Exploring the Potential Molecular Mechanism of the Shugan Jieyu Capsule
in the Treatment of Depression through Network Pharmacology, Molecular
Docking, and Molecular Dynamics Simulation

Zhiyao      Liu; Hailiang      Huang; Ying      Yu; Yuqi      Jia; Lingling      Li; Xin      Shi; Fangqi      Wang

doi:10.2174/1573409919666230619105254

Abstract

Background: Shugan Jieyu Capsule (SJC) is a pure Chinese medicine compound prepared with Hypericum perforatum and Acanthopanacis senticosi. SJC has been approved for the clinical treatment of depression, but the mechanism of action is still unclear.

Objectives: Network pharmacology, molecular docking, and molecular dynamics simulation (MDS) were applied in the present study to explore the potential mechanism of SJC in the treatment of depression.

Methods: TCMSP, BATMAN-TCM, and HERB databases were used, and related literature was reviewed to screen the effective active ingredients of Hypericum perforatum and Acanthopanacis senticosi. TCMSP, BATMAN-TCM, HERB, and STITCH databases were used to predict the potential targets of effective active ingredients. GeneCards database, DisGeNET database, and GEO data set were used to obtain depression targets and clarify the intersection targets of SJC and depression. STRING database and Cytoscape software were used to build a protein-protein interaction (PPI) network of intersection targets and screen the core targets. The enrichment analysis on the intersection targets was conducted. Then the receiver operator characteristic (ROC) curve was constructed to verify the core targets. The pharmacokinetic characteristics of core active ingredients were predicted by SwissADME and pkCSM. Molecular docking was performed to verify the docking activity of the core active ingredients and core targets, and molecular dynamics simulations were performed to evaluate the accuracy of the docking complex.

Results: We obtained 15 active ingredients and 308 potential drug targets with quercetin, kaempferol, luteolin, and hyperforin as the core active ingredients. We obtained 3598 targets of depression and 193 intersection targets of SJC and depression. A total of 9 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2) were screened with Cytoscape 3.8.2 software. A total of 442 GO entries and 165 KEGG pathways (p <0.01) were obtained from the enrichment analysis of the intersection targets, mainly enriched in IL-17, TNF, and MAPK signaling pathways. The pharmacokinetic characteristics of the 4 core active ingredients indicated that they could play a role in SJC antidepressants with fewer side effects. Molecular docking showed that the 4 core active components could effectively bind to the 8 core targets (AKT1, TNF, IL6, IL1B, VEGFA, JUN, CASP3, MAPK3, PTGS2), which were related to depression by the ROC curve. MDS showed that the docking complex was stable.

Conclusion: SJC may treat depression by using active ingredients such as quercetin, kaempferol, luteolin, and hyperforin to regulate targets such as PTGS2 and CASP3 and signaling pathways such as IL-17, TNF, and MAPK, and participate in immune inflammation, oxidative stress, apoptosis, neurogenesis, etc.

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Graphical Abstract

[1]
Kessler, R.C.; Berglund, P.; Demler, O.; Jin, R.; Koretz, D.; Merikangas, K.R.; Rush, A.J.; Walters, E.E.; Wang, P.S. The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA,  2003, 289(23), 3095-3105.
 [http://dx.doi.org/10.1001/jama.289.23.3095] [PMID: 12813115]

[2]

Depression and other common mental disorders: global health estimates; World Health Organization: Geneva, 2017, p. 24.

[3]
Kupfer, D.J.; Frank, E.; Phillips, M.L. Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet,  2012, 379(9820), 1045-1055.
 [http://dx.doi.org/10.1016/S0140-6736(11)60602-8] [PMID: 22189047]

[4]
Uchida, S.; Yamagata, H.; Seki, T.; Watanabe, Y. Epigenetic mechanisms of major depression: Targeting neuronal plasticity. Psychiatry Clin. Neurosci.,  2018, 72(4), 212-227.
 [http://dx.doi.org/10.1111/pcn.12621] [PMID: 29154458]

[5]
Hepgul, N.; Cattaneo, A.; Zunszain, P.A.; Pariante, C.M. Depression pathogenesis and treatment: what can we learn from blood mRNA expression? BMC Med.,  2013, 11(1), 28.
 [http://dx.doi.org/10.1186/1741-7015-11-28] [PMID: 23384232]

