Amalgamated Pharmacoinformatics Study to Investigate the Mechanism of Xiao Jianzhong Tang against Chronic Atrophic Gastritis

  • 作者: Lian X.1, Fan K.2, Qin X.3, Liu Y.4
  • 隶属关系:
    1. Modern Research Center for Traditional Chinese Medicine, The Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University
    2. Modern Research Center for Traditional Chinese Medicine, The Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education,, Shanxi University
    3. Modern Research Center for Traditional Chinese Medicine, The Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education,, Shanxi University,
    4. Modern Research Center for Traditional Chinese Medicine, The Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University,
  • 期: 卷 20, 编号 5 (2024)
  • 页面: 598-615
  • 栏目: Chemistry
  • URL: https://ruspoj.com/1573-4099/article/view/644179
  • DOI: https://doi.org/10.2174/1573409919666230720141115
  • ID: 644179

如何引用文章

全文:

详细

Background:Traditional Chinese medicine (TCM) Xiaojianzhong Tang (XJZ) has a favorable efficacy in the treatment of chronic atrophic gastritis (CAG). However, its pharmacological mechanism has not been fully explained.

Objective:The purpose of this study was to find the potential mechanism of XJZ in the treatment of CAG using pharmacocoinformatics approaches.

Methods:Network pharmacology was used to screen out the key compounds and key targets, MODELLER and GNNRefine were used to repair and refine proteins, Autodock vina was employed to perform molecular docking, Δ Lin_F9XGB was used to score the docking results, and Gromacs was used to perform molecular dynamics simulations (MD).

Results:Kaempferol, licochalcone A, and naringenin, were obtained as key compounds, while AKT1, MAPK1, MAPK14, RELA, STAT1, and STAT3 were acquired as key targets. Among docking results, 12 complexes scored greater than five. They were run for 50ns MD. The free binding energy of AKT1-licochalcone A and MAPK1-licochalcone A was less than -15 kcal/mol and AKT1-naringenin and STAT3-licochalcone A was less than -9 kcal/mol. These complexes were crucial in XJZ treating CAG.

Conclusion:Our findings suggest that licochalcone A could act on AKT1, MAPK1, and STAT3, and naringenin could act on AKT1 to play the potential therapeutic effect on CAG. The work also provides a powerful approach to interpreting the complex mechanism of TCM through the amalgamation of network pharmacology, deep learning-based protein refinement, molecular docking, machine learning-based binding affinity estimation, MD simulations, and MM-PBSA-based estimation of binding free energy.

作者简介

Xu Lian

Modern Research Center for Traditional Chinese Medicine, The Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University

Email: info@benthamscience.net

Kaidi Fan

Modern Research Center for Traditional Chinese Medicine, The Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education,, Shanxi University

Email: info@benthamscience.net

Xuemei Qin

Modern Research Center for Traditional Chinese Medicine, The Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education,, Shanxi University,

编辑信件的主要联系方式.
Email: info@benthamscience.net

Yuetao Liu

Modern Research Center for Traditional Chinese Medicine, The Key Laboratory of Chemical Biology and Molecular Engineering of Ministry of Education, Shanxi University,

