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image of Therapeutic Potential of Quercetin in Type 2 Diabetes Based on a Network Pharmacology Study

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.

© 2025 Bentham Science Publishers
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2025-02-13
2025-04-16

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References

  1. Galicia-Garcia U. Benito-Vicente A. Jebari S. Larrea-Sebal A. Siddiqi H. Uribe K.B. Ostolaza H. Martín C. Pathophysiology of type 2 diabetes mellitus. Int. J. Mol. Sci. 2020 21 17 6275 10.3390/ijms21176275 32872570
    [Google Scholar]
  2. ElSayed N.A. Aleppo G. Aroda V.R. Bannuru R.R. Brown F.M. Bruemmer D. Collins B.S. Gaglia J.L. Hilliard M.E. Isaacs D. Johnson E.L. Kahan S. Khunti K. Leon J. Lyons S.K. Perry M.L. Prahalad P. Pratley R.E. Seley J.J. Stanton R.C. Gabbay R.A. 2. Classification and diagnosis of diabetes: Standards of care in diabetes—2023. Diabetes Care 2023 46 Suppl. 1 S19 S40 10.2337/dc23‑S002 36507649
    [Google Scholar]
  3. Laakso M. Biomarkers for type 2 diabetes. Mol. Metab. 2019 27 Suppl. S139 S146 10.1016/j.molmet.2019.06.016 31500825
    [Google Scholar]
  4. IDF Diabetes Atlas 2021. 2021 Available from: https://diabetesatlas.org/resources/?gad_source=1&gclid=Cj0KCQiAvvO7BhC-ARIsAGFyToXnl4htywlYna4M2_bWuG6tUlkOpQ65Hx6QeYE-vpWQyD4wvQDdtoIaAgiLEALw_wcB
  5. Artasensi A. Pedretti A. Vistoli G. Fumagalli L. Type 2 diabetes mellitus: A review of multi-target drugs. Molecules 2020 25 8 1987 10.3390/molecules25081987 32340373
    [Google Scholar]
  6. Martínez-Esquivias F. Guzmán-Flores J.M. Pérez-Larios A. Rico J.L. Becerra-Ruiz J.S. A review of the effects of Gold, Silver, Selenium, and Zinc nanoparticles on diabetes mellitus in murine models. Mini Rev. Med. Chem. 2021 21 14 1798 1812 10.2174/18755607MTEziOTEv4 33535949
    [Google Scholar]
  7. McGovern A. Tippu Z. Hinton W. Munro N. Whyte M. de Lusignan S. Comparison of medication adherence and persistence in type 2 diabetes: A systematic review and meta‐analysis. Diabetes Obes. Metab. 2018 20 4 1040 1043 10.1111/dom.13160 29135080
    [Google Scholar]
  8. Buse J.B. Wexler D.J. Tsapas A. Rossing P. Mingrone G. Mathieu C. D’Alessio D.A. Davies M.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 Care 2020 43 2 487 493 10.2337/dci19‑0066 31857443
    [Google Scholar]
  9. Pivari F. Mingione A. Brasacchio C. Soldati L. Curcumin and type 2 diabetes mellitus: Prevention and treatment. Nutrients 2019 11 8 1837 10.3390/nu11081837 31398884
    [Google Scholar]
  10. Kashyap D. Garg V.K. Tuli H.S. Yerer M.B. Sak K. Sharma A.K. Kumar M. Aggarwal V. Sandhu S.S. Fisetin and quercetin: Promising flavonoids with chemopreventive potential. Biomolecules 2019 9 5 174 10.3390/biom9050174 31064104
    [Google Scholar]
  11. Deepika Maurya P.K. Health benefits of quercetin in age-related diseases. Molecules 2022 27 8 2498 10.3390/molecules27082498 35458696
    [Google Scholar]
  12. Ansari P. Choudhury S.T. Seidel V. Rahman A.B. Aziz M.A. Richi A.E. Rahman A. Jafrin U.H. Hannan J.M.A. Abdel-Wahab Y.H.A. Therapeutic potential of quercetin in the management of type-2 diabetes mellitus. Life 2022 12 8 1146 10.3390/life12081146 36013325
    [Google Scholar]
  13. Shi L. Wang J. He C. Huang Y. Fu W. Zhang H. An Y. Wang M. Shan Z. Li H. Lv Y. Wang C. Cheng L. Dai H. Duan Y. Zhao H. Zhao B. 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. 2023 23 1 308 10.1186/s12906‑023‑04140‑3 37667364
    [Google Scholar]
