Adaptive stress response pathways play a key role in the switch between adaptation and adversity, and are important in druginduced liver injury. Previously, we have established an HepG2 fluorescent protein reporter platform to monitor adaptive stress response activation following drug treatment. HepG2 cells are often used in high-throughput primary toxicity screening, but metabolizing capacity in these cells is low and repeated dose toxicity testing inherently difficult. Here, we applied our bacterial artificial chromosome-based GFP reporter cell lines representing Nrf2 activation (Srxn1-GFP and NQO1-GFP), unfolded protein response (BiP-GFP and Chop-GFP), and DNA damage response (p21-GFP and Btg2-GFP) as long-term differentiated 3D liver-like spheroid cultures. All HepG2 GFP reporter lines differentiated into 3D spheroids similar to wild-type HepG2 cells. We systematically optimized the automated imaging and quantification of GFP reporter activity in individual spheroids using high-throughput confocal microscopy with a reference set of DILI compounds that activate these three stress response pathways at the transcriptional level in primary human hepatocytes. A panel of 33 compounds with established DILI liability was further tested in these six 3D GFP reporters in single 48 h treatment or 6 day daily repeated treatment. Strongest stress response activation was observed after 6-day repeated treatment, with the BiP and Srxn1-GFP reporters being most responsive and identified particular severe-DILI-onset compounds. Compounds that showed no GFP reporter activation in two-dimensional (2D) monolayer demonstrated GFP reporter stress response activation in 3D spheroids. Our data indicate that the application of BAC-GFP HepG2 cellular stress reporters in differentiated 3D spheroids is a promising strategy for mechanism-based identification of compounds with liability for DILI.

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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.

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Understanding toxicity pathways of engineered nanomaterials (ENM) has recently been brought forward as a key step in twenty-first century ENM risk assessment. Molecular mechanisms linked to phenotypic end points is a step towards the development of toxicity tests based on key events, which may allow for grouping of ENM according to their modes of action. This study identified molecular mechanisms underlying mitochondrial dysfunction in human bronchial epithelial BEAS 2B cells following exposure to one of the most studied multi-walled carbon nanotubes (Mitsui MWCNT-7). Asbestos was used as a positive control and a non-carcinogenic glass wool material was included as a negative fibre control. Decreased mitochondrial membrane potential (MMP↓) was observed for MWCNTs at a biologically relevant dose (0.25 μg/cm(2)) and for asbestos at 2 μg/cm(2), but not for glass wool. Extensive temporal transcriptomic and microRNA expression analyses identified a 330-gene signature (including 26 genes with known mitochondrial function) related to MWCNT- and asbestos-induced MMP↓. Forty-nine of the MMP↓-associated genes showed highly similar expression patterns over time (six time points) and the majority was found to be regulated by two transcription factors strongly involved in mitochondrial homeostasis, APP and NRF1. In addition, four miRNAs were correlated with MMP↓ and one of them, miR-1275, was found to negatively correlate with a large part of the MMP↓-associated genes. Cellular processes such as gluconeogenesis, mitochondrial LC-fatty acid β-oxidation and spindle microtubule function were enriched among the MMP↓-associated genes and miRNAs. These results are expected to be useful in the identification of key events in ENM-related toxicity pathways for the development of molecular screening techniques.

Creating biomaterials that are suited for clinical application is still hampered by a lack of understanding of the interaction between a cell and the biomaterial surface it grows on. This surface communication can strongly impact cellular behavior, which in turn affects the chances of a successful interaction between a material and the host tissue. Transcriptomics data have previously been linked to measurements of biomaterial properties in order to explain the biological mechanisms underlying these cell-biomaterial interactions. However, such multi-assay data are highly complex and therefore require careful and unambiguous characterization and storage. Failure to do so may result in loss of valuable data or erroneous data analysis. In order to start a new initiative that tackles these issues and offers a platform for innovative biomaterial development, we have created a publically accessible repository called The Compendium for Biomaterial Transcriptomics (cBiT, https://cbit.maastrichtuniversity.nl). cBiT is a data warehouse that gives users the opportunity to search through biomaterial-based transcriptomics data sets using a web interface. Data of interest can be selected and downloaded, together with associated measurements of material properties. Researchers are also invited to add their data to cBiT in order to further enhance its scientific value. We aim to make cBiT the hub for biomaterial-associated data, thereby enabling major contributions to a more efficient development of new materials with improved body integration. Here, we describe the structure of cBiT and provide a use case with clinically applied materials to demonstrate how cBiT can be used to correlate data across transcriptomics studies.

Acetaminophen (APAP) is a readily available over-the-counter drug and is one of the most commonly used analgesics/antipyretics worldwide. Large interindividual variation in susceptibility toward APAP-induced liver failure has been reported. However, the exact underlying factors causing this variability in susceptibility are still largely unknown. The aim of this study was to better understand this variability in response to APAP by evaluating interindividual differences in gene expression changes and APAP metabolite formation in primary human hepatocytes (PHH) from several donors (n = 5) exposed in vitro to a non-toxic to toxic APAP dose range. To evaluate interindividual variation, gene expression data/levels of metabolites were plotted against APAP dose/donor. The correlation in APAP dose response between donors was calculated by comparing data points from one donor to the data points of all other donors using a Pearson-based correlation analysis. From that, a correlation score/donor for each gene/metabolite was defined, representing the similarity of the omics response to APAP in PHH of a particular donor to all other donors. The top 1 % highest variable genes were selected for further evaluation using gene set overrepresentation analysis. The biological processes in which the genes with high interindividual variation in expression were involved include liver regeneration, inflammatory responses, mitochondrial stress responses, hepatocarcinogenesis, cell cycle, and drug efficacy. Additionally, the interindividual variation in the expression of these genes could be associated with the variability in expression levels of hydroxyl/methoxy-APAP and C8H13O5N-APAP-glucuronide. The before-mentioned metabolites or their derivatives have also been reported in blood of humans exposed to therapeutic APAP doses. Possibly these findings can contribute to elucidating the causative factors of interindividual susceptibility toward APAP. Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-015-1545-2) contains supplementary material, which is available to authorized users.

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