Chemico-biological interactions · 2026
Multi-dimensional analysis revealing the mechanism of T-2 toxin driving liver fibrosis: integration of network toxicology, machine learning, experimental verification, and molecular docking.
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Qiao Lichun, Shi Shaoteng, Wang Liangjia, Shafiq Muhammad Aftab, Tang Jing, Li Miaoqian, Wan Ping, Zhou Jingxuan, Dai Xulei, Han Jing, Guo Yijie
Аннотация
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.
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.