编辑信件的主要联系方式.
Email: info@benthamscience.net

参考

  1. Koulis, A.; Buckle, A.; Boussioutas, A. Premalignant lesions and gastric cancer: Current understanding. World J. Gastrointest. Oncol., 2019, 11(9), 665-678. doi: 10.4251/wjgo.v11.i9.665 PMID: 31558972
  2. Li, Y.; Xia, R.; Zhang, B.; Li, C. Chronic Atrophic Gastritis: A Review. J. Environ. Pathol. Toxicol. Oncol., 2018, 37(3), 241-259. doi: 10.1615/JEnvironPatholToxicolOncol.2018026839 PMID: 30317974
  3. Rodriguez-Castro, K.I.; Franceschi, M.; Noto, A.; Miraglia, C.; Nouvenne, A.; Leandro, G.; Meschi, T.; De’ Angelis, G.L.; Di Mario, F. Clinical manifestations of chronic atrophic gastritis. Acta Biomed., 2018, 89(8-S), 88-92. PMID: 30561424
  4. Woodford, A.M.; Chaudhry, R.; Conte, G.A.; Gupta, V.; Anne, M. Chronic atrophic gastritis presenting as hemolytic anemia due to severe Vitamin B12 deficiency. Case Rep. Hematol., 2021, 2021, 1-5. doi: 10.1155/2021/9571072 PMID: 34373795
  5. Wei, W.; Lin, S.; Zhu, Y. Effects of Anwei decoction on the protein expression of TFF in rats with chronic atrophic gastritis. Mod. Res. Inflamm., 2014, 3(1), 1-6. doi: 10.4236/mri.2014.31001
  6. Ou, J.; Wang, L. Efficacy of Self-made Hewei Decoction for chronic atrophic gastritis and its effect on gastrin and pepsinogen expression levels. Contrast Media Mol. Imaging, 2022, 2022, 1-8. doi: 10.1155/2022/1092695 PMID: 35694708
  7. Wen, J.; Wu, S.; Ma, X.; Zhao, Y. Zuojin Pill attenuates Helicobacter pylori-induced chronic atrophic gastritis in rats and improves gastric epithelial cells function in GES-1 cells. J. Ethnopharmacol., 2022, 285, 114855. doi: 10.1016/j.jep.2021.114855 PMID: 34808298
  8. Yin, J.; Yi, J.; Yang, C.; Xu, B.; Lin, J.; Hu, H.; Wu, X.; Shi, H.; Fei, X. Weiqi Decoction attenuated chronic atrophic gastritis with precancerous lesion through regulating microcirculation disturbance and HIF-1α signaling pathway. Evid. Based Complement. Alternat. Med., 2019, 2019, 1-12. doi: 10.1155/2019/2651037 PMID: 31320912
  9. Guo, C.Y. Observation on the curative effect of Xiaojianzhong Decoction in treating chronic gastritis. Mod J Integr Tradit Chin West Med, 2022, 2, 2464-2465.
  10. Li, S.; Zhang, B. Traditional Chinese medicine network pharmacology: Theory, methodology and application. Chin. J. Nat. Med., 2013, 11(2), 110-120. doi: 10.1016/S1875-5364(13)60037-0 PMID: 23787177
  11. Meng, X.Y.; Zhang, H.X.; Mezei, M.; Cui, M. Molecular docking: A powerful approach for structure-based drug discovery. Curr. Computeraided Drug Des., 2011, 7(2), 146-157. doi: 10.2174/157340911795677602 PMID: 21534921
  12. Chai, X-L.; Pan, Q.; Zhang, Z-Q.; Tian, C-Y.; Yu, T.; Yang, R. Effect and signaling pathways of Nelumbinis folium in the treatment of hyperlipidemia assessed by network pharmacology. World J. Tradit. Chin. Med., 2021, 7(4), 445-455. doi: 10.4103/2311-8571.328619
  13. Zhao, T.T.; Lan, R.R.; Liang, S.D.; Schmalzing, G.; Gao, H.W.; Verkhratsky, A.; He, C.H.; Nie, H. An exploration in the potential substance basis and mechanism of Chuanxiong Rhizoma and Angelicae Dahuricae Radix on analgesia based on network pharmacology and molecular docking. World J. Tradit. Chin. Med., 2021, 7(2), 201-208.
  14. Karplus, M.; McCammon, J.A. Molecular dynamics simulations of biomolecules. Nat. Struct. Biol., 2002, 9(9), 646-652. doi: 10.1038/nsb0902-646 PMID: 12198485
