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2000
Volume 28, Issue 3
  • ISSN: 1386-2073
  • E-ISSN: 1875-5402

Abstract

Aims

The aim of this study is to explore the anti-depressant mechanism of Chaihu-Shugan San based on serum medicinal chemistry and network pharmacology methods.

Background

Depression lacks effective treatments, with current anti-depressants ineffective in 40% of patients. Chaihu-Shugan San (CHSGS) is a well-known traditional Chinese medicine compound to treat depression. However, the chemical components and the underlying mechanisms targeting the liver and brain in the anti-depressant effects of CHSGS need to be elucidated.

Methods

The chemical components of CHSGS in most current network pharmacology studies are screened from TCMSP and TCMID databases. In this study, we investigated the mechanism and material basis of soothing the liver and relieving depression in the treatment of depression by CHSGS based on serum pharmacochemistry. The anti-depressant mechanism of CHSGS was further verified by proteomics and high-throughput data.

Results

Through serum medicinal chemistry, we obtained 9 bioactive substances of CHSGS. These ingredients have good human oral bioavailability and are non-toxic. Based on liver ChIP-seq data, CHSGS acts on 8 targets specifically localized in the liver, such as FGA, FGB, and FGG. The main contributors to CHSGS soothing the liver targets are hesperetin, nobiletin, ferulic acid, naringin and albiflorin. In addition, network pharmacology analysis identified 9 blood components of CHSGS that corresponded to 63 anti-depressant targets in the brain. Among them, nobiletin has the largest number of anti-depressant targets, followed by glycyrrhizic acid, ferulic acid, albiflorin and hesperetin. We also validated the anti-depressant mechanism of CHSGS based on hippocampal proteomics. CHSGS exerts anti-depressant effects on synaptic structure and neuronal function by targeting multiple synapse related proteins.

Conclusion

This study not only provides a theoretical basis for further expanding the clinical application of CHSGS, but also provides a series of potential lead compounds for the development of depression drugs.

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2024-03-28
2025-04-02
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References

