Maarten L. J. Coonen

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.

Extensive temporal transcriptome and microRNA analyses identify molecular mechanisms underlying mitochondrial dysfunction induced by multi-walled carbon nanotubes in human lung cells

Abstract 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/cm2) and for asbestos at 2 μg/cm2, 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.

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.

Drug-induced liver injury classification model based on in vitro human transcriptomics and in vivo rat clinical chemistry data

In this study, we developed a transcriptomics based human in vitro model for predicting DILI in humans. The transcriptomics data (Affymetrix GeneChip Human Genome U133 Plus 2.0) from primary human hepatocytes were provided by the Japanese Toxicogenomics Project (TGP). The selected compounds were divided into two groups, i.e., most-DILI and no-DILI, based on FDA-approved drug labels. The compounds were further grouped in a training and validation set. The training set, containing the most extreme most-DILI and no-DILI compounds based on the in vivo rat clinical chemistry measurements from TGP, was used to develop the prediction model. The validation set showed high accuracy (> 90%) and performed better than splitting the compounds into training and validation set randomly.

Evaluating microRNA profiles reveals discriminative responses following genotoxic or non-genotoxic carcinogen exposure in primary mouse hepatocytes

Chemical carcinogenesis can be induced by genotoxic (GTX) or non-genotoxic (NGTX) carcinogens. GTX carcinogens have a well-described mode of action. However, the complex mechanisms by which NGTX carcinogens act are less clear and may result in conflicting results between species [e.g. Wy-14,643 (Wy)]. We hypothesise that common microRNA response pathways exist for each class of carcinogenic agents. Therefore, this study compares and integrates mRNA and microRNA expression profiles following short term acute exposure (24 and 48h) to three GTX [aflatoxin B1 (AFB1), benzo[a]pyrene (BaP) and cisplatin (CisPl)] or three NGTX (2,3,7,8-tetrachloordibenzodioxine (TCDD), cyclosporine A (CsA) and Wy) carcinogens in primary mouse hepatocytes. Discriminative gene sets, microRNAs (not for 24h) and processes were identified following 24 and 48h of exposure. From the three discriminative microRNAs found following 48h of exposure, mmu-miR-503-5p revealed to have an interaction with mRNA target gene cyclin D2 (Ccnd2 - 12444) which was involved in the discriminative process of p53 signalling and metabolism. Following exposure to NGTX carcinogens Mmu-miR-503-5p may have an oncogenic function by stimulating Ccnd2 possibly leading to a tumourigenic cell cycle progression. By contrast, after GTX carcinogen exposure it may have a tumour-suppressive function (repressing Ccnd2) leading to cell cycle arrest and to increased DNA repair activities. In addition, compound-specific microRNA-mRNA interactions [mmu-miR-301b-3p-Papss2 (for AFB1), as well as mmu-miR-29b-3p-Col4a2 and mmu-miR-24-3p-Flna (for BaP)] were found to contribute to a better understanding of microRNAs in cell cycle arrest and the impairment of the DNA damage repair, an important hallmark of GTX-induced carcinogenesis. Overall, our results indicate that microRNAs represent yet another relevant intracellular regulatory level in chemical carcinogenesis.

Exploiting microRNA and mRNA profiles generated in vitro from carcinogen-exposed primary mouse hepatocytes for predicting in vivo genotoxicity and carcinogenicity

