Two-Hit Hypothesis: Digital Liver Modeling and Multi-Omics Reveal Mitochondrial and Lipid Metabolic Dysregulation in PM2.5-Induced Liver Fibrosis

Long-term 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 on 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 liver disease progression through a developed digital rodent liver model utilizing machine learning (ML) techniques. Shotgun mass spectrometry (MS) imaging data were acquired from mouse liver disease models across a continuum of fibrosis, cirrhosis, and HCC for training a multi-class 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 both data-driven and knowledge-based approaches, 14 disease progression biomarkers were identified for modeling. Our results suggest that chronic real-world PM2.5 exposure presents a cancer risk. Incorporating metabolomics, lipidomics, and transcriptomics, we proposed that chronic PM2.5 exposure induces mitochondrial dysfunction, activates AMPK signaling, and increases ceramide accumulation, potentially mediating insulin resistance and contributing to non-alcoholic fatty liver disease progression and HCC. This work introduces a digital liver model, representing a significant advancement in assessing hepatic toxicity 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 and therapies.