Systems-level analysis of androgen in aging and age-related diseases: a network pharmacology, molecular docking and microbiota-integrated approach - Imankulov , Kassym; Sariyeva , Inzhu; Kudiyar , Safura; Bolat , Leila; Nurtileu , Mirat (Eurasian Journal of Applied Biotechnology, 2025)
Aging has been associated with hormonal changes, namely in androgen signaling, which contribute to the development of age-related disorders such as Parkinson’s disease and osteoporosis[1]. Network pharmacology and molecular docking were utilized in this study combined with gut microbiota data and bioinformatics databases to study the role of principal androgens-testosterone, dihydrotestosterone, androstenedione, dehydroepiandrosterone, and its sulfate ester in processes of aging. The SwissTarget Prediction and ChEMBL were utilized to predict the androgen-related targets. Disease genes were downloaded from the GeneCards and OMIM for the aging process, Parkinson’s disease, and osteoporosis. The overlap analysis was carried out by the Venny software, and protein-protein interaction networks were created by the STRING and Cytoscape [2, 3]. The GO and KEGG analyses revealed the enrichments in the xenobiotic metabolism, calcium signaling, androstenedione neuroactive ligand-receptor pathways[4]. Molecular docking demonstrated strong interactions between testosterone and hub targets such as CYP19A1 and IL6[5]. Integration of gut microbiota genes further had a visual impact on enrichment profiles, emphasizing the gut-brain and gut-bone axes. This comprehensive analysis highlights the major roles of androgen-responsive pathways in aging process and aging-related diseases’ development. Our findings provide a basis for further experimental evaluation and therapeutic exploration targeting endocrine and microbial regulators to alleviate age-related disorders. Key words: network pharmacology, androgen, aging, metabolites, age-related diseases. References: Tuck, S. P., & Francis, R. M. (2008). Testosterone, bone and osteoporosis. Frontiers of Hormone Research, 123–132. https://doi.org/10.1159/000176049 Oliveros, J.C. (2007-2015) Venny. An interactive tool for comparing lists with Venn's diagrams. https://bioinfogp.cnb.csic.es/tools/venny/index.html Szklarczyk, D., Kirsch, R., Koutrouli, M., Nastou, K., Mehryary, F., Hachilif, R., Gable, A. L., Fang, T., Doncheva, N. T., Pyysalo, S., Bork, P., Jensen, L. J., & von Mering, C. (2023). The STRING database in 2023: protein-protein association networks and functional enrichment analyses for any sequenced genome of interest. Nucleic acids research, 51(D1), D638–D646. https://doi.org/10.1093/nar/gkac1000 He Z, Tang F, Lu Z, Huang Y, Lei H, Li Z, Zeng G. Analysis of differentially expressed genes, clinical value and biological pathways in prostate cancer. Am J Transl Res. 2018 May 15;10(5):1444-1456. PMID: 29887958; PMCID: PMC5992552. Li J, Ding Z, Wang Z, Lu JF, Maity SN, Navone NM, Logothetis CJ, Mills GB, Kim J. Androgen regulation of 5α-reductase isoenzymes in prostate cancer: implications for prostate cancer prevention. PLoS One. 2011;6(12):e28840. doi: 10.1371/journal.pone.0028840. Epub 2011 Dec 14. PMID: 22194926; PMCID: PMC3237548.