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BMC Complementary Medicine and Therapies
Published in: BMC Complementary Medicine and Therapies 1/2020

Open Access 01-12-2020 | Type 2 Diabetes | Research article

A systematic analysis of natural α-glucosidase inhibitors from flavonoids of Radix scutellariae using ultrafiltration UPLC-TripleTOF-MS/MS and network pharmacology

Authors: Le Wang, Nana Tan, Huan Wang, Jingbo Hu, Wenbo Diwu, Xiaoling Wang

Published in: BMC Complementary Medicine and Therapies | Issue 1/2020

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Abstract

Background

Flavonoids from plant medicines are supposed to be viable alternatives for the treatment of type 2 diabetes (T2D) as less toxicity and side effects. Radix scutellariae (RS) is a widely used traditional medicine in Asia. It has shown great potential in the research of T2D. However, the pharmacological actions remain obscured due to the complex chemical nature of plant medicines.

Methods

In the present study, a systematic method combining ultrafiltration UPLC-TripleTOF-MS/MS and network pharmacology was developed to screen α-glucosidase inhibitors from flavonoids of RS, and explore the underlying mechanism for the treatment of T2D.

Results

The n-butanol part of ethanol extract from RS showed a strong α-glucosidase inhibition activity (90.55%, IC50 0.551 mg/mL) against positive control acarbose (90.59%, IC50 1.079 mg/mL). A total of 32 kinds of flavonoids were identified from the extract, and their ESI-MS/MS behaviors were elucidated. Thirteen compounds were screened as α-glucosidase inhibitors, including viscidulin III, 2′,3,5,6′,7-pentahydroxyflavanone, and so on. A compound-target-pathway (CTP) network was constructed by integrating these α-glucosidase inhibitors, target proteins, and related pathways. This network exhibited an uneven distribution and approximate scale-free property. Chrysin (k = 87), 5,8,2′-trihydroxy-7-methoxyflavone (k = 21) and wogonin (k = 20) were selected as the main active constituents with much higher degree values. A protein-protein interaction (PPI) weighted network was built for target proteins of these α-glucosidase inhibitors and drug targets of T2D. PPARG (Cd = 0.165, Cb = 0.232, Cc = 0.401), ACACB (Cd = 0.155, Cb = 0.184, Cc = 0.318), NFKB1 (Cd = 0.233, Cb = 0.161, Cc = 0.431), and PGH2 (Cd = 0.194, Cb = 0.157, Cc = 0.427) exhibited as key targets with the highest scores of centrality indices. Furthermore, a core subnetwork was extracted from the CTP and PPI weighted network. Type II diabetes mellitus (hsa04930) and PPAR signaling pathway (hsa03320) were confirmed as the critical pathways.

