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Combinatorial Chemistry & High Throughput Screening

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ISSN (Print): 1386-2073
ISSN (Online): 1875-5402

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Research Article

Network Pharmacology Research Indicates that Wu-Mei-Wan Treats Obesity by Inhibiting Th17 Cell Differentiation and Alleviating Metabolic Inflammation

Author(s): Zhe Cheng, Xinyu Xiong, Fan Wu, Yan Zhao, Ruolan Dong, Shujun Jiang, Ke Fang, Panpan Huang* and Guang Chen*

Volume 26, Issue 1, 2023

Published on: 17 May, 2022

Page: [30 - 48] Pages: 19

DOI: 10.2174/1386207325666220221121919

Price: $65

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Abstract

Background: Wu-Mei-Wan (WMW), a traditional Chinese medicine (TCM) formula, has a good effect on the treatment of obesity and has been proven helpful to promote the metabolism of adipose tissue. However, its underlying mechanism remains to be studied. This study aims to explore the potential pharmacological mechanism of WMW in the treatment of obesity.

Methods: Network pharmacology was used to sort out the relationship between WMW putative targets and obesity-related drug targets or disease targets, which indicated the mechanism of WMW in treating obesity from two aspects of clinical drugs approved by the Food and Drug Administration (FDA) and obesity-related diseases. Databases such as Traditional Chinese Medicine Systems Pharmacology (TCMSP), PubChem, DrugBank, DisGeNET, and Genecards were used to collect information about targets. String platform was used to convert the data into gene symbol of “homo sapiens”, and perform gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses. With the Human Protein Reference Database (HPRD) as background data, Cytoscape 3.6.0 software was used to construct a new protein-protein interaction (PPI) network. Mechanism diagrams of key pathways were obtained from the KEGG database. AutoDock Vina software was used to conduct molecular docking verification.

Results: The number of targets in the overlap between WMW putative targets and obesity-related drug targets accounted for more than 50% of the latter, and HTR3A, SLC6A4, and CYP3A4 were core targets. In obesity-related disease targets-WMW putative targets PPI network, the Th17 cell differentiation pathway, and the IL-17 signaling pathway were key pathways, and the 1st module and the 7th module were central function modules that were highly associated with immunity and inflammation. Molecular docking verified that STAT3, TGFB1, MMP9, AHR, IL1B, and CCL2 were core targets in the treatment of WMW on obesity.

Conclusion: WMW has similar effects on lipid and drug metabolism as the current obesity-related drugs, and is likely to treat obesity by inhibiting Th17 cell differentiation and alleviating metabolic inflammation.

Keywords: Wu-Mei-Wan; Obesity; Th17 cell; IL-17; Network pharmacology, Obesity.

