Model steatogenic compounds (amiodarone, valproic acid, and tetracycline) alter lipid metabolism by different mechanisms in mouse liver slices [Necrotic compounds exposures]
Although drug induced steatosis represents a mild type of hepatotoxicity, it can progress into more severe non-alcoholic steatohepatitis. Current models used for safety assessment in drug development and chemical risk assessment do not accurately predict steatosis in humans. Therefore, new models need to be developed to screen compounds for steatogenic properties. We have studied the usefulness of mouse precision-cut liver slices (PCLS) as an alternative to animal testing to gain more insight into the mechanisms involved in the steatogenesis. To this end, PCLS were incubated 24h with the model steatogenic compounds, amiodarone (AMI), valproic acid (VA), and tetracycline (TET). Transcriptome analysis using DNA microarrays was used to identify genes and processes affected by these compounds. AMI and VA upregulated lipid metabolism, whereas processes associated with extracellular matrix remodelling and inflammation were downregulated. TET downregulated mitochondrial functions, lipid metabolism, and fibrosis. From the transcriptomics data it was hypothesized that all 3 compounds affect peroxisome proliferator activated-receptor (PPAR) signalling. Application of PPAR reporter assays classified AMI and VA as PPARγ and triple PPARα/ (β/δ)/γ agonist, respectively, whereas TET had no effect on any of the PPARs. Some of the differentially expressed genes were considered as potential candidate biomarkers to identify PPAR agonists (i.e. AMI and VA) or compounds impairing mitochondrial functions (i.e. TET). Finally, comparison of our findings with publicly available transcriptomics data showed that a number of processes altered in the mouse PCLS was also affected in mouse livers and human primary hepatocytes exposed to known PPAR agonists. Thus mouse PCLS are a valuable model to identify mechanisms of action of compounds altering lipid metabolism. Two sets of candidate biomarkers could be used to screen compounds interfering with lipid metabolism by different mechanisms
