Generic placeholder image

Combinatorial Chemistry & High Throughput Screening

Editor-in-Chief

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

Research Article

Systematic Evaluation of the Mechanisms of Mulberry Leaf (Morus alba Linne) Acting on Diabetes Based on Network Pharmacology and Molecular Docking

Author(s): Qiguo Wu* and Yeqing Hu

Volume 24, Issue 5, 2021

Published on: 14 September, 2020

Page: [668 - 682] Pages: 15

DOI: 10.2174/1386207323666200914103719

Price: $65

Abstract

Background: Diabetes mellitus is one of the most common endocrine metabolic disorder- related diseases. The application of herbal medicine to control glucose levels and improve insulin action might be a useful approach in the treatment of diabetes. Mulberry leaves (ML) have been reported to exert important activities of anti-diabetic.

Objective: In this work, we aimed to explore the multi-targets and multi-pathways regulatory molecular mechanism of Mulberry leaves (ML, Morus alba Linne) acting on diabetes.

Methods: Identification of active compounds of Mulberry leaves using Traditional Chinese Medicine Systems Pharmacology (TCMSP) database was carried out. Bioactive components were screened by FAF-Drugs4 website (Free ADME-Tox Filtering Tool). The targets of bioactive components were predicted from SwissTargetPrediction website, and the diabetes related targets were screened from GeneCards database. The common targets of ML and diabetes were used for Gene Ontology (GO) and pathway enrichment analysis. The visualization networks were constructed by Cytoscape 3.7.1 software. The biological networks were constructed to analyze the mechanisms as follows: (1) compound-target network; (2) common target-compound network; (3) common targets protein interaction network; (4) compound-diabetes protein-protein interactions (ppi) network; (5) target-pathway network; and (6) compound-target-pathway network. At last, the prediction results of network pharmacology were verified by molecular docking method.

Results: 17 active components were obtained by TCMSP database and FAF-Drugs4 website. 51 potential targets (11 common targets and 40 associated indirect targets) were obtained and used to build the PPI network by the String database. Furthermore, the potential targets were used for GO and pathway enrichment analysis. Eight key active compounds (quercetin, Iristectorigenin A, 4- Prenylresveratrol, Moracin H, Moracin C, Isoramanone, Moracin E and Moracin D) and 8 key targets (AKT1, IGF1R, EIF2AK3, PPARG, AGTR1, PPARA, PTPN1 and PIK3R1) were obtained to play major roles in Mulberry leaf acting on diabetes. And the signal pathways involved in the mechanisms mainly include AMPK signaling pathway, PI3K-Akt signaling pathway, mTOR signaling pathway, insulin signaling pathway and insulin resistance. The molecular docking results show that the 8 key active compounds have good affinity with the key target of AKT1, and the 5 key targets (IGF1R, EIF2AK3, PPARG, PPARA and PTPN1) have better affinity than AKT1 with the key compound of quercetin.

Conclusion: Based on network pharmacology and molecular docking, this study provided an important systematic and visualized basis for further understanding of the synergy mechanism of ML acting on diabetes.

Keywords: Mulberry leaf, diabetes, network pharmacology, active compounds, target, molecular docking.

