Network Pharmacology, Molecular Docking and Experimental Verification Revealing the Mechanism of Fule Cream against Childhood Atopic Dermatitis

  • Authors: Liu C.1, Liu Y.2, Liu Y.1, Guan J.3, Gao Y.4, Ou L.1, Qi Y.1, Lv X.5, Zhang J.1
  • Affiliations:
    1. Drug Clinical Trial Institution, Children's Hospital of Capital Institute of Pediatrics
    2. Immunology and Cancer Pharmacology Group, State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College
    3. Preparation Research Laboratory, Children’s Hospital, Capital Institute of Pediatrics,
    4. Department of Dermatology,, Children’s Hospital, Capital Institute of Pediatrics
    5. Immunology and Cancer Pharmacology Group, State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College,
  • Issue: Vol 20, No 6 (2024)
  • Pages: 860-875
  • Section: Chemistry
  • URL: https://rjpbr.com/1573-4099/article/view/644379
  • DOI: https://doi.org/10.2174/0115734099257922230925074407
  • ID: 644379

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Full Text

Abstract

Background:The Fule Cream (FLC) is an herbal formula widely used for the treatment of pediatric atopic dermatitis (AD), however, the main active components and functional mechanisms of FLC remain unclear. This study performed an initial exploration of the potential acting mechanisms of FLC in childhood AD treatment through analyses of an AD mouse model using network pharmacology, molecular docking technology, and RNA-seq analysis.

Materials and Methods:The main bioactive ingredients and potential targets of FLC were collected from the Traditional Chinese Medicine Systems Pharmacology Database (TCMSP) and SwissTargetPrediction databases. An herb-compound-target network was built using Cytoscape 3.7.2. The disease targets of pediatric AD were searched in the DisGeNET, Therapeutic Target Database (TTD), OMIM, DrugBank and GeneCards databases. The overlapping targets between the active compounds and the disease were imported into the STRING database for the construction of the protein-protein interaction (PPI) network. Gene Ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of the intersection targets were performed, and molecular docking verification of the core compounds and targets was then performed using AutoDock Vina 1.1.2. The AD mouse model for experimental verification was induced by MC903.

Results:The herb-compound-target network included 415 nodes and 1990 edges. Quercetin, luteolin, beta-sitosterol, wogonin, ursolic acid, apigenin, stigmasterol, kaempferol, sitogluside and myricetin were key nodes. The targets with higher degree values were IL-4, IL-10, IL-1α, IL-1β, TNFα, CXCL8, CCL2, CXCL10, CSF2, and IL-6. GO enrichment and KEGG analyses illustrated that important biological functions involved response to extracellular stimulus, regulation of cell adhesion and migration, inflammatory response, cellular response to cytokine stimulus, and cytokine receptor binding. The signaling pathways in the FLC treatment of pediatric AD mainly involve the PI3K-Akt signaling pathway, cytokine‒cytokine receptor interaction, chemokine signaling pathway, TNF signaling pathway, and NF-κB signaling pathway. The binding energy scores of the compounds and targets indicate a good binding activity. Luteolin, quercetin, and kaempferol showed a strong binding activity with TNFα and IL-4.

Conclusion:This study illustrates the main bioactive components and potential mechanisms of FLC in the treatment of childhood AD, and provides a basis and reference for subsequent exploration.

About the authors

Chang Liu

Drug Clinical Trial Institution, Children's Hospital of Capital Institute of Pediatrics

Email: info@benthamscience.net

Yuxin Liu

Immunology and Cancer Pharmacology Group, State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College

Email: info@benthamscience.net

Yi Liu

Drug Clinical Trial Institution, Children's Hospital of Capital Institute of Pediatrics

Email: info@benthamscience.net

Jing Guan

Preparation Research Laboratory, Children’s Hospital, Capital Institute of Pediatrics,

Email: info@benthamscience.net

Ying Gao

Department of Dermatology,, Children’s Hospital, Capital Institute of Pediatrics

Email: info@benthamscience.net

Ling Ou

Drug Clinical Trial Institution, Children's Hospital of Capital Institute of Pediatrics