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[1]
Sung, H.; Siegel, R.L.; Torre, L.A.; Pearson-Stuttard, J.; Is-lami, F.; Fedewa, S.A.; Goding Sauer, A.; Shuval, K.; Gapstur, S.M.; Jacobs, E.J.; Giovannucci, E.L.; Jemal, A. Global pat-terns in excess body weight and the associated cancer burden. CA Cancer J. Clin., 2019, 69(2), 88-112.
[PMID: 30548482]
[2]
Wu, F.; Yang, X.; Hu, M.; Shao, Q.; Fang, K.; Li, J.; Zhao, Y.; Xu, L.; Zou, X.; Lu, F.; Chen, G. Wu-Mei-Wan prevents high-fat diet-induced obesity by reducing white adipose tissue and enhancing brown adipose tissue function. Phytomedicine, 2020, 76, 153258.
[http://dx.doi.org/10.1016/j.phymed.2020.153258] [PMID: 32563018]
[3]
Yang, X.; Lu, F.; Li, L.; Li, J.; Luo, J.; Zhang, S.; Liu, X.; Chen, G. Wu-Mei-wan protects pancreatic β cells by inhibit-ing NLRP3 Inflammasome activation in diabetic mice. BMC Complement. Altern. Med., 2019, 19(1), 35.
[http://dx.doi.org/10.1186/s12906-019-2443-6] [PMID: 30704457]
[4]
Yang, X.; Li, L.; Fang, K.; Dong, R.; Li, J.; Zhao, Y.; Dong, H.; Yi, P.; Huang, Z.; Chen, G.; Lu, F. Wu-Mei-Wan reduces insulin resistance via inhibition of NLRP3 inflammasome ac-tivation in HepG2 cells. Evid. Based Complement. Alternat. Med., 2017, 2017, 7283241.
[http://dx.doi.org/10.1155/2017/7283241] [PMID: 28928791]
[5]
Hopkins, A.L. Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol., 2008, 4(11), 682-690.
[http://dx.doi.org/10.1038/nchembio.118] [PMID: 18936753]
[6]
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.V. STRING v11: pro-tein-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]
[7]
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 dis-covery from herbal medicines. J. Cheminform., 2014, 6, 13.
[http://dx.doi.org/10.1186/1758-2946-6-13] [PMID: 24735618]
[8]
Kim, S.; Thiessen, P.A.; Bolton, E.E.; Chen, J.; Fu, G.; Gin-dulyte, A.; Han, L.; He, J.; He, S.; Shoemaker, B.A.; Wang, J.; Yu, B.; Zhang, J.; Bryant, S.H. PubChem substance and com-pound databases. Nucleic Acids Res., 2016, 44(D1), D1202-D1213.
[http://dx.doi.org/10.1093/nar/gkv951] [PMID: 26400175]
[9]
Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res., 2018, 46(D1), D1074-D1082.
[http://dx.doi.org/10.1093/nar/gkx1037] [PMID: 29126136]
[10]
Piñero, J.; Ramírez-Anguita, J.M.; Saüch-Pitarch, J.; Ronzano, F.; Centeno, E.; Sanz, F.; Furlong, L.I. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res., 2020, 48(D1), D845-D855.
[PMID: 31680165]
[11]
Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y. The genecards suite: From gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinformatics, 2016, 54, 30.
[http://dx.doi.org/10.1002/cpbi.5]
[12]
Keshava Prasad, T.S.; Goel, R.; Kandasamy, K.; Keerthiku-mar, S.; Kumar, S.; Mathivanan, S.; Telikicherla, D.; Raju, R.; Shafreen, B.; Venugopal, A.; Balakrishnan, L.; Marimuthu, A.; Banerjee, S.; Somanathan, D.S.; Sebastian, A.; Rani, S.; Ray, S.; Harrys Kishore, C.J.; Kanth, S.; Ahmed, M.; Kashyap, M.K.; Mohmood, R.; Ramachandra, Y.L.; Krishna, V.; Rahiman, B.A.; Mohan, S.; Ranganathan, P.; Ramabadran, S.; Chaerkady, R.; Pandey, A. Human protein reference database-2009 update. Nucleic Acids Res., 2009, 37(Database issue), D767-D772.
[http://dx.doi.org/10.1093/nar/gkn892] [PMID: 18988627]
[13]
Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cyto-scape: a software environment for integrated models of bio-molecular interaction networks. Genome Res., 2003, 13(11), 2498-2504.
[http://dx.doi.org/10.1101/gr.1239303] [PMID: 14597658]
[14]
Kanehisa, M.; Sato, Y. KEGG Mapper for inferring cellular functions from protein sequences. Protein Sci., 2020, 29(1), 28-35.
[http://dx.doi.org/10.1002/pro.3711] [PMID: 31423653]