  1. CharlsonF. van OmmerenM. FlaxmanA. CornettJ. WhitefordH. SaxenaS. New WHO prevalence estimates of mental disorders in conflict settings: A systematic review and meta-analysis.Lancet20193941019424024810.1016/S0140‑6736(19)30934‑1 31200992
    [Google Scholar]
  2. HerrmanH. KielingC. McGorryP. HortonR. SargentJ. PatelV. Reducing the global burden of depression: A lancet–world psychiatric association commission.Lancet201939310189e42e4310.1016/S0140‑6736(18)32408‑5 30482607
    [Google Scholar]
  3. LuJ. XuX. HuangY. LiT. MaC. XuG. YinH. XuX. MaY. WangL. HuangZ. YanY. WangB. XiaoS. ZhouL. LiL. ZhangY. ChenH. ZhangT. YanJ. DingH. YuY. KouC. ShenZ. JiangL. WangZ. SunX. XuY. HeY. GuoW. JiangL. LiS. PanW. WuY. LiG. JiaF. ShiJ. ShenZ. ZhangN. Prevalence of depressive disorders and treatment in China: A cross-sectional epidemiological study.Lancet Psychiatry202181198199010.1016/S2215‑0366(21)00251‑0 34559991
    [Google Scholar]
  4. Global, regional, and national incidence, prevalence, and years lived with disability for 354 diseases and injuries for 195 countries and territories, 1990-2017: A systematic analysis for the global burden of disease study 2017.Lancet2018392101591789185810.1016/S0140‑6736(18)32279‑7
    [Google Scholar]
  5. DuY. LiW. LiY. YangJ. WangX. YinS. WangX. Velez de-la-PazO.I. GaoY. ChenH. YinX. ShiH. Repeated arctigenin treatment produces antidepressant- and anxiolytic-like effects in mice.Brain Res. Bull.2019146798610.1016/j.brainresbull.2018.12.005 30597190
    [Google Scholar]
  6. JiangN. LvJ. WangH. LuC. WangQ. XiaT. BaoY. LiS. LiuX. Dammarane sapogenins alleviates depression-like behaviours induced by chronic social defeat stress in mice through the promotion of the BDNF signalling pathway and neurogenesis in the hippocampus.Brain Res. Bull.201915323924910.1016/j.brainresbull.2019.09.007 31542427
    [Google Scholar]
  7. GerhardD.M. WohlebE.S. DumanR.S. Emerging treatment mechanisms for depression: Focus on glutamate and synaptic plasticity.Drug Discov. Today201621345446410.1016/j.drudis.2016.01.016 26854424
    [Google Scholar]
  8. WangY.S. ShenC.Y. JiangJ.G. Antidepressant active ingredients from herbs and nutraceuticals used in TCM: pharmacological mechanisms and prospects for drug discovery.Pharmacol. Res.201915010452010.1016/j.phrs.2019.104520 31706012
    [Google Scholar]
  9. ScheidV. Depression, constraint, and the liver: (Dis)assembling the treatment of emotion-related disorders in Chinese medicine.Cult. Med. Psychiatry2013371305810.1007/s11013‑012‑9290‑y 23315392
    [Google Scholar]
  10. SunY. XuX. ZhangJ. ChenY. Treatment of depression with Chai Hu Shu Gan San: A systematic review and meta-analysis of 42 randomized controlled trials.BMC Complement. Altern. Med.20181816610.1186/s12906‑018‑2130‑z 29454341
    [Google Scholar]
  11. YeungW.F. ChungK.F. NgK.Y. YuY.M. ZieaE.T.C. NgB.F.L. A systematic review on the efficacy, safety and types of Chinese herbal medicine for depression.J. Psychiatr. Res.20145716517510.1016/j.jpsychires.2014.05.016 24974002
    [Google Scholar]
  12. PicardK. BishtK. PogginiS. GarofaloS. GoliaM.T. BasilicoB. AbdallahF. AlbaneseC.N. AmreinI. VernouxN. SharmaK. HuiC.W. SavageC. J.; Limatola, C.; Ragozzino, D.; Maggi, L.; Branchi, I.; Tremblay, M.È. Microglial-glucocorticoid receptor depletion alters the response of hippocampal microglia and neurons in a chronic unpredictable mild stress paradigm in female mice.Brain Behav. Immun.20219742343910.1016/j.bbi.2021.07.022 34343616
    [Google Scholar]
