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Beauvericin (BEA), an ionophoric cyclic hexadepsipeptide mycotoxin, is able to increase oxidative stress by altering membrane ion permeability and uncoupling oxidative phosphorylation. A toxicogenomic study was performed to investigate gene expression changes triggered by BEA exposure (1.5, 3 and 5 μM; 24 h) in Jurkat cells through RNA-sequencing and differential gene expression analysis. Perturbed gene expression was observed in a concentration dependent manner, with 43 differentially expressed genes (DEGs) overlapped in the three studied concentrations. Gene ontology (GO) analysis showed several biological processes related to electron transport chain, oxidative phosphorylation, and cellular respiration significantly altered. Molecular functions linked to mitochondrial respiratory chain and oxidoreductase activity were over-represented (q-value < 0.01). Pathway analysis revealed oxidative phosphorylation and electron transport chain as the most significantly altered pathways in all studied doses (z-score > 1.96; adj p-value < 0.05). 77 genes involved in the respiratory chain were significantly down-regulated at least at one dose. Moreover, 21 genes related to apoptosis and programmed cell death, and 12 genes related to caspase activity were significantly altered, mainly affecting initiator caspases 8, 9 and 10. The results demonstrated BEA-induced mitochondrial damage affecting the respiratory chain, and pointing to apoptosis through the caspase cascade in human lymphoblastic T cells., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Drug-induced liver injury remains the most common cause of acute liver failure and a frequently indicated reason for withdrawal of drugs. For the purpose of evaluating the relevance of liver cell models for assessing hepatotoxic risks in intact humans, we here aimed to benchmark 'omics-derived mechanistic data from three in vitro models for parenchymal liver function, intended for the investigation of drug-induced cholestasis, against 'omics data from cholestatic patients. Transcriptomic changes in HepG2 cells, primary mouse hepatocytes and primary human hepatocytes exposed to known cholestatic compounds were analyzed using microarrays. Some of the differentially expressed genes in HepG2 cells were also differentially expressed into the same direction in human cholestasis. The overlap between drug-induced transcriptomic responses in primary mouse hepatocytes and primary human hepatocytes appeared limited and no genes overlapping with in vivo cholestasis were found. Thereupon, a pathway for drug-induced cholestasis was used to map the drug-induced transcriptomic modifications involved in bile salt homeostasis. Indications of an adaptive response to prevent and reduce intracellular bile salt accumulation were observed in vivo as well as in the in vitro liver models. Furthermore, drug-specific changes were found, which may be indicative for their cholestatic properties. Furthermore, connectivity mapping was applied in order to investigate the predictive value of the in vitro models for in vivo cholestasis. This analysis resulted in a positive connection score for most compounds, which may indicate that for identifying cholestatic compounds the focus should be on gene expression signatures rather than on differentially expressed genes., (Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Transcription factors (TFs) are important modulators of the inducible portion of the transcriptome, and therefore relevant in the context of exposure to exogenous compounds. Current approaches to predict the activity of TFs in biological systems are usually restricted to a few entities at a time due to low-throughput techniques targeting a limited fraction of annotated human TFs. Therefore, high-throughput alternatives may help to identify new targets of mechanistic and predictive value in toxicological investigations. In this study, we inferred the activity multiple TFs using publicly available microarray data from primary human hepatocytes exposed to hundreds of chemicals and evaluated these molecular profiles using multiple correspondence analysis. Our results demonstrate that the lowest dose and latest exposure time (24h) in a subset of chemicals generates a signature indicative of carcinogenicity possibly due to DNA-damaging properties. Furthermore, profiles from the earliest exposure time (2h) and highest dose creates clusters of chemicals implicated in the development of diverse forms of drug-induced liver injury (DILI). Both approaches yielded a number of TFs with similar activity across groups of chemicals, including TFs known in toxicological responses such as AhR, NFE2L2 (Nrf2), NF-κB and PPARG. FOXM1, IRF1 and E2F4 were some of the TFs identified that may be relevant in genotoxic carcinogenesis. SMADs (SMAD1, SMAD2, SMAD5) and KLF5 were identified as some of potentially new TFs whose inferred activities were linked to acute and progressive outcomes in DILI. In conclusion this study offers a novel mechanistic approach targeting TF activity during chemical exposure., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Many studies now produce parallel data sets from different omics technologies; however, the task of interpreting the acquired data in an integrated fashion is not trivial. This review covers those methods that have been used over the past decade to statistically integrate and interpret metabolomics and transcriptomic data sets. It defines four categories of approaches, correlation-based integration, concatenation-based integration, multivariate-based integration and pathway-based integration, into which all existing statistical methods fit. It also explores the choices in study design for generating samples for analysis by these omics technologies and the impact that these technical decisions have on the subsequent data analysis options., (© The Author 2015. Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.)