[15]
Burley, S.K.; Berman, H.M.; Bhikadiya, C.; Bi, C.; Chen, L.; Di Costanzo, L.; Christie, C.; Dalenberg, K.; Duarte, J.M.; Dutta, S.; Feng, Z.; Ghosh, S.; Goodsell, D.S.; Green, R.K.; Guranovic, V.; Guzenko, D.; Hudson, B.P.; Kalro, T.; Liang, Y.; Lowe, R.; Namkoong, H.; Peisach, E.; Periskova, I.; Prlic, A.; Randle, C.; Rose, A.; Rose, P.; Sala, R.; Sekharan, M.; Shao, C.; Tan, L.; Tao, Y.P.; Valasatava, Y.; Voigt, M.; West-brook, J.; Woo, J.; Yang, H.; Young, J.; Zhuravleva, M.; Zar-decki, C. RCSB protein data bank: biological macromolecular structures enabling research and education in fundamental bi-ology, biomedicine, biotechnology and energy. Nucleic Acids Res., 2019, 47(D1), D464-D474.
[http://dx.doi.org/10.1093/nar/gky1004] [PMID: 30357411]
[16]
Trott, O.; Olson, A.J. AutoDock Vina: improving the speed and accuracy of docking with a new scoring function, effi-cient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461.
[PMID: 19499576]
[17]
Hu, M.; Wu, F.; Luo, J.; Gong, J.; Fang, K.; Yang, X.; Li, J.; Chen, G.; Lu, F. The role of berberine in the prevention of HIF-1α activation to alleviate adipose tissue fibrosis in high-fat-diet-induced obese mice. Evid. Based Complement. Alternat. Med., 2018, 2018, 4395137.
[http://dx.doi.org/10.1155/2018/4395137] [PMID: 30622603]
[18]
Barabási, A.L.; Oltvai, Z.N. Network biology: Understanding the cell’s functional organization. Nat. Rev. Genet., 2004, 5(2), 101-113.
[http://dx.doi.org/10.1038/nrg1272] [PMID: 14735121]
[19]
Bai, L.; Zhou, H.; Xu, R.; Zhao, Y.; Chinnaswamy, K.; McEachern, D.; Chen, J.; Yang, C.Y.; Liu, Z.; Wang, M.; Liu, L.; Jiang, H.; Wen, B.; Kumar, P.; Meagher, J.L.; Sun, D.; Stuckey, J.A.; Wang, S. A potent and selective small-molecule degrader of STAT3 achieves complete tumor regression in vi-vo. Cancer Cell, 2019, 36(5), 498-511.e17.
[http://dx.doi.org/10.1016/j.ccell.2019.10.002] [PMID: 31715132]
[20]
Radaev, S.; Zou, Z.; Huang, T.; Lafer, E.M.; Hinck, A.P.; Sun, P.D. Ternary complex of transforming growth factor-beta1 reveals isoform-specific ligand recognition and receptor re-cruitment in the superfamily. J. Biol. Chem., 2010, 285(19), 14806-14814.
[http://dx.doi.org/10.1074/jbc.M109.079921] [PMID: 20207738]
[21]
Nuti, E.; Cuffaro, D.; Bernardini, E.; Camodeca, C.; Panelli, L.; Chaves, S.; Ciccone, L.; Tepshi, L.; Vera, L.; Orlandini, E.; Nencetti, S.; Stura, E.A.; Santos, M.A.; Dive, V.; Rossello, A. Development of thioaryl-based matrix metalloproteinase-12 inhibitors with alternative zinc-binding groups: synthesis, potentiometric, NMR, and crystallographic studies. J. Med. Chem., 2018, 61(10), 4421-4435.
[http://dx.doi.org/10.1021/acs.jmedchem.8b00096] [PMID: 29727184]
[22]
Schulte, K.W.; Green, E.; Wilz, A.; Platten, M.; Daumke, O. Structural basis for aryl hydrocarbon receptor-mediated gene activation. Structure, 2017, 25(7), 1025-1033.e3.
[http://dx.doi.org/10.1016/j.str.2017.05.008] [PMID: 28602820]
[23]
Hou, J.; Townson, S.A.; Kovalchin, J.T.; Masci, A.; Kiner, O.; Shu, Y.; King, B.M.; Schirmer, E.; Golden, K.; Thomas, C.; Garcia, K.C.; Zarbis-Papastoitsis, G.; Furfine, E.S.; Barnes, T.M. Design of a superior cytokine antagonist for topical ophthalmic use. Proc. Natl. Acad. Sci. USA, 2013, 110(10), 3913-3918.
[http://dx.doi.org/10.1073/pnas.1217996110] [PMID: 23431173]
[24]
Grygiel, T.L.; Teplyakov, A.; Obmolova, G.; Stowell, N.; Holland, R.; Nemeth, J.F.; Pomerantz, S.C.; Kruszynski, M.; Gilliland, G.L. Synthesis by native chemical ligation and crys-tal structure of human CCL2. Biopolymers, 2010, 94(3), 350-359.
[http://dx.doi.org/10.1002/bip.21390] [PMID: 20091676]
[25]