  13. XieJ. BiB. QinY. DongW. ZhongJ. LiM. ChengY. XuJ. WangH. Inhibition of phosphodiesterase-4 suppresses HMGB1/RAGE signaling pathway and NLRP3 inflammasome activation in mice exposed to chronic unpredictable mild stress.Brain Behav. Immun.202192677710.1016/j.bbi.2020.11.029 33221489
    [Google Scholar]
  14. GuoQ. LinX.M. DiZ. ZhangQ.A. JiangS. Electroacupuncture ameliorates CUMS-induced depression-like behavior: Involvement of the glutamatergic system and apoptosis in rats.Comb. Chem. High Throughput Screen.2021247996100410.2174/1386207323666201027121423 33109036
    [Google Scholar]
  15. JiaK.K. PanS.M. DingH. LiuJ.H. ZhengY.J. WangS.J. PanY. KongL.D. Chaihu-shugan san inhibits inflammatory response to improve insulin signaling in liver and prefrontal cortex of CUMS rats with glucose intolerance.Biomed. Pharmacother.20181031415142810.1016/j.biopha.2018.04.171 29864926
    [Google Scholar]
  16. FanD-H. CaoM-Q. LiuQ. SunN-N. WuZ-Z. Chaihu-Shugan-San exerts an antidepressive effect by downregulating miR-124 and releasing inhibition of the MAPK14 and Gria3 signaling pathways.Neural Regen. Res.201813583784510.4103/1673‑5374.232478 29863014
    [Google Scholar]
  17. ZhuX. LiT. HuE. DuanL. ZhangC. WangY. TangT. YangZ. FanR. Proteomics study reveals the anti-depressive mechanisms and the compatibility advantage of chaihu-shugan-san in a rat model of chronic unpredictable mild stress.Front. Pharmacol.20221279109710.3389/fphar.2021.791097 35111057
    [Google Scholar]
  18. QiuJ. HuS.Y. ZhangC.H. ShiG.Q. WangS. XiangT. The effect of Chaihu-Shugan-San and its components on the expression of ERK5 in the hippocampus of depressed rats.J. Ethnopharmacol.2014152232032610.1016/j.jep.2014.01.004 24486208
    [Google Scholar]
  19. LiY.H. ZhangC.H. QiuJ. WangS.E. HuS.Y. HuangX. XieY. WangY. ChengT.L. Antidepressant-like effects of Chaihu-Shugan-San via SAPK/JNK signal transduction in rat models of depression.Pharmacogn. Mag.2014103927127710.4103/0973‑1296.137367 25210314
    [Google Scholar]
  20. WangY. FanR. HuangX. Meta-analysis of the clinical effectiveness of traditional Chinese medicine formula Chaihu-Shugan-San in depression.J. Ethnopharmacol.2012141257157710.1016/j.jep.2011.08.079 21933701
    [Google Scholar]
  21. QinF. LiuJ.Y. YuanJ.H. Chaihu-Shugan-San, an oriental herbal preparation, for the treatment of chronic gastritis: A meta-analysis of randomized controlled trials.J. Ethnopharmacol.2013146243343910.1016/j.jep.2013.01.029 23376045
    [Google Scholar]
  22. SuZ.H. ZouG.A. PreissA. ZhangH.W. ZouZ.M. Online identification of the antioxidant constituents of traditional Chinese medicine formula Chaihu-Shu-Gan-San by LC–LTQ-Orbitrap mass spectrometry and microplate spectrophotometer.J. Pharm. Biomed. Anal.201053345446110.1016/j.jpba.2010.05.014 20580508
    [Google Scholar]
  23. ZhuY. WangH. XueH. ZhangY. ChengQ. ChenL. QiuX. Simultaneous determination of five components of chaihu-shugan-san in beagle plasma by HPLC-MS/MS and its application to a pharmacokinetic study after a single dose of chaihu-shugan-san.J. Anal. Methods Chem.2020202011110.1155/2020/8831938 32923002
    [Google Scholar]
  24. LiuY. WangW. ChenY. YanH. WuD. XuJ. ShiS. ShenX. HuangX. Simultaneous quantification of nine components in the plasma of depressed rats after oral administration of Chaihu-Shugan-San by ultra-performance liquid chromatography/quadrupole-time-of-flight mass spectrometry and its application to pharmacokinetic studies.J. Pharm. Biomed. Anal.202018611331010.1016/j.jpba.2020.113310 32348951
    [Google Scholar]
