Integrating Network Toxicology and Untargeted Metabolomics to Identify Candidate Mechanisms and Metabolic Markers of α-Amanitin-Induced Hepatorenal Injury in Mice

Abstract

Highlights What are the main findings? Metabolomic pathway analysis of plasma from α-amanitin-poisoned mice preliminarily reveals that plasma metabolites follow a temporally dynamic progression across seven different groups. PTGS2 emerges as a potential candidate target; propionylcarnitine and 2-arachidonoylglycerol (2-AG) are the key associated metabolites. What are the implications of the main findings? Similar to the clinical symptomatology, α-amanitin-induced liver and kidney injury follows a temporally dynamic evolutionary process, which may inform reference criteria for poisoning treatment. By integrating untargeted metabolomics with network toxicology, this study couples the phenotypic endpoints represented by differential metabolites with the regulatory networks mediated by potential candidate targets. This approach enables a multi-level elucidation of the mechanisms underlying α-amanitin-induced hepatorenal injury, which might provide a reference basis for early toxicity warning and precision intervention. Abstract Background/Objectives: Mushroom poisoning is a leading cause of death from foodborne illness, and α-amanitin is its most potent toxin. However, the mechanisms and biomarkers of α-amanitin-induced hepatorenal injury remain inadequately understood. Methods: First, the potential candidate targets and metabolic pathways were identified using network toxicology. Subsequently, untargeted metabolomics was employed to screen for potential metabolic markers of poisoning in α-amanitin-treated mice across eight time groups: 0 h, 3 h, 8 h, 12 h, 24 h, 2 d, 4 d, and 7 d. Following this, a compound–reaction–enzyme–gene network was constructed based on the differential metabolites to identify relevant genes, which were integrated with the core candidate target genes derived from network toxicology. The findings were further validated by molecular docking and immunohistochemistry. Results: Network toxicology identified eight candidate targets associated with α-amanitin-induced combined hepatic and renal injury. Metabolomic analysis revealed that α-amanitin-induced hepatorenal injury followed a temporally dynamic progression, and that propionylcarnitine was the common differential metabolite across all time points. Integration of the network toxicology and untargeted metabolomics results indicated that PTGS2 served as a potential candidate target and 2-arachidonoylglycerol (2-AG) was selected as a potential differential metabolite in the combined hepatic and renal injury caused by α-amanitin. Conclusions: By integrating network toxicology and untargeted metabolomics, this study preliminarily reveals that α-amanitin-induced hepatorenal injury follows a temporally dynamic progression, which is consistent with the toxicological injury–response–adaptation model. PTGS2 is tentatively annotated as the potential candidate target, while propionylcarnitine and 2-AG are the potential candidate metabolites. These findings may provide valuable guidance for poisoning treatment and identification in cases of mushroom poisoning caused by Amanita species.