  14. Noor F. Tahir ul Qamar M. Ashfaq U.A. Albutti A. Alwashmi A.S.S. Aljasir M.A. Network pharmacology approach for medicinal plants: Review and assessment. Pharmaceuticals 2022 15 5 572 10.3390/ph15050572 35631398
    [Google Scholar]
  15. Martínez-Esquivias F. Guzmán-Flores J.M. Chávez-Díaz I.F. Iñiguez-Muñoz L.E. Reyes-Chaparro A. Pharmacological network study on the effect of 6-Gingerol on cervical cancer using computerized databases. j. biomol. struct. dyn. 2023 ••• 1 12 37776009
    [Google Scholar]
  16. Jiao X. Jin X. Ma Y. Yang Y. Li J. Liang L. Liu R. Li Z. 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. 2021 90 107402 10.1016/j.compbiolchem.2020.107402 33338839
    [Google Scholar]
  17. Guzmán-Flores J.M. Pérez-Vázquez V. Martínez-Esquivias F. Isiordia-Espinoza M.A. Viveros-Paredes J.M. Molecular docking integrated with network pharmacology explores the therapeutic mechanism of Cannabis sativa against type 2 diabetes. Curr. Issues Mol. Biol. 2023 45 9 7228 7241 10.3390/cimb45090457 37754241
    [Google Scholar]
  18. Rappaport N. Twik M. Plaschkes I. Nudel R. Iny Stein T. Levitt J. Gershoni M. Morrey C.P. Safran M. Lancet D. MalaCards: An amalgamated human disease compendium with diverse clinical and genetic annotation and structured search. Nucleic Acids Res. 2017 45 D1 D877 D887 10.1093/nar/gkw1012 27899610
    [Google Scholar]
  19. Piñero J. Saüch J. Sanz F. Furlong L.I. The DisGeNET cytoscape app: Exploring and visualizing disease genomics data. Comput. Struct. Biotechnol. J. 2021 19 2960 2967 10.1016/j.csbj.2021.05.015 34136095
    [Google Scholar]
  20. 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 10.1093/nar/gkz382 31106366
    [Google Scholar]
  21. Davis A.P. Wiegers T.C. Wiegers J. Wyatt B. Johnson R.J. Sciaky D. Barkalow F. Strong M. Planchart A. Mattingly C.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. 2023 195 2 155 168 10.1093/toxsci/kfad069 37486259
    [Google Scholar]
  22. Venny 2.1.0 2023 Available from: https://bioinfogp.cnb.csic.es/tools/venny/
  23. Ge S.X. Jung D. Yao R. Shiny G.O. ShinyGO: A graphical gene-set enrichment tool for animals and plants. Bioinformatics 2020 36 8 2628 2629 10.1093/bioinformatics/btz931 31882993
    [Google Scholar]
  24. Otasek D. Morris J.H. Bouças J. Pico A.R. Demchak B. Cytoscape Automation: Empowering workflow-based network analysis. Genome Biol. 2019 20 1 185 10.1186/s13059‑019‑1758‑4 31477170
    [Google Scholar]
  25. Varadi Mihaly. AlphaFold Protein Structure Database: Massively expanding the structural coverage of protein-sequence space with high-accuracy models. Nucleic Acids Res. 2022 50 D1 D439 D444
    [Google Scholar]
  26. Velankar S. Burley S.K. Kurisu G. Hoch J.C. Markley J.L. The protein data bank archive. Methods Mol. Biol. 2021 2305 3 21 10.1007/978‑1‑0716‑1406‑8_1 33950382
    [Google Scholar]
  27. Kim 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 2023 update. Nucleic Acids Res. 2023 51 D1 D1373 D1380 10.1093/nar/gkac956 36305812
    [Google Scholar]
  28. Gabella C. Duvaud S. Durinx C. Managing the life cycle of a portfolio of open data resources at the SIB Swiss Institute of Bioinformatics. Brief. Bioinform. 2022 23 1 bbab478 10.1093/bib/bbab478 34850820
    [Google Scholar]
  29. Shiah J.V. Grandis J.R. Johnson D.E. Targeting STAT3 with proteolysis targeting chimeras and next-generation Antisense Oligonucleotides. Mol. Cancer Ther. 2021 20 2 219 228 10.1158/1535‑7163.MCT‑20‑0599 33203730
    [Google Scholar]
  30. Acher F.C. Cabayé A. Eshak F. Goupil-Lamy A. Pin J.P. Metabotropic glutamate receptor orthosteric ligands and their binding sites. Neuropharmacology 2022 204 108886 10.1016/j.neuropharm.2021.108886 34813860
