In Silico Analysis of Tea Leaf Compounds Targeting Inflammatory Pathways and Acne-Related Genes
DOI:
https://doi.org/10.15408/jkv.v11i2.46468Keywords:
Acne vulgaris, Camellia sinensis, differentially expressed genes, key genes, PI3K/AktAbstract
Acne (Acne vulgaris) is a chronic skin disease affected by Cutibacterium acnes infection and inflammatory pathways that trigger innate immune responses, such as inflammasome activation. The expression of inflammation-related genes plays a critical role in acne pathogenesis and immune modulation. This study aims to identify compounds from tea leaves (Camellia sinensis var. assamica) that can treat acne by influencing the expression of inflammatory-related genes through in silico analysis. The GSE6475 dataset was utilized to identify differentially expressed genes (DEGs) between acne-affected and normal skin samples (each group n=6). A total of 573 DEGs were identified and mapped to the KEGG inflammatory pathway. The hub gene analysis results showed six genes, including CXCL1, STAT1, and PIK3 (adj. P-value < 0.05). These key genes were then used to cross-validate skin grouping with acne lesions and normal skin. The structure of compounds (natural products) in tea leaves (C. sinensis var. assamica) was obtained from the PubChem database, and their activity against target proteins associated with the identified key genes was predicted using the SkelSpheres descriptor and Support Vector Regression method. This quantitative structure–activity relationship (QSAR)-based machine learning approach was selected because it enables high-throughput prediction of inhibitory potential using chemical descriptors and experimentally derived bioactivity data, providing broader predictive power than conventional molecular docking or molecular dynamics, which rely mainly on structural and energetic estimations. The in-silico prediction results showed that compounds such as theobromine, assamsaponin, procyanidin, and caffeine have exhibited good predicted activity (IC₅₀ ranging from 1.125 to 1.320 μM) as potential inhibitors of the PI3K/Akt pathway, which is known to play a role in the pathogenesis of acne.
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References
1. Vasam M, Korutla S, Bohara RA. Acne vulgaris: A review of the pathophysiology, treatment, and recent nanotechnology based advances. Biochem Biophys Rep. 2023;36:101578. doi:10.1016/j.bbrep.2023.101578
2. Ramli R, Malik AS, Hani AFM, Jamil A. Acne analysis, grading and computational assessment methods: an overview. Skin Res Technol. 2012;18(1):1-14. doi:10.1111/j.1600-0846.2011.00542.x
3. Mias C, Mengeaud V, Bessou‐Touya S, Duplan H. Recent advances in understanding inflammatory acne: Deciphering the relationship between Cutibacterium acnes and Th17 inflammatory pathway. J. Eur. Acad. Dermatol. Venereol. 2023;37(S2):3-11. doi:10.1111/jdv.18794
4. Tan MFJKL, Stein Gold MLF, Alexis MMAF, Harper MJC. Current Concepts in Acne Pathogenesis: Pathways to Inflammation. Semin Cutan Med Surg. 2018;37(3S):S60-S62. doi:10.12788/J.SDER.2018.024
5. Rosen J, Friedman AJ. Inflammatory acne: new developments in pathogenesis and treatment. Cutis. 2014;94(6):266-267.
6. Chen B, Zheng Y, Liang Y. Analysis of Potential Genes and Pathways Involved in the Pathogenesis of Acne by Bioinformatics. Biomed Res Int. 2019;2019:1-8. doi:10.1155/2019/3739086
7. Törőcsik D, Kovács D, Póliska S, Szentkereszty-Kovács Z, Lovászi M, Hegyi K, Szegedi A, Zouboilis CC, Ståhle M. Genome wide analysis of TLR1/2- and TLR4-activated SZ95 sebocytes reveals a complex immune-competence and identifies serum amyloid A as a marker for activated sebaceous glands. PLoS One. 2018;13(6):e0198323. doi:10.1371/journal.pone.0198323
8. Trivedi NR, Gilliland KL, Zhao W, Liu W, Thiboutot DM. Gene Array Expression Profiling in Acne Lesions Reveals Marked Upregulation of Genes Involved in Inflammation and Matrix Remodeling. J. Investig. Dermatol. 2006;126(5):1071-1079. doi:10.1038/sj.jid.5700213
9. Widyaningrum N, Fudholi A, S, Setyowati EP. Stability of Epigallocatechin Gallate (EGCG) from Green Tea (Camellia sinensis) and its Antibacterial Activity against Staphylococcus epidermidis ATCC 35984 and Propionibacterium acnes ATCC 6919. Asian J. Biol. Sci.. 2015;8(2):93-101. doi:10.3923/ajbs.2015.93.101
10. Im M, Kim SY, Sohn KC, Choi D et al. Epigallocatechin-3-Gallate Suppresses IGF-I-Induced Lipogenesis and Cytokine Expression in SZ95 Sebocytes. J. Investig. Dermatol. 2012;132(12):2700-2708. doi:10.1038/jid.2012.202
11. Fasihah MS, Hadi RS, Mustofa S. Effects of Green Tea Leaf Extract on Viability, Apoptosis, and Expression of Interleukin-6 in Keloid Fibroblasts. Mutiara Medika: Jurnal Kedokteran dan Kesehatan. 2023;24(1):9-18. doi:10.18196/mmjkk.v24i1.20146
12. Sugihartini N, Fudholi A, Pramono S, Sismindari S. Validasi Metode Analisa Penetapan Kadar Epigalokatekin Galat Dengan Kromatografi Cair Kinerja Tinggi. Pharmaciana. 2014;4(2). doi:10.12928/pharmaciana.v4i2.1567
13. Rachmania RA, Hariyanti, Zikriah R, Soultan A. In Silico Study of Alkaloid Herba Bakung Putih (Crinum Asiaticum L.) on Inhibition of Cyclooxygenase Enzyme (COX). Jurnal Kimia Valensi. 2018;4(2):124-136.
