Proteomic analysis of methapyrilene-induced effects in primary rat hepatocytes
Omics-based approaches can be used to predict toxicity and to obtain information about the involved mode and mechanism of action of toxicants. Due to the ethical problems of the use of animal models, their high cost and time requirement and the changed regulatory legislation in the EU, the development of powerful in vitro test systems has become increasingly important. We could recently demonstrate that cultivation of rat primary hepatocytes in a collagen-sandwich is most appropriate to reproducibly detect gene expression alterations induced by non-genotoxic (methapyrilene - MP, piperonylbutoxide - PBO) and genotoxic (2-nitrofluorene - 2NF, aflatoxin B1 - AB1) carcinogens and that a qualitatively good correlation between in vivo and in vitro observed deregulations exists for the majority of analyzed genes. In order to further demonstrate the potential of the established in vitro system, we investigated its applicability for the identification of biomarkers for non-genotoxic carcinogens via a proteomic approach. Primary hepatocytes were treated for 24 hours with different concentrations of the non-genotoxic carcinogen MP and the protein expression was analyzed using two-dimensional gel electrophoresis. Compared to two-dimensional gel electrophoresis of extracts obtained from hepatocytes cultured as monolayer and from rat liver tissue, we received qualitatively good results after removal of the overlaying collagen layer from the sandwich culture prior to extraction of proteins. We could identify about 31 at least 2fold up- and 16 at least 2fold down-regulated protein spots in response to treatment with the highest used concentration of MP (100 µM). Some of the up-regulated proteins could also be detected in the protein extracts of hepatocytes exposed to 6,25 µM MP. The ongoing identification of deregulated proteins by MALDI-MS will finally reveal, if the established in vitro system is also suited to detect carcinogen-induced expression changes on the protein level and if the application of transcriptomics and proteomics in combination can improve the identification of biomarkers by providing complementary information.