  25. ZengQ. LiL. SiuW. JinY. CaoM. LiW. ChenJ. CongW. MaM. ChenK. WuZ. A combined molecular biology and network pharmacology approach to investigate the multi-target mechanisms of Chaihu Shugan San on Alzheimer’s disease.Biomed. Pharmacother.201912010937010.1016/j.biopha.2019.109370 31563815
    [Google Scholar]
  26. ZhangS. LuY. ChenW. ShiW. ZhaoQ. ZhaoJ. LiL. Network pharmacology and experimental evidence: PI3K/AKT signaling pathway is involved in the antidepressive roles of Chaihu Shugan San.Drug Des. Devel. Ther.2021153425344110.2147/DDDT.S315060 34385814
    [Google Scholar]
  27. XieZ. XieZ. TrujilloN.P. YangT. YangC. Exploring mechanisms of Chaihu-Shugan-San against liver fibrosis by integrated multi-omics and network pharmacology approach.Biosci. Rep.2022427BSR2022103010.1042/BSR20221030 35791909
    [Google Scholar]
  28. SuZ. JiaH. ZhangH. FengY. AnL. ZouZ. Hippocampus and serum metabolomic studies to explore the regulation of Chaihu-Shu-Gan-San on metabolic network disturbances of rats exposed to chronic variable stress.Mol. Biosyst.201410354956110.1039/c3mb70377k 24398477
    [Google Scholar]
  29. KimS. ChenJ. ChengT. GindulyteA. HeJ. HeS. LiQ. ShoemakerB.A. ThiessenP.A. YuB. ZaslavskyL. ZhangJ. BoltonE.E. PubChem in 2021: New data content and improved web interfaces.Nucleic Acids Res.202149D1D1388D139510.1093/nar/gkaa971 33151290
    [Google Scholar]
  30. XiongG. WuZ. YiJ. FuL. YangZ. HsiehC. YinM. ZengX. WuC. LuA. ChenX. HouT. CaoD. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties.Nucleic Acids Res.202149W1W5W1410.1093/nar/gkab255 33893803
    [Google Scholar]
  31. WeiM. ZhangX. PanX. WangB. JiC. QiY. ZhangJ.Z.H. HobPre: Accurate prediction of human oral bioavailability for small molecules.J. Cheminform.2022141110.1186/s13321‑021‑00580‑6 34991690
    [Google Scholar]
  32. BanerjeeP. EckertA.O. SchreyA.K. PreissnerR. ProTox-II: A webserver for the prediction of toxicity of chemicals.Nucleic Acids Res.201846W1W257W26310.1093/nar/gky318 29718510
    [Google Scholar]
  33. ZengP. LiuY.C. WangX.M. YeC.Y. SunY.W. SuH.F. QiuS.W. LiY.N. WangY. WangY.C. MaJ. LiM. TianQ. Targets and mechanisms of Alpinia oxyphylla Miquel fruits in treating neurodegenerative dementia.Front. Aging Neurosci.202214101389110.3389/fnagi.2022.1013891 36533181
    [Google Scholar]
  34. WeiZ. GaoY. MengF. ChenX. GongY. ZhuC. JuB. ZhangC. LiuZ. LiuQ. iDMer: An integrative and mechanism-driven response system for identifying compound interventions for sudden virus outbreak.Brief. Bioinform.202122297698710.1093/bib/bbaa341 33302292
    [Google Scholar]
  35. LiX. TangQ. MengF. DuP. ChenW. INPUT: An intelligent network pharmacology platform unique for traditional Chinese medicine.Comput. Struct. Biotechnol. J.2022201345135110.1016/j.csbj.2022.03.006 35356545
    [Google Scholar]
  36. DainaA. MichielinO. ZoeteV. SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules.Nucleic Acids Res.201947W1W357W36410.1093/nar/gkz382 31106366
    [Google Scholar]
  37. FangS. DongL. LiuL. GuoJ. ZhaoL. ZhangJ. BuD. LiuX. HuoP. CaoW. DongQ. WuJ. ZengX. WuY. ZhaoY. HERB: A high-throughput experiment- and reference-guided database of traditional Chinese medicine.Nucleic Acids Res.202149D1D1197D120610.1093/nar/gkaa1063 33264402
    [Google Scholar]
  38. ZengP. SuH.F. YeC.Y. QiuS.W. ShiA. WangJ.Z. ZhouX.W. TianQ. A Tau pathogenesis-based network pharmacology approach for exploring the protections of Chuanxiong Rhizoma in alzheimer’s disease.Front. Pharmacol.20221387780610.3389/fphar.2022.877806 35529440
    [Google Scholar]