Email: info@benthamscience.net

Yuenan Qi

Drug Clinical Trial Institution, Children's Hospital of Capital Institute of Pediatrics

Email: info@benthamscience.net

Xiaoxi Lv

Immunology and Cancer Pharmacology Group, State Key Laboratory of Bioactive Substance and Function of Natural Medicines, Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College,

Author for correspondence.
Email: info@benthamscience.net

Jianmin Zhang

Drug Clinical Trial Institution, Children's Hospital of Capital Institute of Pediatrics

Author for correspondence.
Email: info@benthamscience.net

References

  1. Langan, S.M.; Irvine, A.D.; Weidinger, S. Atopic dermatitis. Lancet, 2020, 396(10247), 345-360. doi: 10.1016/S0140-6736(20)31286-1 PMID: 32738956
  2. Weidinger, S.; Beck, L.A.; Bieber, T.; Kabashima, K.; Irvine, A.D. Atopic dermatitis. Nat. Rev. Dis. Primers, 2018, 4(1), 1. doi: 10.1038/s41572-018-0001-z PMID: 29930242
  3. Bieber, T. Atopic dermatitis: An expanding therapeutic pipeline for a complex disease. Nat. Rev. Drug Discov., 2022, 21(1), 21-40. doi: 10.1038/s41573-021-00266-6 PMID: 34417579
  4. Silverberg, J.I. Public health burden and epidemiology of atopic dermatitis. Dermatol. Clin., 2017, 35(3), 283-289. doi: 10.1016/j.det.2017.02.002 PMID: 28577797
  5. Ariëns, L.; Nimwegen, K.; Shams, M.; Bruin, D.; Schaft, J.; Os-Medendorp, H.; Bruin-Weller, M. Economic burden of adult patients with moderate to severe atopic dermatitis indicated for systemic treatment. Acta Derm. Venereol., 2019, 99(9), 762-768. doi: 10.2340/00015555-3212 PMID: 31073619
  6. Tsoi, L.C.; Rodriguez, E.; Stölzl, D.; Wehkamp, U.; Sun, J.; Gerdes, S.; Sarkar, M.K.; Hübenthal, M.; Zeng, C.; Uppala, R.; Xing, X.; Thielking, F.; Billi, A.C.; Swindell, W.R.; Shefler, A.; Chen, J.; Patrick, M.T.; Harms, P.W.; Kahlenberg, J.M.; Perez White, B.E.; Maverakis, E.; Gudjonsson, J.E.; Weidinger, S. Progression of acute-to-chronic atopic dermatitis is associated with quantitative rather than qualitative changes in cytokine responses. J. Allergy Clin. Immunol., 2020, 145(5), 1406-1415. doi: 10.1016/j.jaci.2019.11.047 PMID: 31891686
  7. Tsoi, L.C.; Rodriguez, E.; Degenhardt, F.; Baurecht, H.; Wehkamp, U.; Volks, N.; Szymczak, S.; Swindell, W.R.; Sarkar, M.K.; Raja, K.; Shao, S.; Patrick, M.; Gao, Y.; Uppala, R.; Perez White, B.E.; Getsios, S.; Harms, P.W.; Maverakis, E.; Elder, J.T.; Franke, A.; Gudjonsson, J.E.; Weidinger, S. Atopic dermatitis is an IL-13–dominant disease with greater molecular heterogeneity compared to psoriasis. J. Invest. Dermatol., 2019, 139(7), 1480-1489. doi: 10.1016/j.jid.2018.12.018 PMID: 30641038
  8. Gittler, J.K.; Shemer, A.; Suárez-Fariñas, M.; Fuentes-Duculan, J.; Gulewicz, K.J.; Wang, C.Q.F.; Mitsui, H.; Cardinale, I.; de Guzman Strong, C.; Krueger, J.G.; Guttman-Yassky, E. Progressive activation of TH2/TH22 cytokines and selective epidermal proteins characterizes acute and chronic atopic dermatitis. J. Allergy Clin. Immunol., 2012, 130(6), 1344-1354. doi: 10.1016/j.jaci.2012.07.012 PMID: 22951056
