Skip to content
2000
Volume 25, Issue 20
  • ISSN: 1568-0266
  • E-ISSN: 1873-4294

Abstract

Introduction

Currently, there are pharmacological treatments for type 2 diabetes (T2D), but these are ineffective. Quercetin is a flavonoid with antidiabetic properties.

Objective

This research aimed to identify the molecular mechanism of Quercetin in T2D from network pharmacology.

Methods

We obtained T2D-related genes from MalaCards and DisGeNET, while potential targets for Quercetin were sourced from SwissTargetPrediction and Comparative Toxicogenomics databases. The overlapping genes were identified and analyzed using ShinyGO 0.77. Subsequently, we constructed a protein-protein interaction network using Cytoscape, conducted molecular docking analyses with SwissDock, and validated the results through molecular dynamics simulation in GROMACS.

Results

Quercetin is involved in apoptotic processes and in the regulation of insulin activity, estrogen, prolactin and EGFR receptor. The key driver genes , , , , , , and showed high concordance in the molecular docking study, and molecular dynamics showed stability between Quercetin and ESR2 and PIK3R1.

Conclusions

Our work helps to identify the molecular mechanism and antidiabetic effect of quercetin, which needs to be studied experimentally.

Loading

Article metrics loading...

/content/journals/ctmc/10.2174/0115680266361598250212030220
2025-02-13
2025-12-27
Loading full text...

Full text loading...