  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. doi: 10.1186/1758-2946-6-13 PMID: 24735618
  16. Xu, H.Y.; Zhang, Y.Q.; Liu, Z.M.; Chen, T.; Lv, C.Y.; Tang, S.H.; Zhang, X.B.; Zhang, W.; Li, Z.Y.; Zhou, R.R.; Yang, H.J.; Wang, X.J.; Huang, L.Q. ETCM: An encyclopaedia of traditional Chinese medicine. Nucleic Acids Res., 2019, 47(D1), D976-D982. doi: 10.1093/nar/gky987 PMID: 30365030
  17. Xiong, G.; Wu, Z.; Yi, J.; Fu, L.; Yang, Z.; Hsieh, C.; Yin, M.; Zeng, X.; Wu, C.; Lu, A.; Chen, X.; Hou, T.; Cao, D. ADMETlab 2.0: An integrated online platform for accurate and comprehensive predictions of ADMET properties. Nucleic Acids Res., 2021, 49(W1), W5-W14. doi: 10.1093/nar/gkab255 PMID: 33893803
  18. Daina, A.; Michielin, O.; Zoete, V. SwissADME: A free web tool to evaluate pharmacokinetics, drug-likeness and medicinal chemistry friendliness of small molecules. Sci. Rep., 2017, 7(1), 42717. doi: 10.1038/srep42717 PMID: 28256516
  19. Nickel, J.; Gohlke, B.O.; Erehman, J.; Banerjee, P.; Rong, W.W.; Goede, A.; Dunkel, M.; Preissner, R. SuperPred: Update on drug classification and target prediction. Nucleic Acids Res., 2014, 42(W1), W26-W31. doi: 10.1093/nar/gku477 PMID: 24878925
  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. doi: 10.1093/nar/gkz382 PMID: 31106366
  21. Liu, X.; Ouyang, S.; Yu, B.; Liu, Y.; Huang, K.; Gong, J.; Zheng, S.; Li, Z.; Li, H.; Jiang, H. PharmMapper server: A web server for potential drug target identification using pharmacophore mapping approach. Nucleic Acids Res., 2010, 38(Web Server issue)(Suppl.2), W609-W614. doi: 10.1093/nar/gkq300 PMID: 20430828
  22. Ochoa, D.; Hercules, A.; Carmona, M.; Suveges, D.; Gonzalez-Uriarte, A.; Malangone, C.; Miranda, A.; Fumis, L.; Carvalho-Silva, D.; Spitzer, M.; Baker, J.; Ferrer, J.; Raies, A.; Razuvayevskaya, O.; Faulconbridge, A.; Petsalaki, E.; Mutowo, P.; Machlitt-Northen, S.; Peat, G.; McAuley, E.; Ong, C.K.; Mountjoy, E.; Ghoussaini, M.; Pierleoni, A.; Papa, E.; Pignatelli, M.; Koscielny, G.; Karim, M.; Schwartzentruber, J.; Hulcoop, D.G.; Dunham, I.; McDonagh, E.M. Open Targets Platform: Supporting systematic drug–target identification and prioritisation. Nucleic Acids Res., 2021, 49(D1), D1302-D1310. doi: 10.1093/nar/gkaa1027 PMID: 33196847
  23. Hamosh, A.; Scott, A.F.; Amberger, J.S.; Bocchini, C.A.; McKusick, V.A. Online Mendelian Inheritance in Man (OMIM), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res., 2005, 33(Database issue), D514-D517. doi: 10.1093/nar/gki033 PMID: 15608251
  24. Stelzer, G.; Rosen, N.; Plaschkes, I.; Zimmerman, S.; Twik, M.; Fishilevich, S.; Stein, T.I.; Nudel, R.; Lieder, I.; Mazor, Y.; Kaplan, S.; Dahary, D.; Warshawsky, D.; Guan-Golan, Y.; Kohn, A.; Rappaport, N.; Safran, M.; Lancet, D. The GeneCards Suite.Practical Guide to Life Science Databases; Springer Nature: London, 2022, pp. 27-56.
  25. Piñero, J.; Bravo, À.; Queralt-Rosinach, N.; Gutiérrez-Sacristán, A.; Deu-Pons, J.; Centeno, E.; García-García, J.; Sanz, F.; Furlong, L.I. DisGeNET: A comprehensive platform integrating information on human disease-associated genes and variants. Nucleic Acids Res., 2017, 45(D1), D833-D839. doi: 10.1093/nar/gkw943 PMID: 27924018
  26. UniProt Consortium. T. UniProt: The universal protein knowledgebase. Nucleic Acids Res., 2018, 46(5), 2699. doi: 10.1093/nar/gky092 PMID: 29425356