  9. Renert-Yuval, Y.; Del Duca, E.; Pavel, A.B.; Fang, M.; Lefferdink, R.; Wu, J.; Diaz, A.; Estrada, Y.D.; Canter, T.; Zhang, N.; Wagner, A.; Chamlin, S.; Krueger, J.G.; Guttman-Yassky, E.; Paller, A.S. The molecular features of normal and atopic dermatitis skin in infants, children, adolescents, and adults. J. Allergy Clin. Immunol., 2021, 148(1), 148-163. doi: 10.1016/j.jaci.2021.01.001 PMID: 33453290
  10. Mancuso, J.B.; Lee, S.S.; Paller, A.S.; Ohya, Y.; Eichenfield, L.F. Management of severe atopic dermatitis in pediatric patients. J. Allergy Clin. Immunol. Pract., 2021, 9(4), 1462-1471. doi: 10.1016/j.jaip.2021.02.017 PMID: 33838839
  11. Mandlik, D.S.; Mandlik, S.K. Atopic dermatitis: New insight into the etiology, pathogenesis, diagnosis and novel treatment strategies. Immunopharmacol. Immunotoxicol., 2021, 43(2), 105-125. doi: 10.1080/08923973.2021.1889583 PMID: 33645388
  12. Wang, Z.; Xia, Q.; Liu, X.; Liu, W.; Huang, W.; Mei, X.; Luo, J.; Shan, M.; Lin, R.; Zou, D.; Ma, Z. Phytochemistry, pharmacology, quality control and future research of Forsythia suspensa (Thunb.) Vahl: A review. J. Ethnopharmacol., 2018, 210, 318-339. doi: 10.1016/j.jep.2017.08.040 PMID: 28887216
  13. Hao, Y.; Li, D.; Piao, X.; Piao, X. Forsythia suspensa extract alleviates hypersensitivity induced by soybean β-conglycinin in weaned piglets. J. Ethnopharmacol., 2010, 128(2), 412-418. doi: 10.1016/j.jep.2010.01.035 PMID: 20083183
  14. Sung, Y.Y.; Lee, A.Y.; Kim, H.K. Forsythia suspensa fruit extracts and the constituent matairesinol confer anti-allergic effects in an allergic dermatitis mouse model. J. Ethnopharmacol., 2016, 187, 49-56. doi: 10.1016/j.jep.2016.04.015 PMID: 27085937
  15. Sung, Y.Y.; Yoon, T.; Jang, S.; Kim, H.K. Forsythia suspensa suppresses house dust mite extract-induced atopic dermatitis in NC/Nga mice. PLoS One, 2016, 11(12), e0167687. doi: 10.1371/journal.pone.0167687 PMID: 27936051
  16. Zhang, H.; Sun, X.; Qi, H.; Ma, Q.; Zhou, Q.; Wang, W.; Wang, K. Pharmacological inhibition of the temperature-sensitive and Ca 2+ -Permeable transient receptor potential vanilloid trpv3 channel by natural forsythoside b attenuates pruritus and cytotoxicity of keratinocytes. J. Pharmacol. Exp. Ther., 2019, 368(1), 21-31. doi: 10.1124/jpet.118.254045 PMID: 30377214
  17. Kim, H.; Yang, B.; Lee, H-B.; Kim, S.; Park, Y.C.; Kim, K. Decoction of Dictamnus Dasycarpus Turcz. Root bark ameliorates skin lesions and inhibits inflammatory reactions in mice with contact dermatitis. Pharmacogn. Mag., 2017, 13(51), 483-487. doi: 10.4103/0973-1296.211034 PMID: 28839376
  18. Gao, P.; Wang, L.; Zhao, L.; Zhang, Q.; Zeng, K.; Zhao, M.; Jiang, Y.; Tu, P.; Guo, X. Anti-inflammatory quinoline alkaloids from the root bark of Dictamnus dasycarpus. Phytochemistry, 2020, 172, 112260. doi: 10.1016/j.phytochem.2020.112260 PMID: 31982646
