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Jian'er Jiedu granule alleviates acute lung injury via NOD2/NF-κB/NLRP3 axis: Network pharmacology, machine learning,
Laijun Yang1, Xiaolu Wang1, Yi Guo1
1The Second Clinical College of Guangzhou University of Chinese Medicine, the Second Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou 510006, China.
Background:
Acute lung injury (ALI) is a severe inflammatory disorder associated with high mortality. Jian'er Jiedu granule (JEJD), a formulation developed by Guangdong Provincial Hospital of Traditional Chinese Medicine, has shown promise in alleviating Omicron-related symptoms; however, its therapeutic effects on ALI remain unclear.
Purpose:
To explore the therapeutic effects and mechanisms of action of JEJD in ALI.
Methods:
The active components and potential targets of JEJD were screened using TCMSP, TCMIP, TCMID, and Herb databases. ALI-related genes were obtained from GeneCards, OMIM, and Disgenet databases. The final candidate genes were determined using a combination of machine learning algorithms, cross-species gene expression analysis, and assessment of clinical diagnostic relevance. The relationship between JEJD, ALI, and the identified genes was further analyzed using functional enrichment analysis, competitive endogenous RNA (ceRNA) regulatory network analysis, single-cell transcriptomic data analysis, molecular docking, and molecular dynamics simulation validation. Finally, an ALI mouse model was established via intratracheal LPS instillation to investigate the effects of JEJD in vivo, and in vitro experiments using MH-S macrophages were performed to validate the anti-inflammatory mechanisms.
Results:
Forty-nine intersection genes were identified using database analysis. Machine learning algorithms combined with protein-protein interaction network analysis screened VCAM1 and CASP1 as candidate genes. Enrichment analysis indicated that these genes functioned as downstream components of the NOD-like receptor signaling pathway. Animal experiments showed JEJD significantly reduced the protein levels of NOD2, p-NF-κB, NLRP3, VCAM1, and Caspase-1, thereby alleviating LPS-induced ALI.
Conclusions:
By integrating network pharmacology, machine learning, and experimental validation, we demonstrated that JEJD alleviates ALI at least partly through the NOD2/NF-κB/NLRP3 signaling axis, with VCAM1 and Caspase-1 functioning as critical downstream effector molecules. These findings highlight the therapeutic potential of JEJD in regulating inflammatory networks.