  27. Shannon, P.; Markiel, A.; Ozier, O.; Baliga, N.S.; Wang, J.T.; Ramage, D.; Amin, N.; Schwikowski, B.; Ideker, T. Cytoscape: A software environment for integrated models of biomolecular interaction networks. Genome Res., 2003, 13(11), 2498-2504. doi: 10.1101/gr.1239303 PMID: 14597658
  28. Huang, D.W.; Sherman, B.T.; Lempicki, R.A. Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources. Nat. Protoc., 2009, 4(1), 44-57. doi: 10.1038/nprot.2008.211 PMID: 19131956
  29. Sherman, B.T.; Hao, M.; Qiu, J.; Jiao, X.; Baseler, M.W.; Lane, H.C.; Imamichi, T.; Chang, W. DAVID: A web server for functional enrichment analysis and functional annotation of gene lists (2021 update). Nucleic Acids Res., 2022, 50(W1), W216-W221. doi: 10.1093/nar/gkac194 PMID: 35325185
  30. Bader, G.D.; Hogue, C.W.V. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinformatics, 2003, 4(1), 2. doi: 10.1186/1471-2105-4-2 PMID: 12525261
  31. Chin, C.H.; Chen, S.H.; Wu, H.H.; Ho, C.W.; Ko, M.T.; Lin, C.Y. cytoHubba: Identifying hub objects and sub-networks from complex interactome. BMC Syst. Biol., 2014, 8(S4)(Suppl. 4), S11. doi: 10.1186/1752-0509-8-S4-S11 PMID: 25521941
  32. Xie, C.; Mao, X.; Huang, J.; Ding, Y.; Wu, J.; Dong, S.; Kong, L.; Gao, G.; Li, C.Y.; Wei, L. KOBAS 2.0: A web server for annotation and identification of enriched pathways and diseases. Nucleic Acids Res., 2011, 39(Web Server issue)(Suppl. 2), W316-W322. doi: 10.1093/nar/gkr483 PMID: 21715386
  33. Martí-Renom, M.A.; Stuart, A.C.; Fiser, A.; Sánchez, R.; Melo, F.; Šali, A. Comparative protein structure modeling of genes and genomes. Annu. Rev. Biophys. Biomol. Struct., 2000, 29(1), 291-325. doi: 10.1146/annurev.biophys.29.1.291 PMID: 10940251
  34. Webb, B.; Sali, A. Comparative protein structure modeling using MODELLER. Curr. Protoc. Bioinformatics, 2016, 5, 5-6. doi: 10.1002/cpbi.3
  35. Eswar, N.; Webb, B.; Marti-Renom, M.A.; Madhusudhan, M.S.; Eramian, D.; Shen, M.Y.; Pieper, U.; Sali, A. Comparative protein structure modeling using Modeller; Curr. Protoc. Bioinformatics, 2006. Chapter 5, 6. PMID: 18428767
  36. Jing, X.; Xu, J. Fast and effective protein model refinement using deep graph neural networks. Nature Computational Science, 2021, 1(7), 462-469. doi: 10.1038/s43588-021-00098-9 PMID: 35321360
  37. Chen, V.B.; Arendall, W.B., III; Headd, J.J.; Keedy, D.A.; Immormino, R.M.; Kapral, G.J.; Murray, L.W.; Richardson, J.S.; Richardson, D.C. MolProbity: All-atom structure validation for macromolecular crystallography. Acta Crystallogr. D Biol. Crystallogr., 2010, 66(1), 12-21. doi: 10.1107/S0907444909042073 PMID: 20057044
  38. Zhang, J.; Zhang, Y. A novel side-chain orientation dependent potential derived from random-walk reference state for protein fold selection and structure prediction. PLoS One, 2010, 5(10), e15386. doi: 10.1371/journal.pone.0015386 PMID: 21060880
  39. Volkamer, A.; Kuhn, D.; Rippmann, F.; Rarey, M. DoGSiteScorer: A web server for automatic binding site prediction, analysis and druggability assessment. Bioinformatics, 2012, 28(15), 2074-2075. doi: 10.1093/bioinformatics/bts310 PMID: 22628523
  40. Trott, O.; Olson, A.J. AutoDock Vina: Improving the speed and accuracy of docking with a new scoring function, efficient optimization, and multithreading. J. Comput. Chem., 2010, 31(2), 455-461. PMID: 19499576