  19. Kim, H.; Kim, M.; Kim, H.; San Lee, G.; Gun An, W.; In Cho, S. Anti-inflammatory activities of Dictamnus dasycarpus Turcz., root bark on allergic contact dermatitis induced by dinitrofluorobenzene in mice. J. Ethnopharmacol., 2013, 149(2), 471-477. doi: 10.1016/j.jep.2013.06.055 PMID: 23850712
  20. Chu, X.; Wei, M.; Yang, X.; Cao, Q.; Xie, X.; Guan, M.; Wang, D.; Deng, X. Effects of an anthraquinone derivative from Rheum officinale Baill, emodin, on airway responses in a murine model of asthma. Food Chem. Toxicol., 2012, 50(7), 2368-2375. doi: 10.1016/j.fct.2012.03.076 PMID: 22484343
  21. Lin, Y.C.; Yang, C.C.; Lin, C.H.; Hsia, T.C.; Chao, W.C.; Lin, C.C. Atractylodin ameliorates ovalbumin induced asthma in a mouse model and exerts immunomodulatory effects on Th2 immunity and dendritic cell function. Mol. Med. Rep., 2020, 22(6), 4909-4918. doi: 10.3892/mmr.2020.11569 PMID: 33174031
  22. Hopkins, A.L. Network pharmacology: The next paradigm in drug discovery. Nat. Chem. Biol., 2008, 4(11), 682-690. doi: 10.1038/nchembio.118 PMID: 18936753
  23. Wang, Y.; Yuan, Y.; Wang, W.; He, Y.; Zhong, H.; Zhou, X.; Chen, Y.; Cai, X.J.; Liu, L. Mechanisms underlying the therapeutic effects of Qingfeiyin in treating acute lung injury based on GEO datasets, network pharmacology and molecular docking. Comput. Biol. Med., 2022, 145, 105454. doi: 10.1016/j.compbiomed.2022.105454 PMID: 35367781
  24. Li, X.; Wei, S.; Niu, S.; Ma, X.; Li, H.; Jing, M.; Zhao, Y. Network pharmacology prediction and molecular docking-based strategy to explore the potential mechanism of Huanglian Jiedu Decoction against sepsis. Comput. Biol. Med., 2022, 144, 105389. doi: 10.1016/j.compbiomed.2022.105389 PMID: 35303581
  25. 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
  26. 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
  27. Tao, W.; Xu, X.; Wang, X.; Li, B.; Wang, Y.; Li, Y.; Yang, L. Network pharmacology-based prediction of the active ingredients and potential targets of Chinese herbal Radix Curcumae formula for application to cardiovascular disease. J. Ethnopharmacol., 2013, 145(1), 1-10. doi: 10.1016/j.jep.2012.09.051 PMID: 23142198
  28. 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. doi: 10.1093/nar/gkac956 PMID: 36305812
  29. Piñero, J.; Ramírez-Anguita, J.M.; Saüch-Pitarch, J.; Ronzano, F.; Centeno, E.; Sanz, F.; Furlong, L.I. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res., 2020, 48(D1), D845-D855. PMID: 31680165
  30. Zhou, Y.; Zhang, Y.; Lian, X.; Li, F.; Wang, C.; Zhu, F.; Qiu, Y.; Chen, Y. Therapeutic target database update 2022: Facilitating drug discovery with enriched comparative data of targeted agents. Nucleic Acids Res., 2022, 50(D1), D1398-D1407. doi: 10.1093/nar/gkab953 PMID: 34718717
  31. Amberger, J.S.; Bocchini, C.A.; Schiettecatte, F.; Scott, A.F.; Hamosh, A. OMIM.org: Online Mendelian Inheritance in Man (OMIM®), an online catalog of human genes and genetic disorders. Nucleic Acids Res., 2015, 43(D1), D789-D798. doi: 10.1093/nar/gku1205 PMID: 25428349
