Current Computer-Aided Drug DesignCurrent Computer-Aided Drug DesignCurrent Computer-Aided Drug Design1573-40991875-6697Bentham Science64411410.2174/1573409919666230619105254ChemistryResearch ArticleExploring the Potential Molecular Mechanism of the Shugan Jieyu Capsule in the Treatment of Depression through Network Pharmacology, Molecular Docking, and Molecular Dynamics SimulationLiuZhiyaoinfo@benthamscience.netHuangHailianginfo@benthamscience.netYuYinginfo@benthamscience.netJiaYuqiinfo@benthamscience.netLiLinglinginfo@benthamscience.netShiXininfo@benthamscience.netWangFangqiinfo@benthamscience.netDepartment of Rehabilitation Medicine, Shandong University of Traditional Chinese MedicineInnovative Institute of Chinese Medicine and Pharmacy, Shandong University of Traditional Chinese MedicineInnovative Institute of Chinese Medicine and Pharmacy,, Shandong University of Traditional Chinese Medicine0105202420550151707012025Copyright ©; 2024, Bentham Science Publishers2024Bentham Science Publishershttps://rjeid.com/1573-4099/article/view/644114

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.

Objective: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.

Depressionshugan jieyu capsulenetwork pharmacologymolecular dockingmolecular dynamics simulationpharmacokinetic.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. doi: 10.1001/jama.289.23.3095 PMID: 12813115Depression and other common mental disorders: global health estimates; World Health Organization: Geneva, 2017, p. 24.Kupfer, D.J.; Frank, E.; Phillips, M.L. Major depressive disorder: new clinical, neurobiological, and treatment perspectives. Lancet, 2012, 379(9820), 1045-1055. doi: 10.1016/S0140-6736(11)60602-8 PMID: 22189047Uchida, S.; Yamagata, H.; Seki, T.; Watanabe, Y. Epigenetic mechanisms of major depression: Targeting neuronal plasticity. Psychiatry Clin. Neurosci., 2018, 72(4), 212-227. doi: 10.1111/pcn.12621 PMID: 29154458Hepgul, 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. doi: 10.1186/1741-7015-11-28 PMID: 23384232Hackett, M.L.; Anderson, C.S.; House, A.; Xia, J. Interventions for treating depression after stroke. Cochrane Database Syst. Rev., 2008, (4), CD003437. PMID: 18843644Mannheimer, 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. doi: 10.1177/02698811211001082 PMID: 33860708Alzoubi, 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. doi: 10.2147/DDDT.S278153 PMID: 33299301Wang, 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. doi: 10.3748/wjg.v11.i9.1373 PMID: 15761979Ng, 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. doi: 10.1016/j.jad.2016.12.048 PMID: 28064110Sun, 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.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. doi: 10.1016/j.jep.2022.115367 PMID: 35562090Sun, 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.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.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. doi: 10.1002/prca.201100077 PMID: 22213655Zhang, W. Network pharmacology: A further description. Net.Pharmacol., 2016, 1(1), 1-14.Li, S.; Zhang, B. Traditional Chinese medicine network pharmacology: theory, methodology and application. Chin. J. Nat. Med., 2013, 11(2), 110-120. doi: 10.1016/S1875-5364(13)60037-0 PMID: 23787177Wu, 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: 28868850Zhang, R.; Zhu, X.; Bai, H.; Ning, K. Network pharmacology databases for traditional chinese medicine: Review and assessment. Front. Pharmacol., 2019, 10, 123. doi: 10.3389/fphar.2019.00123 PMID: 30846939Hao, D.C.; Xiao, P.G. Network pharmacology: a Rosetta Stone for traditional Chinese medicine. Drug Dev. Res., 2014, 75(5), 299-312. doi: 10.1002/ddr.21214 PMID: 25160070Kitchen, 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. doi: 10.1038/nrd1549 PMID: 15520816Buch, 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. doi: 10.1073/pnas.1103547108 PMID: 21646537Ru, 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. doi: 10.1186/1758-2946-6-13 PMID: 24735618Liu, 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. doi: 10.1038/srep21146 PMID: 26879404Fang, 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. doi: 10.1093/nar/gkaa1063 PMID: 33264402Szklarczyk, 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. doi: 10.1093/nar/gkv1277 PMID: 26590256UniProt: a worldwide hub of protein knowledge. Nucleic Acids Res., 2019, 47(D1), D506-D515. doi: 10.1093/nar/gky1049 PMID: 30395287Shannon, 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. doi: 10.1101/gr.1239303 PMID: 14597658Safran, 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. doi: 10.1093/nar/gkg050 PMID: 12519968Piñero, 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. doi: 10.1093/nar/gkw943 PMID: 27924018Iwamoto, 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. doi: 10.1038/sj.mp.4001437 PMID: 14743183Oliveros, J.C. Venny. 2007. Available from: http://bioinfogp.cnb.csic.es/tools/venny/index.htmlSzklarczyk, 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. doi: 10.1093/nar/gky1131 PMID: 30476243Zhou, 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. doi: 10.1038/s41467-019-09234-6 PMID: 30944313Huey, 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. doi: 10.1002/jcc.20634 PMID: 17274016Goodsell, D.S.; Morris, G.M.; Olson, A.J. Automated docking of flexible ligands: Applications of autodock. J. Mol. Recognit., 1996, 9(1), 1-5. doi: 10.1002/(SICI)1099-1352(199601)9:13.0.CO;2-6 PMID: 8723313Morris, 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. doi: 10.1007/BF00124499 PMID: 8877701Zhou, 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. doi: 10.1155/2022/8255550 PMID: 35936218Shukla, 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. doi: 10.3389/fmolb.2022.1078987 PMID: 36741918Kim, 