[6]
Hackett, M.L.; Anderson, C.S.; House, A.; Xia, J. Interventions for treating depression after stroke. Cochrane Database Syst. Rev.,  2008, (4), CD003437.
 [PMID: 18843644]

[7]
Mannheimer, B.; Falhammar, H.; Calissendorff, J.; Skov, J.; Lindh, J.D. Time-dependent association between selective serotonin reuptake inhibitors and hospitalization due to hyponatremia. J. Psychopharmacol.,  2021, 35(8), 928-933.
 [http://dx.doi.org/10.1177/02698811211001082] [PMID: 33860708]

[8]
Alzoubi, K.H.; Abdel-Hafiz, L.; Khabour, O.F.; El-Elimat, T.; Alzubi, M.A.; Alali, F.Q. Evaluation of the effect of Hypericum triquetrifolium turra on memory impairment induced by chronic psychosocial stress in rats: Role of BDNF. Drug Des. Devel. Ther.,  2020, 14, 5299-5314.
 [http://dx.doi.org/10.2147/DDDT.S278153] [PMID: 33299301]

[9]
Wang, G.H.; Dong, H.Y.; Dong, W.G.; Wang, X.P.; Luo, H.S.; Yu, J.P. Protective effect of Radix Acanthopanacis senticosi capsule on colon of rat depression model. World J. Gastroenterol.,  2005, 11(9), 1373-1377.
 [http://dx.doi.org/10.3748/wjg.v11.i9.1373] [PMID: 15761979]

[10]
Ng, Q.X.; Venkatanarayanan, N.; Ho, C.Y.X. Clinical use of Hypericum perforatum (St John’s wort) in depression: A meta-analysis. J. Affect. Disord.,  2017, 210, 211-221.
 [http://dx.doi.org/10.1016/j.jad.2016.12.048] [PMID: 28064110]

[11]
Sun, X.Y.; Chen, A.Q.; Xu, X.F.; Zhang, H.G.; Zhang, H.Y. Randomized, double blind, placebo-controlled trial of Shuganjieyu capsule in the treatment of mild or moderate depression. Zhongguo Xin Yao Zazhi, 2009.

[12]
Wu, T.; Yue, T.; Yang, P.; Jia, Y. Notable efficacy of Shugan Jieyu capsule in treating adult with post-stroke depression: A PRISMA-compliant meta-analysis of randomized controlled trials. J. Ethnopharmacol.,  2022, 294, 115367.
 [http://dx.doi.org/10.1016/j.jep.2022.115367] [PMID: 35562090]

[13]
Sun, Y.; Tian, G.; Shi, K.; Sun, X.; Li, X.; Zeng, W.; Li, H.; Zhang, B.; Tian, F. A comparison between Shugan Jieyu Capsule and escitalopram oxalate in treatment of hypertension complicated by anxiety-depression. Chinese J. Evid. Based Cardiovascul. Med., 2018.

[14]
H, Q.; KZ, W. Clinical effect of Shugan Jieyu capsule combined with escitalopram in the treatment of senile depression. Contemp. Med.,  2019, 2019, 80-81.

[15]
Colinge, J.; Rix, U.; Bennett, K.L.; Superti-Furga, G. Systems biology analysis of protein-drug interactions. Proteomics Clin. Appl.,  2012, 6(1-2), 102-116.
 [http://dx.doi.org/10.1002/prca.201100077] [PMID: 22213655]

[16]
Zhang, W. Network pharmacology: A further description. Net.Pharmacol.,  2016, 1(1), 1-14.

[17]
Li, S.; Zhang, B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin. J. Nat. Med.,  2013, 11(2), 110-120.
 [http://dx.doi.org/10.1016/S1875-5364(13)60037-0] [PMID: 23787177]

[18]
Wu, C.W.; Lu, L.; Liang, S.W.; Chen, C.; Wang, S.M. Application of drug-target prediction technology in network pharmacology of traditional Chinese medicine. Zhongguo Zhongyao Zazhi,  2016, 41(3), 377-382.
 [PMID: 28868850]

[19]
Zhang, R.; Zhu, X.; Bai, H.; Ning, K. Network pharmacology databases for traditional chinese medicine: Review and assessment. Front. Pharmacol.,  2019, 10, 123.
 [http://dx.doi.org/10.3389/fphar.2019.00123] [PMID: 30846939]

