Karen Brauers

Assessing the Metabolic Competence of Sandwich-Cultured Mouse Primary Hepatocytes

Primary human and rat hepatocyte cultures are well established in vitro systems used in toxicological studies. However, whereas transgenic mouse models provide an opportunity for studying mechanisms of toxicity, mouse primary hepatocyte cultures are less well described. The potential usefulness of a mouse hepatocyte-based in vitro model was assessed in this study by investigating time-dependent competence for xenobiotic metabolism and gene expression profiles. Primary mouse hepatocytes, isolated using two-step collagenase perfusion, were cultured in a collagen sandwich configuration. Gene expression profiles and the activities of various cytochrome P450 (P450) enzymes were determined after 0, 42, and 90 h in culture. Principal component analysis of gene expression profiles shows that replicates per time point are similar. Gene expression levels of most phase I biotransformation enzymes decrease to approximately 69 and 57% of the original levels at 42 and 90 h, respectively, whereas enzyme activities for most of the studied P450s decrease to 59 and 34%. The decrease for phase II gene expression is only to 96 and 92% of the original levels at 42 and 90 h, respectively. Pathway analysis reveals initial effects at the level of proteins, external signaling pathways, and energy production. Later effects are observed for transcription, translation, membranes, and cell cycle-related gene sets. These results indicate that the sandwich-cultured primary mouse hepatocyte system is robust and seems to maintain its metabolic competence better than that of the rat hepatocyte system.

Discrimination for genotoxic and nongenotoxic carcinogens by gene expression profiling in primary mouse hepatocytes improves with exposure time.

Assessing the potential carcinogenicity of chemicals for humans represents an ongoing challenge. Chronic rodent bioassays predict human cancer risk at only limited reliability and are simultaneously expensive and long lasting. In order to seek for alternatives, the ability of a transcriptomics-based primary mouse hepatocyte model to classify carcinogens by their modes of action was evaluated. As it is obvious that exposure will induce a cascade of gene expression modifications, in particular, the influence of exposure time in vitro on discriminating genotoxic (GTX) carcinogens from nongenotoxic (NGTX) carcinogens class discrimination was investigated. Primary mouse hepatocytes from male C57Bl6 mice were treated for 12, 24, 36, and 48 h with two GTX and two NGTX carcinogens. For validation, two additional GTX compounds were studied at 24 and 48 h. Immunostaining of gammaH2AX foci was applied in order to phenotypically verify DNA damage. It confirmed significant induction of DNA damage after treatment with GTX compounds but not with NGTX compounds. Whole-genome gene expression modifications were analyzed by means of Affymetrix microarrays. When using differentially expressed genes from data sets normalized by Robust Multi-array Average, the two classes and various compounds were better separated from each other by hierarchical clustering when increasing the treatment period. Discrimination of GTX and NGTX carcinogens by Prediction Analysis of Microarray improved with time and resulted in correct classification of the validation compounds. The present study shows that gene expression profiling in primary mouse hepatocytes is promising for discriminating GTX from NGTX compounds and that this discrimination improves with increasing treatment period.

Inhibition of LPS-induced pulmonary inflammation by specific flavonoids.

In the present study, the anti-inflammatory effects of the flavonoids flavone, fisetin and tricetin were evaluated in a mouse model of LPS-induced acute pulmonary inflammation. The flavonoid fisetin significantly reduced lung myeloperoxidase-levels and gene-expression of inflammatory mediators such as IL-6, TNF-alpha, IL-1beta, MIP-1alpha and MIP-2. The LPS-induced gene transcription of HO-1 and SOD2 was also significantly reduced by fisetin. Overall, the anti-inflammatory effects of fisetin in this in vivo model were much more pronounced as compared to the observed effects of flavone or tricetin and the anti-inflammatory glucocorticoid dexamethasone. The results of this study indicate that flavonoids such as fisetin might be potential candidates as pharmaceuticals or nutraceuticals in the treatment of pulmonary inflammatory diseases.

