Report of the First ONTOX Stakeholder Network Meeting: Digging Under the Surface of ONTOX Together With the Stakeholders.

The first Stakeholder Network Meeting of the EU Horizon 2020-funded ONTOX project was held on 13-14 March 2023, in Brussels, Belgium. The discussion centred around identifying specific challenges, barriers and drivers in relation to the implementation of non-animal new approach methodologies (NAMs) and probabilistic risk assessment (PRA), in order to help address the issues and rank them according to their associated level of difficulty. ONTOX aims to advance the assessment of chemical risk to humans, without the use of animal testing, by developing non-animal NAMs and PRA in line with 21st century toxicity testing principles. Stakeholder groups (regulatory authorities, companies, academia, non-governmental organisations) were identified and invited to participate in a meeting and a survey, by which their current position in relation to the implementation of NAMs and PRA was ascertained, as well as specific challenges and drivers highlighted. The survey analysis revealed areas of agreement and disagreement among stakeholders on topics such as capacity building, sustainability, regulatory acceptance, validation of adverse outcome pathways, acceptance of artificial intelligence (AI) in risk assessment, and guaranteeing consumer safety. The stakeholder network meeting resulted in the identification of barriers, drivers and specific challenges that need to be addressed. Breakout groups discussed topics such as hazard versus risk assessment, future reliance on AI and machine learning, regulatory requirements for industry and sustainability of the ONTOX Hub platform. The outputs from these discussions provided insights for overcoming barriers and leveraging drivers for implementing NAMs and PRA. It was concluded that there is a continued need for stakeholder engagement, including the organisation of a 'hackathon' to tackle challenges, to ensure the successful implementation of NAMs and PRA in chemical risk assessment.

Correction: Bischoff et al. The Effects of the Food Additive Titanium Dioxide (E171) on Tumor Formation and Gene Expression in the Colon of a Transgenic Mouse Model for Colorectal Cancer. Nanomaterials 2022, 12, 1256.

In the published publication [...].

Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data.

The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains.

A dynamic model of the intestinal epithelium integrates multiple sources of preclinical data and enables clinical translation of drug-induced toxicity.

We have built a quantitative systems toxicology modeling framework focused on the early prediction of oncotherapeutic-induced clinical intestinal adverse effects. The model describes stem and progenitor cell dynamics in the small intestinal epithelium and integrates heterogeneous epithelial-related processes, such as transcriptional profiles, citrulline kinetics, and probability of diarrhea. We fitted a mouse-specific version of the model to quantify doxorubicin and 5-fluorouracil (5-FU)-induced toxicity, which included pharmacokinetics and 5-FU metabolism and assumed that both drugs led to cell cycle arrest and apoptosis in stem cells and proliferative progenitors. The model successfully recapitulated observations in mice regarding dose-dependent disruption of proliferation which could lead to villus shortening, decrease of circulating citrulline, increased diarrhea risk, and transcriptional induction of the p53 pathway. Using a human-specific epithelial model, we translated the cytotoxic activity of doxorubicin and 5-FU quantified in mice into human intestinal injury and predicted with accuracy clinical diarrhea incidence. However, for gefitinib, a specific-molecularly targeted therapy, the mice failed to reproduce epithelial toxicity at exposures much higher than those associated with clinical diarrhea. This indicates that, regardless of the translational modeling approach, preclinical experimental settings have to be suitable to quantify drug-induced clinical toxicity with precision at the structural scale of the model. Our work demonstrates the usefulness of translational models at early stages of the drug development pipeline to predict clinical toxicity and highlights the importance of understanding cross-settings differences in toxicity when building these approaches.

Reply to Kaminski, N.E.; Cohen, S.M. Comment on "Bischoff et al. The Effects of the Food Additive Titanium Dioxide (E171) on Tumor Formation and Gene Expression in the Colon of a Transgenic Mouse Model for Colorectal Cancer. Nanomaterials 2022, 12, 1256".

We appreciate the interest in our article describing transcriptome changes in a transgenic mouse model carrying an APC gene mutation and would like to reply to the reader [...].

