Figure 1.
Viability of mouse liver slices upon treatment with steatogenic drugs.
Liver slices were incubated for 24(AMI) 25, 50, and 100 µM, valproic acid (VA) 50, 200, and 500 µM, and tetracycline (TET) 5, 40, and 100 µM. ATP content (nmol/mg of protein) in slices treated with different concentrations of hepatotoxicants was compared to control slices. Each point is the mean±SD of 5 independent experiments (liver slices were isolated from livers of 5 mice) and each measurement was made in duplicate. There were no significant differences between the tested conditions.
Figure 2.
Effects of steatogenic drugs on gene expression in mouse PCLS.
A. PCLS obtained from 5 mice were treated with 50 µM amiodarone (AMI), 200 µM of valproic acid (VA), 40 µM of tetracycline (TET) or vehicle for 24 h and subjected to Affymetrix microarray analysis. The biological processes in the heat map correspond to gene sets significantly affected according to GSEA (p<0.05, FDR<0.05). Processes that were upregulated are represented by red colour, the downregulated processes are depicted in green, and unaffected processes in black. B. Gene Ontology (GO) analysis of the significant genes identified by GSEA (p<0.05, FDR<0.05) was performed in DAVID. GO terms were considered to be significant if p<0.005, FDR<0.005. The significant GO terms were grouped into GO annotation clusters and are depicted as a heat map. For explanation of the colours see Figure 2A.
Figure 3.
Functional clustering of genes involved in energy metabolism (amiodarone).
Genes related to energy metabolism identified by GSEA as being significantly altered upon amiodarone (AMI) treatment were subjected to functional clustering in STRING. Functional clusters such as lipid synthesis, β-oxidation, mitochondria, peroxisomes, and PPARα -dependent lipid metabolism were identified. Information about fold change (FC = treatment vs. control) for the analysed genes in individual mice is presented as a heat map. Genes that did not form connected nodes were removed from the presented clusters. Thicker lines represent stronger associations between genes. Inter-cluster edges are represented by dashed-lines. The bigger spheres represent genes coding for proteins with known structure. Smaller spheres represent genes coding proteins for which no structural information is available.
Figure 4.
Functional clustering of genes involved in energy metabolism (valproic acid).
Genes related to energy metabolism identified by GSEA as being significantly altered upon valproic acid (VA) treatment were subjected to functional clustering in STRING. Functional clusters such as lipid synthesis, lipid catabolism, β-oxidation, glucose metabolism, and bile acid metabolism have been identified. Information about fold change (FC = treatment vs. control) for the analysed genes in individual mice is presented as a heat map. For further explanation of the networks see Fig. 3.
Figure 5.
Functional clustering of genes involved in energy metabolism (tetracycline).
Genes related to energy metabolism identified by GSEA as being significantly altered upon tetracycline (TET) treatment were subjected to functional clustering in STRING. Functional clusters such as lipid synthesis, β-oxidation, PPARα signaling, inflammation/apoptosis, amino acids (aa)/glucose/lipid metabolism, and cholesterol/bile acid homeostasis were identified. Information about fold change (FC = treatment vs. control) for the analysed genes in individual mice is presented as a heat map. For explanation of the networks see Fig. 3.
Figure 6.
Effect of valproic acid and amiodarone on PPARα, PPAR β/δ, and PPARγ gene reporter assays.
Luciferase activity of PPARα CALUX cells upon exposure to PPARα agonists: GW7647 (A) and valproic acid (B). Luciferase activity of PPAR β/δ CALUX cells upon exposure to PPAR β/δ agonists: L-165, 041 (C), and valproic acid (D). Luciferase activity of PPARγ CALUX cells upon exposure to PPARγ agonists: rosiglitazone (E), valproic acid (F), and amiodarone (G). Data are corrected for solvent control values and expressed as means±standard errors (n = 3). X axis represents concentration of the compounds [M] and y axis represents luciferase units. AMI stands for amiodarone, VA-valproic acid, and TET-tetracycline.
Figure 7.
Identification of potential biomarkers for PPAR agonists in mouse PCLS.
PCLS obtained from 4 or 5(amiodarone (A), valproic acid (B), or tetracycline(C)), cholestasis (cyclosporin A (D), chlorpromazine (E), or ethinyl estradiol (F)), necrosis (acetaminophen (G), isoniazid (H), or paraquat (I)), or controls. GSEA led to the identification of 8 genes upregulated by amiodarone and valproic acid, which were considered as candidate biomarkers for PPAR agonists. mRNA expression values for the selected biomarkers are derived from DNA-microarrays and results are presented as heat maps of log2, median centered gene expression values subjected to HCA. Red and green indicate expression higher and lower, respectively, than the average expression of all samples within the same heat map. AMI stands for amiodarone, VA-valproic acid, TET- tetracycline, CsA-cyclosporin A, CPZ- chlorpromazine, EE- ethinyl estradiol, APAP-acetaminophen, ISND- isoniazid, PQ- paraquat, and ctr- controls, M1 represents PCLS obtained from liver of mouse nr 1 etc.
Figure 8.
Identification of potential biomarkers for tetracycline-like acting compounds in mouse PCLS.
PCLS obtained from 4 or 5 mice were exposed for 24(amiodarone (A), valproic acid (B), or tetracycline (C)), cholestasis (cyclosporin A (D), chlorpromazine (E), or ethinyl estradiol (F)), necrosis (acetaminophen (G), isoniazid (H), or paraquat(I)), or controls. GSEA led to the identification of 19 genes downregulated by tetracycline (TET) treatment, which were considered as candidate biomarkers for TET-like acting compounds. mRNA expression values for the selected biomarkers are derived from DNA-microarrays, and results are presented as heat maps of log2, median centered gene expression values subjected to HCA. For explanation of the colours and abbreviations see Figure 7.
Figure 9.
Comparative data analysis: relevance for mouse in vivo and human primary hepatocytes.
Publically available transcriptomics data (Gene Expression Omnibus) relevant for the actions of known PPAR agonists in mouse liver in vivo and human primary hepatocytes were used. The heat map represents significant gene sets (GSEA p<0.05, FDR<0.05), which were subjected to HCA. Gene sets were obtained using the ANNI text mining tool. Processes that were upregulated are represented by red colour, the downregulated processes are depicted in green, and unaffected processes are in black. Ale stands for aleglitazar (double PPARα/γ agonist), Pio/Feno-pioglitazone/fenofibrate (PPAR γ/PPARα agonists), Tesa-Tesaglitazar (double PPAR γ/α agonist), AMI-amiodarone (PPAR γ agonist), VA-valproic acid (triple PPARα/(β/δ)/γ agonist), TET-tetracycline, Wy-Wy14643, FO-fish oil, m-mouse, h-human, PCLS-precision cut liver slices, PH-primary hepatocytes, L- liver in vivo.