Conclusions

These results improved current understanding of natural flavonoids on the treatment of T2D. The combination of ultrafiltration UPLC-TripleTOF-MS/MS and network pharmacology provides a novel strategy for the research of plant medicines and complex diseases.
Appendix
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Literature
1.
Chatterjee S, Khunti K, Davies MJ. Type 2 diabetes. Lancet. 2017;58(1):63.
2.
Hays NP, Galassetti PR, Coker RH. Prevention and treatment of type 2 diabetes: current role of lifestyle, natural product, and pharmacological interventions. Pharmacol Ther. 2008;118(2):181–91.PubMedPubMedCentralCrossRef
3.
Shapiro K, Gong WC. Natural products used for diabetes. J Am Pharm Assoc. 2002;42(2):217–26.
4.
Alam F, Islam MA, Kamal MA, Gan SH. Updates on managing type 2 diabetes mellitus with natural products: towards antidiabetic drug development. Curr Med Chem. 2016;25(39):5395–431.CrossRef
5.
Salimifar M, Fatehi-Hassanabad Z, Fatehi M. A review on natural products for controlling type 2 diabetes with an emphasis on their mechanisms of actions. Curr Diabetes Rev. 2013;9(5):402–11.PubMedCrossRef
6.
Li C, Lin G, Zuo Z. Pharmacological effects and pharmacokinetics properties of Radix Scutellariae and its bioactive flavones. Biopharm Drug Dispos. 2011;32(8):427–45.PubMedCrossRef
7.
Luo J-L, Lu F-L, Liu Y-C, Lo C-F. Identification of Scutellaria Baicalensis in traditional Chinese medicine preparations by LC/MS/MS fingerprinting method. J Food Drug Anal. 2012;20(4):887–899+984.
8.
Liu G, Ma J, Chen Y, Tian Q, Shen Y, Wang X, Chen B, Yao S. Investigation of flavonoid profile of Scutellaria bacalensis Georgi by high performance liquid chromatography with diode array detection and electrospray ion trap mass spectrometry. J Chromatogr A. 2009;1216(23):4809–14.PubMedCrossRef
9.
Zhao T, Tang H, Xie L, Zheng Y, Ma Z, Sun Q, Li X. Scutellaria baicalensis Georgi.(Lamiaceae): a review of its traditional uses, botany, phytochemistry, pharmacology and toxicology. J Pharm Pharmacol. 2019;71(9):1353–69.PubMedCrossRef
10.
Song KH, Lee SH, Kim BY, Park AY, Kim JY. Extracts of Scutellaria baicalensis reduced body weight and blood triglyceride in db/db mice. Phytother Res. 2013;27(2):244–50.PubMedCrossRef
11.
Park JH, Kim RY, Park E. Antioxidant and α-glucosidase inhibitory activities of different solvent extracts of skullcap (Scutellaria baicalensis). Food Sci Biotechnol. 2011;20(4):1107–12.CrossRef
12.
Liu SZ, Deng YX, Chen B, Zhang XJ, Shi QZ, Qiu XM. Antihyperglycemic effect of the traditional Chinese scutellaria-coptis herb couple and its main components in streptozotocin-induced diabetic rats. J Ethnopharmacol. 2013;145(2):490–8.PubMedCrossRef
13.
Waisundara VY, Hsu A, Huang D, Tan BK. Scutellaria baicalensis enhances the anti-diabetic activity of metformin in streptozotocin-induced diabetic Wistar rats. Am J Chin Med. 2008;36(3):517.PubMedCrossRef
14.
Park J, Jang HJ. Anti-diabetic effects of natural products an overview of therapeutic strategies. Mol Cell Toxicol. 2017;13(1):1–20.CrossRef
15.
Bischoff H. The mechanism of alpha-glucosidase inhibition in the management of diabetes. Clin Invest Med. 1995;18(4):303.PubMed
16.
Yang Y, Lian G, Yu B. Naturally occurring polyphenolic glucosidase inhibitors. Cheminform. 2015;55(3–4):268–84.
17.