Transcriptomic Analysis in Human 3D Skin Model Injected with Resorbable Hyaluronic Acid Fillers Reveals Foreign Body Response.

Usage of injectable dermal fillers applied for aesthetic purposes has extensively increased over the years. As such, the number of related adverse reactions has increased, including patients showing severe complications such as product migration, topical swelling and inflammatory reactions of the skin. In order to understand the underlying molecular events of these adverse reactions we performed a genome-wide gene expression study on the multi-cell type human Phenion® Full-Thickness Skin Model exposed to five experimental hyaluronic acid (HA) preparations with increasing cross-linking degree, four commercial fillers from Perfectha®, and non-resorbable filler Bio-Alcamid®. In addition, we evaluated whether cross-linking degree or particle size of the HA-based fillers could be associated with the occurrence of adverse effects. In all cases, exposure to different HA fillers resulted in a clearly elevated gene expression of cytokines and chemokines related to acute inflammation as part of the foreign body response. Furthermore, for one experimental filler genes of OXPHOS complexes I-V were significantly down-regulated (adjusted p-value < 0.05), resulting in mitochondrial dysfunction which can be linked to over-expression of pro-inflammatory cytokines TNFα and IL-1ß and chemokine CCL2. Our hypothesis that cross-linking degree or particle size of the HA-based fillers is related to the biological responses induced by these fillers could only partially be confirmed for particle size. In conclusion, our innovative approach resulted in gene expression changes from a human 3D skin model exposed to dermal fillers that mechanistically substantiate aforementioned adverse reactions, and thereby adds to the weight of evidence that these fillers may induce inflammatory and fibrotic responses.

Gene expression responses reflecting 5-FU-induced toxicity: Comparison between patient colon tissue and 3D human colon organoids.

Capecitabine is a chemotherapeutic drug that is widely used as a monotherapy option in advanced cancer patients. After administration, it is converted into its active metabolite 5-fluorouracil (5-FU), a cytotoxic compound that may also induce adverse side effects in the gastrointestinal (GI) tract. Although these side effects can interfere with the continuation of the chemotherapy, diagnostic tools to detect early onset and prevention strategies are not available. In this explorative case study, we aim to identify differentially expressed genes (DEGs) that provide insight into the molecular mechanisms of toxicity induced by 5-FU in healthy colon tissue of breast cancer patients receiving capecitabine. Gene expression responses observed in patients were compared with those established in an in vitro model of healthy colon organoids. Colon biopsies from two patients with advanced breast cancer were collected before and after the treatment with capecitabine and used for RNA sequencing to determine transcriptomic responses. Differential expression analysis resulted in 31 affected genes, showing that the most affected pathways were transport of small molecules, cellular responses to stress, folate metabolism, NF-kB signalling pathway and immune system responses. The most biologically relevant genes were haemoglobin subunits encoding genes, involved in several processes; ATP12A, SLC26A3 and AQP8, involved in the transport of ions and water; TRIM31, a regulator of NF-kB signalling pathway; MST1P2 and MST1L, stimulators of macrophages. Comparison of human in vitro and in vivo responses showed that the gene expression of TRIM31 was similarly altered in the colon organoids exposed to 5-FU. Therefore, this gene constitutes a potential biomarker of colon toxicity that might be used in future in vitro drug safety design and screening.

The Effects of the Food Additive Titanium Dioxide (E171) on Tumor Formation and Gene Expression in the Colon of a Transgenic Mouse Model for Colorectal Cancer.

