Toxicogenomic Data Research Articles

Application of dynamic topic models to toxicogenomics data.

BackgroundAll biological processes are inherently dynamic. Biological systems evolve transiently or sustainably according to sequential time points after perturbation by environment insults, drugs and chemicals. Investigating the temporal behavior of molecular events has been an important subject to understand the underlying mechanisms governing the biological system in response to, such as, drug treatment. The intrinsic complexity of time series data requires appropriate computational algorithms for data interpretation. In this study, we propose, for the first time, the application of dynamic topic models (DTM) for analyzing time-series gene expression data.ResultsA large time-series toxicogenomics dataset was studied. It contains over 3144 microarrays of gene expression data corresponding to rat livers treated with 131 compounds (most are drugs) at two doses (control and high dose) in a repeated schedule containing four separate time points (4-, 8-, 15- and 29-day). We analyzed, with DTM, the topics (consisting of a set of genes) and their biological interpretations over these four time points. We identified hidden patterns embedded in this time-series gene expression profiles. From the topic distribution for compound-time condition, a number of drugs were successfully clustered by their shared mode-of-action such as PPARɑ agonists and COX inhibitors. The biological meaning underlying each topic was interpreted using diverse sources of information such as functional analysis of the pathways and therapeutic uses of the drugs. Additionally, we found that sample clusters produced by DTM are much more coherent in terms of functional categories when compared to traditional clustering algorithms.ConclusionsWe demonstrated that DTM, a text mining technique, can be a powerful computational approach for clustering time-series gene expression profiles with the probabilistic representation of their dynamic features along sequential time frames. The method offers an alternative way for uncovering hidden patterns embedded in time series gene expression profiles to gain enhanced understanding of dynamic behavior of gene regulation in the biological system.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-016-1225-0) contains supplementary material, which is available to authorized users.

The Comparative Toxicogenomics Database: update 2017.

The Comparative Toxicogenomics Database (CTD; http://ctdbase.org/) provides information about interactions between chemicals and gene products, and their relationships to diseases. Core CTD content (chemical-gene, chemical-disease and gene-disease interactions manually curated from the literature) are integrated with each other as well as with select external datasets to generate expanded networks and predict novel associations. Today, core CTD includes more than 30.5 million toxicogenomic connections relating chemicals/drugs, genes/proteins, diseases, taxa, Gene Ontology (GO) annotations, pathways, and gene interaction modules. In this update, we report a 33% increase in our core data content since 2015, describe our new exposure module (that harmonizes exposure science information with core toxicogenomic data) and introduce a novel dataset of GO-disease inferences (that identify common molecular underpinnings for seemingly unrelated pathologies). These advancements centralize and contextualize real-world chemical exposures with molecular pathways to help scientists generate testable hypotheses in an effort to understand the etiology and mechanisms underlying environmentally influenced diseases.

Prediction of developmental chemical toxicity based on gene networks of human embryonic stem cells.

Predictive toxicology using stem cells or their derived tissues has gained increasing importance in biomedical and pharmaceutical research. Here, we show that toxicity category prediction by support vector machines (SVMs), which uses qRT-PCR data from 20 categorized chemicals based on a human embryonic stem cell (hESC) system, is improved by the adoption of gene networks, in which network edge weights are added as feature vectors when noisy qRT-PCR data fail to make accurate predictions. The accuracies of our system were 97.5–100% for three toxicity categories: neurotoxins (NTs), genotoxic carcinogens (GCs) and non-genotoxic carcinogens (NGCs). For two uncategorized chemicals, bisphenol-A and permethrin, our system yielded reasonable results: bisphenol-A was categorized as an NGC, and permethrin was categorized as an NT; both predictions were supported by recently published papers. Our study has two important features: (i) as the first study to employ gene networks without using conventional quantitative structure-activity relationships (QSARs) as input data for SVMs to analyze toxicogenomics data in an hESC validation system, it uses additional information of gene-to-gene interactions to significantly increase prediction accuracies for noisy gene expression data; and (ii) using only undifferentiated hESCs, our study has considerable potential to predict late-onset chemical toxicities, including abnormalities that occur during embryonic development.