Chen J, Cheng YQ, Yamaki K, Li LT. Anti-α-glucosidase activity of Chinese traditionally fermented soybean (douchi). Food Chem. 2007;103(4):1091–6.CrossRef
18.
Ríos JL, Francini F, Schinella GR. Natural products for the treatment of type 2 diabetes mellitus. Planta Med. 2015;81(12–13):975.PubMed
19.
Habtemariam S. Targeting intestinal digestive enzymes by natural products: synergistic effect of flavonoids. Planta Med. 2011;77(12):1404.CrossRef
20.
Shibano M, Kakutani K, Taniguchi M, Yasuda M, Baba K. Antioxidant constituents in the dayflower (Commelina communis L.) and their alpha-glucosidase-inhibitory activity. J Nat Med. 2008;62(3):349–53.PubMedCrossRef
21.
Matsuda H, Morikawa T, Toguchida I, Yoshikawa M. Structural requirements of flavonoids and related compounds for aldose reductase inhibitory activity. Chem Pharm Bull (Tokyo). 2002;50(6):788.CrossRef
22.
Xu L-N, Li Y, Dai Y, Peng J-Y. Natural products for the treatment of type 2 diabetes mellitus: pharmacology and mechanisms. Pharmacol Res. 2018;130:451.PubMedCrossRef
23.
Yang JR, Luo JG, Kong LY. Determination of α-glucosidase inhibitors from Scutellaria baicalensis using liquid chromatography with quadrupole time of flight tandem mass spectrometry coupled with centrifugal ultrafiltration. Chin J Nat Med. 2015;13(3):208–14.PubMed
24.
Kishida K. Simplified extraction of tetracycline antibiotics from milk using a centrifugal ultrafiltration device. Food Chem. 2011;126(2):687–90.CrossRef
25.
Yang Z, Zhang Y, Sun L, Wang Y, Gao X, Cheng Y. An ultrafiltration high-performance liquid chromatography coupled with diode array detector and mass spectrometry approach for screening and characterising tyrosinase inhibitors from mulberry leaves. Anal Chim Acta. 2012;719(6):87.PubMedCrossRef
26.
Zhang Y, Peng M, Liu L, Shi S, Peng S. Screening, identification, and potential interaction of active compounds from Eucommia ulmodies leaves binding with bovine serum albumin. J Agric Food Chem. 2012;60(12):3119–25.PubMedCrossRef
27.
Wang J, Liu S, Li S, Song F, Zhang Y, Liu Z, Liu C-M. Ultrafiltration LC-PDA-ESI/MS combined with reverse phase-medium pressure liquid chromatography for screening and isolation potential α-glucosidase inhibitors from Scutellaria baicalensis Georgi. Anal Methods. 2014;6(15):5918.CrossRef
28.
Beutler JA. Natural products as a foundation for drug discovery. Curr Protoc Pharmacol. 2009;46(1):1–21.CrossRef
29.
30.
Hopkins AL. Network pharmacology: the next paradigm in drug discovery. Nat Chem Biol. 2008;4(11):682–90.PubMedCrossRef
31.
Zhang X-Z, Gu J-Y, Cao L, Li N, Ma Y-M, Su Z-Z, Ding G, Chen L-R, Xu X-J, Xiao W. Network pharmacology study on the mechanism of traditional Chinese medicine for upper respiratory tract infection. Mol BioSyst. 2014;10(10):2517.PubMedCrossRef
32.
Li B-H, Tao W-Y, Zheng C-L, Shar PA, Huang C, Fu Y-X, Wang Y-H. Systems pharmacology-based approach for dissecting the addition and subtraction theory of traditional Chinese medicine: an example using Xiao-Chaihu-Decoction and Da-Chaihu-Decoction. Comput Biol Med. 2014;53:19–29.PubMedCrossRef
33.
34.
Wang L-L, Li Z, Shao Q, Li X, Ai N, Zhao X-P, Fan X-H. Dissecting active ingredients of Chinese medicine by content-weighted ingredient-target network. Mol BioSyst. 2014;10(7):1905.PubMedCrossRef
35.
Chen L, Lv D, Wang D, Chen X, Zhu Z, Cao Y, Chai Y. A novel strategy of profiling the mechanism of herbal medicines by combining network pharmacology with plasma concentration determination and affinity constant measurement. Mol BioSyst. 2016;12(11):3347.PubMedCrossRef