  32. Wishart, D.S.; Feunang, Y.D.; Guo, A.C.; Lo, E.J.; Marcu, A.; Grant, J.R.; Sajed, T.; Johnson, D.; Li, C.; Sayeeda, Z.; Assempour, N.; Iynkkaran, I.; Liu, Y.; Maciejewski, A.; Gale, N.; Wilson, A.; Chin, L.; Cummings, R.; Le, D.; Pon, A.; Knox, C.; Wilson, M. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res., 2018, 46(D1), D1074-D1082. doi: 10.1093/nar/gkx1037 PMID: 29126136
  33. 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: From gene data mining to disease genome sequence analyses. Curr. Protoc. Bioinformatics, 2016, 54, 1.30.1-1.30.33.
  34. 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
  35. Tang, Y.; Li, M.; Wang, J.; Pan, Y.; Wu, F.X. CytoNCA: A cytoscape plugin for centrality analysis and evaluation of protein interaction networks. Biosystems, 2015, 127, 67-72. doi: 10.1016/j.biosystems.2014.11.005 PMID: 25451770
  36. Raman, K.; Damaraju, N.; Joshi, G.K. The organisational structure of protein networks: Revisiting the centrality–lethality hypothesis. Syst. Synth. Biol., 2014, 8(1), 73-81. doi: 10.1007/s11693-013-9123-5 PMID: 24592293
  37. Missiuro, P.V.; Liu, K.; Zou, L.; Ross, B.C.; Zhao, G.; Liu, J.S.; Ge, H. Information flow analysis of interactome networks. PLOS Comput. Biol., 2009, 5(4), e1000350. doi: 10.1371/journal.pcbi.1000350 PMID: 19503817
  38. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2021. Available from: https://www.eea.europa.eu/data-and-maps/indicators/oxygen-consuming-substances-in-rivers/r-development-core-team-2006
  39. Chen, H. R package VennDiagram: Generate High-Resolution Venn and Euler Plots. 2021. Available from: https://rdrr.io/cran/VennDiagram/#:~:text=A%20set%20of%20functions%20to,of%20plot%20shape%20and%20structure.
  40. 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. 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. doi: 10.1093/nar/gky1131 PMID: 30476243
  41. 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), S11. doi: 10.1186/1752-0509-8-S4-S11 PMID: 25521941
  42. Zhou, Y.; Zhou, B.; Pache, L.; Chang, M.; Khodabakhshi, A.H.; Tanaseichuk, O.; Benner, C.; Chanda, S.K. Metascape provides a biologist-oriented resource for the analysis of systems-level datasets. Nat. Commun., 2019, 10(1), 1523. doi: 10.1038/s41467-019-09234-6 PMID: 30944313
  43. Wickham, H. ggplot2: Elegant Graphics for Data Analysis; Springer, 2016.
  44. Neuwirth, E. RColorBrewer: ColorBrewer Palettes(Version 1.1-2). 2014. Available from: http://cran.nexr.com/web/packages/RColorBrewer/index.html
  45. Csardi, G. The igraph software package for complex network research. InterJ. Complex Syst., 2006, 1695(5), 1-9.
  46. Wu, T.; Hu, E.; Xu, S.; Chen, M.; Guo, P.; Dai, Z.; Feng, T.; Zhou, L.; Tang, W.; Zhan, L.; Fu, X.; Liu, S.; Bo, X.; Yu, G. clusterProfiler 4.0: A universal enrichment tool for interpreting omics data. Innovation, 2021, 2(3), 100141. doi: 10.1016/j.xinn.2021.100141 PMID: 34557778