    [Google Scholar]
  31. Abraham M.J. Murtola T. Schulz R. Páll S. Smith J.C. Hess B. Lindahl E. GROMACS: High performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 2015 1-2 19 25 10.1016/j.softx.2015.06.001
    [Google Scholar]
  32. Jo S. Cheng X. Islam S.M. Huang L. Rui H. Zhu A. Lee H.S. Qi Y. Han W. Vanommeslaeghe K. MacKerell A.D. Jr Roux B. Im W. CHARMM-GUI PDB manipulator for advanced modeling and simulations of proteins containing nonstandard residues. Adv. Protein Chem. Struct. Biol. 2014 96 235 265 10.1016/bs.apcsb.2014.06.002 25443960
    [Google Scholar]
  33. O’Boyle N.M. Banck M. James C.A. Morley C. Vandermeersch T. Hutchison G.R. Open Babel: An open chemical toolbox. J. Cheminform. 2011 3 1 33 10.1186/1758‑2946‑3‑33 21982300
    [Google Scholar]
  34. Kim S. Lee J. Jo S. Brooks C.L. III Lee H.S. Im W. CHARMM-GUI ligand reader and modeler for CHARMM force field generation of small molecules. J. Comput. Chem. 2017 38 21 1879 1886 10.1002/jcc.24829 28497616
    [Google Scholar]
  35. Lee J. Cheng X. Swails J.M. Yeom M.S. Eastman P.K. Lemkul J.A. Wei S. Buckner J. Jeong J.C. Qi Y. Jo S. Pande V.S. Case D.A. Brooks C.L. III MacKerell A.D. Jr Klauda J.B. Im W. CHARMM-GUI input generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM simulations using the CHARMM36 additive force field. J. Chem. Theory Comput. 2016 12 1 405 413 10.1021/acs.jctc.5b00935 26631602
    [Google Scholar]
  36. Bussi G. Donadio D. Parrinello M. Canonical sampling through velocity rescaling. J. Chem. Phys. 2007 126 1 014101 10.1063/1.2408420 17212484
    [Google Scholar]
  37. Vanommeslaeghe K. Hatcher E. Acharya C. Kundu S. Zhong S. Shim J. Darian E. Guvench O. Lopes P. Vorobyov I. Mackerell A.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. 2010 31 4 671 690 10.1002/jcc.21367 19575467
    [Google Scholar]
  38. DeFronzo R.A. Ferrannini E. Groop L. Henry R.R. Herman W.H. Holst J.J. Hu F.B. Kahn C.R. Raz I. Shulman G.I. Simonson D.C. Testa M.A. Weiss R. Type 2 diabetes mellitus. Nat. Rev. Dis. Primers 2015 1 1 15019 10.1038/nrdp.2015.19 27189025
    [Google Scholar]
  39. Forbes J.M. Cooper M.E. Mechanisms of diabetic complications. Physiol. Rev. 2013 93 1 137 188 10.1152/physrev.00045.2011 23303908
    [Google Scholar]
  40. Franz M.J. Boucher J.L. Rutten-Ramos S. VanWormer J.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. 2015 115 9 1447 1463 10.1016/j.jand.2015.02.031 25935570
    [Google Scholar]
  41. Xu L. Li Y. Dai Y. Peng J. Natural products for the treatment of type 2 diabetes mellitus: Pharmacology and mechanisms. Pharmacol. Res. 2018 130 451 465 10.1016/j.phrs.2018.01.015 29395440
    [Google Scholar]
  42. Zu G. Sun K. Li L. Zu X. Han T. Huang H. Mechanism of quercetin therapeutic targets for Alzheimer disease and type 2 diabetes mellitus. Sci. Rep. 2021 11 1 22959 10.1038/s41598‑021‑02248‑5 34824300
    [Google Scholar]
  43. Wang J-Y. Nie Y-X. Dong B-Z. Cai Z-C. Zeng X-K. Du L. Zhu X. Yin X-X. Quercetin protects islet β-cells from oxidation-induced apoptosis via Sirt3 in T2DM. Iran. J. Basic Med. Sci. 2021 24 5 629 635 34249264
    [Google Scholar]
  44. Yang D.K. Kang H.S. Anti-diabetic effect of cotreatment with quercetin and resveratrol in streptozotocin-induced diabetic rats. Biomol. Ther. 2018 26 2 130 138 10.4062/biomolther.2017.254 29462848
    [Google Scholar]
  45. Zhang F. Feng J. Zhang J. Kang X. Qian D. 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. 2020 20 6 1 10.3892/etm.2020.9410 33200005
    [Google Scholar]
