlights: Seasonal and pandemic influenza infections are serious threats to public health. Computational research serves as a time-saving alternative to the experimental research that yields promising compounds and targets. A network pharmacology-based strategy was used to predict potential compounds and target genes from Fritillaria thunbergii (FT) against influenza. Compound-target (C-T), Compound-Disease Target (C-D), protein-protein interaction (PPI) and Compound-Disease Target-Pathway (C-D-P) networks were constructed to analyze FT's effect against influenza. Network analyses predicted two compounds (beta-sitosterol and pelargonidin) derived from FT that may be potent candidates for influenza treatment. Abstract: Seasonal and pandemic influenza infections are serious threats to public health and the global economy. Since antigenic drift reduces the effectiveness of conventional therapies against the virus, herbal medicine has been proposed as an alternative. Fritillaria thunbergii (FT) have been traditionally used to treat airway inflammatory diseases such as coughs, bronchitis, pneumonia, and fever-based illnesses. Herein, we used a network pharmacology-based strategy to predict potential compounds from Fritillaria thunbergii (FT), target genes, and cellular pathways to better combat influenza and influenza-associated diseases. We identified five compounds, and 47 target genes using a compound-target network (C-T). Two compounds (beta-sitosterol and pelargonidin) and nine target genes ( BCL2, CASP3, HSP90AA1, ICAM1, JUN, NOS2, PPARG, PTGS1, PTGS2 ) were identified using a compound-influenza disease target network (C-D). Protein-protein interaction (PPI) network was constructed and we identified eight proteins from nine target genes formed a network. The compound-disease-pathway network (C-D-P) revealed three classes of pathways linked to influenza: cancer, viral diseases, and inflammation. Taken together, our systems biology data from C-T, C-D, PPI and C-D-P networks predicted potent compounds from FT and new therapeutic targets and pathways involved in influenza.