  47. Walter, W.; Sánchez-Cabo, F.; Ricote, M. GOplot: An R package for visually combining expression data with functional analysis. Bioinformatics, 2015, 31(17), 2912-2914. doi: 10.1093/bioinformatics/btv300 PMID: 25964631
  48. 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
  49. 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 2019 update: Improved access to chemical data. Nucleic Acids Res., 2019, 47(D1), D1102-D1109. doi: 10.1093/nar/gky1033 PMID: 30371825
  50. 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. doi: 10.1186/1758-2946-3-33 PMID: 21982300
  51. Morris, G.M.; Huey, R.; Lindstrom, W.; Sanner, M.F.; Belew, R.K.; Goodsell, D.S.; Olson, A.J. AutoDock4 and AutoDockTools4: Automated docking with selective receptor flexibility. J. Comput. Chem., 2009, 30(16), 2785-2791. doi: 10.1002/jcc.21256 PMID: 19399780
  52. Seeliger, D.; de Groot, B.L. Ligand docking and binding site analysis with PyMOL and Autodock/Vina. J. Comput. Aided Mol. Des., 2010, 24(5), 417-422. doi: 10.1007/s10822-010-9352-6 PMID: 20401516
  53. Burley, S.K.; Bhikadiya, C.; Bi, C.; Bittrich, S.; Chen, L.; Crichlow, G.V.; Christie, C.H.; Dalenberg, K.; Di Costanzo, L.; Duarte, J.M.; Dutta, S.; Feng, Z.; Ganesan, S.; Goodsell, D.S.; Ghosh, S.; Green, R.K.; Guranović, V.; Guzenko, D.; Hudson, B.P.; Lawson, C.L.; Liang, Y.; Lowe, R.; Namkoong, H.; Peisach, E.; Persikova, I.; Randle, C.; Rose, A.; Rose, Y.; Sali, A.; Segura, J.; Sekharan, M.; Shao, C.; Tao, Y.P.; Voigt, M.; Westbrook, J.D.; Young, J.Y.; Zardecki, C.; Zhuravleva, M. RCSB Protein Data Bank: Powerful new tools for exploring 3D structures of biological macromolecules for basic and applied research and education in fundamental biology, biomedicine, biotechnology, bioengineering and energy sciences. Nucleic Acids Res., 2021, 49(D1), D437-D451. doi: 10.1093/nar/gkaa1038 PMID: 33211854
  54. DeLano, W.L. The PyMOL Molecular Graphics System (Version 2.5.0). 2021. Available from: https://mybiosoftware.com/pymol-molecular-visualization-system.html
  55. 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
  56. Dai, J.; Choo, M.K.; Park, J.M.; Fisher, D.E. Topical ror inverse agonists suppress inflammation in mouse models of atopic dermatitis and acute irritant dermatitis. J. Invest. Dermatol., 2017, 137(12), 2523-2531. doi: 10.1016/j.jid.2017.07.819 PMID: 28774591
  57. Hou, D.D.; Zhang, W.; Gao, Y.L.; Sun, Y.; Wang, H.X.; Qi, R.Q.; Chen, H.D.; Gao, X.H. Anti-inflammatory effects of quercetin in a mouse model of MC903-induced atopic dermatitis. Int. Immunopharmacol., 2019, 74, 105676. doi: 10.1016/j.intimp.2019.105676 PMID: 31181406
  58. Karuppagounder, V.; Arumugam, S.; Thandavarayan, R.A.; Sreedhar, R.; Giridharan, V.V.; Watanabe, K. Molecular targets of quercetin with anti-inflammatory properties in atopic dermatitis. Drug Discov. Today, 2016, 21(4), 632-639. doi: 10.1016/j.drudis.2016.02.011 PMID: 26905599