Titanium dioxide (TiO2) is present in many different food products as the food additive E171, which is currently scrutinized due to its potential adverse effects, including the stimulation of tumor formation in the gastrointestinal tract. We developed a transgenic mouse model to examine the effects of E171 on colorectal cancer (CRC), using the Cre-LoxP system to create an Apc-gene-knockout model which spontaneously develops colorectal tumors. A pilot study showed that E171 exposed mice developed colorectal adenocarcinomas, which were accompanied by enhanced hyperplasia in epithelial cells, lymphatic nodules at the base of the polyps, and increased tumor size. In the main study, tumor formation was studied following the exposure to 5 mg/kgbw/day of E171 for 9 weeks (Phase I). E171 exposure showed a statistically nonsignificant increase in the number of colorectal tumors in these transgenic mice, as well as a statistically nonsignificant increase in the average number of mice with tumors. Gene expression changes in the colon were analyzed after exposure to 1, 2, and 5 mg/kgbw/day of E171 for 2, 7, 14, and 21 days (Phase II). Whole-genome mRNA analysis revealed the modulation of genes in pathways involved in the regulation of gene expression, cell cycle, post-translational modification, nuclear receptor signaling, and circadian rhythm. The processes associated with these genes might be involved in the enhanced tumor formation and suggest that E171 may contribute to tumor formation and progression by modulation of events related to inflammation, activation of immune responses, cell cycle, and cancer signaling.

A Transcriptomic Approach to Elucidate the Mechanisms of Gefitinib-Induced Toxicity in Healthy Human Intestinal Organoids.

Gefitinib is a tyrosine kinase inhibitor (TKI) that selectively inhibits the epidermal growth factor receptor (EGFR), hampering cell growth and proliferation. Due to its action, gefitinib has been used in the treatment of cancers that present abnormally increased expression of EGFR. However, side effects from gefitinib therapy may occur, among which diarrhoea is most common, that can lead to interruption of the planned therapy in the more severe cases. The mechanisms underlying intestinal toxicity induced by gefitinib are not well understood. Therefore, this study aims at providing insight into these mechanisms based on transcriptomic responses induced in vitro. A 3D culture of healthy human colon and small intestine (SI) organoids was exposed to 0.1, 1, 10 and 30 µM of gefitinib, for a maximum of three days. These drug concentrations were selected using physiologically-based pharmacokinetic simulation considering patient dosing regimens. Samples were used for the analysis of viability and caspase 3/7 activation, image-based analysis of structural changes, as well as RNA isolation and sequencing via high-throughput techniques. Differential gene expression analysis showed that gefitinib perturbed signal transduction pathways, apoptosis, cell cycle, FOXO-mediated transcription, p53 signalling pathway, and metabolic pathways. Remarkably, opposite expression patterns of genes associated with metabolism of lipids and cholesterol biosynthesis were observed in colon versus SI organoids in response to gefitinib. These differences in the organoids' responses could be linked to increased activated protein kinase (AMPK) activity in colon, which can influence the sensitivity of the colon to the drug. Therefore, this study sheds light on how gefitinib induces toxicity in intestinal organoids and provides an avenue towards the development of a potential tool for drug screening and development.

Integrating SNPs-based genetic risk factor with blood epigenomic response of differentially arsenic-exposed rural subjects reveals disease-associated signaling pathways.

Arsenic (As) contamination in groundwater is responsible for numerous adverse health outcomes among millions of people. Epigenetic alterations are among the most widely studied mechanisms of As toxicity. To understand how As exposure alters gene expression through epigenetic modifications, a systematic genome-wide study was designed to address the impact of multiple important single nucleotide polymorphisms (SNPs) related to As exposure on the methylome of drinking water As-exposed rural subjects from Pakistan. Urinary As levels were used to stratify subjects into low, medium and high exposure groups. Genome-wide DNA methylation was investigated using MeDIP in combination with NimbleGen 2.1 M Deluxe Promotor arrays. Transcriptome levels were measured using Agilent 8 × 60 K expression arrays. Genotyping of selected SNPs (As3MT, DNMT1a, ERCC2, EGFR and MTHFR) was measured and an integrated genetic risk factor for each respondent was calculated by assigning a specific value to the measured genotypes based on known risk allele numbers. To select a representative model related to As exposure we compared 9 linear mixed models comprising of model 1 (including the genetic risk factor), model 2 (without the genetic risk factor) and models with individual SNPs incorporated into the methylome data. Pathway analysis was performed using ConsensusPathDB. Model 1 comprising the integrated genetic risk factor disclosed biochemical pathways including muscle contraction, cardio-vascular diseases, ATR signaling, GPCR signaling, methionine metabolism and chromatin modification in association with hypo- and hyper-methylated gene targets. A unique pathway (direct P53 effector) was found associated with the individual DNMT1a polymorphism due to hyper-methylation of CSE1L and TRRAP. Most importantly, we provide here the first evidence of As-associated DNA methylation in relation with gene expression of ATR, ATF7IP, TPM3, UBE2J2. We report the first evidence that integrating SNPs data with methylome data generates a more representative epigenome profile and discloses a better insight in disease risks of As-exposed individuals.

