Adverse Outcome Pathway-Driven Analysis of Liver Steatosis in Vitro: A Case Study with Cyproconazole

Claudia Luckert, Albert Braeuning, Georges de Sousa, Sigrid Durinck, Efrosini S. Katsanou, Parthena Konstantinidou, Kyriaki Machera, Emanuela S. Milani, Ad A. C. M. Peijnenburg, Roger Rahmani, Andreja Rajkovic, Deborah Rijkers (+7 others)
2018 Zenodo  
Adverse outcome pathways (AOPs) describe causal relationships between molecular perturbation and adverse cellular effects and are being increasingly adopted for linking in vitro mechanistic toxicology to in vivo data from regulatory toxicity studies. In this work, a case study was performed by developing a bioassay toolbox to assess key events in the recently proposed AOP for chemically induced liver steatosis. The toolbox is comprised of in vitro assays to measure nuclear receptor activation,
more » ... ene and protein expression, lipid accumulation, mitochondrial respiration, and formation of fatty liver cells. Assay evaluation was performed in human HepaRG hepatocarcinoma cells exposed to the model compound cyproconazole, a fungicide inducing steatosis in rodents. Cyproconazole dose-dependently activated RARα and PXR, two molecular initiating events in the steatosis AOP. Moreover, cyproconazole provoked a disruption of mitochondrial functions and induced triglyceride accumulation and the formation of fatty liver cells as described in the AOP. Gene and protein expression analysis, however, showed expression changes different from those proposed in the AOP, thus suggesting that the current version of the AOP might not fully reflect the complex mechanisms linking nuclear receptor activation and liver steatosis. Our study shows that cyproconazole induces steatosis in human liver cells in vitro and demonstrates the utility of systems-based approaches in the mechanistic assessment of molecular and cellular key events in an AOP. AOP-driven in vitro testing as demonstrated can further improve existing AOPs, provide insight regarding molecular mechanisms of toxicity, and inform predictive risk assessment
doi:10.5281/zenodo.3051458 fatcat:gu4olprngvdnrmop2ay4t4gxee