  59. Karuppagounder, V.; Arumugam, S.; Thandavarayan, R.A.; Pitchaimani, V.; Sreedhar, R.; Afrin, R.; Harima, M.; Suzuki, H.; Nomoto, M.; Miyashita, S.; Suzuki, K.; Nakamura, M.; Watanabe, K. Modulation of HMGB1 translocation and RAGE/NF κ B cascade by quercetin treatment mitigates atopic dermatitis in NC/Nga transgenic mice. Exp. Dermatol., 2015, 24(6), 418-423. doi: 10.1111/exd.12685 PMID: 25739980
  60. Lee, H.N.; Shin, S.A.; Choo, G.S.; Kim, H.J.; Park, Y.S.; Kim, B.S.; Kim, S.K.; Cho, S.D.; Nam, J.S.; Choi, C.S.; Che, J.H.; Park, B.K.; Jung, J.Y. Anti inflammatory effect of quercetin and galangin in LPS stimulated RAW264.7 macrophages and DNCB induced atopic dermatitis animal models. Int. J. Mol. Med., 2018, 41(2), 888-898. PMID: 29207037
  61. Beken, B.; Serttas, R.; Yazicioglu, M.; Turkekul, K.; Erdogan, S. Quercetin improves inflammation, oxidative stress, and impaired wound healing in atopic dermatitis model of human keratinocytes. Pediatr. Allergy Immunol. Pulmonol., 2020, 33(2), 69-79. doi: 10.1089/ped.2019.1137 PMID: 34678092
  62. Gendrisch, F.; Esser, P.R.; Schempp, C.M.; Wölfle, U. Luteolin as a modulator of skin aging and inflammation. Biofactors, 2021, 47(2), 170-180. doi: 10.1002/biof.1699 PMID: 33368702
  63. Gugliandolo, E.; Palma, E.; Cordaro, M.; D’Amico, R.; Peritore, A.F.; Licata, P.; Crupi, R. Canine atopic dermatitis: Role of luteolin as new natural treatment. Vet. Med. Sci., 2020, 6(4), 926-932. doi: 10.1002/vms3.325 PMID: 32741111
  64. Han, N.R.; Kim, H.M.; Jeong, H.J. The β-sitosterol attenuates atopic dermatitis-like skin lesions through down-regulation of TSLP. Exp. Biol. Med., 2014, 239(4), 454-464. doi: 10.1177/1535370213520111 PMID: 24510054
  65. Moon, P.D.; Han, N.R.; Lee, J.; Kim, H.M.; Jeong, H.J. Ursolic acid downregulates thymic stromal lymphopoietin through the blockade of intracellular calcium/caspase 1/NF κB signaling cascade in HMC 1 cells. Int. J. Mol. Med., 2019, 43(5), 2252-2258. doi: 10.3892/ijmm.2019.4144 PMID: 30976816
  66. Yano, S.; Umeda, D.; Yamashita, S.; Yamada, K.; Tachibana, H. Dietary apigenin attenuates the development of atopic dermatitis-like skin lesions in NC/Nga mice. J. Nutr. Biochem., 2009, 20(11), 876-881. doi: 10.1016/j.jnutbio.2008.08.002 PMID: 18993046
  67. Che, D.N.; Cho, B.O.; Shin, J.Y.; Kang, H.J.; Kim, J.S.; Oh, H.; Kim, Y.S.; Jang, S.I. Apigenin inhibits IL-31 cytokine in human mast cell and mouse skin tissues. Molecules, 2019, 24(7), 1290. doi: 10.3390/molecules24071290 PMID: 30987029
  68. Lee, H.S.; Jeong, G.S. Therapeutic effect of kaempferol on atopic dermatitis by attenuation of T cell activity via interaction with multidrug resistance‐associated protein 1. Br. J. Pharmacol., 2021, 178(8), 1772-1788. doi: 10.1111/bph.15396 PMID: 33555623
  69. Hou, D.D.; Gu, Y.J.; Wang, D.C.; Niu, Y.; Xu, Z.R.; Jin, Z.Q.; Wang, X.X.; Li, S.J. Therapeutic effects of myricetin on atopic dermatitis in vivo and in vitro. Phytomedicine, 2022, 102, 154200. doi: 10.1016/j.phymed.2022.154200 PMID: 35671605