New insights into the mechanisms underlying 5-fluorouracil-induced intestinal toxicity based on transcriptomic and metabolomic responses in human intestinal organoids.

5-Fluorouracil (5-FU) is a widely used chemotherapeutical that induces acute toxicity in the small and large intestine of patients. Symptoms can be severe and lead to the interruption of cancer treatments. However, there is limited understanding of the molecular mechanisms underlying 5-FU-induced intestinal toxicity. In this study, well-established 3D organoid models of human colon and small intestine (SI) were used to characterize 5-FU transcriptomic and metabolomic responses. Clinically relevant 5-FU concentrations for in vitro testing in organoids were established using physiologically based pharmacokinetic simulation of dosing regimens recommended for cancer patients, resulting in exposures to 10, 100 and 1000 µM. After treatment, different measurements were performed: cell viability and apoptosis; image analysis of cell morphological changes; RNA sequencing; and metabolome analysis of supernatant from organoids cultures. Based on analysis of the differentially expressed genes, the most prominent molecular pathways affected by 5-FU included cell cycle, p53 signalling, mitochondrial ATP synthesis and apoptosis. Short time-series expression miner demonstrated tissue-specific mechanisms affected by 5-FU, namely biosynthesis and transport of small molecules, and mRNA translation for colon; cell signalling mediated by Rho GTPases and fork-head box transcription factors for SI. Metabolomic analysis showed that in addition to the effects on TCA cycle and oxidative stress in both organoids, tissue-specific metabolic alterations were also induced by 5-FU. Multi-omics integration identified transcription factor E2F1, a regulator of cell cycle and apoptosis, as the best key node across all samples. These results provide new insights into 5-FU toxicity mechanisms and underline the relevance of human organoid models in the safety assessment in drug development.

Use of deep learning methods to translate drug-induced gene expression changes from rat to human primary hepatocytes.

In clinical trials, animal and cell line models are often used to evaluate the potential toxic effects of a novel compound or candidate drug before progressing to human trials. However, relating the results of animal and in vitro model exposures to relevant clinical outcomes in the human in vivo system still proves challenging, relying on often putative orthologs. In recent years, multiple studies have demonstrated that the repeated dose rodent bioassay, the current gold standard in the field, lacks sufficient sensitivity and specificity in predicting toxic effects of pharmaceuticals in humans. In this study, we evaluate the potential of deep learning techniques to translate the pattern of gene expression measured following an exposure in rodents to humans, circumventing the current reliance on orthologs, and also from in vitro to in vivo experimental designs. Of the applied deep learning architectures applied in this study the convolutional neural network (CNN) and a deep artificial neural network with bottleneck architecture significantly outperform classical machine learning techniques in predicting the time series of gene expression in primary human hepatocytes given a measured time series of gene expression from primary rat hepatocytes following exposure in vitro to a previously unseen compound across multiple toxicologically relevant gene sets. With a reduction in average mean absolute error across 76 genes that have been shown to be predictive for identifying carcinogenicity from 0.0172 for a random regression forest to 0.0166 for the CNN model (p < 0.05). These deep learning architecture also perform well when applied to predict time series of in vivo gene expression given measured time series of in vitro gene expression for rats.

Drug-induced gene expression profile changes in relation to intestinal toxicity: State-of-the-art and new approaches.