[1]
Mandrup-Poulsen, T. Diabetes. BMJ, 1998, 316(7139), 1221-1225.
[http://dx.doi.org/10.1136/bmj.316.7139.1221] [PMID: 9553001]
[2]
Shi, J.; Hu, H.; Harnett, J.; Zheng, X.; Liang, Z.; Wang, Y.T.; Ung, C.O.L. An evaluation of randomized controlled trials on nutraceuticals containing traditional Chinese medicines for diabetes management: a systematic review. Chin. Med., 2019, 14, 54.
[http://dx.doi.org/10.1186/s13020-019-0276-3] [PMID: 31798675]
[3]
Joh, B.; Jeon, E.S.; Lim, S.H.; Park, Y.L.; Park, W.; Chae, H. Intercultural Usage of Mori Folium: Comparison Review from a Korean Medical Perspective. Evid. Based Complement. Alternat. Med., 2015, 2015379268
[http://dx.doi.org/10.1155/2015/379268] [PMID: 26539223]
[4]
Asano, N.; Yamashita, T.; Yasuda, K.; Ikeda, K.; Kizu, H.; Kameda, Y.; Kato, A.; Nash, R.J.; Lee, H.S.; Ryu, K.S. Polyhydroxylated alkaloids isolated from mulberry trees (Morusalba L.) and silkworms (Bombyx mori L.). J. Agric. Food Chem., 2001, 49(9), 4208-4213.
[http://dx.doi.org/10.1021/jf010567e] [PMID: 11559112]
[5]
Chan, E.W.; Lye, P.Y.; Wong, S.K. Phytochemistry, pharmacology, and clinical trials of Morus alba. Chin. J. Nat. Med., 2016, 14(1), 17-30.
[PMID: 26850343]
[6]
Daniel, M. Riche, Krista D Riche, Honey E East, Elizabeth K Barrett, Warren L May. Impact of Mulberry Leaf Extract on Type 2 Diabetes (Mul-DM): A Randomized, Placebo-Controlled Pilot Study. Complement. Ther. Med., 2017, 32, 105-108.
[http://dx.doi.org/10.1016/j.ctim.2017.04.006]
[7]
Józefczuk, J.; Malikowska, K.; Glapa, A.; Stawińska-Witoszyńska, B.; Nowak, J.K.; Bajerska, J.; Lisowska, A.; Walkowiak, J. Mulberry leaf extract decreases digestion and absorption of starch in healthy subjects-A randomized, placebo-controlled, crossover study. Adv. Med. Sci., 2017, 62(2), 302-306.
[http://dx.doi.org/10.1016/j.advms.2017.03.002] [PMID: 28501729]
[8]
Król, E.; Jeszka-Skowron, M.; Krejpcio, Z.; Flaczyk, E.; Wójciak, R.W. The Effects of Supplementary Mulberry Leaf (Morus alba) Extracts on the Trace Element Status (Fe, Zn and Cu) in Relation to Diabetes Management and Antioxidant Indices in Diabetic Rats. Biol. Trace Elem. Res., 2016, 174(1), 158-165.
[http://dx.doi.org/10.1007/s12011-016-0696-1] [PMID: 27071614]
[9]
Sheng, Y.; Zheng, S.; Ma, T.; Zhang, C.; Ou, X.; He, X.; Xu, W.; Huang, K. Mulberry leaf alleviates streptozotocin-induced diabetic rats by attenuating NEFA signaling and modulating intestinal microflora. Sci. Rep., 2017, 7(1), 12041.
[http://dx.doi.org/10.1038/s41598-017-12245-2] [PMID: 28935866]
[10]
Liu, C-G.; Ma, Y-P.; Zhang, X-J. Effects of mulberry leaf polysaccharide on oxidative stress in pancreatic β-cells of type 2 diabetic rats. Eur. Rev. Med. Pharmacol. Sci., 2017, 21(10), 2482-2488.
[PMID: 28617536]
[11]
Gu, P.; Chen, H. Modern bioinformatics meets traditional Chinese medicine. Brief. Bioinform., 2014, 15(6), 984-1003.
[http://dx.doi.org/10.1093/bib/bbt063] [PMID: 24067932]
[12]
Liang, X-Z.; Li, R.; Xu, B.; Luo, D.; Liu, G.B.; Peng, J.; Li, G. Systematic evaluation of the mechanisms of zoledronic acid based on network pharmacology. Comput. Biol. Chem., 2019, 83107097
[http://dx.doi.org/10.1016/j.compbiolchem.2019.107097] [PMID: 31446368]
[13]