14. Demšar J. Orange: Data Mining Toolbox in Python. J Mach Learn Res. 2013;14:2349-2353.
15. Sander T, Freyss J, von Korff M, Rufener C. DataWarrior: An Open-Source Program For Chemistry Aware Data Visualization And Analysis. J Chem Inf Model. 2015;55(2):460-473. doi:10.1021/ci500588j
16. Sayers EW, Beck J, Bolton EE, et al. Database resources of the National Center for Biotechnology Information. Nucleic Acids Res. 2021;49(D1):D10-D17. doi:10.1093/nar/gkaa892
17. Clough E, Barrett T, Wilhite SE, et al. NCBI GEO: archive for gene expression and epigenomics data sets: 23-year update. Nucleic Acids Res. 2024;52(D1):D138-D144. doi:10.1093/nar/gkad965
18. Yu D, Lim J, Wang X, Liang F, Xiao G. Enhanced construction of gene regulatory networks using hub gene information. BMC Bioinformatics. 2017;18(1):186. doi:10.1186/s12859-017-1576-1
19. Frimayanti N, Yam ML, Lee HB, Othman R, Zain SM, Rahman NA. Validation of quantitative structure-activity relationship (QSAR) model for photosensitizer activity prediction. Int J Mol Sci. 2011;12(12):8626-8644. doi:10.3390/ijms12128626
20. Zou YF, Zhang SY, Li LW, et al. Hub genes for early diagnosis and therapy of adamantinomatous craniopharyngioma. Medicine. 2022;101(37):e30278. doi:10.1097/MD.0000000000030278
21. Khanduja DK, Kaur S. The Categorization of Documents Using Support Vector Machines. International Journal of Scientific Research in Computer Science and Engineering. 2023;11(6):1-12. doi:10.26438/ijsrcse/v11i6.112
22. Li J, Ding J, Zhi DU, Gu K, Wang H. Identification of Type 2 Diabetes Based on a Ten-Gene Biomarker Prediction Model Constructed Using a Support Vector Machine Algorithm. Biomed Res Int. 2022;2022:1-15. doi:10.1155/2022/1230761
23. C. Melnik B. Acneigenic Stimuli Converge in Phosphoinositol-3 Kinase/ Akt/Foxo1 Signal Transduction. J. Clin. Exp. Dermatol. 2010;01(01). doi:10.4172/2155-9554.1000101
24. Bharti S, Vadlamudi HC. A strategic review on the involvement of receptors, transcription factors and hormones in acne pathogenesis. J. Recept. Signal Transduct.. 2021;41(2):105-116. doi:10.1080/10799893.2020.1805626
25. Awad SM, Tawfik YM, El‐Mokhtar MA, El‐Gazzar AF, Abdel Motaleb AA. Activation of Janus kinase signaling pathway in acne lesions. Dermatol Ther. 2021;34(1). doi:10.1111/dth.14563
26. Burke SJ, Lu D, Sparer TE, et al. NF-κB and STAT1 control CXCL1 and CXCL2 gene transcription. Am. J. Physiol. Endocrinol. Metab. 2014;306(2):E131-E149. doi:10.1152/ajpendo.00347.2013
27. Nguyen H, Ramana C V., Bayes J, Stark GR. Roles of Phosphatidylinositol 3-Kinase in Interferon-γ-dependent Phosphorylation of STAT1 on Serine 727 and Activation of Gene Expression. J. Biol. Chem.. 2001;276(36):33361-33368. doi:10.1074/jbc.M105070200
28. Indrayanto G, Putra GS, Suhud F. Validation of in-vitro bioassay methods: Application in herbal drug research. In: ; 2021:273-307. doi:10.1016/bs.podrm.2020.07.005
29. Singh N, Shreshtha AK, Thakur MS, Patra S. Xanthine scaffold: scope and potential in drug development. Heliyon. 2018;4(10):e00829. doi:10.1016/j.heliyon.2018.e00829
30. Mattos MMG, Filho SA, Martins GR, et al. Antimicrobial and antibiofilm properties of procyanidins: potential for clinical and biotechnological applications. Crit Rev Microbiol. Published online September 20, 2024:1-24. doi:10.1080/1040841X.2024.2404509
31. Ruan S, Xiang S, Wu W, et al. Potential role of mTORC1 and the PI3K-Akt pathway in anti-acne properties of licorice flavonoids. J Funct Foods. 2020;70:103968. doi:10.1016/j.jff.2020.103968
32. Lin Y, Jiao Y, Yuan Y, et al. Propionibacterium acnes induces intervertebral disc degeneration by promoting nucleus pulposus cell apoptosis via the TLR2/JNK/mitochondrial-mediated pathway. Emerg Microbes Infect. 2018;7(1):1-8. doi:10.1038/s41426-017-0002-0
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