The well-defined battery of in vitro systems applied within chemical cancer risk assessment is often characterised by a high false-positive rate, thus repeatedly failing to correctly predict the in vivo genotoxic and carcinogenic properties of test compounds. Toxicogenomics, i.e. mRNA-profiling, has been proven successful in improving the prediction of genotoxicity in vivo and the understanding of underlying mechanisms. Recently, microRNAs have been discovered as post-transcriptional regulators of mRNAs. It is thus hypothesised that using microRNA response-patterns may further improve current prediction methods. This study aimed at predicting genotoxicity and non-genotoxic carcinogenicity in vivo, by comparing microRNA- and mRNA-based profiles, using a frequently applied in vitro liver model and exposing this to a range of well-chosen prototypical carcinogens. Primary mouse hepatocytes (PMH) were treated for 24 and 48h with 21 chemical compounds [genotoxins (GTX) vs. non-genotoxins (NGTX) and non-genotoxic carcinogens (NGTX-C) versus non-carcinogens (NC)]. MicroRNA and mRNA expression changes were analysed by means of Exiqon and Affymetrix microarray-platforms, respectively. Classification was performed by using Prediction Analysis for Microarrays (PAM). Compounds were randomly assigned to training and validation sets (repeated 10 times). Before prediction analysis, pre-selection of microRNAs and mRNAs was performed by using a leave-one-out t-test. No microRNAs could be identified that accurately predicted genotoxicity or non-genotoxic carcinogenicity in vivo. However, mRNAs could be detected which appeared reliable in predicting genotoxicity in vivo after 24h (7 genes) and 48h (2 genes) of exposure (accuracy: 90% and 93%, sensitivity: 65% and 75%, specificity: 100% and 100%). Tributylinoxide and para-Cresidine were misclassified. Also, mRNAs were identified capable of classifying NGTX-C after 24h (5 genes) as well as after 48h (3 genes) of treatment (accuracy: 78% and 88%, sensitivity: 83% and 83%, specificity: 75% and 93%). Wy-14,643, phenobarbital and ampicillin trihydrate were misclassified. We conclude that genotoxicity and non-genotoxic carcinogenicity probably cannot be accurately predicted based on microRNA profiles. Overall, transcript-based prediction analyses appeared to clearly outperform microRNA-based analyses.

MagiCMicroRna: a web implementation of AgiMicroRna using shiny

BackgroundMicroRNA expression can be quantified using sequencing techniques or commercial microRNA-expression arrays. Recently, the AgiMicroRna R-package was published that enabled systematic preprocessing and statistical analysis for Agilent microRNA arrays. Here we describe MagiCMicroRna, which is a user-friendly web interface for this package, together with a new filtering approach.ResultsWe used MagiCMicroRna to normalize and filter an Agilent miRNA microarray dataset of cancerous and normal tissues from 14 different patients. With the standard filtering procedure, 250 out of 817 microRNAs remained, whereas the new group-specific filtering approach resulted in broader datasets for further analysis in most groups (>279 microRNAs remaining).ConclusionsThe user-friendly web interface of MagiCMicroRna enables researchers to normalize and filter Agilent microarrays by the click of one button. Furthermore, MagiCMicroRna provides flexibility in choosing the filtering method. The new group-specific filtering approach lead to an increased number and additional tissue-specific microRNAs remaining for subsequent analysis compared to the standard procedure. The MagiCMicroRna web interface and source code can be downloaded from https://bitbucket.org/mutgx/magicmicrorna.git.

cBiT: A transcriptomics database for innovative biomaterial engineering

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.

Hepatotoxicity Screening on In Vitro Models and the Role of ’Omics

The liver is one of the five most common target organs of toxicity, both during acute and chronic (repeated dose) toxicity, not only for drugs but also for cosmetic ingredients. Chemical entities can trigger liver damage in humans, and hepatotoxicity is the leading cause of withdrawal of drugs from the market, accounting for 40% of withdrawals worldwide. It has been estimated that only 50% of human liver toxicities could be predicted using animal models, and there is a clear need for accurately predictive toxicity and safety testing methods. Hepatocellular injury can manifest in a number of ways, including hepatitis, steatosis, cirrhosis, inflammation, phospholipidosis, and cholestasis. Cholestasis and steatosis are among the most prominent and well-documented types of liver injury and are the focus of this chapter. Furthermore, attention is also paid to necrosis, a mode of cell death that is observed in most of the aforementioned types of hepatocellular injury.