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]
[14]
Huang, X.; Liu, G.; Guo, J.; Su, Z. The PI3K/AKT pathway in obesity and type 2 diabetes. Int. J. Biol. Sci., 2018, 14(11), 1483-1496.
[http://dx.doi.org/10.7150/ijbs.27173] [PMID: 30263000]
[15]
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]
[16]
FAF-Drugs2: a free ADME/tox filtering tool to assist drug discovery and chemical biology projects. Lagorce D, Sperandio O, Galons H, Miteva MA, Villoutreix BO. BMC Bioinformatics, 2008, 9, 396.
[http://dx.doi.org/10.1186/1471-2105-9-396]
[17]
Daina, A.; Michielin, O.; Zoete, V. SwissTargetPrediction: updated data and new features for efficient prediction of protein targets of small molecules. Nucleic Acids Res., 2019, 47(W1), W357-W364.
[http://dx.doi.org/10.1093/nar/gkz382] [PMID: 31106366]
[18]
Stelzer, G; Rosen, R; Plaschkes, I; Zimmerman, S; Twik, M; Fishilevich, S; Iny Stein, T; Nudel, R; Lieder, I; Mazor, Y; Kaplan, S; Dahary, D; Warshawsky, D The GeneCards suite: from gene data mining to disease genome sequence analysis. Current Protocols in Bioinformatics, 2016, 54
[19]
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: 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]
[20]
Huang, W.; Sherman, B.T.; Lempicki, R.A. Bioinformatics enrichment tools: paths toward the comprehensive functional analysis of large gene lists. Nucleic Acids Res., 2009, 37(1), 1-13.
[http://dx.doi.org/10.1093/nar/gkn923] [PMID: 19033363]
[21]
Liu, Y.; Grimm, M.; Dai, W.T.; Hou, M.C.; Xiao, Z.X.; Cao, Y. CB-Dock: a web server for cavity detection-guided protein-ligand blind docking. Acta Pharmacol. Sin., 2020, 41(1), 138-144.
[http://dx.doi.org/10.1038/s41401-019-0228-6] [PMID: 31263275]
[22]
D’Andrea, G. Quercetin: A flavonol with multifaceted therapeutic applications? Fitoterapia, 2015, 106, 256-271.
[http://dx.doi.org/10.1016/j.fitote.2015.09.018] [PMID: 26393898]
[23]
Khan, F.; Niaz, K.; Maqbool, F.; Ismail Hassan, F.; Abdollahi, M.; Nagulapalli Venkata, K.C.; Nabavi, S.M.; Bishayee, A. Molecular targets underlying the anticancer effects of quercetin: an update. Nutrients, 2016, 8(9), 529.
[http://dx.doi.org/10.3390/nu8090529] [PMID: 27589790]
[24]
Miltonprabu, S; Tomczyk, M; Skalicka-Woźniak, K Hepatoprotective effect of quercetin: from chemistry to medicine. Food Chem. Toxicol., 2017, 108(Part B), 365-374.
[http://dx.doi.org/10.1016/j.fct.2016.08.034]
[25]
Li, X.; Zhou, N.; Wang, J.; Liu, Z.; Wang, X.; Zhang, Q.; Liu, Q.; Gao, L.; Wang, R. Quercetin suppresses breast cancer stem cells (CD44+/CD24-) by inhibiting the PI3K/Akt/mTOR-signaling pathway. Life Sci., 2018, 196, 56-62.
[http://dx.doi.org/10.1016/j.lfs.2018.01.014] [PMID: 29355544]
[26]
Yin, X.; Xu, Z.; Zhang, Z.; Li, L.; Pan, Q.; Zheng, F.; Li, H. Association of PI3K/AKT/mTOR pathway genetic variants with type 2 diabetes mellitus in Chinese. Diabetes Res. Clin. Pract., 2017, 128, 127-135.
[http://dx.doi.org/10.1016/j.diabres.2017.04.002] [PMID: 28477532]
[27]
Ong, M.; Peng, J.; Jin, X.; Qu, X. Chinese herbal medicine for the optimal management of polycystic ovary syndrome. Am. J. Chin. Med., 2017, 45(3), 405-422.
[http://dx.doi.org/10.1142/S0192415X17500252] [PMID: 28359195]
[28]
