Targeting macrophage-myofibroblast transition with

Introduction: Methods: The AECS was qualitative analyzed by UHPLC-Q-Exactive Orbitrap-MS. Potential targets of AECS were predicted, and RIF disease targets were collated from databases. A Venn diagram was generated using the EVenn platform, and drug-active ingredient-target network diagrams were constructed with Cytoscape 3.10.1 software. The PPI network was generated through the STRING database, and GO and KEGG enrichment analyses were executed via the DAVID platform. Molecular docking predictions of active ingredients binding with core targets were conducted using the CB-Dock2 platform. Finally, the anti-RIF effect of AECS was evaluated in an adenine-induced rat model. Results: A total of 64 chemical constituents were identified in the AECS. 97 common targets for treating RIF were identified through mining multiple databases. These key targets, particularly AKT1, EGFR and IL6, mediated biological functions such as protein phosphorylation and regulated several signaling pathways, including PI3K/Akt. Molecular docking studies demonstrated that ingredients like licochalcone A exhibited strong binding affinity with hub genes such as AKT1, EGFR and IL6. In an RIF rat model, treatment groups showed reduced renal tissue damage. Furthermore, treatment with AECS significantly ameliorated renal dysfunction in RIF rats, along with a downregulation of RIF markers α-SMA and fibronectin. Compared to the AECSL group, the LST and AECSH groups (300mg/kg/d) exhibited more significant therapeutic effects. Ultimately, RIF model rats showed increased expression of pan-macrophage marker CD68 and M2-specific marker CD206, along with α-SMA co-expression, indicating differentiation into MMT cells displaying CD68 Conclusion: Our study is expected to provide the pharmacological mechanisms by which CS may be a promising anti-RIF drug for future clinical trials.