| First Author | Harrill AH | Year | 2009 |
| Journal | Toxicol Sci | Volume | 110 |
| Issue | 1 | Pages | 235-43 |
| PubMed ID | 19420014 | Mgi Jnum | J:150021 |
| Mgi Id | MGI:3849546 | Doi | 10.1093/toxsci/kfp096 |
| Citation | Harrill AH, et al. (2009) Population-based discovery of toxicogenomics biomarkers for hepatotoxicity using a laboratory strain diversity panel. Toxicol Sci 110(1):235-43 |
| abstractText | Toxicogenomic studies are increasingly used to uncover potential biomarkers of adverse health events, enrich chemical risk assessment, and to facilitate proper identification and treatment of persons susceptible to toxicity. Current approaches to biomarker discovery through gene expression profiling usually utilize a single or few strains of rodents, limiting the ability to detect biomarkers that may represent the wide range of toxicity responses typically observed in genetically heterogeneous human populations. To enhance the utility of animal models to detect response biomarkers for genetically diverse populations, we used a laboratory mouse strain diversity panel. Specifically, mice from 36 inbred strains derived from Mus mus musculus, Mus mus castaneous, and Mus mus domesticus origins were treated with a model hepatotoxic agent, acetaminophen (300 mg/kg, ig). Gene expression profiling was performed on liver tissue collected at 24 h after dosing. We identified 26 population-wide biomarkers of response to acetaminophen hepatotoxicity in which the changes in gene expression were significant across treatment and liver necrosis score but not significant for individual mouse strains. Importantly, most of these biomarker genes are part of the intracellular signaling involved in hepatocyte death and include genes previously associated with acetaminophen-induced hepatotoxicity, such as cyclin-dependent kinase inhibitor 1A (p21) and interleukin 6 signal transducer (Il6st), and genes not previously associated with acetaminophen, such as oncostatin M receptor (Osmr) and MLX interacting protein like (Mlxipl). Our data demonstrate that a multistrain approach may provide utility for understanding genotype-independent toxicity responses and facilitate identification of novel targets of therapeutic intervention. |