  39. YuG. WangL.G. HanY. HeQ.Y. clusterProfiler: An R package for comparing biological themes among gene clusters.OMICS201216528428710.1089/omi.2011.0118 22455463
    [Google Scholar]
  40. ZengP. SuH.F. YeC.Y. QiuS.W. TianQ. Therapeutic mechanism and key alkaloids of Uncaria rhynchophylla in alzheimer’s disease from the perspective of pathophysiological processes.Front. Pharmacol.20211280698410.3389/fphar.2021.806984 34975502
    [Google Scholar]
  41. SzklarczykD. GableA.L. NastouK.C. LyonD. KirschR. PyysaloS. DonchevaN.T. LegeayM. FangT. BorkP. JensenL.J. von MeringC. The STRING database in 2021: Customizable protein–protein networks, and functional characterization of user-uploaded gene/measurement sets.Nucleic Acids Res.202149D1D605D61210.1093/nar/gkaa1074 33237311
    [Google Scholar]
  42. ShannonP. MarkielA. OzierO. BaligaN.S. WangJ.T. RamageD. AminN. SchwikowskiB. IdekerT. Cytoscape: A software environment for integrated models of biomolecular interaction networks.Genome Res.200313112498250410.1101/gr.1239303 14597658
    [Google Scholar]
  43. FarleyW.D. DonaldsonS.L. ComesO. ZuberiK. BadrawiR. ChaoP. FranzM. GrouiosC. KaziF. LopesC.T. MaitlandA. MostafaviS. MontojoJ. The GeneMANIA prediction server: Biological network integration for gene prioritization and predicting gene function.Nucleic Acids Res.201038W214W22010.1093/nar/gkq537
    [Google Scholar]
  44. XuM. ZhangD.F. LuoR. WuY. ZhouH. KongL.L. BiR. YaoY.G. A systematic integrated analysis of brain expression profiles reveals YAP1 and other prioritized hub genes as important upstream regulators in Alzheimer’s disease.Alzheimers Dement.201814221522910.1016/j.jalz.2017.08.012 28923553
    [Google Scholar]
  45. DarmanisS. SloanS.A. ZhangY. EngeM. CanedaC. ShuerL.M. GephartH.M.G. BarresB.A. QuakeS.R. A survey of human brain transcriptome diversity at the single cell level.Proc. Natl. Acad. Sci. 2015112237285729010.1073/pnas.1507125112 26060301
    [Google Scholar]
  46. WuY. TangJ. ZhouC. ZhaoL. ChenJ. ZengL. RaoC. ShiH. LiaoL. LiangZ. YangY. ZhouJ. XieP. Quantitative proteomics analysis of the liver reveals immune regulation and lipid metabolism dysregulation in a mouse model of depression.Behav. Brain Res.201631133033910.1016/j.bbr.2016.05.057 27247144
    [Google Scholar]
  47. WeiH. ZhouT. TanB. ZhangL. LiM. XiaoZ. XuF. Impact of chronic unpredicted mild stress-induced depression on repaglinide fate via glucocorticoid signaling pathway.Oncotarget2017827443514436510.18632/oncotarget.17874 28574832
    [Google Scholar]
  48. WangZ. SunH. YaoX. LiD. XuL. LiY. TianS. HouT. Comprehensive evaluation of ten docking programs on a diverse set of protein–ligand complexes: The prediction accuracy of sampling power and scoring power.Phys. Chem. Chem. Phys.20161818129641297510.1039/C6CP01555G 27108770
    [Google Scholar]
  49. KimS. ThiessenP.A. ChengT. YuB. ShoemakerB.A. WangJ. BoltonE.E. WangY. BryantS.H. Literature information in PubChem: Associations between PubChem records and scientific articles.J. Cheminform.2016813210.1186/s13321‑016‑0142‑6 27293485
    [Google Scholar]
  50. BurleyS.K. BhikadiyaC. BiC. BittrichS. ChenL. CrichlowG.V. DuarteJ.M. DuttaS. FayaziM. FengZ. FlattJ.W. GanesanS.J. GoodsellD.S. RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D.Protein Sci.2022311187208
    [Google Scholar]
  51. PatilS.A. AkkiA.J. RaghuA.V. KulkarniR.V. AkamanchiK.G. Sugarcane polyphenol oxidase: Structural elucidation using molecular modeling and docking analyses.Process Biochem.202313424324910.1016/j.procbio.2023.09.013
    [Google Scholar]