Hotamisligil, G.S. Inflammation, metaflammation and im-munometabolic disorders. Nature, 2017, 542(7640), 177-185.
[http://dx.doi.org/10.1038/nature21363] [PMID: 28179656]
[26]
McLaughlin, T.; Liu, L.F.; Lamendola, C.; Shen, L.; Morton, J.; Rivas, H.; Winer, D.; Tolentino, L.; Choi, O.; Zhang, H.; Hui Yen Chng, M.; Engleman, E. T-cell profile in adipose tis-sue is associated with insulin resistance and systemic in-flammation in humans. Arterioscler. Thromb. Vasc. Biol., 2014, 34(12), 2637-2643.
[http://dx.doi.org/10.1161/ATVBAHA.114.304636] [PMID: 25341798]
[27]
Lee, J.Y.; Hall, J.A.; Kroehling, L.; Wu, L.; Najar, T.; Nguyen, H.H.; Lin, W.Y.; Yeung, S.T.; Silva, H.M.; Li, D.; Hine, A.; Loke, P.; Hudesman, D.; Martin, J.C.; Kenigsberg, E.; Merad, M.; Khanna, K.M.; Littman, D.R. Serum amyloid a proteins induce pathogenic Th17 cells and promote inflammatory dis-ease. Cell, 2020, 180(1), 79-91.e16.
[http://dx.doi.org/10.1016/j.cell.2019.11.026] [PMID: 31866067]
[28]
Feuerer, M.; Herrero, L.; Cipolletta, D.; Naaz, A.; Wong, J.; Nayer, A.; Lee, J.; Goldfine, A.B.; Benoist, C.; Shoelson, S.; Mathis, D. Lean, but not obese, fat is enriched for a unique population of regulatory T cells that affect metabolic parame-ters. Nat. Med., 2009, 15(8), 930-939.
[http://dx.doi.org/10.1038/nm.2002] [PMID: 19633656]
[29]
Han, J.M.; Wu, D.; Denroche, H.C.; Yao, Y.; Verchere, C.B.; Levings, M.K. IL-33 reverses an obesity-induced deficit in visceral adipose tissue ST2+ T regulatory cells and amelio-rates adipose tissue inflammation and insulin resistance. J. Immunol., 2015, 194(10), 4777-4783.
[http://dx.doi.org/10.4049/jimmunol.1500020] [PMID: 25870243]
[30]
Ahmed, M.; Gaffen, S.L. IL-17 in obesity and adipogenesis. Cytokine Growth Factor Rev., 2010, 21(6), 449-453.
[http://dx.doi.org/10.1016/j.cytogfr.2010.10.005] [PMID: 21084215]
[31]
Csóka, B.; Pacher, P.; Bai, P.; Haskó, G. New piece in the jigsaw puzzle: Adipose tissue-derived stem cells from obese subjects drive Th17 polarization. Diabetes, 2015, 64(7), 2341-2343.
[http://dx.doi.org/10.2337/db15-0437] [PMID: 26106197]
[32]
Foucher, E.D.; Blanchard, S.; Preisser, L.; Descamps, P.; Ifrah, N.; Delneste, Y.; Jeannin, P. IL-34- and M-CSF-induced macrophages switch memory T cells into Th17 cells via membrane IL-1α. Eur. J. Immunol., 2015, 45(4), 1092-1102.
[http://dx.doi.org/10.1002/eji.201444606] [PMID: 25545357]
[33]
Chang, H.; Zhao, F.; Xie, X.; Liao, Y.; Song, Y.; Liu, C.; Wu, Y.; Wang, Y.; Liu, D.; Wang, Y.; Zou, J.; Qi, Z. PPARα sup-presses Th17 cell differentiation through IL-6/STAT3/RORγt pathway in experimental autoimmune myocarditis. Exp. Cell Res., 2019, 375(1), 22-30.
[http://dx.doi.org/10.1016/j.yexcr.2018.12.005] [PMID: 30557558]
[34]
Huber, S.; Stahl, F.R.; Schrader, J.; Lüth, S.; Presser, K.; Ca-rambia, A.; Flavell, R.A.; Werner, S.; Blessing, M.; Herkel, J.; Schramm, C. Activin a promotes the TGF-beta-induced con-version of CD4+CD25- T cells into Foxp3+ induced regulato-ry T cells. J. Immunol., 2009, 182(8), 4633-4640.
[http://dx.doi.org/10.4049/jimmunol.0803143] [PMID: 19342638]
[35]
Zhang, S. The role of transforming growth factor β in T help-er 17 differentiation. Immunology, 2018, 155(1), 24-35.
[http://dx.doi.org/10.1111/imm.12938] [PMID: 29682722]
[36]
Zhao, R.X.; He, Q.; Sha, S.; Song, J.; Qin, J.; Liu, P.; Sun, Y.J.; Sun, L.; Hou, X.G.; Chen, L. Increased AHR transcripts correlate with pro-inflammatory T-helper lymphocytes polar-ization in both metabolically healthy obesity and type 2 dia-betic patients. Front. Immunol., 2020, 11, 1644.
[http://dx.doi.org/10.3389/fimmu.2020.01644] [PMID: 32849564]

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