[20]
Hao, D.C.; Xiao, P.G. Network pharmacology: a Rosetta Stone for traditional Chinese medicine. Drug Dev. Res.,  2014, 75(5), 299-312.
 [http://dx.doi.org/10.1002/ddr.21214] [PMID: 25160070]

[21]
Kitchen, D.B.; Decornez, H.; Furr, J.R.; Bajorath, J. Docking and scoring in virtual screening for drug discovery: methods and applications. Nat. Rev. Drug Discov.,  2004, 3(11), 935-949.
 [http://dx.doi.org/10.1038/nrd1549] [PMID: 15520816]

[22]
Buch, I.; Giorgino, T.; De Fabritiis, G. Complete reconstruction of an enzyme-inhibitor binding process by molecular dynamics simulations. Proc. Natl. Acad. Sci.,  2011, 108(25), 10184-10189.
 [http://dx.doi.org/10.1073/pnas.1103547108] [PMID: 21646537]

[23]
Ru, J.; Li, P.; Wang, J.; Zhou, W.; Li, B.; Huang, C.; Li, P.; Guo, Z.; Tao, W.; Yang, Y.; Xu, X.; Li, Y.; Wang, Y.; Yang, L. TCMSP: a database of systems pharmacology for drug discovery from herbal medicines. J. Cheminform.,  2014, 6(1), 13.
 [http://dx.doi.org/10.1186/1758-2946-6-13] [PMID: 24735618]

[24]
Liu, Z.; Guo, F.; Wang, Y.; Li, C.; Zhang, X.; Li, H.; Diao, L.; Gu, J.; Wang, W.; Li, D.; He, F. BATMAN-TCM: A bioinformatics analysis tool for molecular mechanism of traditional chinese medicine. Sci. Rep.,  2016, 6(1), 21146.
 [http://dx.doi.org/10.1038/srep21146] [PMID: 26879404]

[25]
Fang, S.; Dong, L.; Liu, L.; Guo, J.; Zhao, L.; Zhang, J.; Bu, D.; Liu, X.; Huo, P.; Cao, W.; Dong, Q.; Wu, J.; Zeng, X.; Wu, Y.; Zhao, Y. HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Res.,  2021, 49(D1), D1197-D1206.
 [http://dx.doi.org/10.1093/nar/gkaa1063] [PMID: 33264402]

[26]
Szklarczyk, D.; Santos, A.; von Mering, C.; Jensen, L.J.; Bork, P.; Kuhn, M. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res.,  2016, 44(D1), D380-D384.
 [http://dx.doi.org/10.1093/nar/gkv1277] [PMID: 26590256]

[27]
UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res.,  2019, 47(D1), D506-D515.
 [http://dx.doi.org/10.1093/nar/gky1049] [PMID: 30395287]

[28]
Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res.,  2003, 13(11), 2498-2504.
 [http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]

[29]
Safran, M.; Chalifa-Caspi, V.; Shmueli, O.; Olender, T.; Lapidot, M.; Rosen, N.; Shmoish, M.; Peter, Y.; Glusman, G.; Feldmesser, E.; Adato, A.; Peter, I.; Khen, M.; Atarot, T.; Groner, Y.; Lancet, D. Human gene-centric databases at the weizmann institute of science: GeneCards, UDB, CroW 21 and HORDE. Nucleic Acids Res.,  2003, 31(1), 142-146.
 [http://dx.doi.org/10.1093/nar/gkg050] [PMID: 12519968]

[30]
Piñero, J.; Bravo, À.; Queralt-Rosinach, N.; Gutiérrez-Sacristán, A.; Deu-Pons, J.; Centeno, E.; García-García, J.; Sanz, F.; Furlong, L.I. DisGeNET: a comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res.,  2017, 45(D1), D833-D839.
 [http://dx.doi.org/10.1093/nar/gkw943] [PMID: 27924018]

[31]
Iwamoto, K.; Kakiuchi, C.; Bundo, M.; Ikeda, K.; Kato, T. Molecular characterization of bipolar disorder by comparing gene expression profiles of postmortem brains of major mental disorders. Mol. Psychiatry,  2004, 9(4), 406-416.
 [http://dx.doi.org/10.1038/sj.mp.4001437] [PMID: 14743183]

