Multi-dimensional analysis revealing the mechanism of T-2 toxin driving liver fibrosis: integration of network toxicology, machine learning, experimental verification, and molecular docking

Background T-2 toxin is a highly toxic mycotoxin commonly present in food and the environment, with accumulating evidence supporting its hepatotoxic potential. However, the molecular events linking T-2 toxin exposure to liver fibrosis remain insufficiently characterized. Methods In this study, a comprehensive strategy integrating network toxicology, transcriptomic analysis, and machine learning algorithms was applied to identify key molecular targets associated with T-2 toxin-related liver fibrosis. A rat model with chronic T-2 toxin exposure was further used for experimental validation through histopathological analysis, gene expression profiling, and assessment of PI3K/Akt signaling activity. Molecular docking was performed to evaluate potential interactions between T-2 toxin and representative target proteins. Results Network toxicology analysis identified 352 potential targets of T-2 toxin-related liver fibrosis, significantly enriched in PI3K/Akt and MAPK signaling pathways. Further combined with the transcriptome data, four key core targets were screened out by machine learning, namely CDH1 , CYP2D6 , NR0B2 and GAS6 , which demonstrated high diagnostic performance. In vivo experiments revealed that T-2 toxin exposure induced hepatic collagen deposition and α-SMA upregulation, accompanied by the suppression of PI3K/Akt signaling. Molecular docking suggested favorable binding affinities between T-2 toxin and the core target proteins. Conclusion Overall, this study delineates a multi-target molecular profile of T-2 toxin-induced liver fibrosis, highlighting the roles of core genes and alterations in the PI3K/Akt signaling pathway.