Exploring the Potential Antidepressant Mechanisms of Pinellia by Using the Network Pharmacology and Molecular Docking

About 350 million people worldwide suffered from depression, but less than half of the patients received effective and regular treatments. Traditional Chinese Medicine (TCM) such as pinellia has been proven effective for antidepressant treatment with fewer side effects. However, the exact mechanisms remain unclear. Herein, we use the methods of network pharmacology and molecular docking to analyze the effective monomer components of pinellia and reveal the involved signaling pathways to produce antidepressant effects. TCMSP, BATMAN-TCM, and TCMID databases were utilized to analyze the bioactive ingredients and target genes derived from pinellia via the screening the molecular weight (MW), oral bioavailability (OB), blood–brain barrier (BBB) and drug similarity (DL). OMIM, TTD, DisGeNET, GeneCards and DrugBank databases were used to obtain key genes of depression. Then, the networks of protein–protein interaction (PPI) and "medicine-ingredients-targets-pathways" were built. The target signaling pathways were enriched by GO and KEGG by using R language. Furthermore, bioactive ingredients binding of the targets were verified by molecular docking. Nine active monomer ingredients and 96 pivotal gene targets were selected from pinellia. 10,124 disease genes and 87 drug-disease intersecting genes were verified. GO analysis proposed that the receptor activity of neurotransmitter, postsynaptic neurotransmitter, G protein-coupled neurotransmitter, and acetylcholine through the postsynaptic membrane could be modulated by pinellia. KEGG pathway analysis revealed that pinellia influenced depression-related neural tissue interaction, cholinergic synapse, serotonin activated synapse and calcium signaling pathway. Besides, the reliability and accuracy of results obtained from the indirect network pharmacology were validated by molecular docking. The bioactive components of pinellia made significant antidepressant effects by regulating the key target genes/proteins in the pathophysiology of depression.