  52. VidyavathiG.T. KumarB.V. RaghuA.V. AravindaT. HaniU. MurthyH.C.A. ShridharA.H. Punica granatum pericarp extract catalyzed green chemistry approach for synthesizing novel ligand and its metal(II) complexes: Molecular docking/DNA interactions.J. Mol. Struct.2022124913165610.1016/j.molstruc.2021.131656
    [Google Scholar]
  53. LaskowskiR.A. SwindellsM.B. LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery.J. Chem. Inf. Model.201151102778278610.1021/ci200227u 21919503
    [Google Scholar]
  54. SnyderJ.S. SoumierA. BrewerM. PickelJ. CameronH.A. Adult hippocampal neurogenesis buffers stress responses and depressive behaviour.Nature2011476736145846110.1038/nature10287 21814201
    [Google Scholar]
  55. NingL.N. ZhangT. ChuJ. QuN. LinL. FangY.Y. ShiY. ZengP. CaiE.L. WangX.M. WangQ. LuY.M. ZhouX.W. ZhangQ. TianQ. Gender-related hippocampal proteomics study from young rats after chronic unpredicted mild stress exposure.Mol. Neurobiol.201855183585010.1007/s12035‑016‑0352‑y 28064424
    [Google Scholar]
  56. HopkinsA.L. Network pharmacology.Nat. Biotechnol.200725101110111110.1038/nbt1007‑1110 17921993
    [Google Scholar]
  57. NogalesC. MamdouhZ.M. ListM. KielC. CasasA.I. SchmidtH.H.H.W. Network pharmacology: Curing causal mechanisms instead of treating symptoms.Trends Pharmacol. Sci.202243213615010.1016/j.tips.2021.11.004 34895945
    [Google Scholar]
  58. ZengP. WangX.M. YeC.Y. SuH.F. TianQ. The main alkaloids in Uncaria rhynchophylla and their anti-alzheimer’s disease mechanism determined by a network pharmacology approach.Int. J. Mol. Sci.2021227361210.3390/ijms22073612 33807157
    [Google Scholar]
  59. LinX. ChiW. GengX. JiangQ. MaB. DaiB. SuiY. JiangJ. Evaluation of the mechanism of yishan formula in treating breast cancer based on network pharmacology and experimental verification.Comb. Chem. High Throughput Screen.20242710.2174/0113862073266004231105164321 38178684
    [Google Scholar]
  60. ZhuJ. HanM. YangY. FengR. HuY. WangY. Exploring the mechanism of Brucea Javanica against ovarian cancer based on network pharmacology and the influence of luteolin on the PI3K/AKT pathway.Comb. Chem. High Throughput Screen.202427115716710.2174/1386207326666230627114111 37366364
    [Google Scholar]
  61. DumanR.S. AghajanianG.K. Synaptic dysfunction in depression: Potential therapeutic targets.Science20123386103687210.1126/science.1222939 23042884
    [Google Scholar]
  62. YiL.T. XuH.L. FengJ. ZhanX. ZhouL.P. CuiC.C. Involvement of monoaminergic systems in the antidepressant-like effect of nobiletin.Physiol. Behav.201110211610.1016/j.physbeh.2010.10.008 20951716
    [Google Scholar]
  63. LiJ. ZhouY. LiuB.B. LiuQ. GengD. WengL.J. YiL.T. Nobiletin ameliorates the deficits in hippocampal BDNF, TrkB, and synapsin I induced by chronic unpredictable mild stress.Evid. Based Complement. Alternat. Med.2013201311110.1155/2013/359682 23573124
    [Google Scholar]
  64. YamamotoY. ShiodaN. HanF. MoriguchiS. NakajimaA. YokosukaA. MimakiY. SashidaY. YamakuniT. OhizumiY. FukunagaK. Nobiletin improves brain ischemia-induced learning and memory deficits through stimulation of CaMKII and CREB phosphorylation.Brain Res.2009129521822910.1016/j.brainres.2009.07.081 19646972
    [Google Scholar]
  65. DongX. HuangR. Ferulic acid: An extraordinarily neuroprotective phenolic acid with anti-depressive properties.Phytomedicine202210515435510.1016/j.phymed.2022.154355 35908520
    [Google Scholar]