36.
Luo F, Gu J, Chen L, Xu X. Systems pharmacology strategies for anticancer drug discovery based on natural products. Mol BioSyst. 2014;10(7):1912.PubMedCrossRef
37.
Gogoi B, Gogoi D, Silla Y, Kakoti BB, Bhau BS. Network pharmacology-based virtual screening of natural products from Clerodendrum species for identification of novel anti-cancer therapeutics. Mol BioSyst. 2017;13(2):406–16.PubMedCrossRef
38.
Kibble M, Saarinen N, Tang J, Wennerberg K, Mäkelä S, Aittokallio T. Network pharmacology applications to map the unexplored target space and therapeutic potential of natural products. Nat Prod Rep. 2015;32(8):1249–66.PubMedCrossRef
39.
Lagunin AA, Goel RK, Gawande DY, Pahwa P, Gloriozova TA, Dmitriev AV, Ivanov SM, Rudik AV, Konova VI, Pogodin PV. Chemo- and bioinformatics resources for in silico drug discovery from medicinal plants beyond their traditional use: a critical review. Nat Prod Rep. 2014;31(11):1585–611.PubMedCrossRef
40.
Kang WY, Song YL, Zhang L. α-Glucosidase inhibitory and antioxidant properties and antidiabetic activity of Hypericum ascyron L. Med Chem Res. 2010;20(7):809–16.CrossRef
41.
Nickel J, Gohlke BO, Erehman J, Banerjee P, Rong WW, Goede A, Dunkel M, Preissner R. SuperPred: update on drug classification and target prediction. Nucleic Acids Res. 2014;42(1):26–31.CrossRef
42.
Benjamini Y, Hochberg Y. Controlling the false discovery rate - a practical and powerful approach to multiple testing. J R Stat Soc B. 1995;57(1):289–300.
43.
Li YH, Yu CY, Li XX, Zhang P, Tang J, Yang Q, Fu T, Zhang X, Cui X, Tu G. Therapeutic target database update 2018: enriched resource for facilitating bench-to-clinic research of targeted therapeutics. Nucleic Acids Res. 2017;46(1):1121–7.
44.
Szklarczyk D, Morris JH, Cook H, Kuhn M, Wyder S, Simonovic M, Santos A, Doncheva NT, Roth A, Bork P. The STRING database in 2017: quality-controlled protein–protein association networks, made broadly accessible. Nucleic Acids Res. 2017;45(Database issue):D362–8.PubMedCrossRef
45.
Zhang L, Zhang R-W, Li Q, Lian J-W, Liang J, Chen X-H, Bi K-S. Development of the fingerprints for the quality evaluation of Scutellariae Radix by HPLC-DAD and LC-MS-MS. Chromatographia. 2007;66(1–2):13–20.CrossRef
46.
Liu G, Ma J, Chen Y, Tian Q, Shen Y, Wang X, Yao S. Investigation of flavonoid profile of Scutellaria bacalensis Georgi by high performance liquid chromatography with diode array detection and electrospray ion trap mass spectrometry. J Chromatogr. 2009;1216(23):4809–14.CrossRef
47.
Ma YL, Li QM, Van den Heuvel H, Claeys M. Characterization of flavone and flavonol aglycones by collision-induced dissociation tandem mass spectrometry. Rapid Commun Mass Spectrom. 2015;11(12):1357–64.CrossRef
48.
Reddy B, Reddy M, Gunasekar D, Murthy MM, Caux C, Bodo B. Two new flavonoids from Andrographis macrobotrys. Indian J Chem B. 2005;44(9):1966–9.
49.
He L, Zhang Z, Lu L, Liu Y, Li S, Wang J, Song Z, Yan Z, Miao J. Rapid identification and quantitative analysis of the chemical constituents in Scutellaria indica L. by UHPLC–QTOF–MS and UHPLC–MS/MS. J Pharm Biomed Anal. 2016;117:125–39.PubMedCrossRef
50.
Azimova SS, Vinogradova VI. Natural compounds: flavonoids. New York: Springer; 2013. p. 104–18.
51.