One of the major complications that patients experience during pharmacological treatment is the occurrence of adverse drug reactions (ADRs). The most affected organs are the liver, kidney, heart and the gastrointestinal-immune system. In comparison to the other organs, less progress has been made on human-relevant prediction of drug-induced intestinal toxicity, evidencing current large data gaps. The most widely used drugs that are associated with intestinal damage include chemotherapeutics, such as 5-Fluorouracil or Tyrosine Kinase Inhibitors (TKIs), as well as non-steroidal anti-inflammatory drugs (NSAIDs). Chemotherapeutics are regarded as inducers of acute intestinal toxicity whereas NSAIDs are associated with chronic inflammation of the intestine. In view of the fact that only a few studies have been dedicated to studying cellular and genomic responses in relation to drug-induced intestinal ADRs, little is known about how intestinal toxicity develops after exposure to such drugs or which molecular mechanisms are involved. Therefore, new models and experiments are required to establish transcriptomic responses and alterations of molecular markers induced by different medicines. This review summarizes the available information about transcriptomic responses and biomarkers of toxicity induced by 5-FU, NSAIDS or TKIs in different experimental models. Future investigation should address the challenges in predicting intestinal toxicity induced by drugs and unveil specific gene expression profiles that can be applied in the development of safer drugs.

Integrative omics data analyses of repeated dose toxicity of valproic acid in vitro reveal new mechanisms of steatosis induction.

Valproic acid (VPA) is a very potent anti-cancer and neuro-protective drug probably by its HDAC inhibiting properties, which may cause steatosis in the liver. The present study investigates the effect of repetitive VPA treatment of primary human hepatocytes (PHH) on whole genome gene expression-, DNA methylation-, and miRNA changes, using microarrays and integrated data analyses. PHH were exposed to a non-cytotoxic dose of VPA for 5days daily which induced lipid accumulation. Part of the PHH was left untreated for 3days for studying the persistence of 'omics' changes. VPA treatment appeared to inhibit the expression of the transcription factors HNF1A and ONECUT1. HNF1A interacted with 41 differentially expressed genes of which 12 were also differentially methylated. None of the genes present in this network were regulated by a DE-miR. The subnetwork of ONECUT1 consisted of 44 differentially expressed genes of which 15 were differentially methylated, and 3 were regulated by a DE-miR. A number of genes in the networks are involved in fatty acid metabolism, and may contribute to the development of steatosis by increasing oxidative stress thereby causing mitochondrial dysfunction, and by shifting metabolism of VPA towards ß-oxidation due to reduced glucuronidation. Part of the changes remained persistent after washing out of VPA, like PMAIP1 which is associated with cellular stress in liver of patients with NASH. The MMP2 gene showed the highest number of interactions with other persistently expressed genes, among which LCN2 which is a key modulator of lipid homeostasis. Furthermore, VPA modulated the expression and DNA methylation level of nuclear receptors and their target genes involved in the adverse outcome pathway of steatosis, thereby expanding our current knowledge of the pathway. In particular, VPA modulated PPARγ, and PPARα, AHR and CD36 on both the gene expression and the DNA methylation level, thereby inhibiting ß-oxidation and increasing uptake of fatty acid into the hepatocytes, respectively. Overall, our integrative data analyses identified novel genes modulated by VPA, which provide more insight into the mechanisms of repeated dose toxicity of VPA, leading to steatosis.

Dose and Time Dependencies in Stress Pathway Responses during Chemical Exposure: Novel Insights from Gene Regulatory Networks.