Fu, J.; Huang, J.; Lin, M.; Xie, T.; You, T. Quercetin promotes diabetic wound healing via  Switching macrophages from M1 to M2 polarization. J. Surg. Res., 2020, 246, 213-223.
[http://dx.doi.org/10.1016/j.jss.2019.09.011] [PMID: 31606511]
[29]
Chen, S.; Jiang, H.; Wu, X.; Fang, J. Therapeutic effects of quercetin on inflammation, obesity, and type 2 diabetes. Mediators Inflamm., 2016, 20169340637
[http://dx.doi.org/10.1155/2016/9340637] [PMID: 28003714]
[30]
Oyedemi, S.O.; Nwaogu, G.; Chukwuma, C.I.; Adeyemi, O.T.; Matsabisa, M.G.; Swain, S.S.; Aiyegoro, O.A. Quercetin modulates hyperglycemia by improving the pancreatic antioxidant status and enzymes activities linked with glucose metabolism in type 2 diabetes model of rats: In silico studies of molecular interaction of quercetin with hexokinase and catalase. J. Food Biochem., 2020, 44(2)e13127
[http://dx.doi.org/10.1111/jfbc.13127] [PMID: 31876980]
[31]
Jiang, X.; Yu, J.; Wang, X.; Ge, J.; Li, N. Quercetin improves lipid metabolism via SCAP-SREBP2-LDLr signaling pathway in early stage diabetic nephropathy. Diabetes Metab. Syndr. Obes., 2019, 12, 827-839.
[http://dx.doi.org/10.2147/DMSO.S195456] [PMID: 31239739]
[32]
Alwhaibi, A.; Verma, A.; Adil, M.S.; Somanath, P.R. The unconventional role of Akt1 in the advanced cancers and in diabetes-promoted carcinogenesis. Pharmacol. Res., 2019, 145104270
[http://dx.doi.org/10.1016/j.phrs.2019.104270] [PMID: 31078742]
[33]
Cheng, K.K.; Akasaki, Y.; Lecommandeur, E.; Lindsay, R.T.; Murfitt, S.; Walsh, K.; Griffin, J.L. Metabolomic analysis of akt1-mediated muscle hypertrophy in models of diet-induced obesity and age-related fat accumulation. J. Proteome Res., 2015, 14(1), 342-352.
[http://dx.doi.org/10.1021/pr500756u] [PMID: 25231380]
[34]
Wang, G. Singularity analysis of the AKT signaling pathway reveals connections between cancer and metabolic diseases. Phys. Biol., 2010, 7(4), 046015.
[http://dx.doi.org/10.1088/1478-3975/7/4/046015] [PMID: 21178243]
[35]
Albury-Warren, T.M.; Pandey, V.; Spinel, L.P.; Masternak, M.M.; Altomare, D.A. Prediabetes linked to excess glucagon in transgenic mice with pancreatic active AKT1. J. Endocrinol., 2016, 228(1), 49-59.
[http://dx.doi.org/10.1530/JOE-15-0288] [PMID: 26487674]
[36]
Peng, J.; Li, Q.; Li, K.; Zhu, L.; Lin, X.; Lin, X.; Shen, Q.; Li, G.; Xie, X. Quercetin improves glucose and lipid metabolism of diabetic rats: involvement of Akt signaling and SIRT1. J. Diabetes Res., 2017, 2017, 3417306.
[http://dx.doi.org/10.1155/2017/3417306] [PMID: 29379801]
[37]
Lu, J.; Wang, Z.; Li, S.; Xin, Q.; Yuan, M.; Li, H.; Song, X.; Gao, H.; Pervaiz, N.; Sun, X.; Lv, W.; Jing, T.; Zhu, Y. Quercetin Inhibits the Migration and Invasion of HCCLM3 Cells by Suppressing the Expression of p-Akt1, Matrix Metalloproteinase (MMP) MMP-2, and MMP-9. Med. Sci. Monit., 2018, 24, 2583-2589.
[http://dx.doi.org/10.12659/MSM.906172] [PMID: 29701200]
[38]
Bowden, D.W. Association of the PTPN1 gene with type 2 diabetes and insulin resistance. Discov. Med., 2004, 4(24), 427-432.
[PMID: 20704943]
[39]