  66. FanR. HuangX. WangY. ChenX. RenP. JiH. XieY. ZhangY. HuangW. QiuX. LiuZ. ZhouH. FanL. GaoL. Ethnopharmacokinetic- and activity-guided isolation of a new antidepressive compound from fructus aurantii found in the traditional chinese medicine chaihu-shugan-san: A new approach and its application.Evid. Based Complement. Alternat. Med.201220121810.1155/2012/607584 22454671
    [Google Scholar]
  67. XieW. QiuX. HuangX. XieY. WuK. WangY. JiH. HeJ. RenP. Comparison between the pharmacokinetics of meranzin hydrate in a rat model of chronic depression and in controls following the oral administration of Chaihu-Shugan-San.Exp. Ther. Med.20136491391810.3892/etm.2013.1229 24137289
    [Google Scholar]
  68. ZhangX. HanL. LiuJ. XuQ. GuoY. ZhengW. WangJ. HuangX. RenP. Pharmacokinetic study of 7 compounds following oral administration of fructus aurantii to depressive rats.Front. Pharmacol.2018913110.3389/fphar.2018.00131 29556193
    [Google Scholar]
  69. LopezJ.P. LimR. CruceanuC. CrapperL. FasanoC. LabonteB. MaussionG. YangJ.P. YerkoV. VigneaultE. El MestikawyS. MechawarN. PavlidisP. TureckiG. miR-1202 is a primate-specific and brain-enriched microRNA involved in major depression and antidepressant treatment.Nat. Med.201420776476810.1038/nm.3582 24908571
    [Google Scholar]
  70. CaoM.Q. ChenD.H. ZhangC.H. WuZ.Z. Screening of specific microRNA in hippocampus of depression model rats and intervention effect of Chaihu Shugan San.Chin. J. Chines. Mater. Med.2013381015851589 23947143
    [Google Scholar]
  71. GecysD. DambrauskieneK. SimonyteS. PatamsyteV. VilkeviciuteA. MusneckisA. Butkute-SliuozieneK. LesauskaiteV. ZemaitisL. UsaiteD. AdomaitieneV. Circulating hsa-let-7e-5p and hsa-miR-125a-5p as possible biomarkers in the diagnosis of major depression and bipolar disorders.Dis. Markers2022202211210.1155/2022/3004338 35178127
    [Google Scholar]
  72. MuddashettyR.S. NalavadiV.C. GrossC. YaoX. XingL. LaurO. WarrenS.T. BassellG.J. Reversible inhibition of PSD-95 mRNA translation by miR-125a, FMRP phosphorylation, and mGluR signaling.Mol. Cell201142567368810.1016/j.molcel.2011.05.006 21658607
    [Google Scholar]
  73. LiY.J. XuM. GaoZ.H. WangY.Q. YueZ. ZhangY.X. LiX.X. ZhangC. XieS.Y. WangP.Y. Alterations of serum levels of BDNF-related miRNAs in patients with depression.PLoS One201385e6364810.1371/journal.pone.0063648 23704927
    [Google Scholar]
  74. KahlK.G. KrügerT. EckermannG. WedemeyerH. [Major depression and liver disease: The role of microbiome and inflammation]Fortschr. Neurol. Psychiatr.2019871122110.1055/s‑0043‑123068 29490382
    [Google Scholar]
  75. Martins-de-SouzaD. MaccarroneG. IsingM. KloiberS. LucaeS. HolsboerF. TurckC.W. Plasma fibrinogen: Now also an antidepressant response marker?Transl. Psychiatry201441e35210.1038/tp.2013.129 24473443
    [Google Scholar]
  76. WangQ. YuC. ShiS. SuX. ZhangJ. DingY. SunY. LiuM. LiC. ZhaoX. JiangW. WeiT. An analysis of plasma reveals proteins in the acute phase response pathway to be candidate diagnostic biomarkers for depression.Psychiatry Res.201927240441010.1016/j.psychres.2018.11.069 30611956
    [Google Scholar]
  77. ChenQ. LiC. TaoE. AsakawaT. ZhangY. Exploration of a brain-liver-communication-related mechanism involved in the experimental perimenopausal depression rat model using Chaihu-Shugan-San.Neurochem. Res.20224751354136810.1007/s11064‑022‑03534‑y 35190952
    [Google Scholar]
  78. JiaH. YuM. MaL.Y. ZhangH. ZouZ. Chaihu-Shu-Gan-San regulates phospholipids and bile acid metabolism against hepatic injury induced by chronic unpredictable stress in rat.J. Chromatogr. B Analyt. Technol. Biomed. Life Sci.20171064142110.1016/j.jchromb.2017.08.003 28886478
    [Google Scholar]
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