RNA-Seq Provides New Insights in the Transcriptome Responses Induced by the Carcinogen Benzo[a]pyrene

Whole-genome transcriptome measurements are pivotal for characterizing molecular mechanisms of chemicals and predicting toxic classes, such as genotoxicity and carcinogenicity, from in vitro and in vivo assays. In recent years, deep sequencing technologies have been developed that hold the promise of measuring the transcriptome in a more complete and unbiased manner than DNA microarrays. Here, we applied this RNA-seq technology for the characterization of the transcriptomic responses in HepG2 cells upon exposure to benzo[a]pyrene (BaP), a well-known DNA damaging human carcinogen. Based on EnsEMBL genes, we demonstrate that RNA-seq detects ca 20% more genes than microarray-based technology but almost threefold more significantly differentially expressed genes. Functional enrichment analyses show that RNA-seq yields more insight into the biology and mechanisms related to the toxic effects caused by BaP, i.e., two- to fivefold more affected pathways and biological processes. Additionally, we demonstrate that RNA-seq allows detecting alternative isoform expression in many genes, including regulators of cell death and DNA repair such as TP53, BCL2 and XPA, which are relevant for genotoxic responses. Moreover, potentially novel isoforms were found, such as fragments of known transcripts, transcripts with additional exons, intron retention or exon-skipping events. The biological function(s) of these isoforms remain for the time being unknown. Finally, we demonstrate that RNA-seq enables the investigation of allele-specific gene expression, although no changes could be observed. Our results provide evidence that RNA-seq is a powerful tool for toxicology, which, compared with microarrays, is capable of generating novel and valuable information at the transcriptome level for characterizing deleterious effects caused by chemicals.

Time Series Analysis of Benzo[A]Pyrene-Induced Transcriptome Changes Suggests That a Network of Transcription Factors Regulates the Effects on Functional Gene Sets

Chemical carcinogens may cause a multitude of effects inside cells, thereby affecting transcript levels of genes by direct activation of transcription factors (TF) or indirectly through the formation of DNA damage. As the temporal profiles of these responses may be profoundly different, examining time-dependent changes may provide new insights in TF networks related to cellular responses to chemical carcinogens. Therefore, we investigated in human hepatoma cells gene expression changes caused by benzo[a]pyrene at 12 time points after exposure, in relation to DNA adduct and cell cycle. Temporal profiles for functional gene sets demonstrate both early and late effects in up- and downregulation of relevant gene sets involved in cell cycle, apoptosis, DNA repair, and metabolism of amino acids and lipids. Many significant transcription regulation networks appeared to be around TF that are proto-oncogenes or tumor suppressor genes. The time series analysis tool Short Time-series Expression Miner (STEM) was used to identify time-dependent correlation of pathways, gene sets, TF networks, and biological parameters. Most correlations are with DNA adduct levels, which is an early response, and less with the later responses on G1 and S phase cells. The majority of the modulated genes in the Reactome pathways can be regulated by several of these TF, e.g., 73% by nuclear factor-kappa B and 34-42% by c-MYC, SRF, AP1, and E2F1. All these TF can also regulate one or more of the others. Our data indicate that a complex network of a few TF is responsible for the majority of the transcriptional changes induced by BaP. This network hardly changes over time, despite that the transcriptional profiles clearly alter, suggesting that also other regulatory mechanisms are involved.

Gene expression profiling in primary mouse hepatocytes discriminates true from false-positive genotoxic compounds.