  41. Cetin, A. In silico studies on stilbenolignan analogues as SARS-CoV-2 Mpro inhibitors. Chem. Phys. Lett., 2021, 771, 138563. doi: 10.1016/j.cplett.2021.138563 PMID: 33776065
  42. Cetin, A. Some flavolignans as potent Sars-Cov-2 inhibitors via molecular docking, molecular dynamic simulations and ADME analysis. Curr. Computeraided Drug Des., 2022, 18(5), 337-346. doi: 10.2174/1573409918666220816113516 PMID: 35975852
  43. Laskowski, R.A.; Swindells, M.B. LigPlot+: Multiple ligand-protein interaction diagrams for drug discovery. J. Chem. Inf. Model., 2011, 51(10), 2778-2786. doi: 10.1021/ci200227u PMID: 21919503
  44. Yang, C.; Zhang, Y. Delta machine learning to improve scoring-ranking-screening performances of protein–ligand scoring functions. J. Chem. Inf. Model., 2022, 62(11), 2696-2712. doi: 10.1021/acs.jcim.2c00485 PMID: 35579568
  45. 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. doi: 10.1016/j.softx.2015.06.001
  46. Zenodo. GROMACS 2022 Manual. 2020. Available From: https://zenodo.org/record/7037337
  47. Lu, T.; Chen, F. Multiwfn: A multifunctional wavefunction analyzer. J. Comput. Chem., 2012, 33(5), 580-592. doi: 10.1002/jcc.22885 PMID: 22162017
  48. Frank, N. Software update: The ORCA program system, version 4.0 Comput. Mol. Sci, 2018, 8(1), e1327. doi: 10.1002/wcms.1327
  49. Frank, N. Software update: The ORCA program system, version 5.0 Comput. Mol. Sci, 2022, 12(5), e1606. doi: 10.1002/wcms.1606
  50. Lu, T. Sobtop: A tool of generating forcefield parameters and GROMACS topology file. 2022. Available From: sobereva.com/soft/Sobtop
  51. Turner, P.J. Center for Coastal And Land-Margin Research (CCALMR); Oregon Graduate Institute of Science and Technology.: Oregon, 2005.
  52. Jing, X. GNNRefine: Fast and effective protein model refinement by deep graph neural networks; , 2021. Available From: https://codeocean.com/capsule/5769140/tree/v1
  53. Lobanov, M.Iu.; Bogatyreva, N.S.; Galzitskaia, O.V. Radius of gyration is indicator of compactness of protein structure. Mol. Biol., 2008, 42(4), 701-706. PMID: 18856071
  54. Borjian Boroujeni, M.; Shahbazi Dastjerdeh, M.; Shokrgozar, M.A.; Rahimi, H.; Omidinia, E. Computational driven molecular dynamics simulation of keratinocyte growth factor behavior at different pH conditions. Informatics in Medicine Unlocked, 2021, 23, 100514. doi: 10.1016/j.imu.2021.100514
  55. Hao, Y.; Zhang, C.; Sun, Y.; Xu, H. Licochalcone A inhibits cell proliferation, migration, and invasion through regulating the PI3K/AKT signaling pathway in oral squamous cell carcinoma. OncoTargets Ther., 2019, 12, 4427-4435. doi: 10.2147/OTT.S201728 PMID: 31239711
  56. Chen, X.; Liu, Z.; Meng, R.; Shi, C.; Guo, N. Antioxidative and anticancer properties of Licochalcone A from licorice. J. Ethnopharmacol., 2017, 198, 331-337. doi: 10.1016/j.jep.2017.01.028 PMID: 28111219
  57. Shu, J.; Cui, X.; Liu, X.; Yu, W.; Zhang, W.; Huo, X.; Lu, C. Licochalcone A inhibits IgE-mediated allergic reaction through PLC/ERK/STAT3 pathway. Int. J. Immunopathol. Pharmacol., 2022, 36, 3946320221135462. doi: 10.1177/03946320221135462 PMID: 36263976
  58. Wu, J.; Ye, X.; Yang, S.; Yu, H.; Zhong, L.; Gong, Q. Systems pharmacology study of the anti-liver injury mechanism of citri reticulatae pericarpium. Front. Pharmacol., 2021, 12, 618846. doi: 10.3389/fphar.2021.618846 PMID: 33912040
  59. Panche, A.N.; Diwan, A.D.; Chandra, S.R. Flavonoids: An overview. J. Nutr. Sci., 2016, 5, e47. doi: 10.1017/jns.2016.41 PMID: 28620474
