Benzo[a]pyrene-Induced Changes in MicroRNA-mRNA Networks

Toxicological studies assessing the safety of compounds for humans frequently use in vitro systems to characterize toxic responses in combination with transcriptomic analyses. Thus far, changes have mostly been investigated at the mRNA level. Recently, microRNAs have attracted attention because they are powerful negative regulators of mRNA levels and, thus, may be responsible for the modulation of important mRNA networks implicated in toxicity. This study aimed to identify possible microRNA-mRNA networks as novel interactions on the gene expression level after a genotoxic insult. We used benzo[a]pyrene (BaP), a polycyclic aromatic hydrocarbon, as a model genotoxic/carcinogenic compound. We analyzed time-dependent effects on mRNA and microRNA profiles in HepG2 cells, a widely used human liver cell line that expresses active p53 and is competent for the biotransformation of BaP. Changes in microRNA expression in response to BaP, in combination with multiple alterations of mRNA levels, were observed. Many of these altered mRNAs are targets of altered microRNAs. Using pathway analysis, we evaluated the relevance of such microRNA deregulations to genotoxicity. This revealed eight microRNAs that appear to participate in specific BaP-responsive pathways relevant to genotoxicity, such as apoptotic signaling, cell cycle arrest, DNA damage response, and DNA damage repair. Our results particularly highlight the potential of microRNA-29b, microRNA-26a-1*, and microRNA-122* as novel players in the BaP response. Therefore, this study demonstrates the added value of an integrated microRNA-mRNA approach for identifying molecular mechanisms induced by BaP in an in vitro human model

Classification of Hepatotoxicants Using HepG2 Cells: A Proof of Principle Study

With the number of new drug candidates increasing every year, there is a need for high-throughput human toxicity screenings. As the liver is the most important organ in drug metabolism and thus capable of generating relatively high levels of toxic metabolites, it is important to find a reliable strategy to screen for drug-induced hepatotoxicity. Microarray-based transcriptomics is a well-established technique in toxicogenomics research and is an ideal approach to screen for drug-induced injury at an early stage. The aim of this study was to prove the principle of classifying known hepatotoxicants and nonhepatotoxicants using their distinctive gene expression profiles in vitro in HepG2 cells. Furthermore, we undertook to subclassify the hepatotoxic compounds by investigating the subclass of cholestatic compounds. Prediction analysis for microarrays was used for classification of hepatotoxicants and nonhepatotoxicants, which resulted in an accuracy of 92% on the training set and 91% on the validation set, using 36 genes. A second model was set up with the goal of finding classifiers for cholestasis, resulting in 12 genes that appeared capable of correctly classifying 8 of the 9 cholestatic compounds, resulting in an accuracy of 93%. We were able to prove the principle that transcriptomic analyses of HepG2 cells can indeed be used to classify chemical entities for hepatotoxicity. Genes selected for classification of hepatotoxicity and cholestasis indicate that endoplasmic reticulum stress and the unfolded protein response may be important cellular effects of drug-induced liver injury. However, the number of compounds in both the training set and the validation set should be increased to improve the reliability of the prediction