Hepatotoxicity Prediction and Multi-omics Reveal Mitochondrial and Lipid Metabolic Dysregulation in PM2.5-Induced Liver Fibrosis.

Prolonged exposure to fine particulate matter (PM2.5) has been linked to chronic liver injury and cancer. However, an alternative risk assessment method to prospective longitudinal studies of exposome-metabolome interactions for liver inflammation-associated hepatocellular carcinoma (HCC) is lacking. This study investigates the risk of long-term real-world PM2.5 exposure in hepatocarcinogenesis through machine learning techniques. Shotgun mass spectrometry (MS) imaging data were acquired from mouse models across a continuum of fibrosis, cirrhosis, and HCC for training a multiclass classification model to identify "No Risk", "Cancer Risk", and "Cancer". Direct infusion-MS data from PM2.5-exposed mouse livers were analyzed to classify risk. By integrating data-driven and knowledge-based approaches, 14 disease progression biomarkers were identified for modeling. Our results suggest that chronic real-world PM2.5 exposure can induce liver fibrosis, presenting cancer risk. Incorporating metabolomics, lipidomics, and transcriptomics, we propose PM2.5 exposure induces mitochondrial dysfunction, activates AMPK signaling, and increases ceramide accumulation, potentially mediating insulin resistance that contributes to nonalcoholic fatty liver disease and HCC progression. This work represents a significant advancement in assessing hepatotoxicity of environmental toxicants by reducing reliance on traditional animal testing methods. It also underscores the potential of emerging technologies in transforming our understanding of PM2.5 exposure, paving the way for targeted interventions.