  46. Bolouki A. Zal F. Mostafavi-pour Z. Bakhtari A. Protective effects of quercetin on uterine receptivity markers and blastocyst implantation rate in diabetic pregnant mice. Taiwan. J. Obstet. Gynecol. 2020 59 6 927 934 10.1016/j.tjog.2020.09.038 33218414
    [Google Scholar]
  47. Lupo G. Cambria M.T. Olivieri M. Rocco C. Caporarello N. Longo A. Zanghì G. Salmeri M. Foti M.C. Anfuso C.D. Anti‐angiogenic effect of quercetin and its 8‐methyl pentamethyl ether derivative in human microvascular endothelial cells. J. Cell. Mol. Med. 2019 23 10 6565 6577 10.1111/jcmm.14455 31369203
    [Google Scholar]
  48. Hao J. Chen C. Huang K. Huang J. Li J. Liu P. Huang H. Polydatin improves glucose and lipid metabolism in experimental diabetes through activating the Akt signaling pathway. Eur. J. Pharmacol. 2014 745 152 165 10.1016/j.ejphar.2014.09.047 25310908
    [Google Scholar]
  49. Cohen P. Goedert M. GSK3 inhibitors: Development and therapeutic potential. Nat. Rev. Drug Discov. 2004 3 6 479 487 10.1038/nrd1415 15173837
    [Google Scholar]
  50. 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 1 10 10.1155/2017/3417306 29379801
    [Google Scholar]
  51. Rahmani A.H. Alsahli M.A. Khan A.A. Almatroodi S.A. Quercetin, a plant flavonol attenuates diabetic complications, renal tissue damage, renal oxidative stress and inflammation in streptozotocin-induced diabetic rats. Metabolites 2023 13 1 130 10.3390/metabo13010130 36677055
    [Google Scholar]
  52. Dini S. Zakeri M. Ebrahimpour S. Dehghanian F. Esmaeili A. Quercetin‑conjugated superparamagnetic iron oxide nanoparticles modulate glucose metabolism-related genes and miR-29 family in the hippocampus of diabetic rats. Sci. Rep. 2021 11 1 8618 10.1038/s41598‑021‑87687‑w 33883592
    [Google Scholar]
  53. Pennuto M. Pandey U.B. Polanco M.J. Insulin-like growth factor 1 signaling in motor neuron and polyglutamine diseases: From molecular pathogenesis to therapeutic perspectives. Front. Neuroendocrinol. 2020 57 100821 10.1016/j.yfrne.2020.100821 32006533
    [Google Scholar]
  54. Wrigley S. Arafa D. Tropea D. Insulin-like growth factor 1: At the crossroads of brain development and aging. Front. Cell. Neurosci. 2017 11 14 10.3389/fncel.2017.00014 28203146
    [Google Scholar]
  55. Krebber M.M. van Dijk C.G.M. Vernooij R.W.M. Brandt M.M. Emter C.A. Rau C.D. Fledderus J.O. Duncker D.J. Verhaar M.C. Cheng C. Joles J.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. 2020 21 18 6742 10.3390/ijms21186742 32937927
    [Google Scholar]
  56. Uemura S. Matsushita H. Li W. Glassford A.J. Asagami T. Lee K.H. Harrison D.G. Tsao P.S. Diabetes mellitus enhances vascular matrix metalloproteinase activity: Role of oxidative stress. Circ. Res. 2001 88 12 1291 1298 10.1161/hh1201.092042 11420306
    [Google Scholar]
  57. Boťanská B. Barteková M. Ferenczyová K. Fogarassyová M. Kindernay L. Barančík M. Matrix metalloproteinases and their role in mechanisms underlying effects of quercetin on heart function in aged zucker diabetic fatty rats. Int. J. Mol. Sci. 2021 22 9 4457 10.3390/ijms22094457 33923282
    [Google Scholar]
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Therapeutic Potential of Quercetin in Type 2 Diabetes Based on a Network Pharmacology Study
Bentham Science Publishers ; https://doi.org/10.2174/0115680266361598250212030220
/content/journals/ctmc/10.2174/0115680266361598250212030220
/content/journals/ctmc/10.2174/0115680266361598250212030220
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  • Article Type:     Research Article
Keywords: T2D ; network pharmacology ; Quercetin ; molecular docking ; bioinformatics ; molecular dynamics

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