[32]
Oliveros, J.C. Venny. 2007. Available from: http://bioinfogp.cnb.csic.es/tools/venny/index.html

[33]
Szklarczyk, D.; Gable, A.L.; Lyon, D.; Junge, A.; Wyder, S.; Huerta-Cepas, J.; Simonovic, M.; Doncheva, N.T.; Morris, J.H.; Bork, P.; Jensen, L.J.; Mering, C. STRING v11: protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res.,  2019, 47(D1), D607-D613.
 [http://dx.doi.org/10.1093/nar/gky1131] [PMID: 30476243]

[34]
Zhou, Y.; Zhou, B.; Pache, L.; Chang, M.; Khodabakhshi, A.H.; Tanaseichuk, O.; Benner, C.; Chanda, S.K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun.,  2019, 10(1), 1523.
 [http://dx.doi.org/10.1038/s41467-019-09234-6] [PMID: 30944313]

[35]
Huey, R.; Morris, G.M.; Olson, A.J.; Goodsell, D.S. A semiempirical free energy force field with charge-based desolvation. J. Comput. Chem.,  2007, 28(6), 1145-1152.
 [http://dx.doi.org/10.1002/jcc.20634] [PMID: 17274016]

[36]
Goodsell, D.S.; Morris, G.M.; Olson, A.J. Automated docking of flexible ligands: Applications of autodock. J. Mol. Recognit.,  1996, 9(1), 1-5.
 [http://dx.doi.org/10.1002/(SICI)1099-1352(199601)9:1<1::AID-JMR241>3.0.CO;2-6] [PMID: 8723313]

[37]
Morris, G.M.; Goodsell, D.S.; Huey, R.; Olson, A.J. Distributed automated docking of flexible ligands to proteins: Parallel applications of AutoDock 2.4. J. Comput. Aided Mol. Des.,  1996, 10(4), 293-304.
 [http://dx.doi.org/10.1007/BF00124499] [PMID: 8877701]

[38]
Zhou, W.; Liu, Q.; Wang, W.; Yuan, X.J.; Xiao, C.C.; Ye, S.D. Comprehensive network analysis reveals the targets and potential multitarget drugs of type 2 Diabetes Mellitus. Oxid. Med. Cell. Longev.,  2022, 2022, 1-12.
 [http://dx.doi.org/10.1155/2022/8255550] [PMID: 35936218]

[39]
Shukla, R.; Kumar, A.; Kelvin, D.J.; Singh, T.R. Disruption of DYRK1A-induced hyperphosphorylation of amyloid-beta and tau protein in Alzheimer’s disease: An integrative molecular modeling approach. Front. Mol. Biosci.,  2023, 9, 1078987.
 [http://dx.doi.org/10.3389/fmolb.2022.1078987] [PMID: 36741918]

[40]
Kim, S.; Chen, J.; Cheng, T.; Gindulyte, A.; He, J.; He, S.; Li, Q.; Shoemaker, B.A.; Thiessen, P.A.; Yu, B.; Zaslavsky, L.; Zhang, J.; Bolton, E.E. PubChem 2019 update: improved access to chemical data. Nucleic Acids Res.,  2019, 47(D1), D1102-D1109.
 [http://dx.doi.org/10.1093/nar/gky1033] [PMID: 30371825]

[41]
Berman, H.M.; Westbrook, J.; Feng, Z.; Gilliland, G.; Bhat, T.N.; Weissig, H.; Shindyalov, I.N.; Bourne, P.E. The protein data bank. Nucleic Acids Res.,  2000, 28(1), 235-242.
 [http://dx.doi.org/10.1093/nar/28.1.235] [PMID: 10592235]

[42]
Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem.,  2009, 30(16), 2785-2791.
 [http://dx.doi.org/10.1002/jcc.21256] [PMID: 19399780]

[43]
Daina, A.; Michielin, O.; Zoete, V. SwissADME: a free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep.,  2017, 7(1), 42717.
 [http://dx.doi.org/10.1038/srep42717] [PMID: 28256516]

[44]
Pires, D.E.V.; Blundell, T.L.; Ascher, D.B. pkCSM: Predicting small-molecule pharmacokinetic and toxicity properties using graph-based signatures. J. Med. Chem.,  2015, 58(9), 4066-4072.
 [http://dx.doi.org/10.1021/acs.jmedchem.5b00104] [PMID: 25860834]