Perturbation of biological networks is often observed during exposure to xenobiotics, and the identification of disturbed processes, their dynamic traits, and dose-response relationships are some of the current challenges for elucidating the mechanisms determining adverse outcomes. In this scenario, reverse engineering of gene regulatory networks (GRNs) from expression data may provide a system-level snapshot embedded within accurate molecular events. Here, we investigate the composition of GRNs inferred from groups of chemicals with two distinct outcomes, namely carcinogenicity [azathioprine (AZA) and cyclophosphamide (CYC)] and drug-induced liver injury (DILI; diclofenac, nitrofurantoin, and propylthiouracil), and a non-carcinogenic/non-DILI group (aspirin, diazepam, and omeprazole). For this, we analyzed publicly available exposed in vitro human data, taking into account dose and time dependencies. Dose-Time Network Identification (DTNI) was applied to gene sets from exposed primary human hepatocytes using four stress pathways, namely endoplasmic reticulum (ER), NF-κB, NRF2, and TP53. Inferred GRNs suggested case specificity, varying in interactions, starting nodes, and target genes across groups. DILI and carcinogenic compounds were shown to directly affect all pathway-based GRNs, while non-DILI/non-carcinogenic chemicals only affected NF-κB. NF-κB-based GRNs clearly illustrated group-specific disturbances, with the cancer-related casein kinase CSNK2A1 being a target gene only in the carcinogenic group, and opposite regulation of NF-κB subunits being observed in DILI and non-DILI/non-carcinogenic groups. Target genes in NRF2-based GRNs shared by DILI and carcinogenic compounds suggested markers of hepatotoxicity. Finally, we indicate several of these group-specific interactions as potentially novel. In summary, our reversed-engineered GRNs are capable of revealing dose dependent, chemical-specific mechanisms of action in stress-related biological networks.

Identification of essential transcription factors for adequate DNA damage response after benzo(a)pyrene and aflatoxin B1 exposure by combining transcriptomics with functional genomics.

DNA damage mediates widespread changes in transcription through activation or repression of transcription factors (TFs). However, the consequences of regulating specific TFs for the outcome of the DNA repair process remain incompletely understood. Here, we combined transcriptomics and TF binding prediction with functional genomics to identify TFs essential for adequate DNA repair in HepG2 liver cells after a non-cytotoxic dose of carcinogens benzo(a)pyrene (BaP) (2µM) and aflatoxin B1 (AFB1) (5µM). BaP and AFB1 induced a largely common transcriptional response, mediated by similar TFs. A lentiviral shRNA screen knocking down the top31 identified TFs, was performed to determine their effect on DNA repair by assessing phosphorylation of H2AX (γ-H2AX). In addition to the top candidate p53, we identified several other interesting TFs that modulated γ-H2AX after BaP and AFB1 treatment. Validation studies confirmed the role of p53 in reducing γ-H2AX formation and DNA breaks measured by COMET assay after BaP and AFB1 exposure. Expression of the cell cycle inhibitor p21 was profoundly impaired upon p53 knock-down. In addition, the expression of 2 genes involved in nucleotide exchange repair, DDB2 and XPC was significantly reduced in p53 knock-down cells. Although p63 knock-down affected DNA damage upon BaP treatment this was not associated with altered expression of DDB2 or XPC. Finally, knock-down of ARNT reduced γ-H2AX in response to BaP, which was associated with reduced CYP1A1 expression. Importantly, our results suggest a new role for ARNT and its dimerization partner AHR in the occurrence of H2AX phosphorylation after AFB1 treatment. These data show that modulation of TF activity impacts on the repair of BaP- and AFB1-induced DNA damage. Our study also demonstrates the potential of combining functional genomics with genome-wide expression analysis to identify yet unknown causal relationships, thereby aiding in the interpretation of complex biological systems.

DTNI: a novel toxicogenomics data analysis tool for identifying the molecular mechanisms underlying the adverse effects of toxic compounds.

Unravelling gene regulatory networks (GRNs) influenced by chemicals is a major challenge in systems toxicology. Because toxicant-induced GRNs evolve over time and dose, the analysis of global gene expression data measured at multiple time points and doses will provide insight in the adverse effects of compounds. Therefore, there is a need for mathematical methods for GRN identification from time-over-dose-dependent data. One of the current approaches for GRN inference is Time Series Network Identification (TSNI). TSNI is based on ordinary differential equations (ODE), describing the time evolution of the expression of each gene, which is assumed to be dependent on the expression of other genes and an external perturbation (i.e. chemical exposure). Here, we present Dose-Time Network Identification (DTNI), a method extending TSNI by including ODE describing how the expression of each gene evolves with dose, which is supposed to depend on the expression of other genes and the exposure time. We also adapted TSNI in order to enable inclusion of time-over-dose-dependent data from multiple compounds. Here, we show that DTNI outperforms TSNI in inferring a toxicant-induced GRN. Moreover, we show that DTNI is a suitable method to infer a GRN dose- and time-dependently induced by a group of compounds influencing a common biological process. Applying DTNI on experimental data from TG-GATEs, we demonstrate that DTNI provides in-depth information on the mode of action of compounds, in particular key events and potential molecular initiating events. Furthermore, DTNI also discloses several unknown interactions which have to be verified experimentally.

