Dorien A.M. van Dartel

Transcriptomics-based identification of developmental toxicants through their interference with cardiomyocyte differentiation of embryonic stem cells.

The embryonic stem cell test (EST) predicts developmental toxicity based on the inhibition of cardiomyocyte differentiation of embryonic stem cells (ESC). The subjective endpoint, the long culture duration together with the undefined applicability domain and related predictivity need further improvement to facilitate implementation of the EST into regulatory strategies. These aspects may be improved by studying gene expression changes in the ESC differentiation cultures and their modulation by compound exposure using transcriptomics. Here, we tested the developmental toxicants monobutyl phthalate and 6-aminonicotinamide. ESC were allowed to differentiated, and cardiomyocyte differentiation was assessed after 10 days of culture. RNA of solvent controls was collected after 0, 24, 48, 72 and 96 h of exposure, and RNA of developmental-toxicant-exposed cultures was collected after 24 and 96 h. Samples were hybridized to DNA microarrays, and 1355 genes were found differentially expressed among the unexposed experimental groups. These regulated genes were involved in differentiation-related processes, and Principal Component Analysis (PCA) based on these genes showed that the unexposed experimental groups appeared in chronological order in the PCA, which can therefore be regarded as a continuous representation of the differentiation track. The developmental-toxicant-exposed cultures appeared to deviate significantly from this differentiation track, which confirms the compound-modulating effects on the differentiation process. The incorporation of transcriptomics in the EST is expected to provide a more informative and improved endpoint in the EST as compared with morphology, allowing early detection of differentiation modulation. Furthermore, this approach may improve the definition of the applicability domain and predictivity of the EST.

Proteome profiling of mouse embryonic stem cells to define markers for cell differentiation and embryotoxicity

In search for alternative methods for developmental toxicity testing, we investigated whether embryonic stem cell (ESC) differentiation and its modulation by toxic exposure could be monitored by proteome profiling. We compared the proteomes of mouse ESC, differentiating ESC (DIF) and differentiating ESC exposed to the embryotoxicant monobutyl phthalate (MBP). Experiments were performed in duplicates for each cell culture and the proteins extracted from the cells were separated by one-dimensional SDS-PAGE. The identified proteins were quantified using a label-free quantitative method based on counting the observed peptides as an index of protein abundance. Fifty-seven proteins were upregulated in DIF relative to ESC, whereas 42 proteins were downregulated. Most of the upregulated proteins could be correlated with cardiomyocyte functionality. In contrast, the downregulated proteins were principally pluripotency markers, chaperones and ribosomal proteins. Higher expression levels of enzymes involved in DNA mismatch repair (MSH6) and in methylation reactions (MAT2A, AHCY) were also detected in the ESC, suggesting that these processes are more active in the ESC. In addition, the detection of gluthatione S-transferase alpha 4 (GSTA4) and Park7 DJ-1 protein (antioxidant) in ESC indicates that these cells have potential detoxification mechanisms. Furthermore, MBP affected the expression of 33 proteins, including MYH6, a cardiomyocyte-specific protein, which was significantly downregulated. MBP also affected the expression levels of chaperones, metabolic enzymes and chromatin modulating proteins, suggesting that MBP alters the differentiation process. Western blot analysis of MYH6 and HSPB1 confirmed the proteomic data. In addition, a favorable correlation was observed between protein expression and published changes in the expression of related genes at the transcriptomics level. Together, the results reveal potential protein markers that may be used as endpoints in an ESC based animal free alternative test for embryotoxicity though further studies are required for confirmation.

Discriminating classes of developmental toxicants using gene expression profiling in the embryonic stem cell test

The embryonic stem cell test (EST) has been shown to be a promising in vitro method for the prediction of developmental toxicity. In our previous studies, we demonstrated that the implementation of gene expression analysis in the EST may further improve the identification of developmental toxicants. In the present study, we investigated if gene expression profiling could be used to discriminate compound classes with distinct modes of action (MoA) using the EST protocol. Gene expression data of our previous study were used and were analyzed of embryonic stem cell (ESC) differentiation cultures exposed to six compounds belonging to two classes with distinct MoA, namely phthalates and triazoles. We used three approaches to study class-characteristic gene regulation that may be useful for discrimination of compound classes. First, at the individual gene level, gene signatures characteristic for each class were identified that successfully discriminated both classes using principal component analysis. Second, at the functional level, enriched gene ontology (GO) biological processes showed their usefulness for class discrimination via hierarchical clustering. Third, two previously identified gene sets, which we designed to predict developmental toxicity, appeared successful in separating phthalate from triazole compounds. In summary, we established the possibility to discriminate between compound classes in the EST system using three different specific transcriptomics-based approaches. Differential gene expression information specific for the class of compound under study may be employed to optimize prioritization of compounds within that class for further testing.