de Souza, K.S.; Ururahy, M.A.; da Costa Oliveira, Y.M.; Loureiro, M.B.; da Silva, H.P.; Bortolin, R.H.; Melo Dos Santos, F.; Luchessi, A.D.; Neto, J.J.; Arrais, R.F.; Hirata, R.D.; das Graças Almeida, M.; Hirata, M.H.; de Rezende, A.A. Low bone mineral density in patients with type 1 diabetes: association with reduced expression of IGF1, IGF1R and TGF B 1 in peripheral blood mononuclear cells. Diabetes Metab. Res. Rev., 2016, 32(6), 589-595.
[http://dx.doi.org/10.1002/dmrr.2772] [PMID: 26663878]
[40]
Gupta, S.; McGrath, B.; Cavener, D.R. PERK (EIF2AK3) regulates proinsulin trafficking and quality control in the secretory pathway. Diabetes, 2010, 59(8), 1937-1947.
[http://dx.doi.org/10.2337/db09-1064] [PMID: 20530744]
[41]
Wang, R.; McGrath, B.C.; Kopp, R.F.; Roe, M.W.; Tang, X.; Chen, G.; Cavener, D.R. Insulin secretion and Ca2+ dynamics in β-cells are regulated by PERK (EIF2AK3) in concert with calcineurin. J. Biol. Chem., 2013, 288(47), 33824-33836.
[http://dx.doi.org/10.1074/jbc.M113.503664] [PMID: 24114838]
[42]
Hu, C.; Zhang, R.; Wang, C.; Wang, J.; Ma, X.; Lu, J.; Qin, W.; Hou, X.; Wang, C.; Bao, Y.; Xiang, K.; Jia, W. PPARG, KCNJ11, CDKAL1, CDKN2A-CDKN2B, IDE-KIF11-HHEX, IGF2BP2 and SLC30A8 are associated with type 2 diabetes in a Chinese population. PLoS One, 2009, 4(10), e7643.
[http://dx.doi.org/10.1371/journal.pone.0007643] [PMID: 19862325]
[43]
Thaipitakwong, T.; Supasyndh, O.; Rasmi, Y.; Aramwit, P. A randomized controlled study of dose-finding, efficacy, and safety of mulberry leaves on glycemic profiles in obese persons with borderline diabetes. Complement. Ther. Med., 2020, 49, 102292.
[http://dx.doi.org/10.1016/j.ctim.2019.102292] [PMID: 32147046]
[44]
Meng, Q.; Qi, X.; Chao, Y.; Chen, Q.; Cheng, P.; Yu, X.; Kuai, M.; Wu, J.; Li, W.; Zhang, Q.; Li, Y.; Bian, H. IRS1/PI3K/AKT pathway signal involved in the regulation of glycolipid metabolic abnormalities by Mulberry (Morus alba L.) leaf extracts in 3T3-L1 adipocytes. Chin. Med., 2020, 15, 1.
[http://dx.doi.org/10.1186/s13020-019-0281-6] [PMID: 31908653]
[45]
Tian, S.; Wang, M.; Liu, C.; Zhao, H.; Zhao, B. Mulberry leaf reduces inflammation and insulin resistance in type 2 diabetic mice by TLRs and insulin Signalling pathway. BMC Complement. Altern. Med., 2019, 19(1), 326.
[http://dx.doi.org/10.1186/s12906-019-2742-y] [PMID: 31752797]
[46]
Meng, Q.; Qi, X.; Fu, Y.; Chen, Q.; Cheng, P.; Yu, X.; Sun, X.; Wu, J.; Li, W.; Zhang, Q.; Li, Y.; Wang, A.; Bian, H. Flavonoids extracted from mulberry (Morus alba L.) leaf improve skeletal muscle mitochondrial function by activating AMPK in type 2 diabetes. J. Ethnopharmacol., 2020, 248, 112326.
[http://dx.doi.org/10.1016/j.jep.2019.112326] [PMID: 31639486]
[47]
Bae, U.J.; Jung, E.S.; Jung, S.J.; Chae, S.W.; Park, B.H. Mulberry leaf extract displays antidiabetic activity in db/db mice via Akt and AMP-activated protein kinase phosphorylation. Food Nutr. Res., 2018, 62, 10.
[http://dx.doi.org/10.29219/fnr.v62.1473] [PMID: 30150922]
[48]
Ge, Q.; Chen, L.; Tang, M.; Zhang, S.; Liu, L.; Gao, L.; Ma, S.; Kong, M.; Yao, Q.; Feng, F.; Chen, K. Analysis of mulberry leaf components in the treatment of diabetes using network pharmacology. Eur. J. Pharmacol., 2018, 833, 50-62.
[http://dx.doi.org/10.1016/j.ejphar.2018.05.021] [PMID: 29782863]

Rights & Permissions Print Export Cite as
© 2022 Bentham Science Publishers | Privacy Policy