Meta-analysis of transcriptomic responses as a means to identify pulmonary disease outcomes for engineered nanomaterials.

BackgroundThe increasing use of engineered nanomaterials (ENMs) of varying physical and chemical characteristics poses a great challenge for screening and assessing the potential pathology induced by these materials, necessitating novel toxicological approaches. Toxicogenomics measures changes in mRNA levels in cells and tissues following exposure to toxic substances. The resulting information on altered gene expression profiles, associated pathways, and the doses at which these changes occur, are used to identify the underlying mechanisms of toxicity and to predict disease outcomes. We evaluated the applicability of toxicogenomics data in identifying potential lung-specific (genomic datasets are currently available from experiments where mice have been exposed to various ENMs through this common route of exposure) disease outcomes following exposure to ENMs.MethodsSeven toxicogenomics studies describing mouse pulmonary responses over time following intra-tracheal exposure to increasing doses of carbon nanotubes (CNTs), carbon black, and titanium dioxide (TiO2) nanoparticles of varying properties were examined to understand underlying mechanisms of toxicity. mRNA profiles from these studies were compared to the publicly available datasets of 15 other mouse models of lung injury/diseases induced by various agents including bleomycin, ovalbumin, TNFα, lipopolysaccharide, bacterial infection, and welding fumes to delineate the implications of ENM-perturbed biological processes to disease pathogenesis in lungs.ResultsThe meta-analysis revealed two distinct clusters—one driven by TiO2 and the other by CNTs. Unsupervised clustering of the genes showing significant expression changes revealed that CNT response clustered with bleomycin injury and bacterial infection models, both of which are known to induce lung fibrosis, in a post-exposure-time dependent manner, irrespective of the CNT’s physical-chemical properties. TiO2 samples clustered separately from CNTs and disease models.ConclusionsThese results indicate that in the absence of apical toxicity data, a tiered strategy beginning with short term, in vivo tissue transcriptomics profiling can effectively and efficiently screen new ENMs that have a higher probability of inducing pulmonary pathogenesis.Electronic supplementary materialThe online version of this article (doi:10.1186/s12989-016-0137-5) contains supplementary material, which is available to authorized users.

Transcriptional Profiling of Dibenzo[def,p]chrysene-induced Spleen Atrophy Provides Mechanistic Insights into its Immunotoxicity in MutaMouse.

Dibenzo[def,p]chrysene (DBC) is the most carcinogenic polycyclic aromatic hydrocarbon (PAH) examined to date. We investigated the immunotoxicity of DBC, manifested as spleen atrophy, following acute exposure of adult MutaMouse males by oral gavage. Mice were exposed to 0, 2.0, 6.2, or 20.0 mg DBC /kg-bw per day, for 3 days. Genotoxic endpoints (DBC-DNA adducts and lacZ mutant frequency in spleen and bone marrow, and red blood cell micronucleus frequency) and global gene expression changes were measured. All of the genotoxicity measures increased in a dose-dependent manner in spleen and bone marrow. Gene expression analysis showed that DBC activates p53 signaling pathways related to cellular growth and proliferation, which was evident even at the low dose. Strikingly, the expression profiles of DBC exposed mouse spleens were highly inversely correlated with the expression profiles of the only published toxicogenomics dataset of enlarged mouse spleen. This analysis suggested a central role for Bnip3l, a pro-apoptotic protein involved in negative regulation of erythroid maturation. RT-PCR confirmed expression changes in several genes related to apoptosis, iron metabolism, and aryl hydrocarbon receptor signaling that are regulated in the opposite direction during spleen atrophy versus benzo[a]pyrene-mediated splenomegaly. In addition, benchmark dose modeling of toxicogenomics data yielded toxicity estimates that are very close to traditional toxicity endpoints. This work illustrates the power of toxicogenomics to reveal rich mechanistic information for immunotoxic compounds and its ability to provide information that is quantitatively similar to that derived from standard toxicity methods in health risk assessment.