Well-established in vitro methods for testing the genotoxic potency of chemicals--such as the Ames/Salmonella test, the mouse lymphoma assay, the micronucleus test and the chromosomal aberration test--show a high false-positive rate for predicting in vivo genotoxicity and carcinogenicity. Thus, there is a need for more reliable in vitro assays. We investigated whether gene expression profiling in metabolically competent primary mouse hepatocytes is capable of discriminating true genotoxic (GTX) compounds from false-positive genotoxic (FP-GTX) compounds. Sandwich-cultured primary hepatocytes from male C57Bl6 mice were treated for 24 and 48 h with five true GTX and five FP-GTX compounds. Whole genome gene expression modifications were analysed by means of Affymetrix mouse genome 430 2.0 microarrays. Filtered genes were used for hierarchical clustering and class prediction methods. Classifiers were generated by prediction analysis of microarray using a leave-one-compound-out method and selecting the genes that were common to the 10 training sets. For the training compounds, all but one were correctly classified. Validation of the classification model with five new compounds resulted in a 100% correct classification at 24 h and 80% at 48 h. The generated classifiers were mostly involved in metabolic and biosynthetic processes, immune responses and apoptosis. Applying genes whose expression change correlates with γH2AX foci, a measure for DNA damage, did not improve the classification. The present study shows that gene expression profiling in primary mouse hepatocytes is capable of discriminating between true GTX and FP-GTX compounds.

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.

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.

Integrative cross-omics analysis in primary mouse hepatocytes unravels mechanisms of cyclosporin A-induced hepatotoxicity.

The liver is responsible for drug metabolism and drug-induced hepatotoxicity is the most frequent reason for drug withdrawal, indicating that better pre-clinical toxicity tests are needed. In order to bypass animal models for toxicity screening, we exposed primary mouse hepatocytes for exploring the prototypical hepatotoxicant cyclosporin A. To elucidate the mechanisms underlying cyclosporin A-induced hepatotoxicity, we analyzed expression levels of proteins, mRNAs, microRNAs and metabolites. Integrative analysis of transcriptomics and proteomics showed that protein disulfide isomerase family A, member 4 was up-regulated on both the protein level and mRNA level. This protein is involved in protein folding and secretion in the endoplasmic reticulum. Furthermore, the microRNA mmu-miR-182-5p which is predicted to interact with the mRNA of this protein, was also differentially expressed, further emphasizing endoplasmic reticulum stress as important event in drug-induced toxicity. To further investigate the interaction between the significantly expressed proteins, a network was created including genes and microRNAs known to interact with these proteins and this network was used to visualize the experimental data. In total 6 clusters could be distinguished which appeared to be involved in several toxicity related processes, including alteration of protein folding and secretion in the endoplasmic reticulum. Metabonomic analyses resulted in 5 differentially expressed metabolites, indicative of an altered glucose, lipid and cholesterol homeostasis which can be related to cholestasis. Single and integrative analyses of transcriptomics, proteomics and metabonomics reveal mechanisms underlying cyclosporin A-induced cholestasis demonstrating that endoplasmic reticulum stress and the unfolded protein response are important processes in drug-induced liver toxicity.

Integrating multiple omics to unravel mechanisms of Cyclosporin A induced hepatotoxicity in vitro

In order to improve attrition rates of candidate-drugs there is a need for a better understanding of the mechanisms underlying drug-induced hepatotoxicity. We aim to further unravel the toxicological response of hepatocytes to a prototypical cholestatic compound by integrating transcriptomic and metabonomic profiling of HepG2 cells exposed to Cyclosporin A. Cyclosporin A exposure induced intracellular cholesterol accumulation and diminished intracellular bile acid levels. Performing pathway analyses of significant mRNAs and metabolites separately and integrated, resulted in more relevant pathways for the latter. Integrated analyses showed pathways involved in cell cycle and cellular metabolism to be significantly changed. Moreover, pathways involved in protein processing of the endoplasmic reticulum, bile acid biosynthesis and cholesterol metabolism were significantly affected. Our findings indicate that an integrated approach combining metabonomics and transcriptomics data derived from representative in vitro models, with bioinformatics can improve our understanding of the mechanisms of action underlying drug-induced hepatotoxicity. Furthermore, we showed that integrating multiple omics and thereby analyzing genes, microRNAs and metabolites of the opposed model for drug-induced cholestasis can give valuable information about mechanisms of drug-induced cholestasis in vitro and therefore could be used in toxicity screening of new drug candidates at an early stage of drug discovery.