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. doi: 10.1093/nar/gky1033 PMID: 30371825Berman, 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. doi: 10.1093/nar/28.1.235 PMID: 10592235Morris, 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. doi: 10.1002/jcc.21256 PMID: 19399780Daina, 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. doi: 10.1038/srep42717 PMID: 28256516Pires, 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. doi: 10.1021/acs.jmedchem.5b00104 PMID: 25860834Szewczyk, 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. doi: 10.1016/j.bbr.2019.112045 PMID: 31220487Mennini, T.; Gobbi, M. The antidepressant mechanism of Hypericum perforatum. Life Sci., 2004, 75(9), 1021-1027. doi: 10.1016/j.lfs.2004.04.005 PMID: 15207650Li, 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. doi: 10.1155/2023/2827938 PMID: 36846049Li, C.; Huang, J.; Cheng, Y.C.; Zhang, Y.W. Traditional chinese medicine in depression treatment: From molecules to systems. Front. Pharmacol., 2020, 11, 586. doi: 10.3389/fphar.2020.00586 PMID: 32457610Youdim, 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. doi: 10.1046/j.1471-4159.2003.01652.x PMID: 12641740Magalingam, K.B.; Radhakrishnan, A.K.; Haleagrahara, N. Protective Mechanisms of Flavonoids in Parkinson’s Disease. Oxid. Med. Cell. Longev., 2015, 2015, 1-14. doi: 10.1155/2015/314560 PMID: 26576219Chen, S.; Jiang, H.; Wu, X.; Fang, J. Therapeutic effects of quercetin on inflammation, obesity, and type 2 Diabetes. Mediators Inflamm., 2016, 2016, 1-5. doi: 10.1155/2016/9340637 PMID: 28003714Khan, 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. doi: 10.1055/a-0748-5518 PMID: 30296804Pei, 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. doi: 10.1016/j.bbrc.2016.07.068 PMID: 27450812Sawmiller, 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. doi: 10.3390/ijms15010895 PMID: 24413756Wang, 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. doi: 10.3892/mmr.2016.5052 PMID: 27035793Achour, 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. doi: 10.3389/fcell.2021.753279 PMID: 34790666Silva 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. doi: 10.3389/fphar.2020.565700 PMID: 33519431Zanoli, P. Role of hyperforin in the pharmacological activities of St. John’s Wort. CNS Drug Rev., 2004, 10(3), 203-218. doi: 10.1111/j.1527-3458.2004.tb00022.x PMID: 15492771Zhang, 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: 30387813Meinke, 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. doi: 10.1016/j.ejpb.2012.03.002 PMID: 22430217Filipović, 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. doi: 10.1002/jnr.22687 PMID: 21656845Novelli, 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. doi: 10.3390/ijms21218108 PMID: 33143088Yucel, 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. doi: 10.1016/j.acthis.2016.02.007 PMID: 26970810Breyer, 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. doi: 10.1146/annurev.pharmtox.41.1.661 PMID: 11264472Shi, 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. doi: 10.4049/jimmunol.0903487 PMID: 20483760Minghetti, L. Role of COX-2 in inflammatory and degenerative brain diseases. Subcell. Biochem., 2007, 42, 127-141. doi: 10.1007/1-4020-5688-5_5 PMID: 17612048Bialek, 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. doi: 10.3390/genes12050667 PMID: 33946816Cassano, 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. doi: 10.1038/sj.tpj.6500385 PMID: 16568149Leonard, 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. doi: 10.1016/j.neubiorev.2011.12.005 PMID: 22197082PerskidskiĭIu, 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: 1524503Cao, 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. doi: 10.1038/ng1395 PMID: 15258583Nowacka, 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. doi: 10.1016/j.npep.2011.05.005 PMID: 21719103Yang, 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: 25767506Yi, 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. doi: 10.3389/fimmu.2020.00535 PMID: 32292408McCusker, 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.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. doi: 10.1016/j.cellimm.2007.09.010 PMID: 18325486Borsello, 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. doi: 10.1038/nm911 PMID: 12937412Medeiros, 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. doi: 10.1523/JNEUROSCI.5047-06.2007 PMID: 17507561Shen, 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. doi: 10.1016/j.neuint.2016.03.004 PMID: 26964766Song, X.; Qian, Y. The activation and regulation of IL-17 receptor mediated signaling. Cytokine, 2013, 62(2), 175-182. doi: 10.1016/j.cyto.2013.03.014 PMID: 23557798Song, X.; Qian, Y. IL-17 family cytokines mediated signaling in the pathogenesis of inflammatory diseases. Cell. Signal., 2013, 25(12), 2335-2347. doi: 10.1016/j.cellsig.2013.07.021 PMID: 23917206Tanoue, T.; Nishida, E. Docking interactions in the mitogen-activated protein kinase cascades. Pharmacol. Ther., 2002, 93(2-3), 193-202. doi: 10.1016/S0163-7258(02)00188-2 PMID: 12191611Wefers, 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. doi: 10.1371/journal.pone.0035035 PMID: 22529971Falcicchia, 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. doi: 10.3390/ijms21165624 PMID: 32781522Duman, 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. doi: 10.1016/j.biopsych.2006.05.047 PMID: 16945347Kopnisky, 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. doi: 10.1016/S0306-4522(02)00573-0 PMID: 12559097Einat, 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. doi: 10.1523/JNEUROSCI.23-19-07311.2003 PMID: 12917364Montanari, F.; Ecker, G.F. Prediction of drug–ABC-transporter interaction - Recent advances and future challenges. Adv. Drug Deliv. Rev., 2015, 86, 17-26. doi: 10.1016/j.addr.2015.03.001 PMID: 25769815