Meta-Analysis of Large-Scale Toxicogenomic Data Finds Neuronal Regeneration Related Protein and Cathepsin D to Be Novel Biomarkers of Drug-Induced Toxicity.

Undesirable toxicity is one of the main reasons for withdrawing drugs from the market or eliminating them as candidates in clinical trials. Although numerous studies have attempted to identify biomarkers capable of predicting pharmacotoxicity, few have attempted to discover robust biomarkers that are coherent across various species and experimental settings. To identify such biomarkers, we conducted meta-analyses of massive gene expression profiles for 6,567 in vivo rat samples and 453 compounds. After applying rigorous feature reduction procedures, our analyses identified 18 genes to be related with toxicity upon comparisons of untreated versus treated and innocuous versus toxic specimens of kidney, liver and heart tissue. We then independently validated these genes in human cell lines. In doing so, we found several of these genes to be coherently regulated in both in vivo rat specimens and in human cell lines. Specifically, mRNA expression of neuronal regeneration-related protein was robustly down-regulated in both liver and kidney cells, while mRNA expression of cathepsin D was commonly up-regulated in liver cells after exposure to toxic concentrations of chemical compounds. Use of these novel toxicity biomarkers may enhance the efficiency of screening for safe lead compounds in early-phase drug development prior to animal testing.

Integrative toxicogenomics-based approach to risk assessment of heavy metal mixtures/complexes: strategies and challenges

Human exposure to metallic elements ranging from single metal ionic salt, metal compounds, and metal mixtures that may occur naturally, as well as from human activities and industrial applications. Some metals including arsenic, cadmium, chromium, lead, mercury, and nickel in both single element and mixture forms render confounding health effects and ultimately cause cancer. Studies of heavy metal-mediated global aberration using non-targeted multiple toxicogenomic technologies might help to elucidate environmentally relevant disorders, as well as to monitor biomarker of exposure and predict the health risk toward environmental toxicants. We describe recent toxicogenomic studies on heavy metal mixtures as well as relevant mechanism of toxicity and molecular signatures. On the basis of system toxicology approach, integrative toxicogenomic and bioinformatic tools might represent the biological pathways linked to disorders. We also strongly suggest that the toxicogenomic data can be adopted to risk assessment process. Furthermore, we mention challenges in utility of toxicogenomic studies data to risk assessment process of toxicity of metal mixtures. Overall, we realize that application and interpretation of toxicogenomic data regarding to their strengths and weaknesses would potentiate chemical risk assessment.

Impact of Genomics Platform and Statistical Filtering on Transcriptional Benchmark Doses (BMD) and Multiple Approaches for Selection of Chemical Point of Departure (PoD).

Many regulatory agencies are exploring ways to integrate toxicogenomic data into their chemical risk assessments. The major challenge lies in determining how to distill the complex data produced by high-content, multi-dose gene expression studies into quantitative information. It has been proposed that benchmark dose (BMD) values derived from toxicogenomics data be used as point of departure (PoD) values in chemical risk assessments. However, there is limited information regarding which genomics platforms are most suitable and how to select appropriate PoD values. In this study, we compared BMD values modeled from RNA sequencing-, microarray-, and qPCR-derived gene expression data from a single study, and explored multiple approaches for selecting a single PoD from these data. The strategies evaluated include several that do not require prior mechanistic knowledge of the compound for selection of the PoD, thus providing approaches for assessing data-poor chemicals. We used RNA extracted from the livers of female mice exposed to non-carcinogenic (0, 2 mg/kg/day, mkd) and carcinogenic (4, 8 mkd) doses of furan for 21 days. We show that transcriptional BMD values were consistent across technologies and highly predictive of the two-year cancer bioassay-based PoD. We also demonstrate that filtering data based on statistically significant changes in gene expression prior to BMD modeling creates more conservative BMD values. Taken together, this case study on mice exposed to furan demonstrates that high-content toxicogenomics studies produce robust data for BMD modelling that are minimally affected by inter-technology variability and highly predictive of cancer-based PoD doses.