Xenobiotic metabolism in differentiated human bronchial epithelial cells.

Differentiated human bronchial epithelial cells in air liquid interface cultures (ALI-PBEC) represent a promising alternative for inhalation studies with rodents as these 3D airway epithelial tissue cultures recapitulate the human airway in multiple aspects, including morphology, cell type composition, gene expression and xenobiotic metabolism. We performed a detailed longitudinal gene expression analysis during the differentiation of submerged primary human bronchial epithelial cells into ALI-PBEC to assess the reproducibility and inter-individual variability of changes in transcriptional activity during this process. We generated ALI-PBEC cultures from four donors and focussed our analysis on the expression levels of 362 genes involved in biotransformation, which are of primary importance for toxicological studies. Expression of various of these genes (e.g., GSTA1, ADH1C, ALDH1A1, CYP2B6, CYP2F1, CYP4B1, CYP4X1 and CYP4Z1) was elevated following the mucociliary differentiation of airway epithelial cells into a pseudo-stratified epithelial layer. Although a substantial number of genes were differentially expressed between donors, the differences in fold changes were generally small. Metabolic activity measurements applying a variety of different cytochrome p450 substrates indicated that epithelial cultures at the early stages of differentiation are incapable of biotransformation. In contrast, mature ALI-PBEC cultures were proficient in the metabolic conversion of a variety of substrates albeit with considerable variation between donors. In summary, our data indicate a distinct increase in biotransformation capacity during differentiation of PBECs at the air-liquid interface and that the generation of biotransformation competent ALI-PBEC cultures is a reproducible process with little variability between cultures derived from four different donors.

Integrative "-Omics" Analysis in Primary Human Hepatocytes Unravels Persistent Mechanisms of Cyclosporine A-Induced Cholestasis.

Cyclosporine A (CsA) is an undecapeptide with strong immunosuppressant activities and is used a lot after organ transplantation. Furthermore, it may induce cholestasis in the liver. In general, the drug-induced cholestasis (DIC) pathway includes genes involved in the uptake, synthesis, conjugation, and secretion of bile acids. However, whether CsA-induced changes in the cholestasis pathway in vitro are persistent for repeated dose toxicity has not yet been investigated. To explore this, primary human hepatocytes (PHH) were exposed to a subcytotoxic dose of 30 µM CsA daily for 3 and 5 days. To investigate the persistence of induced changes upon terminating CsA exposure after 5 days, a subset of PHH was subjected to a washout period (WO-period) of 3 days. Multiple -omics analyses, comprising whole genome analysis of DNA methylation, gene expression, and microRNA expression, were performed. The CsA-treatment resulted after 3 and 5 days, respectively, in 476 and 20 differentially methylated genes (DMGs), 1353 and 1481 differentially expressed genes (DEGs), and in 22 and 29 differentially expressed microRNAs (DE-miRs). Cholestasis-related pathways appeared induced during CsA-treatment. Interestingly, 828 persistent DEGs and 6 persistent DE-miRs but no persistent DMGs were found after the WO-period. These persistent DEGs and DE-miRs showed concordance for 22 genes. Furthermore, 29 persistent DEGs changed into the same direction as observed in livers from cholestasis patients. None of those 29 DEGs which among others relate to oxidative stress and lipid metabolism are yet present in the DIC pathway or cholestasis adverse outcome pathway (AOP) thus presenting novel findings. In summary, we have demonstrated for the first time a persistent impact of repeated dose administration of CsA on genes and microRNAs related to DIC in the gold standard human liver in vitro model with PHH.

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.