[45]
Szewczyk, B.; Pochwat, B.; Muszyńska, B.; Opoka, W.; Krakowska, A.; Rafało-Ulińska, A.; Friedland, K.; Nowak, G. Antidepressant-like activity of hyperforin and changes in BDNF and zinc levels in mice exposed to chronic unpredictable mild stress. Behav. Brain Res.,  2019, 372, 112045.
 [http://dx.doi.org/10.1016/j.bbr.2019.112045] [PMID: 31220487]

[46]
Mennini, T.; Gobbi, M. The antidepressant mechanism of Hypericum perforatum. Life Sci.,  2004, 75(9), 1021-1027.
 [http://dx.doi.org/10.1016/j.lfs.2004.04.005] [PMID: 15207650]

[47]
Li, F.; Zhou, Z.; Lu, C.; Pang, G.; Lu, Z. To investigate the potential mechanism of huanglian jiangtang formula lowering blood sugar in view of network pharmacology and molecular docking technology. Evid. Based Complement. Alternat. Med.,  2023, 2023, 1-11.
 [http://dx.doi.org/10.1155/2023/2827938] [PMID: 36846049]

[48]
Li, C.; Huang, J.; Cheng, Y.C.; Zhang, Y.W. Traditional chinese medicine in depression treatment: From molecules to systems. Front. Pharmacol.,  2020, 11, 586.
 [http://dx.doi.org/10.3389/fphar.2020.00586] [PMID: 32457610]

[49]
Youdim, K.A.; Dobbie, M.S.; Kuhnle, G.; Proteggente, A.R.; Abbott, N.J.; Rice-Evans, C. Interaction between flavonoids and the blood-brain barrier: in vitro studies. J. Neurochem.,  2003, 85(1), 180-192.
 [http://dx.doi.org/10.1046/j.1471-4159.2003.01652.x] [PMID: 12641740]

[50]
Magalingam, K.B.; Radhakrishnan, A.K.; Haleagrahara, N. Protective Mechanisms of Flavonoids in Parkinson’s Disease. Oxid. Med. Cell. Longev.,  2015, 2015, 1-14.
 [http://dx.doi.org/10.1155/2015/314560] [PMID: 26576219]

[51]
Chen, S.; Jiang, H.; Wu, X.; Fang, J. Therapeutic effects of quercetin on inflammation, obesity, and type 2 Diabetes. Mediators Inflamm.,  2016, 2016, 1-5.
 [http://dx.doi.org/10.1155/2016/9340637] [PMID: 28003714]

[52]
Khan, K.; Najmi, A.K.; Akhtar, M. A natural phenolic compound quercetin showed the usefulness by targeting inflammatory, oxidative stress markers and augment 5-ht levels in one of the animal models of depression in mice. Drug Res. (Stuttg.),  2019, 69(7), 392-400.
 [http://dx.doi.org/10.1055/a-0748-5518] [PMID: 30296804]

[53]
Pei, B.; Yang, M.; Qi, X.; Shen, X.; Chen, X.; Zhang, F. Quercetin ameliorates ischemia/reperfusion-induced cognitive deficits by inhibiting ASK1/JNK3/caspase-3 by enhancing the Akt signaling pathway. Biochem. Biophys. Res. Commun.,  2016, 478(1), 199-205.
 [http://dx.doi.org/10.1016/j.bbrc.2016.07.068] [PMID: 27450812]

[54]
Sawmiller, D.; Li, S.; Shahaduzzaman, M.; Smith, A.; Obregon, D.; Giunta, B.; Borlongan, C.; Sanberg, P.; Tan, J. Luteolin reduces Alzheimer’s disease pathologies induced by traumatic brain injury. Int. J. Mol. Sci.,  2014, 15(1), 895-904.
 [http://dx.doi.org/10.3390/ijms15010895] [PMID: 24413756]

[55]
Wang, H.; Wang, H.; Cheng, H.; Che, Z. Ameliorating effect of luteolin on memory impairment in an Alzheimer’s disease model. Mol. Med. Rep.,  2016, 13(5), 4215-4220.
 [http://dx.doi.org/10.3892/mmr.2016.5052] [PMID: 27035793]