High-throughput concentration-response analysis for omics datasets.

Omics-based methods are increasingly used in current ecotoxicology. Therefore, a large number of observations for various toxic substances and organisms are available and may be used for identifying modes of action, adverse outcome pathways, or novel biomarkers. For these purposes, good statistical analysis of toxicogenomic data is vital. In contrast to established ecotoxicological techniques, concentration-response modeling is rarely used for large datasets. Instead, statistical hypothesis testing is prevalent, which provides only a limited scope for inference. The present study therefore applied automated concentration-response modeling for 3 different ecotoxicotranscriptomic and ecotoxicometabolomic datasets. The modeling process was performed by simultaneously applying 9 different regression models, representing distinct mechanistic, toxicological, and statistical ideas that result in different curve shapes. The best-fitting models were selected by using Akaike's information criterion. The linear and exponential models represented the best data description for more than 50% of responses. Models generating U-shaped curves were frequently selected for transcriptomic signals (30%), and sigmoid models were identified as best fit for many metabolomic signals (21%). Thus, selecting the models from an array of different types seems appropriate, because concentration-response functions may vary because of the observed response type, and they also depend on the compound, the organism, and the investigated concentration and exposure duration range. The application of concentration-response models can help to further tap the potential of omics data and is a necessary step for quantitative mixture effect assessment at the molecular response level.

In silico評価モデル、Toxicogenomicsデータを用いた肝毒性予測の現状

In silico評価モデル、Toxicogenomicsデータを用いた肝毒性予測の現状

Characterization of Conserved Toxicogenomic Responses in Chemically Exposed Hepatocytes across Species and Platforms.

BackgroundGenome-wide expression profiling is increasingly being used to identify transcriptional changes induced by drugs and environmental stressors. In this context, the Toxicogenomics Project–Genomics Assisted Toxicity Evaluation system (TG-GATEs) project generated transcriptional profiles from rat liver samples and human/rat cultured primary hepatocytes exposed to more than 100 different chemicals.ObjectivesTo assess the capacity of the cell culture models to recapitulate pathways induced by chemicals in vivo, we leveraged the TG-GATEs data set to compare the early transcriptional responses observed in the liver of rats treated with a large set of chemicals with those of cultured rat and human primary hepatocytes challenged with the same compounds in vitro.MethodsWe developed a new pathway-based computational pipeline that efficiently combines gene set enrichment analysis (GSEA) using pathways from the Reactome database with biclustering to identify common modules of pathways that are modulated by several chemicals in vivo and in vitro across species.ResultsWe found that some chemicals induced conserved patterns of early transcriptional responses in in vitro and in vivo settings, and across human and rat genomes. These responses involved pathways of cell survival, inflammation, xenobiotic metabolism, oxidative stress, and apoptosis. Moreover, our results support the transforming growth factor beta receptor (TGF-βR) signaling pathway as a candidate biomarker associated with exposure to environmental toxicants in primary human hepatocytes.ConclusionsOur integrative analysis of toxicogenomics data provides a comprehensive overview of biochemical perturbations affected by a large panel of chemicals. Furthermore, we show that the early toxicological response occurring in animals is recapitulated in human and rat primary hepatocyte cultures at the molecular level, indicating that these models reproduce key pathways in response to chemical stress. These findings expand our understanding and interpretation of toxicogenomics data from human hepatocytes exposed to environmental toxicants.CitationEl-Hachem N, Grossmann P, Blanchet-Cohen A, Bateman AR, Bouchard N, Archambault J, Aerts HJ, Haibe-Kains B. 2016. Characterization of conserved toxicogenomic responses in chemically exposed hepatocytes across species and platforms. Environ Health Perspect 124:313–320; http://dx.doi.org/10.1289/ehp.1409157