The assessment of the carcinogenic potential of chemicals with alternative, human-based in vitro systems has become a major goal of toxicogenomics. The central read-out of these assays is the transcriptome, and while many studies exist that explored the gene expression responses of such systems, reports on robustness and reproducibility, when testing them independently in different laboratories, are still uncommon. Furthermore, there is limited knowledge about variability induced by the data analysis protocols. We have conducted an inter-laboratory study for testing chemical carcinogenicity evaluating two human in vitro assays: hepatoma-derived cells and hTERT-immortalized renal proximal tubule epithelial cells, representing liver and kidney as major target organs. Cellular systems were initially challenged with thirty compounds, genome-wide gene expression was measured with microarrays, and hazard classifiers were built from this training set. Subsequently, each system was independently established in three different laboratories, and gene expression measurements were conducted using anonymized compounds. Data analysis was performed independently by two separate groups applying different protocols for the assessment of inter-laboratory reproducibility and for the prediction of carcinogenic hazard. As a result, both workflows came to very similar conclusions with respect to (1) identification of experimental outliers, (2) overall assessment of robustness and inter-laboratory reproducibility and (3) re-classification of the unknown compounds to the respective toxicity classes. In summary, the developed bioinformatics workflows deliver accurate measures for inter-laboratory comparison studies, and the study can be used as guidance for validation of future carcinogenicity assays in order to implement testing of human in vitro alternatives to animal testing.

Benzo(a)pyrene (BaP) is a ubiquitous carcinogen resulting from incomplete combustion of organic compounds and also present at high levels in cigarette smoke. A wide range of biological effects has been attributed to BaP and its genotoxic metabolite BPDE, but the contribution to BaP toxicity of intermediary metabolites generated along the detoxification path remains unknown. Here, we report for the first time how 3-OH-BaP, 9,10-diol and BPDE, three major BaP metabolites, temporally relate to BaP-induced transcriptomic alterations in HepG2 cells. Since BaP is also known to induce AhR activation, we additionally evaluated TCDD to source the expression of non-genotoxic AhR-mediated patterns. 9,10-Diol was shown to activate several transcription factor networks related to BaP metabolism (AhR), oxidative stress (Nrf2) and cell proliferation (HIF-1α, AP-1) in particular at early time points, while BPDE influenced expression of genes involved in cell energetics, DNA repair and apoptotic pathways. Also, in order to grasp the role of BaP and its metabolites in chemical hepatocarcinogenesis, we compared expression patterns from BaP(-metabolites) and TCDD to a signature set of approximately nine thousand gene expressions derived from hepatocellular carcinoma (HCC) patients. While transcriptome modulation by TCDD appeared not significantly related to HCC, BaP and BPDE were shown to deregulate metastatic markers via non-genotoxic and genotoxic mechanisms and activate inflammatory pathways (NF-κβ signaling, cytokine-cytokine receptor interaction). BaP also showed strong repression of genes involved in cholesterol and fatty acid biosynthesis. Altogether, this study provides new insights into BaP-induced toxicity and sheds new light onto its mechanism of action as a hepatocarcinogen.

The lack of accurate in vitro assays for predicting in vivo toxicity of chemicals together with new legislations demanding replacement and reduction of animal testing has triggered the development of alternative methods. This study aimed at developing a transcriptomics-based in vitro prediction assay for in vivo genotoxicity. Transcriptomics changes induced in the human liver cell line HepG2 by 34 compounds after treatment for 12, 24, and 48 h were used for the selection of gene-sets that are capable of discriminating between in vivo genotoxins (GTX) and in vivo nongenotoxins (NGTX). By combining transcriptomics with publicly available results for these chemicals from standard in vitro genotoxicity studies, we developed several prediction models. These models were validated by using an additional set of 28 chemicals. The best prediction was achieved after stratification of chemicals according to results from the Ames bacterial gene mutation assay prior to transcriptomics evaluation after 24h of treatment. A total of 33 genes were selected for discriminating GTX from NGTX for Ames-positive chemicals and 22 for Ames-negative chemicals. Overall, this method resulted in 89% accuracy and 91% specificity, thereby clearly outperforming the standard in vitro test battery. Transcription factor network analysis revealed HNF3a, HNF4a, HNF6, androgen receptor, and SP1 as main factors regulating the expression of classifiers for Ames-positive chemicals. Thus, the classical bacterial gene mutation assay in combination with in vitro transcriptomics in HepG2 is proposed as an upgraded in vitro approach for predicting in vivo genotoxicity of chemicals holding a great promise for reducing animal experimentations on genotoxicity.