[56]
Achour, M.; Ferdousi, F.; Sasaki, K.; Isoda, H. Luteolin modulates neural stem cells fate determination: In vitro study on human neural stem cells, and in vivo Study on LPS-induced depression mice model. Front. Cell Dev. Biol.,  2021, 97, 53279.
 [http://dx.doi.org/10.3389/fcell.2021.753279] [PMID: 34790666]

[57]
Silva dos Santos, J.; Gonçalves Cirino, J.P.; de Oliveira Carvalho, P.; Ortega, M.M. The pharmacological action of kaempferol in central nervous system diseases: A review. Front. Pharmacol.,  2021, 11, 565700.
 [http://dx.doi.org/10.3389/fphar.2020.565700] [PMID: 33519431]

[58]
Zanoli, P. Role of hyperforin in the pharmacological activities of St. John’s Wort. CNS Drug Rev.,  2004, 10(3), 203-218.
 [http://dx.doi.org/10.1111/j.1527-3458.2004.tb00022.x] [PMID: 15492771]

[59]
Zhang, Y.; Yu, P.; Liu, H.; Yao, H.; Yao, S.; Yuan, S.Y.; Zhang, J.C. Hyperforin improves post-stroke social isolation induced exaggeration of PSD and PSA via TGF-β. Int. J. Mol. Med.,  2019, 43(1), 413-425.
 [PMID: 30387813]

[60]
Meinke, M.C.; Schanzer, S.; Haag, S.F.; Casetti, F.; Müller, M.L.; Wölfle, U.; Kleemann, A.; Lademann, J.; Schempp, C.M. In vivo photoprotective and anti-inflammatory effect of hyperforin is associated with high antioxidant activity in vitro and ex vivo. Eur. J. Pharm. Biopharm.,  2012, 81(2), 346-350.
 [http://dx.doi.org/10.1016/j.ejpb.2012.03.002] [PMID: 22430217]

[61]
Filipović, D.; Zlatković, J.; Inta, D.; Bjelobaba, I.; Stojiljkovic, M.; Gass, P. Chronic isolation stress predisposes the frontal cortex but not the hippocampus to the potentially detrimental release of cytochrome c from mitochondria and the activation of caspase-3. J. Neurosci. Res.,  2011, 89(9), 1461-1470.
 [http://dx.doi.org/10.1002/jnr.22687] [PMID: 21656845]

[62]
Novelli, M.; Masiello, P.; Beffy, P.; Menegazzi, M. Protective role of St. John’s Wort and its components hyperforin and hypericin against diabetes through inhibition of inflammatory signaling: Evidence from in vitro and in vivo studies. Int. J. Mol. Sci.,  2020, 21(21), 8108.
 [http://dx.doi.org/10.3390/ijms21218108] [PMID: 33143088]

[63]
Yucel, A.; Yucel, N.; Ozkanlar, S.; Polat, E.; Kara, A.; Ozcan, H.; Gulec, M. Effect of agomelatine on adult hippocampus apoptosis and neurogenesis using the stress model of rats. Acta Histochem.,  2016, 118(3), 299-304.
 [http://dx.doi.org/10.1016/j.acthis.2016.02.007] [PMID: 26970810]

[64]
Breyer, R.M.; Bagdassarian, C.K.; Myers, S.A.; Breyer, M.D. Prostanoid receptors: subtypes and signaling. Annu. Rev. Pharmacol. Toxicol.,  2001, 41(1), 661-690.
 [http://dx.doi.org/10.1146/annurev.pharmtox.41.1.661] [PMID: 11264472]

[65]
Shi, J.; Johansson, J.; Woodling, N.S.; Wang, Q.; Montine, T.J.; Andreasson, K. The prostaglandin E2 E-prostanoid 4 receptor exerts anti-inflammatory effects in brain innate immunity. J. Immunol.,  2010, 184(12), 7207-7218.
 [http://dx.doi.org/10.4049/jimmunol.0903487] [PMID: 20483760]

[66]
Minghetti, L. Role of COX-2 in inflammatory and degenerative brain diseases. Subcell. Biochem.,  2007, 42, 127-141.
 [http://dx.doi.org/10.1007/1-4020-5688-5_5] [PMID: 17612048]