Computational systems toxicology: Emergence, development and application

系统生物学的发展为传统毒理学的研究提供了新机遇, 逐步形成了系统毒理学. 计算系统毒理学旨在整合毒理组学实验数据, 构建多水平、多尺度的预测模型, 定量评估化合物的安全性. 目前已经发展了静态网络分析预测、动态网络模拟和有害结局路径等几种研究方法. 虽起步不久, 但在全面理解毒理学机制、发现新的生物标志物及化合物安全性综合评估等方面已表现出良好的应用前景. 本文主要综述了计算系统毒理学的诞生背景、数据资源、研究方法、应用领域及未来展望.

RNA sequencing provides insights into the toxicogenomic response of ZF4 cells to methyl methanesulfonate

Whole genome transcriptomic studies are powerful for characterizing the molecular mechanisms underlying the physiological effects of chemicals, and are informative for environmental health risk assessment. Alkylating agents are an abundant class of chemicals that can damage DNA in the environment, and are used for anticancer treatments. Currently, little is known regarding the molecular mechanisms of toxic alkylating agents in zebrafish cell lines. In this study, RNA-sequencing was used to investigate the transcriptomic responses of zebrafish ZF4 cells following exposure to the model genotoxicant methyl methanesulfonate (MMS). The half-maximal inhibitory concentration (IC50 ) of MMS was 639.16 ± 61.8 µm, and apoptosis was induced within 24 h of exposure. RNA sequencing identified 3601 differentially expressed genes (DEGs) that were upregulated and 3037 that were downregulated. Gene ontology enrichment analysis revealed that most DEGs belonged to synthesis and metabolism categories. RNA-associated processes were the most upregulated, while cell cycle and adhesion were the most repressed processes, and neuron-related processes were the most downregulated developmental process. Kyoto Encyclopedia of Genes and Genomes pathway enrichment analysis identified DNA damage repair, cell cycle, apoptosis and spliceosome as overrepresented terms. Six types of alternative splicing were detected. In total, 1156 alternative splicing DEGs were specifically expressed following MMS treatment, many of which belonged to metabolism and catabolic process categories. Cluster analysis of orthologs was able to extrapolate toxicotranscriptomic data between zebrafish and yeast. These results provide insight into the genome-wide response of ZF4 cells following exposure to MMS, and this knowledge will inform future toxicogenomic data analysis and environmental health risk assessment.

Effects of phthalate exposure on asthma may be mediated through alterations in DNA methylation.

BackgroundPhthalates may increase the asthma risk in children. Mechanisms underlying this association remain to be addressed. This study assesses the effect of phthalate exposures on epigenetic changes and the role of epigenetic changes for asthma. In the first step, urine and blood samples from 256 children of the Childhood Environment and Allergic diseases Study (CEAS) were analyzed. Urine 5OH-MEHP levels were quantified as an indicator of exposure, and asthma information was collected. DNA methylation (DNA-M) was measured by quantitative PCR. In the screening part of step 1, DNA-M of 21 potential human candidate genes suggested by a toxicogenomic data were investigated in 22 blood samples. Then, in the testing part of step 1, positively screened genes were tested in a larger sample of 256 children and then validated by protein measurements. In step 2, we replicated the association between phthalate exposure and gene-specific DNA-M in 54 children in the phthalate contaminated food event. In step 3, the risk of DNA-M for asthma was tested in 256 children from CEAS and corroborated in 270 children from the Isle of Wight (IOW) birth cohort.ResultsDifferential methylation in three genes (AR, TNFα, and IL-4) was identified through screening. Testing in 256 children showed that methylation of the TNFα gene promoter was lower when children had higher urine 5OH-MEHP values (β = −0.138, P = 0.040). Functional validation revealed that TNFα methylation was inversely correlated with TNFα protein levels (β = −0.18, P = 0.041). In an additional sample of 54 children, we corroborated that methylation of the TNFα gene promoter was lower when urine 5OH-MEHP concentrations were higher. Finally, we found that a lower methylation of 5′CGI region of TNFα was associated with asthma in 256 CEAS children (OR = 2.15, 95% CI = 1.01 to 4.62). We replicated this in 270 children from the IOW birth cohort study. Methylation of the CpG site cg10717214 was negatively associated with asthma, when children had ‘AA’ or ‘AG’ genotype of the TNFα single nucleotide rs1800610.ConclusionsEffects of phthalate exposure on asthma may be mediated through alterations in DNA methylation.Electronic supplementary materialThe online version of this article (doi:10.1186/s13148-015-0060-x) contains supplementary material, which is available to authorized users.