References

  1. Galicia-GarciaU. Benito-VicenteA. JebariS. Larrea-SebalA. SiddiqiH. UribeK.B. OstolazaH. MartínC. Pathophysiology of type 2 diabetes mellitus.Int. J. Mol. Sci.20202117627510.3390/ijms21176275 32872570
    [Google Scholar]
  2. ElSayedN.A. AleppoG. ArodaV.R. BannuruR.R. BrownF.M. BruemmerD. CollinsB.S. GagliaJ.L. HilliardM.E. IsaacsD. JohnsonE.L. KahanS. KhuntiK. LeonJ. LyonsS.K. PerryM.L. PrahaladP. PratleyR.E. SeleyJ.J. StantonR.C. GabbayR.A. 2. Classification and diagnosis of diabetes: Standards of care in diabetes—2023.Diabetes Care202346Suppl. 1S19S4010.2337/dc23‑S002 36507649
    [Google Scholar]
  3. LaaksoM. Biomarkers for type 2 diabetes.Mol. Metab.201927Suppl.S139S14610.1016/j.molmet.2019.06.016 31500825
    [Google Scholar]
  4. IDF Diabetes Atlas 2021.2021Available from: https://diabetesatlas.org/resources/?gad_source=1&gclid=Cj0KCQiAvvO7BhC-ARIsAGFyToXnl4htywlYna4M2_bWuG6tUlkOpQ65Hx6QeYE-vpWQyD4wvQDdtoIaAgiLEALw_wcB
  5. ArtasensiA. PedrettiA. VistoliG. FumagalliL. Type 2 diabetes mellitus: A review of multi-target drugs.Molecules2020258198710.3390/molecules25081987 32340373
    [Google Scholar]
  6. Martínez-EsquiviasF. Guzmán-FloresJ.M. Pérez-LariosA. RicoJ.L. Becerra-RuizJ.S. A review of the effects of Gold, Silver, Selenium, and Zinc nanoparticles on diabetes mellitus in murine models.Mini Rev. Med. Chem.202121141798181210.2174/18755607MTEziOTEv4 33535949
    [Google Scholar]
  7. McGovernA. TippuZ. HintonW. MunroN. WhyteM. de LusignanS. Comparison of medication adherence and persistence in type 2 diabetes: A systematic review and meta‐analysis.Diabetes Obes. Metab.20182041040104310.1111/dom.13160 29135080
    [Google Scholar]
  8. BuseJ.B. WexlerD.J. TsapasA. RossingP. MingroneG. MathieuC. D’AlessioD.A. DaviesM.J. 2019 Update to: Management of hyperglycemia in type 2 diabetes, 2018. A consensus report by the American Diabetes Association (ADA) and the European Association for the study of diabetes (EASD).Diabetes Care202043248749310.2337/dci19‑0066 31857443
    [Google Scholar]
  9. PivariF. MingioneA. BrasacchioC. SoldatiL. Curcumin and type 2 diabetes mellitus: Prevention and treatment.Nutrients2019118183710.3390/nu11081837 31398884
    [Google Scholar]
  10. KashyapD. GargV.K. TuliH.S. YererM.B. SakK. SharmaA.K. KumarM. AggarwalV. SandhuS.S. Fisetin and quercetin: Promising flavonoids with chemopreventive potential.Biomolecules20199517410.3390/biom9050174 31064104
    [Google Scholar]
  11. Deepika MauryaP.K. Health benefits of quercetin in age-related diseases.Molecules2022278249810.3390/molecules27082498 35458696
    [Google Scholar]
  12. AnsariP. ChoudhuryS.T. SeidelV. RahmanA.B. AzizM.A. RichiA.E. RahmanA. JafrinU.H. HannanJ.M.A. Abdel-WahabY.H.A. Therapeutic potential of quercetin in the management of type-2 diabetes mellitus.Life2022128114610.3390/life12081146 36013325
    [Google Scholar]
  13. ShiL. WangJ. HeC. HuangY. FuW. ZhangH. AnY. WangM. ShanZ. LiH. LvY. WangC. ChengL. DaiH. DuanY. ZhaoH. ZhaoB. Identifying potential therapeutic targets of mulberry leaf extract for the treatment of type 2 diabetes: A TMT-based quantitative proteomic analysis.BMC Complement. Med. Ther.202323130810.1186/s12906‑023‑04140‑3 37667364
    [Google Scholar]
  14. NoorF. Tahir ul QamarM. AshfaqU.A. AlbuttiA. AlwashmiA.S.S. AljasirM.A. Network pharmacology approach for medicinal plants: Review and assessment.Pharmaceuticals202215557210.3390/ph15050572 35631398
    [Google Scholar]
  15. Martínez-EsquiviasF. Guzmán-FloresJ.M. Chávez-DíazI.F. Iñiguez-MuñozL.E. Reyes-ChaparroA. Pharmacological network study on the effect of 6-Gingerol on cervical cancer using computerized databases.j. biomol. struct. dyn.202311237776009