The embryonic stem cell test combined with toxicogenomics as an alternative testing model for the assessment of developmental toxicity

One of the most studied in vitro alternative testing methods for identification of developmental toxicity is the embryonic stem cell test (EST). Although the EST has been formally validated, the applicability domain as well as the predictability of the model needs further study to allow successful implementation of the EST as an alternative testing method in regulatory toxicity testing. Genomics technologies have already provided a proof of principle of their value in identification of toxicants such as carcinogenic compounds. Also within the EST, gene expression profiling has shown its value in the identification of developmental toxicity and in the evaluation of factors critical for risk assessment, such as dose and time responses. It is expected that the implementation of genomics into the EST will provide a more detailed end point evaluation as compared to the classical morphological scoring of differentiation cultures. Therefore, genomics may contribute to improvement of the EST, both in terms of definition of its applicability domain as well as its predictive capacity. In the present review, we present the progress that has been made with regard to the prediction of developmental toxicity using the EST combined with transcriptomics. Furthermore, we discuss the developments of additional aspects required for further optimization of the EST, including kinetics, the use of human embryonic stem cells (ESC) and computational toxicology. Finally, the current and future use of the EST model for prediction of developmental toxicity in testing strategies and in regulatory toxicity evaluations is discussed.

Comparison of MeHg-induced toxicogenomic responses across in vivo and in vitro models used in developmental toxicology.

Toxicogenomic evaluations may improve toxicity prediction of in vitro-based developmental models, such as whole embryo culture (WEC) and embryonic stem cells (ESC), by providing a robust mechanistic marker which can be linked with responses associated with developmental toxicity in vivo. While promising in theory, toxicogenomic comparisons between in vivo and in vitro models are complex due to inherent differences in model characteristics and experimental design. Determining factors which influence these global comparisons are critical in the identification of reliable mechanistic-based markers of developmental toxicity. In this study, we compared available toxicogenomic data assessing the impact of the known teratogen, methylmercury (MeHg) across a diverse set of in vitro and in vivo models to investigate the impact of experimental variables (i.e. model, dose, time) on our comparative assessments. We evaluated common and unique aspects at both the functional (Gene Ontology) and gene level of MeHg-induced response. At the functional level, we observed stronger similarity in MeHg-response between mouse embryos exposed in utero (2 studies), ESC, and WEC as compared to liver, brain and mouse embryonic fibroblast MeHg studies. These findings were strongly correlated to the presence of a MeHg-induced developmentally related gene signature. In addition, we identified specific MeHg-induced gene expression alterations associated with developmental signaling and heart development across WEC, ESC and in vivo systems. However, the significance of overlap between studies was highly dependent on traditional experimental variables (i.e. dose, time). In summary, we identify promising examples of unique gene expression responses which show in vitro-in vivo similarities supporting the relevance of in vitro developmental models for predicting in vivo developmental toxicity.

Gene set assembly for quantitative prediction of developmental toxicity in the embryonic stem cell test.

The embryonic stem cell test (EST) is an in vitro method for predicting developmental toxicity based on compound-induced inhibition of embryonic stem cell (ESC) differentiation. We previously described how gene expression analysis in the EST can be used to describe normal ESC differentiation as well as identify compound developmental toxicity, by means of our differentiation track algorithm. In this study, we combined raw data from our three previous studies in a new integrated analysis, to identify a gene set that allows for improved prediction. By evaluating predictions of 100,000 randomly selected gene sets, we identified which genes contribute significantly to the prediction reliability. By additional cross-validation, we identified a set of 52 genes that allows for improved prediction of toxicity. The correlation between the predictions using this gene set and the magnitude of the EST endpoint was 0.85, and the gene set predicted developmental toxicity with 83% accuracy (area under the curve 89%). If compounds with ineffective data because of a too low tested concentration or too much variation between samples were excluded, even 100% accuracy could be reached based on 15 compounds. This novel gene set consists mainly of genes involved in the stem cell differentiation or other developmental processes. We expect that this set can be of use in future studies aimed at improving the EST for risk assessment, thus making a next step towards regulatory implementation of this method.

Complementary Detection of Embryotoxic Properties of Substances in the Neural and Cardiac Embryonic Stem Cell Tests

In developmental toxicity testing, in vitro screening assays are highly needed to increase efficiency and to reduce animal use. A promising in vitro assay is the cardiac embryonic stem cell test (ESTc), in which the effect of developmental toxicants on cardiomyocyte differentiation is assessed. Recently, we developed a neural differentiation variant of the stem cell test (neural embryonic stem cell test [ESTn]). In both of these models, we have previously performed a series of transcriptomic studies to characterize gene expression changes (1) across time during normal differentiation and (2) in response to a series of developmental toxicants in the ESTn and ESTc. Here, using the cumulative of these studies, we compared gene expression profiles of ESTn and ESTc over time as well as model-specific changes induced by seven compounds, comprising six known in vivo developmental toxicants and one negative control. Time-related gene expression profiles showed similarities between the two EST systems. However, specific genes could be identified changing over time differently in each model related to the two different lineages of differentiation. Interestingly, compound-induced gene expression changes were generally model specific, especially for methylmercury and flusilazole, which were predicted better in ESTn and ESTc, respectively. Valproic acid-induced gene expression changes were most comparable out of the six developmental toxicants between the ESTn and ESTc. Direct transcriptomic comparisons between the ESTn and ESTc indicate that combined transcriptomic analyses support and complement each other. Therefore, a combined approach incorporating ESTc and ESTn may improve developmental toxicant detection over individual assays.