  70. Behniafard, N.; Gharagozlou, M.; Farhadi, E.; Khaledi, M.; Sotoudeh, S.; Darabi, B.; Fathi, S.M.; Gholizadeh Moghaddam, Z.; Mahmoudi, M.; Aghamohammadi, A.; Amirzargar, A.A.; Rezaei, N. TNF-alpha single nucleotide polymorphisms in atopic dermatitis. Eur. Cytokine Netw., 2012, 23(4), 163-165. doi: 10.1684/ecn.2012.0323 PMID: 23328497
  71. Gharagozlou, M.; Farhadi, E.; Khaledi, M.; Behniafard, N.; Sotoudeh, S.; Salari, R.; Darabi, B.; Fathi, S.M.; Mahmoudi, M.; Aghamohammadi, A.; Amirzargar, A.A.; Rezaei, N. Association between the interleukin 6 genotype at position -174 and atopic dermatitis. J. Investig. Allergol. Clin. Immunol., 2013, 23(2), 89-93. PMID: 23654074
  72. Stavric, K.; Peova, S.; Trajkov, D.; Spiroski, M. Gene polymorphisms of 22 cytokines in Macedonian children with atopic dermatitis. Iran. J. Allergy Asthma Immunol., 2012, 11(1), 37-50. PMID: 22427475
  73. Kayserova, J.; Sismova, K.; Zentsova-Jaresova, I.; Katina, S.; Vernerova, E.; Polouckova, A.; Capkova, S.; Malinova, V.; Striz, I.; Sediva, A. A prospective study in children with a severe form of atopic dermatitis: Clinical outcome in relation to cytokine gene polymorphisms. J. Investig. Allergol. Clin. Immunol., 2012, 22(2), 92-101. PMID: 22533231
  74. Hulshof, L.; Hack, D.P.; Hasnoe, Q.C.J.; Dontje, B.; Jakasa, I.; Riethmüller, C.; McLean, W.H.I.; Aalderen, W.M.C.; van’t Land, B.; Kezic, S.; Sprikkelman, A.B.; Middelkamp-Hup, M.A. A minimally invasive tool to study immune response and skin barrier in children with atopic dermatitis. Br. J. Dermatol., 2019, 180(3), 621-630. doi: 10.1111/bjd.16994 PMID: 29989151
  75. Lyubchenko, T.; Collins, H.K.; Goleva, E.; Leung, D.Y.M. Skin tape sampling technique identifies proinflammatory cytokines in atopic dermatitis skin. Ann. Allergy Asthma Immunol., 2021, 126(1), 46-53.e2. doi: 10.1016/j.anai.2020.08.397 PMID: 32896640
  76. Lee, Y.; Choi, H.K.; N’deh, K.P.U.; Choi, Y.J.; Fan, M.; Kim, E.; Chung, K.H.; An, J.H. Inhibitory effect of centella asiatica extract on dncb-induced atopic dermatitis in hacat cells and BALB/c mice. Nutrients, 2020, 12(2), 411. doi: 10.3390/nu12020411 PMID: 32033291
  77. Danso, M.O.; van Drongelen, V.; Mulder, A.; van Esch, J.; Scott, H.; van Smeden, J.; El Ghalbzouri, A.; Bouwstra, J.A. TNF-α and Th2 cytokines induce atopic dermatitis-like features on epidermal differentiation proteins and stratum corneum lipids in human skin equivalents. J. Invest. Dermatol., 2014, 134(7), 1941-1950. doi: 10.1038/jid.2014.83 PMID: 24518171
  78. Howell, M.D.; Kim, B.E.; Gao, P.; Grant, A.V.; Boguniewicz, M.; DeBenedetto, A.; Schneider, L.; Beck, L.A.; Barnes, K.C.; Leung, D.Y.M. Cytokine modulation of atopic dermatitis filaggrin skin expression. J. Allergy Clin. Immunol., 2007, 120(1), 150-155. doi: 10.1016/j.jaci.2007.04.031 PMID: 17512043

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