Xiao L, Wang H, Song S, Zhang G, Song H, Xu S. Isolation and identification of the chemical constituents of roots of Scutellaria amoena CH Wright. Shenyang Yaoke Daxue Xuebao. 2003;20(3):181–3.
52.
Barberan F, Ferreres F, Tomas F, Guirado A. Electron impact mass spectrometric differentiation of 5, 6-dihydroxy-7, 8-dimethoxy-and 5, 8-dihydroxy-6, 7-dimethoxyflavones. Phytochemistry. 1986;25(4):923–5.CrossRef
53.
Han J, Ye M, Xu M, Sun J, Wang B, Guo D. Characterization of flavonoids in the traditional Chinese herbal medicine-Huangqin by liquid chromatography coupled with electrospray ionization mass spectrometry. J Chromatogr B Analyt Technol Biomed Life Sci. 2007;848(2):355–62.PubMedCrossRef
54.
Guo M, Zhang Y, Wang Y, Zhao X. Development of a rapid screening method for discovering neuroprotective components from traditional Chinese medicine. Zhongguo Zhong Yao Za Zhi. 2013;38(10):1581–4.PubMed
55.
Cieśla Ł, Moaddel R. Comparison of analytical techniques for the identification of bioactive compounds from natural products. Nat Prod Rep. 2016;33(10):1131–45.PubMedCrossRef
56.
Szklarczyk D, Santos A, Von Mering C, Jensen LJ, Bork P, Kuhn M. STITCH 5: augmenting protein–chemical interaction networks with tissue and affinity data. Nucleic Acids Res. 2016;44(1):380–4.CrossRef
57.
Moller DE. New drug targets for type 2 diabetes and the metabolic syndrome. Nature. 2001;414(6865):821–7.PubMedCrossRef
58.
Kaur P, Mittal A, Nayak S, Vyas M, Mishra V, Khatik G. Current strategies and drug targets in the management of type 2 diabetes mellitus. Curr Drug Targets. 2018;19(15):1738–66.PubMedCrossRef
59.
Carmeli B, Erhan B, Koyama T, Rhrissorrakrai K, Royyuru AK, Utro F, Waks Z. Relevancy assessment and visualization of biological pathways. In: U.S. patent application, vol. 14/745; 2016. p. 616.
60.
Wang X-J, Zhang A-H, Sun H, Han Y, Yan G-L. Discovery and development of innovative drug from traditional medicine by integrated chinmedomics strategies in the post-genomic era. TrAC Trends Anal Chem. 2016;76:86–94.CrossRef
61.
Martínez Gómez LE, Cruz M, Martínez Nava GA, Madrid Marina V, Parra E, García Mena J, Espinoza Rojo M, Estrada Velasco BI, Piza Roman LF, Aguilera P. A replication study of the IRS1, CAPN10, TCF7L2, and PPARG gene polymorphisms associated with type 2 diabetes in two different populations of Mexico. Ann Hum Genet. 2011;75(5):612–20.PubMedCrossRef
62.
Wang L, Tan N, Hu J, Wang H, Duan D, Ma L, Xiao J, Wang X. Analysis of the main active ingredients and bioactivities of essential oil from Osmanthus fragrans Var. thunbergii using a complex network approach. BMC Syst Biol. 2017;11(1):144.PubMedPubMedCentralCrossRef
63.
Mp VDH, Sporns O. Network hubs in the human brain. Trends Cogn Sci. 2013;17(12):683–96.CrossRef
64.
Samarghandian PS, Nezhad MA, Samini F, Farkhondeh T. Chrysin treatment improves diabetes and its complications in liver, brain, and pancreas in streptozotocin-induced diabetic rats. Can J Physiol Pharmacol. 2016;94(4):388–93.PubMedCrossRef
65.
Yang T, Liu H, Zhao B, Xia Z, Zhang Y, Zhang D, Li M, Cao Y, Zhang Z, Bi Y. Wogonin enhances intracellular adiponectin levels and suppresses adiponectin secretion in 3T3-L1 adipocytes. Endocr J. 2016;64(1):15–26.PubMedCrossRef
66.
Bobilya DJ. Flavonoid antioxidants: chemistry, metabolism and structure-activity relationships. J Nutr Biochem. 2002;13(10):572–84.PubMedCrossRef
67.