    [Google Scholar]
  16. JiaoX. JinX. MaY. YangY. LiJ. LiangL. LiuR. LiZ. A comprehensive application: Molecular docking and network pharmacology for the prediction of bioactive constituents and elucidation of mechanisms of action in component-based Chinese medicine.Comput. Biol. Chem.20219010740210.1016/j.compbiolchem.2020.107402 33338839
    [Google Scholar]
  17. Guzmán-FloresJ.M. Pérez-VázquezV. Martínez-EsquiviasF. Isiordia-EspinozaM.A. Viveros-ParedesJ.M. Molecular docking integrated with network pharmacology explores the therapeutic mechanism of Cannabis sativa against type 2 diabetes.Curr. Issues Mol. Biol.20234597228724110.3390/cimb45090457 37754241
    [Google Scholar]
  18. RappaportN. TwikM. PlaschkesI. NudelR. Iny SteinT. LevittJ. GershoniM. MorreyC.P. SafranM. LancetD. MalaCards: An amalgamated human disease compendium with diverse clinical and genetic annotation and structured search.Nucleic Acids Res.201745D1D877D88710.1093/nar/gkw1012 27899610
    [Google Scholar]
  19. PiñeroJ. SaüchJ. SanzF. FurlongL.I. The DisGeNET cytoscape app: Exploring and visualizing disease genomics data.Comput. Struct. Biotechnol. J.2021192960296710.1016/j.csbj.2021.05.015 34136095
    [Google Scholar]
  20. DainaA. MichielinO. ZoeteV. SwissTargetPrediction: Updated data and new features for efficient prediction of protein targets of small molecules.Nucleic Acids Res.201947W1W357W36410.1093/nar/gkz382 31106366
    [Google Scholar]
  21. DavisA.P. WiegersT.C. WiegersJ. WyattB. JohnsonR.J. SciakyD. BarkalowF. StrongM. PlanchartA. MattinglyC.J. CTD tetramers: A new online tool that computationally links curated chemicals, genes, phenotypes, and diseases to inform molecular mechanisms for environmental health.Toxicol. Sci.2023195215516810.1093/toxsci/kfad069 37486259
    [Google Scholar]
  22. Venny 2.1.0Available from: https://bioinfogp.cnb.csic.es/tools/venny/ 2023
  23. GeS.X. JungD. YaoR. ShinyG.O. ShinyGO: A graphical gene-set enrichment tool for animals and plants.Bioinformatics20203682628262910.1093/bioinformatics/btz931 31882993
    [Google Scholar]
  24. OtasekD. MorrisJ.H. BouçasJ. PicoA.R. DemchakB. Cytoscape Automation: Empowering workflow-based network analysis.Genome Biol.201920118510.1186/s13059‑019‑1758‑4 31477170
    [Google Scholar]
  25. VaradiMihaly AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models.Nucleic Acids Res.202250D1D439D444
    [Google Scholar]
  26. VelankarS. BurleyS.K. KurisuG. HochJ.C. MarkleyJ.L. The protein data bank archive.Methods Mol. Biol.2021230532110.1007/978‑1‑0716‑1406‑8_1 33950382
    [Google Scholar]
  27. KimS. ChenJ. ChengT. GindulyteA. HeJ. HeS. LiQ. ShoemakerB.A. ThiessenP.A. YuB. ZaslavskyL. ZhangJ. BoltonE.E. PubChem 2023 update.Nucleic Acids Res.202351D1D1373D138010.1093/nar/gkac956 36305812
    [Google Scholar]
  28. GabellaC. DuvaudS. DurinxC. Managing the life cycle of a portfolio of open data resources at the SIB Swiss Institute of Bioinformatics.Brief. Bioinform.2022231bbab47810.1093/bib/bbab478 34850820
    [Google Scholar]
  29. ShiahJ.V. GrandisJ.R. JohnsonD.E. Targeting STAT3 with proteolysis targeting chimeras and next-generation Antisense Oligonucleotides.Mol. Cancer Ther.202120221922810.1158/1535‑7163.MCT‑20‑0599 33203730
    [Google Scholar]
  30. AcherF.C. CabayéA. EshakF. Goupil-LamyA. PinJ.P. Metabotropic glutamate receptor orthosteric ligands and their binding sites.Neuropharmacology202220410888610.1016/j.neuropharm.2021.108886 34813860
    [Google Scholar]
  31. AbrahamM.J. MurtolaT. SchulzR. PállS. SmithJ.C. HessB. LindahlE. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers.SoftwareX20151-2192510.1016/j.softx.2015.06.001