[67]
Bialek, K.; Czarny, P.; Wigner, P.; Synowiec, E.; Barszczewska, G.; Bijak, M.; Szemraj, J.; Niemczyk, M.; Tota-Glowczyk, K.; Papp, M.; Sliwinski, T. Chronic mild stress and venlafaxine treatment were associated with altered expression level and methylation status of new candidate inflammatory genes in pbmcs and brain structures of wistar rats. Genes (Basel),  2021, 12(5), 667.
 [http://dx.doi.org/10.3390/genes12050667] [PMID: 33946816]

[68]
Cassano, P.; Hidalgo, A.; Burgos, V.; Adris, S.; Argibay, P. Hippocampal upregulation of the cyclooxygenase-2 gene following neonatal clomipramine treatment (a model of depression). Pharmacogenomics J.,  2006, 6(6), 381-387.
 [http://dx.doi.org/10.1038/sj.tpj.6500385] [PMID: 16568149]

[69]
Leonard, B.; Maes, M. Mechanistic explanations how cell-mediated immune activation, inflammation and oxidative and nitrosative stress pathways and their sequels and concomitants play a role in the pathophysiology of unipolar depression. Neurosci. Biobehav. Rev.,  2012, 36(2), 764-785.
 [http://dx.doi.org/10.1016/j.neubiorev.2011.12.005] [PMID: 22197082]

[70]
PerskidskiĭIu, V.; Barshteĭn Iu, A. Biological manifestations of the tumor necrosis factor effect and its role in the pathogenesis of various diseases. Arkh. Patol.,  1992, 54, 5-10.
 [PMID: 1524503]

[71]
Cao, L.; Jiao, X.; Zuzga, D.S.; Liu, Y.; Fong, D.M.; Young, D.; During, M.J. VEGF links hippocampal activity with neurogenesis, learning and memory. Nat. Genet.,  2004, 36(8), 827-835.
 [http://dx.doi.org/10.1038/ng1395] [PMID: 15258583]

[72]
Nowacka, M.M.; Obuchowicz, E. Vascular endothelial growth factor (VEGF) and its role in the central nervous system: A new element in the neurotrophic hypothesis of antidepressant drug action. Neuropeptides,  2012, 46(1), 1-10.
 [http://dx.doi.org/10.1016/j.npep.2011.05.005] [PMID: 21719103]

[73]
Yang, C.; Sun, N.; Ren, Y.; Sun, Y.; Xu, Y.; Li, A.; Wu, K.; Zhang, K. Association between AKT1 gene polymorphisms and depressive symptoms in the Chinese Han population with major depressive disorder. Neural Regen. Res.,  2012, 7(3), 235-239.
 [PMID: 25767506]

[74]
Yi, H.; Zhang, Y.; Yang, X.; Li, M.; Hu, H.; Xiong, J.; Wang, N.; Jin, J.; Zhang, Y.; Song, Y.; Wang, X.; Chen, L.; Lian, J. Hepatitis B core antigen impairs the polarization while promoting the production of inflammatory cytokines of M2 macrophages via the TLR2 pathway. Front. Immunol.,  2020, 11, 535.
 [http://dx.doi.org/10.3389/fimmu.2020.00535] [PMID: 32292408]

[75]
McCusker, R.H.; Strle, K.; Broussard, S.R.; Dantzer, R.; Bluthé, R.; Kelley, K.W. Crosstalk between insulin-like growth factors and proinflammatory cytokines; , 2007. Elsevier. 

[76]
O’Connor, J.C.; McCusker, R.H.; Strle, K.; Johnson, R.W.; Dantzer, R.; Kelley, K.W. Regulation of IGF-I function by proinflammatory cytokines: At the interface of immunology and endocrinology. Cell. Immunol.,  2008, 252(1-2), 91-110.
 [http://dx.doi.org/10.1016/j.cellimm.2007.09.010] [PMID: 18325486]

[77]
Borsello, T.; Clarke, P.G.H.; Hirt, L.; Vercelli, A.; Repici, M.; Schorderet, D.F.; Bogousslavsky, J.; Bonny, C. A peptide inhibitor of c-Jun N-terminal kinase protects against excitotoxicity and cerebral ischemia. Nat. Med.,  2003, 9(9), 1180-1186.
 [http://dx.doi.org/10.1038/nm911] [PMID: 12937412]