Dose response analysis of monophthalates in the murine embryonic stem cell test assessed by cardiomyocyte differentiation and gene expression

The embryonic stem cell test (EST) is based on compound-induced inhibition of cardiomyocyte differentiation of pluripotent stem cells. We examined the use of transcriptomics to assess concentration-effect relationships and performed potency ranking within a chemical class. Three embryotoxic phthalate monoesters, monobutyl phthalate (MBuP), monobenzyl phthalate (MBzP) and mono-(2-ethylhexyl) phthalate (MEHP) and the non-embryotoxic monomethyl phthalate (MMP) were studied for their effects on gene expression. Effects on gene expression were observed at concentrations that did not inhibit cardiomyocyte differentiation or induce cytotoxicity. The embryotoxic phthalate monoesters altered the expression of 668 commonly expressed genes in a concentration-dependent fashion. The same potency ranking was observed for morphology and gene expression (MEHP>MBzP>MBuP>MMP). These results indicate that integrating transcriptomics provides a sensitive method to measure the dose-dependent effects of phthalate monoester exposure and enables potency ranking based on a common mode of action within a class of compounds. Transcriptomic approaches may improve the applicability of the EST, in terms of sensitivity and specificity.

Next-generation text-mining mediated generation of chemical response-specific gene sets for interpretation of gene expression data

BackgroundAvailability of chemical response-specific lists of genes (gene sets) for pharmacological and/or toxic effect prediction for compounds is limited. We hypothesize that more gene sets can be created by next-generation text mining (next-gen TM), and that these can be used with gene set analysis (GSA) methods for chemical treatment identification, for pharmacological mechanism elucidation, and for comparing compound toxicity profiles.MethodsWe created 30,211 chemical response-specific gene sets for human and mouse by next-gen TM, and derived 1,189 (human) and 588 (mouse) gene sets from the Comparative Toxicogenomics Database (CTD). We tested for significant differential expression (SDE) (false discovery rate -corrected p-values < 0.05) of the next-gen TM-derived gene sets and the CTD-derived gene sets in gene expression (GE) data sets of five chemicals (from experimental models). We tested for SDE of gene sets for six fibrates in a peroxisome proliferator-activated receptor alpha (PPARA) knock-out GE dataset and compared to results from the Connectivity Map. We tested for SDE of 319 next-gen TM-derived gene sets for environmental toxicants in three GE data sets of triazoles, and tested for SDE of 442 gene sets associated with embryonic structures. We compared the gene sets to triazole effects seen in the Whole Embryo Culture (WEC), and used principal component analysis (PCA) to discriminate triazoles from other chemicals.ResultsNext-gen TM-derived gene sets matching the chemical treatment were significantly altered in three GE data sets, and the corresponding CTD-derived gene sets were significantly altered in five GE data sets. Six next-gen TM-derived and four CTD-derived fibrate gene sets were significantly altered in the PPARA knock-out GE dataset. None of the fibrate signatures in cMap scored significant against the PPARA GE signature. 33 environmental toxicant gene sets were significantly altered in the triazole GE data sets. 21 of these toxicants had a similar toxicity pattern as the triazoles. We confirmed embryotoxic effects, and discriminated triazoles from other chemicals.ConclusionsGene set analysis with next-gen TM-derived chemical response-specific gene sets is a scalable method for identifying similarities in gene responses to other chemicals, from which one may infer potential mode of action and/or toxic effect.

Identification by Gene Coregulation Mapping of Novel Genes Involved in Embryonic Stem Cell Differentiation

A combined analysis of data from a series of literature studies can lead to more reliable results than that based on a single study. A common problem in performing combined analyses of literature microarray gene expression data is that the original raw data are not always available and not always easy to combine in one analysis. We propose an approach that does not require analyzing original raw data, but instead takes literature gene sets derived from (supplementary) tables as input and uses gene co-occurrence in these sets for mapping a co-regulation network. An algorithm for this method was applied to a collection of literature-derived gene sets related to embryonic stem cell (ESC) differentiation. In the resulting network, genes involved in similar biological processes or expressed at similar time points during differentiation were found to cluster together. Using this information, we identified 43 genes not previously associated with cardiac ESC differentiation for which we were able to assign a putative novel biological function. For 6 of these genes (Apobec2, Cth, Ptges, Rrad, Zfp57, and 2410146L05Rik), literature data on mouse knockout phenotypes support their putative function. Three other genes (Rcor2, Zfp503, and Hspb3) are part of major pathways within the network and therefore likely mechanistically relevant candidate genes. We anticipate that these 43 genes can help to improve the understanding of the molecular events underlying ESC differentiation. Moreover, the approach introduced here can be more widely applied to identify possible novel gene functions in biological processes.