Li Y, Huang TH, Yamahara J. Salacia root, a unique Ayurvedic medicine, meets multiple targets in diabetes and obesity. Life Sci. 2008;82(21):1045–9.PubMedCrossRef
68.
Bashan N, Dorfman K, Tarnovscki T, Harman Boehm I, Liberty IF, Blüher M, Ovadia S, Maymon Zilberstein T, Potashnik R, Stumvoll M. Mitogen-activated protein kinases, inhibitory-κB kinase, and insulin signaling in human omental versus subcutaneous adipose tissue in obesity. Endocrinology. 2007;148(6):2955–62.PubMedCrossRef
69.
Thauvinrobinet C, Auclair M, Duplomb L, Carondebarle M, Avila M, Stonge J, Merrer ML, Luyer BL, Héron D, Mathieudramard M. PIK3R1 mutations cause syndromic insulin resistance with lipoatrophy. Am J Hum Genet. 2013;93(1):141–9.CrossRef
70.
Liu C, Bousman C, Pantelis C, Skafidas E, Zhang D, Yue W, Everall I. Pathway-wide association study identifies five shared pathways associated with schizophrenia in three ancestral distinct populations. Transl Psychiatry. 2017;7(2):e1037.PubMedPubMedCentralCrossRef
71.
La LP, Listì A, Caruso S, Amodeo V, Passiglia F, Bazan V, Fanale D. Potential role of ANGPTL4 in the cross talk between metabolism and cancer through PPAR signaling pathway. PPAR Res. 2017;2017:1–15.
72.
Gallagher EJ, Leroith D. Diabetes, cancer, and metformin: connections of metabolism and cell proliferation. Ann N Y Acad Sci. 2011;1243(1):54–68.PubMedCrossRef
73.
Zanger UM, Schwab M. Cytochrome P450 enzymes in drug metabolism: regulation of gene expression, enzyme activities, and impact of genetic variation. Pharmacol Ther. 2013;138(1):103–41.PubMedCrossRef
74.
Schultze SM, Hemmings BA, Niessen M, Tschopp O. PI3K/AKT, MAPK and AMPK signalling: protein kinases in glucose homeostasis. Expert Rev Mol Med. 2012;14:e1.PubMedCrossRef
75.
Holcombe M, Adra S, Bicak M, Chin S, Coakley S, Graham AI, Green J, Greenough C, Jackson D, Kiran M. Modelling complex biological systems using an agent-based approach. Integr Biol. 2012;4(1):53–64.CrossRef
76.
Papaetis GS, Papakyriakou P, Panagiotou TN. Central obesity, type 2 diabetes and insulin: exploring a pathway full of thorns. Arch Med Sci. 2015;11(3):463–82.PubMedPubMedCentralCrossRef
77.
Barrat A, Barthelemy M, Vespignani A. The architecture of complex weighted networks: measurements and models. In: Large scale structure and dynamics of complex networks: from information technology to finance and natural science. edn. New Jersey: World Scientific; 2007. p. 67–92.CrossRef
78.
Wang L, Hou E-T, Wang L-J, Wang Y-J, Yang L-J, Zheng X-H, Xie G-Q, Sun Q, Liang M-Y, Tian Z-M. Reconstruction and analysis of correlation networks based on GC-MS metabolomics data for young hypertensive men. Anal Chim Acta. 2015;854:95–105.PubMedCrossRef
79.
Stumvoll M, Häring H. The peroxisome proliferator-activated receptor-γ2 Pro12Ala polymorphism. Diabetes. 2002;51(8):2341–7.PubMedCrossRef
80.
Black MH, Wu J, Takayanagi M, Wang N, Taylor KD, Haritunians T, Trigo E, Lawrence JM, Watanabe RM, Buchanan TA. Variation in PPARG is associated with longitudinal change in insulin resistance in mexican americans at risk for type 2 diabetes. J Clin Endocrinol Metab. 2015;100(3):1187–95.PubMedPubMedCentralCrossRef
81.
Konheim YL, Wolford JK. Association of a promoter variant in the inducible cyclooxygenase-2 gene (PTGS2) with type 2 diabetes mellitus in Pima Indians. Hum Genet. 2003;113(5):377–81.PubMedCrossRef
82.