    [Google Scholar]
  32. JoS. ChengX. IslamS.M. HuangL. RuiH. ZhuA. LeeH.S. QiY. HanW. VanommeslaegheK. MacKerellA.D.Jr RouxB. ImW. CHARMM-GUI PDB manipulator for advanced modeling and simulations of proteins containing nonstandard residues.Adv. Protein Chem. Struct. Biol.20149623526510.1016/bs.apcsb.2014.06.002 25443960
    [Google Scholar]
  33. O’BoyleN.M. BanckM. JamesC.A. MorleyC. VandermeerschT. HutchisonG.R. Open Babel: An open chemical toolbox.J. Cheminform.2011313310.1186/1758‑2946‑3‑33 21982300
    [Google Scholar]
  34. KimS. LeeJ. JoS. BrooksC.L.III LeeH.S. ImW. CHARMM-GUI ligand reader and modeler for CHARMM force field generation of small molecules.J. Comput. Chem.201738211879188610.1002/jcc.24829 28497616
    [Google Scholar]
  35. LeeJ. ChengX. SwailsJ.M. YeomM.S. EastmanP.K. LemkulJ.A. WeiS. BucknerJ. JeongJ.C. QiY. JoS. PandeV.S. CaseD.A. BrooksC.L.III MacKerellA.D.Jr KlaudaJ.B. ImW. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field.J. Chem. Theory Comput.201612140541310.1021/acs.jctc.5b00935 26631602
    [Google Scholar]
  36. BussiG. DonadioD. ParrinelloM. Canonical sampling through velocity rescaling.J. Chem. Phys.2007126101410110.1063/1.2408420 17212484
    [Google Scholar]
  37. VanommeslaegheK. HatcherE. AcharyaC. KunduS. ZhongS. ShimJ. DarianE. GuvenchO. LopesP. VorobyovI. MackerellA.D.Jr CHARMM general force field: A force field for drug‐like molecules compatible with the CHARMM all‐atom additive biological force fields.J. Comput. Chem.201031467169010.1002/jcc.21367 19575467
    [Google Scholar]
  38. DeFronzoR.A. FerranniniE. GroopL. HenryR.R. HermanW.H. HolstJ.J. HuF.B. KahnC.R. RazI. ShulmanG.I. SimonsonD.C. TestaM.A. WeissR. Type 2 diabetes mellitus.Nat. Rev. Dis. Primers2015111501910.1038/nrdp.2015.19 27189025
    [Google Scholar]
  39. ForbesJ.M. CooperM.E. Mechanisms of diabetic complications.Physiol. Rev.201393113718810.1152/physrev.00045.2011 23303908
    [Google Scholar]
  40. FranzM.J. BoucherJ.L. Rutten-RamosS. VanWormerJ.J. Lifestyle weight-loss intervention outcomes in overweight and obese adults with type 2 diabetes: A systematic review and meta-analysis of randomized clinical trials.J. Acad. Nutr. Diet.201511591447146310.1016/j.jand.2015.02.031 25935570
    [Google Scholar]
  41. XuL. LiY. DaiY. PengJ. Natural products for the treatment of type 2 diabetes mellitus: Pharmacology and mechanisms.Pharmacol. Res.201813045146510.1016/j.phrs.2018.01.015 29395440
    [Google Scholar]
  42. ZuG. SunK. LiL. ZuX. HanT. HuangH. Mechanism of quercetin therapeutic targets for Alzheimer disease and type 2 diabetes mellitus.Sci. Rep.20211112295910.1038/s41598‑021‑02248‑5 34824300
    [Google Scholar]
  43. WangJ-Y. NieY-X. DongB-Z. CaiZ-C. ZengX-K. DuL. ZhuX. YinX-X. Quercetin protects islet β-cells from oxidation-induced apoptosis via Sirt3 in T2DM.Iran. J. Basic Med. Sci.2021245629635 34249264
    [Google Scholar]
  44. YangD.K. KangH.S. Anti-diabetic effect of cotreatment with quercetin and resveratrol in streptozotocin-induced diabetic rats.Biomol. Ther.201826213013810.4062/biomolther.2017.254 29462848
    [Google Scholar]
  45. ZhangF. FengJ. ZhangJ. KangX. QianD. Quercetin modulates AMPK/SIRT1/NF κB signaling to inhibit inflammatory/oxidative stress responses in diabetic high fat diet induced atherosclerosis in the rat carotid artery.Exp. Ther. Med.2020206110.3892/etm.2020.9410 33200005
    [Google Scholar]
  46. BoloukiA. ZalF. Mostafavi-pourZ. BakhtariA. Protective effects of quercetin on uterine receptivity markers and blastocyst implantation rate in diabetic pregnant mice.Taiwan. J. Obstet. Gynecol.202059692793410.1016/j.tjog.2020.09.038 33218414