Revealing the mechanism of Fructus meliae toosendan-induced liver injury in mice by integrating microRNA and mRNA-based toxicogenomics data

Fructus meliae toosendan-induced liver injury in mice was investigated by integrating the data from miroRNA and mRNA expression profiles combined with the general toxicological assessments method.

DiXa: a data infrastructure for chemical safety assessment.

Motivation: The field of toxicogenomics (the application of ‘-omics’ technologies to risk assessment of compound toxicities) has expanded in the last decade, partly driven by new legislation, aimed at reducing animal testing in chemical risk assessment but mainly as a result of a paradigm change in toxicology towards the use and integration of genome wide data. Many research groups worldwide have generated large amounts of such toxicogenomics data. However, there is no centralized repository for archiving and making these data and associated tools for their analysis easily available.Results: The Data Infrastructure for Chemical Safety Assessment (diXa) is a robust and sustainable infrastructure storing toxicogenomics data. A central data warehouse is connected to a portal with links to chemical information and molecular and phenotype data. diXa is publicly available through a user-friendly web interface. New data can be readily deposited into diXa using guidelines and templates available online. Analysis descriptions and tools for interrogating the data are available via the diXa portal.Availability and implementation: http://www.dixa-fp7.euContact: d.hendrickx@maastrichtuniversity.nl; info@dixa-fp7.euSupplementary information: Supplementary data are available at Bioinformatics online.

Cross-species toxicogenomic analyses and phenotypic anchoring in response to groundwater low-level pollution.

BackgroundComparison of toxicogenomic data facilitates the identification of deregulated gene patterns and maximizes health risk prediction in human.ResultsHere, we performed phenotypic anchoring on the effects of acute exposure to low-grade polluted groundwater using mouse and zebrafish. Also, we evaluated two windows of chronic exposure in mouse, starting in utero and at the end of lactation. Bioinformatic analysis of livers microarray data showed that the number of deregulated biofunctions and pathways is higher after acute exposure, compared to the chronic one. It also revealed specific profiles of altered gene expression in all treatments, pointing to stress response/mitochondrial pathways as major players of environmental toxicity. Of note, dysfunction of steroid hormones was also predicted by bioinformatic analysis and verified in both models by traditional approaches, serum estrogens measurement and vitellogenin mRNA determination in mice and zebrafish, respectively.ConclusionsIn our report, phenotypic anchoring in two vertebrate model organisms highlights the toxicity of low-grade pollution, with varying susceptibility based on exposure window. The overlay of zebrafish and mice deregulated pathways, more than single genes, is useful in risk identification from chemicals implicated in the observed effects.Electronic supplementary materialThe online version of this article (doi:10.1186/1471-2164-15-1067) contains supplementary material, which is available to authorized users.

Workshop report: Identifying opportunities for global integration of toxicogenomics databases, 26-27 June 2013, Research Triangle Park, NC, USA.