Nitz I, Fisher E, Grallert H, Li Y, Gieger C, Rubin D, Boeing H, Spranger J, Lindner I, Schreiber S. Association of prostaglandin E synthase 2 (PTGES2) Arg298His polymorphism with type 2 diabetes in two German study populations. J Clin Endocrinol Metab. 2007;92(8):3183–8.PubMedCrossRef
83.
Zain M, Awan FR, Najam SS, Islam M, Khan AR, Bilal A, Bellili N, Marre M, Roussel R, Fumeron F. Association of ACACB gene polymorphism (rs2268388, G> A) with type 2 diabetes and end stage renal disease in Pakistani Punjabi population. Meta Gene. 2017;12:109–12.CrossRef
84.
Riancho JA, Vázquez L, García Pérez MA, Sainz J, Olmos JM, Hernández JL, Pérez López J, Amado JA, Zarrabeitia MT, Cano A. Association of ACACB polymorphisms with obesity and diabetes. Mol Genet Metab. 2011;104(4):670–6.PubMedCrossRef
85.
Coto E, Díaz Corte C, Tranche S, Gómez J, Alonso B, Iglesias S, Reguero JR, López Larrea C, Coto Segura P. Gene variants in the NF-KB pathway (NFKB1, NFKBIA, NFKBIZ) and their association with type 2 diabetes and impaired renal function. Hum Immunol. 2018;79(6):494–8.PubMedCrossRef
86.
Stentz FB, Kitabchi AE. Transcriptome and proteome expressions involved in insulin resistance in muscle and activated T-lymphocytes of patients with type 2 diabetes. Genomics Proteomics Bioinformatics. 2007;5(3–4):216–35.PubMedCrossRef
87.
Tanti JF, Jager J. Cellular mechanisms of insulin resistance: role of stress-regulated serine kinases and insulin receptor substrates (IRS) serine phosphorylation. Curr Opin Pharm. 2009;9(6):753–62.CrossRef
88.
Banks AS, Mcallister FE, Camporez JPG, Zushin PJH, Jurczak MJ, Laznikbogoslavski D, Shulman GI, Gygi SP, Spiegelman BM. An Erk/Cdk5 axis controls the diabetogenic actions of PPARγ. Nature. 2015;517(7534):391–5.PubMedCrossRef
89.
Fanale D, Amodeo V, Caruso S. The interplay between metabolism, PPAR signaling pathway, and cancer. PPAR Res. 2017;2017:1–2.CrossRef
90.
Cui X, Shen YM, Jiang S, Qian DW, Shang EX, Zhu ZH, Duan JA. Comparative analysis of the main active components and hypoglycemic effects after the compatibility of Scutellariae Radix and Coptidis Rhizoma. J Sep Sci. 2019;42(8):1520–7.PubMedCrossRef
91.
Tahtah Y, Kongstad KT, Wubshet SG, Nyberg NT, Jønsson LH, Jäger AK, Qinglei S, Staerk D. Triple aldose reductase/α-glucosidase/radical scavenging high-resolution profiling combined with high-performance liquid chromatography–high-resolution mass spectrometry–solid-phase extraction–nuclear magnetic resonance spectroscopy for identification of antidiabetic constituents in crude extract of Radix Scutellariae. J Chromatogr. 2015;1408:125–32.CrossRef
92.
Zhang X, Liu S, Xing J, Pi Z, Liu Z, Song F. Systematic study on metabolism and activity evaluation of Radix Scutellaria extract in rat plasma using UHPLC with quadrupole time-of-flight mass spectrometry and microdialysis intensity-fading mass spectrometry. J Sep Sci. 2018;41(7):1704–10.PubMedCrossRef
Metadata
Title
A systematic analysis of natural α-glucosidase inhibitors from flavonoids of Radix scutellariae using ultrafiltration UPLC-TripleTOF-MS/MS and network pharmacology
Authors
Le Wang
Nana Tan
Huan Wang
Jingbo Hu
Wenbo Diwu
Xiaoling Wang
Publication date
01-12-2020
Publisher
BioMed Central
Keyword
Type 2 Diabetes
Published in
BMC Complementary Medicine and Therapies / Issue 1/2020
Electronic ISSN: 2662-7671
DOI
https://doi.org/10.1186/s12906-020-2871-3

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