Integrating network pharmacology with non-targeted serum metabolomics for elucidating the acting mechanism of Sini San for depression
OBJECTIVE To preliminarily explore the acting mechanism of traditional Chinese medicine compound Sini San for depression.METHODS The chemical constituents of Sini San were examined by UPLC-Q-TOF-MS/MS.The databases of SwissTargetPrediction and PubChem were accessed for selecting the targets of the antidepressant components of Sini San.And the database of DisGeNET was utilized for screening out the targets of depression,Venny tool software for locating the intersect-ing targets of Sini San,STRING data platform for constructing an inter-target PPI network and Cytoscape for screening out the core genes of Sini San.And the intersecting genes were subjected to GO and KEGG enrichment analyses by platform Metascape for predicting the potential signaling pathways of antidepressant.A depression model was established by an approach of chronic unpredictable mild stress(CUMS).Based upon body weight,60 male rats were randomized into four groups of control,model,Sini San(3.17 g·kg-1)and fluoxetine(1.58 mg·kg-1)(n=15 each).Except for control group,other rats underwent 6-week CUMS modeling.Then each group was evaluated behaviorally.Immunofluorescent detections of indoleamine 2,3-dioxygenase 1(IDO 1)and ionized calcium binding adapter molecule 1(IBA 1)were performed in rat hippocampus.And non-targeted metabo-lomic analyses of serum samples were conducted.RESULT A total of 61 chemical components were identified by UPLC-Q-TOF-MS/MS.In conjunctions with network pharmacology analysis,535 component targets,1 479 disease targets and 205 inter-section targets were obtained.Twenty-one core targets of AKT1,TP53,TNF and IL6 were mainly involved in the signaling pathways of mitogen-activated protein kinases(MAPK),Ras and PI3K-Akt.Behavioral results revealed that,as compared with blank group,model group showed reduced sucrose preference(P＜0.05),longer rest time in open-field box,reduced total num-ber of activities and shorter total distance travelled for exercise(P＜0.05).And dosing of Sini San significantly improved depression-like behaviors(P＜0.05).Immunofluorescent results revealed that fluorescent intensity of IDO 1/IBA 1 was signifi-cantly higher than that of blank group(P＜0.05)and a marked reversal of IDO 1 and IBA 1 occurred after Sini San dosing(P＜0.05).Serum metabolomics could detect a total of 19 differential metabolites,including L-tyrosine and arachidonic acid,mostly enriching 10 metabolic pathways including phenylalanine,tyrosine/tryptophan biosynthesis as well as arachidonic acid/sphingo-lipid metabolism.CONCLUSION Based upon network pharmacology and non-targeted metabolomics techniques,this study has preliminarily predicted that the major chemical constituents of Sini San's antidepressant include Nomilin,Enoxolone and 7,4-dihydroxyflavone are closely involved in the signaling pathways of MAPK,Ras and PI3K-Akt,revealing 19 differential metabo-lites and 10 metabolic pathways.It may further provide scientific rationales for the clinical application Sini San as a classic antidepressant.
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