  60. Chu, X.; Ci, X.; Wei, M.; Yang, X.; Cao, Q.; Guan, M.; Li, H.; Deng, Y.; Feng, H.; Deng, X. Licochalcone a inhibits lipopolysaccharide-induced inflammatory response in vitro and in vivo. J. Agric. Food Chem., 2012, 60(15), 3947-3954. doi: 10.1021/jf2051587 PMID: 22400806
  61. Furuhashi, I.; Iwata, S.; Sato, T.; Inoue, H.; Shibata, S. Inhibition by licochalcone A, a novel flavonoid isolated from liquorice root, of IL-1β-induced PGE2 production in human skin fibroblasts. J. Pharm. Pharmacol., 2010, 57(12), 1661-1666. doi: 10.1211/jpp.57.12.0017 PMID: 16354411
  62. Chang, J.; Zhang, Y.; Shen, N.; Zhou, J.; Zhang, H. MiR-129-5p prevents depressive-like behaviors by targeting MAPK1 to suppress inflammation. Exp. Brain Res., 2021, 239(11), 3359-3370. doi: 10.1007/s00221-021-06203-8 PMID: 34482419
  63. Lee, H.; Jeong, A.J.; Ye, S.K. Highlighted STAT3 as a potential drug target for cancer therapy. BMB Rep., 2019, 52(7), 415-423. doi: 10.5483/BMBRep.2019.52.7.152 PMID: 31186087
  64. Pan, C.; Liu, Q.; Wu, X. HIF1α/miR-520a-3p/AKT1/mTOR feedback promotes the proliferation and glycolysis of gastric cancer cells. Cancer Manag. Res., 2019, 11, 10145-10156. doi: 10.2147/CMAR.S223473 PMID: 31819647
  65. Xue, L.; Zhang, W.J.; Fan, Q.X.; Wang, L.X. Licochalcone A inhibits PI3K/Akt/mTOR signaling pathway activation and promotes autophagy in breast cancer cells. Oncol. Lett., 2018, 15(2), 1869-1873. PMID: 29399197
  66. Huang, C.F.; Yang, S.F.; Chiou, H.L.; Hsu, W.H.; Hsu, J.C.; Liu, C.J.; Hsieh, Y.H. Licochalcone A inhibits the invasive potential of human glioma cells by targeting the MEK/ERK and ADAM9 signaling pathways. Food Funct., 2018, 9(12), 6196-6204. doi: 10.1039/C8FO01643G PMID: 30465574
  67. Funakoshi-Tago, M.; Tago, K.; Nishizawa, C.; Takahashi, K.; Mashino, T.; Iwata, S.; Inoue, H.; Sonoda, Y.; Kasahara, T. Licochalcone A is a potent inhibitor of TEL-Jak2-mediated transformation through the specific inhibition of Stat3 activation. Biochem. Pharmacol., 2008, 76(12), 1681-1693. doi: 10.1016/j.bcp.2008.09.012 PMID: 18848530
  68. Fukai, T.; Marumo, A.; Kaitou, K.; Kanda, T.; Terada, S.; Nomura, T. Anti-Helicobacter pylori flavonoids from licorice extract. Life Sci., 2002, 71(12), 1449-1463. doi: 10.1016/S0024-3205(02)01864-7 PMID: 12127165
  69. Park, J.M.; Park, S.H.; Hong, K.S.; Han, Y.M.; Jang, S.H.; Kim, E.H.; Hahm, K.B. Special licorice extracts containing lowered glycyrrhizin and enhanced licochalcone A prevented Helicobacter pylori-initiated, salt diet-promoted gastric tumorigenesis. Helicobacter, 2014, 19(3), 221-236. doi: 10.1111/hel.12121 PMID: 24646026
  70. Den Hartogh, D.J.; Tsiani, E. Antidiabetic properties of naringenin: A citrus fruit polyphenol. Biomolecules, 2019, 9(3), 99. doi: 10.3390/biom9030099 PMID: 30871083
  71. Ge, Y.; Chen, H.; Wang, J.; Liu, G.; Cui, S.W.; Kang, J.; Jiang, Y.; Wang, H. Naringenin prolongs lifespan and delays aging mediated by IIS and MAPK in Caenorhabditis elegans. Food Funct., 2021, 12(23), 12127-12141. doi: 10.1039/D1FO02472H PMID: 34787618
  72. Wu, J.; Ye, X.; Yang, S.; Yu, H.; Zhong, L.; Gong, Q. Systems pharmacology study of the anti-liver injury mechanism of citri reticulatae pericarpium. Front Pharmacol, 2021, 12, 618846. doi: 10.3389/fphar.2021.61884

补充文件

附件文件
动作
1. JATS XML
下载

版权所有 © Bentham Science Publishers, 2024