A joint US-EU workshop on enhancing data sharing and exchange in toxicogenomics was held at the National Institute for Environmental Health Sciences. Currently, efficient reuse of data is hampered by problems related to public data availability, data quality, database interoperability (the ability to exchange information), standardization and sustainability. At the workshop, experts from universities and research institutes presented databases, studies, organizations and tools that attempt to deal with these problems. Furthermore, a case study showing that combining toxicogenomics data from multiple resources leads to more accurate predictions in risk assessment was presented. All participants agreed that there is a need for a web portal describing the diverse, heterogeneous data resources relevant for toxicogenomics research. Furthermore, there was agreement that linking more data resources would improve toxicogenomics data analysis. To outline a roadmap to enhance interoperability between data resources, the participants recommend collecting user stories from the toxicogenomics research community on barriers in data sharing and exchange currently hampering answering to certain research questions. These user stories may guide the prioritization of steps to be taken for enhancing integration of toxicogenomics databases.

Global transcriptional analysis of short-term hepatic stress responses in Atlantic salmon (Salmo salar) exposed to depleted uranium.

Potential environmental hazards of radionuclides are often studied at the individual level. Sufficient toxicogenomics data at the molecular/cellular level for understanding the effects and modes of toxic action (MoAs) of radionuclide is still lacking. The current article introduces transcriptomic data generated from a recent ecotoxicological study, with the aims to characterize the MoAs of a metallic radionuclide, deplete uranium (DU) in an ecologically and commercially important fish species, Atlantic salmon (Salmo salar). Salmon were exposed to three concentrations (0.25, 0.5 and 1.0 mg/L) of DU for 48 h. Short-term global transcriptional responses were studied using Agilent custom-designed high density 60,000-feature (60 k) salmonid oligonucleotide microarrays (oligoarray). The microarray datasets deposited at Gene Expression Omnibus (GEO ID: GSE58824) were associated with a recently published study by Song et al. (2014) in BMC Genomics. The authors describe the experimental data herein to build a platform for better understanding the toxic mechanisms and ecological hazard of radionuclides such as DU in fish.

From transient transcriptome responses to disturbed neurodevelopment: role of histone acetylation and methylation as epigenetic switch between reversible and irreversible drug effects.

The superordinate principles governing the transcriptome response of differentiating cells exposed to drugs are still unclear. Often, it is assumed that toxicogenomics data reflect the immediate mode of action (MoA) of drugs. Alternatively, transcriptome changes could describe altered differentiation states as indirect consequence of drug exposure. We used here the developmental toxicants valproate and trichostatin A to address this question. Neurally differentiating human embryonic stem cells were treated for 6 days. Histone acetylation (primary MoA) increased quickly and returned to baseline after 48 h. Histone H3 lysine methylation at the promoter of the neurodevelopmental regulators PAX6 or OTX2 was increasingly altered over time. Methylation changes remained persistent and correlated with neurodevelopmental defects and with effects on PAX6 gene expression, also when the drug was washed out after 3–4 days. We hypothesized that drug exposures altering only acetylation would lead to reversible transcriptome changes (indicating MoA), and challenges that altered methylation would lead to irreversible developmental disturbances. Data from pulse-chase experiments corroborated this assumption. Short drug treatment triggered reversible transcriptome changes; longer exposure disrupted neurodevelopment. The disturbed differentiation was reflected by an altered transcriptome pattern, and the observed changes were similar when the drug was washed out during the last 48 h. We conclude that transcriptome data after prolonged chemical stress of differentiating cells mainly reflect the altered developmental stage of the model system and not the drug MoA. We suggest that brief exposures, followed by immediate analysis, are more suitable for information on immediate drug responses and the toxicity MoA.Electronic supplementary materialThe online version of this article (doi:10.1007/s00204-014-1279-6) contains supplementary material, which is available to authorized users.