Network Pharmacology and Bioinformatics Methods Reveal the Mechanism of Sishenwan in the Treatment of diabetic nephropathy
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.02.14.528457v1?rss=1

Authors: Deng, Y., Ma, Y., Zhang, S., Zhang, M.

Abstract: Objective: In order to decipher the bioactive components and potential mechanisms of the traditional Chinese medicine (TCM) formula Sishenwan (SSW) for diabetic nephropathy (DN), we integrated network pharmacology and bioinformatics. Methods: The candidate compounds of SSW and relative targets were obtained from the TCMSP, BATMAN-TCM, SiwssTartgetPrediction, STITCH, and ChEMBL web servers. The UniProt database was used to translate the target names into gene names, and then constructed the herbal-compound-target network. DN-related targets were ascertained based on OMIM, CTD, GeneCards, DisGeNET, and GEO. Furthermore, there was a protein-protein interaction (PPI) network to explore the overlapping targets between SSW and DN, which focused on screening the pivotal targets by topology. GO and KEGG enrichment analyses were carried out to further understand the potential functions associated with the effect of SSW against DN. Eventually, molecular docking simulations were performed to validate the binding affinity between major bioactive components and hub genes. Results: A total of 120 candidate active compounds and 542 corresponding drug targets were derived, in which 195 targets intersected with DN. Then, KEGG pathway analysis showed that several signaling pathways were closely related to the process of SSW against DN, including the AGE-RAGE signaling pathway in diabetic complications, the TNF signaling pathway, and IL-17 signaling pathway, ect. The PPI network analysis identified PTGS2, CREB1, ESR1, TNF, IL1B, INS, AKT1, PPARG, and JUN were the top 9 hub targets. The molecular docking confirmed that the bioactive compounds of SSW had a firm binding affinity with hub targets. Conclusions: As a whole, the present study revealed that SSW exerted therapeutic effects on DN via modulating multi-targets with multi-compounds through multi-pathways.

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