[78]
Medeiros, R.; Prediger, R.D.S.; Passos, G.F.; Pandolfo, P.; Duarte, F.S.; Franco, J.L.; Dafre, A.L.; Di Giunta, G.; Figueiredo, C.P.; Takahashi, R.N.; Campos, M.M.; Calixto, J.B. Connecting TNF-alpha signaling pathways to iNOS expression in a mouse model of Alzheimer’s disease: relevance for the behavioral and synaptic deficits induced by amyloid beta protein. J. Neurosci.,  2007, 27(20), 5394-5404.
 [http://dx.doi.org/10.1523/JNEUROSCI.5047-06.2007] [PMID: 17507561]

[79]
Shen, X.; Ma, L.; Dong, W.; Wu, Q.; Gao, Y.; Luo, C.; Zhang, M.; Chen, X.; Tao, L. Autophagy regulates intracerebral hemorrhage induced neural damage via apoptosis and NF-κB pathway. Neurochem. Int.,  2016, 96, 100-112.
 [http://dx.doi.org/10.1016/j.neuint.2016.03.004] [PMID: 26964766]

[80]
Song, X.; Qian, Y. The activation and regulation of IL-17 receptor mediated signaling. Cytokine,  2013, 62(2), 175-182.
 [http://dx.doi.org/10.1016/j.cyto.2013.03.014] [PMID: 23557798]

[81]
Song, X.; Qian, Y. IL-17 family cytokines mediated signaling in the pathogenesis of inflammatory diseases. Cell. Signal.,  2013, 25(12), 2335-2347.
 [http://dx.doi.org/10.1016/j.cellsig.2013.07.021] [PMID: 23917206]

[82]
Tanoue, T.; Nishida, E. Docking interactions in the mitogen-activated protein kinase cascades. Pharmacol. Ther.,  2002, 93(2-3), 193-202.
 [http://dx.doi.org/10.1016/S0163-7258(02)00188-2] [PMID: 12191611]

[83]
Wefers, B.; Hitz, C.; Hölter, S.M.; Trümbach, D.; Hansen, J.; Weber, P.; Pütz, B.; Deussing, J.M.; de Angelis, M.H.; Roenneberg, T.; Zheng, F.; Alzheimer, C.; Silva, A.; Wurst, W.; Kühn, R. MAPK signaling determines anxiety in the juvenile mouse brain but depression-like behavior in adults. PLoS One,  2012, 7(4), e35035.
 [http://dx.doi.org/10.1371/journal.pone.0035035] [PMID: 22529971]

[84]
Falcicchia, C.; Tozzi, F.; Arancio, O.; Watterson, D.M.; Origlia, N. Involvement of p38 MAPK in Synaptic Function and Dysfunction. Int. J. Mol. Sci.,  2020, 21(16), 5624.
 [http://dx.doi.org/10.3390/ijms21165624] [PMID: 32781522]

[85]
Duman, C.H.; Schlesinger, L.; Kodama, M.; Russell, D.S.; Duman, R.S. A role for MAP kinase signaling in behavioral models of depression and antidepressant treatment. Biol. Psychiatry,  2007, 61(5), 661-670.
 [http://dx.doi.org/10.1016/j.biopsych.2006.05.047] [PMID: 16945347]

[86]
Kopnisky, K.L.; Chalecka-Franaszek, E.; Gonzalez-Zulueta, M.; Chuang, D.M. Chronic lithium treatment antagonizes glutamate-induced decrease of phosphorylated CREB in neurons via reducing protein phosphatase 1 and increasing MEK activities. Neuroscience,  2003, 116(2), 425-435.
 [http://dx.doi.org/10.1016/S0306-4522(02)00573-0] [PMID: 12559097]

[87]
Einat, H.; Yuan, P.; Gould, T.D.; Li, J.; Du, J.; Zhang, L.; Manji, H.K.; Chen, G. The role of the extracellular signal-regulated kinase signaling pathway in mood modulation. J. Neurosci.,  2003, 23(19), 7311-7316.
 [http://dx.doi.org/10.1523/JNEUROSCI.23-19-07311.2003] [PMID: 12917364]

[88]
Montanari, F.; Ecker, G.F. Prediction of drug–ABC-transporter interaction - Recent advances and future challenges. Adv. Drug Deliv. Rev.,  2015, 86, 17-26.
 [http://dx.doi.org/10.1016/j.addr.2015.03.001] [PMID: 25769815]

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DOI https://dx.doi.org/10.2174/1573409919666230619105254	Print ISSN 1573-4099
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