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Objective: Longdan Xiegan Decoction (LXD) is a famous herbal formula in China. It has been proved that LXD has been shown to have a significant inhibitory effect on suppresses the inflammatory cells associated with uveitis. However, the key functional combination of component groups and their possible mechanisms remain unclear.
Methods: The community detecting model of the network, the functional response space, and reverse prediction model were utilized to decode the key components group (KCG) and possible mechanism of LXD in treating uveitis. Finally, MTT assay, NO assay and ELISA assay were applied to verify the effectiveness of KCG and the accuracy of our strategy.
Results: In the components-targets-pathogenic genes-disease (CTP) network, a combination of Huffman coding and random walk algorithm was used and eight foundational acting communities (FACs) were discovered with important functional significance. Verification has shown that FACs can represent the corresponding C-T network for treating uveitis. A novel node importance calculation method was designed to construct the functional response space and pick out 349 effective proteins. A total of 54 components were screened and defined as KCG. The pathway enrichment results showed that KCG and their targets enriched signal pathways of IL-17, Toll-like receptor, and T cell receptor played an important role in the pathogenesis of uveitis. Furthermore, experimental verification results showed that important KCG quercetin and sitosterol markedly inhibited the production of nitric oxide and significantly regulated the level of TNF-α and IFN-γ in Lipopolysaccharide-induced RAW264.7 cells.
Discussion: In this research, we decoded the potential mechanism of the multi-components-genes-pathways of LXD's pharmacological action mode against uveitis based on an integrated pharmacology approach. The results provided a new perspective for the future studies of the anti-uveitis mechanism of traditional Chinese medicine.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC9677932 | PMC |
http://dx.doi.org/10.2147/DDDT.S385136 | DOI Listing |
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