整合网络药理学与非靶向血清代谢组学探讨四逆散治疗抑郁症的作用机制

Integrating network pharmacology with non-targeted serum metabolomics for elucidating the acting mechanism of Sini San for depression

目的:初步探讨中药复方四逆散治疗抑郁症的作用机制。方法:采用UPLC-Q-TOF-MS/MS对四逆散的化学成分进行系统鉴定;利用SwissTargetPrediction、PubChem数据库筛选出四逆散抗抑郁的成分作用靶点,DisGeNET数据库筛选出抑郁症疾病靶点,并结合Venny工具软件筛选出四逆散抗抑郁的交集靶点,通过STRING数据平台构建靶点基因间PPI网络,利用Cytoscape筛选出四逆散抗抑郁的核心基因,Metascape平台对交集基因进行GO和KEGG富集分析,预测四逆散抗抑郁所涉及的潜在信号通路;采用慢性不可预知温和刺激(chronic unpredictable mild stress,CUMS)方法制备抑郁模型,将60只雄性SD大鼠按体质量大小随机分组为空白组、模型组、四逆散组(3.17 g·kg^(–1))和氟西汀组(1.58 mg·kg^(–1)),每组15只,除空白组外,其余大鼠进行CUMS造模。造模6周后,对各组大鼠进行行为学评价;采用免疫荧光对大鼠海马区进行吲哚胺2,3-双加氧酶1(indoleamine 2,3-dioxygenase 1,IDO 1)、离子钙结合适配器分子1(ionized calcium binding adapter molecule 1,IBA 1)检测;采用非靶向代谢组学对各组大鼠血清进行分析。结果:液质联用分析共鉴定出61个化学成分;结合网络药理学分析,得到535个成分靶点、1479个疾病靶点和205个交集靶点,其中包括AKT1、TP53、TNF、IL6等21个核心靶点,主要涉及丝裂原活化蛋白激酶(mitogen-activated protein kinase,MAPK)、Ras、PI3K-Akt等信号通路;行为学结果发现,与空白组相比,模型组大鼠蔗糖偏好降低、旷场箱中静止时间增加、活动总次数减少以及运动总路程减少(均为P<0.05),四逆散给药后能显著改善抑郁样行为(均为P<0.05);免疫荧光结果发现,与空白组相比,IDO 1与IBA 1的荧光强度均显著升高(P<0.05),经过四逆散给药后均明显回转(P<0.05);血清代谢组学筛选出19个差异代谢物,包括L-酪氨酸、花生四烯酸等,主要富集到苯丙氨酸、酪氨酸和色氨酸的生物合成以及花生四烯酸代谢、鞘脂代谢等10条代谢通路。结论:该研究基于网络药理学与非靶向代谢组学技术,初步预测到诺米林、甘草次酸、7,4-二羟基黄酮等为四逆散抗抑郁的主要化学成分,涉及MAPK、Ras、PI3K-Akt等信号通路,并挖掘19个差异代谢物和10条代谢通路,可进一步为经典复方四逆散抗抑郁的临床应用提供现代科学依据。

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 intersecting 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 metabolomic analyses of serum samples were conducted.RESULT A total of 61 chemical components were identified by UPLC-QTOF-MS/MS.In conjunctions with network pharmacology analysis,535 component targets,1479 disease targets and 205 intersection 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 number 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 significantly 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/sphingolipid 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 metabolites and 10 metabolic pathways.It may further provide scientific rationales for the clinical application Sini San as a classic antidepressant.