    [Google Scholar]
  47. LupoG. CambriaM.T. OlivieriM. RoccoC. CaporarelloN. LongoA. ZanghìG. SalmeriM. FotiM.C. AnfusoC.D. Anti‐angiogenic effect of quercetin and its 8‐methyl pentamethyl ether derivative in human microvascular endothelial cells.J. Cell. Mol. Med.201923106565657710.1111/jcmm.14455 31369203
    [Google Scholar]
  48. HaoJ. ChenC. HuangK. HuangJ. LiJ. LiuP. HuangH. Polydatin improves glucose and lipid metabolism in experimental diabetes through activating the Akt signaling pathway.Eur. J. Pharmacol.201474515216510.1016/j.ejphar.2014.09.047 25310908
    [Google Scholar]
  49. CohenP. GoedertM. GSK3 inhibitors: Development and therapeutic potential.Nat. Rev. Drug Discov.20043647948710.1038/nrd1415 15173837
    [Google Scholar]
  50. PengJ. LiQ. LiK. ZhuL. LinX. LinX. ShenQ. LiG. XieX. Quercetin improves glucose and lipid metabolism of diabetic rats: Involvement of Akt signaling and SIRT1.J. Diabetes Res.2017201711010.1155/2017/3417306 29379801
    [Google Scholar]
  51. RahmaniA.H. AlsahliM.A. KhanA.A. AlmatroodiS.A. Quercetin, a plant flavonol attenuates diabetic complications, renal tissue damage, renal oxidative stress and inflammation in streptozotocin-induced diabetic rats.Metabolites202313113010.3390/metabo13010130 36677055
    [Google Scholar]
  52. DiniS. ZakeriM. EbrahimpourS. DehghanianF. EsmaeiliA. Quercetin conjugated superparamagnetic iron oxide nanoparticles modulate glucose metabolism-related genes and miR-29 family in the hippocampus of diabetic rats.Sci. Rep.2021111861810.1038/s41598‑021‑87687‑w 33883592
    [Google Scholar]
  53. PennutoM. PandeyU.B. PolancoM.J. Insulin-like growth factor 1 signaling in motor neuron and polyglutamine diseases: From molecular pathogenesis to therapeutic perspectives.Front. Neuroendocrinol.20205710082110.1016/j.yfrne.2020.100821 32006533
    [Google Scholar]
  54. WrigleyS. ArafaD. TropeaD. Insulin-like growth factor 1: At the crossroads of brain development and aging.Front. Cell. Neurosci.2017111410.3389/fncel.2017.00014 28203146
    [Google Scholar]
  55. KrebberM.M. van DijkC.G.M. VernooijR.W.M. BrandtM.M. EmterC.A. RauC.D. FledderusJ.O. DunckerD.J. VerhaarM.C. ChengC. JolesJ.A. Matrix metalloproteinases and tissue inhibitors of metalloproteinases in extracellular matrix remodeling during left ventricular diastolic dysfunction and heart failure with preserved ejection fraction: A systematic review and meta-analysis.Int. J. Mol. Sci.20202118674210.3390/ijms21186742 32937927
    [Google Scholar]
  56. UemuraS. MatsushitaH. LiW. GlassfordA.J. AsagamiT. LeeK.H. HarrisonD.G. TsaoP.S. Diabetes mellitus enhances vascular matrix metalloproteinase activity: Role of oxidative stress.Circ. Res.200188121291129810.1161/hh1201.092042 11420306
    [Google Scholar]
  57. BoťanskáB. BartekováM. FerenczyováK. FogarassyováM. KindernayL. BarančíkM. Matrix metalloproteinases and their role in mechanisms underlying effects of quercetin on heart function in aged zucker diabetic fatty rats.Int. J. Mol. Sci.2021229445710.3390/ijms22094457 33923282
    [Google Scholar]
/content/journals/ctmc/10.2174/0115680266361598250212030220
Loading
/content/journals/ctmc/10.2174/0115680266361598250212030220
Loading

Data & Media loading...


  • Article Type:
    Research Article
Keyword(s): bioinformatics; molecular docking; molecular dynamics; network pharmacology; Quercetin; T2D
This is a required field
Please enter a valid email address
Approval was a Success
Invalid data
An Error Occurred
Approval was partially successful, following selected items could not be processed due to error
Please enter a valid_number test