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A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors.

Rempel E, Hoelting L, Waldmann T, Balmer NV, Schildknecht S, Grinberg M, Das Gaspar JA, Shinde V, Stöber R, Marchan R, van Thriel C, Liebing J, Meisig J, Blüthgen N, Sachinidis A, Rahnenführer J, Hengstler JG, Leist M - Arch. Toxicol. (2015)

Bottom Line: Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants.Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1 and LHX2) are sufficient to separate HDACi from mercurials.Our data demonstrate how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, TU Dortmund University, 44139, Dortmund, Germany.

ABSTRACT
Test systems to identify developmental toxicants are urgently needed. A combination of human stem cell technology and transcriptome analysis was to provide a proof of concept that toxicants with a related mode of action can be identified and grouped for read-across. We chose a test system of developmental toxicity, related to the generation of neuroectoderm from pluripotent stem cells (UKN1), and exposed cells for 6 days to the histone deacetylase inhibitors (HDACi) valproic acid, trichostatin A, vorinostat, belinostat, panobinostat and entinostat. To provide insight into their toxic action, we identified HDACi consensus genes, assigned them to superordinate biological processes and mapped them to a human transcription factor network constructed from hundreds of transcriptome data sets. We also tested a heterogeneous group of 'mercurials' (methylmercury, thimerosal, mercury(II)chloride, mercury(II)bromide, 4-chloromercuribenzoic acid, phenylmercuric acid). Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants. A support vector machine (SVM)-based classifier predicted all HDACi correctly. For validation, the classifier was applied to legacy data sets of HDACi, and for each exposure situation, the SVM predictions correlated with the developmental toxicity. Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1 and LHX2) are sufficient to separate HDACi from mercurials. Our data demonstrate how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants. Extension of this concept to mechanisms beyond HDACi would allow prediction of human developmental toxicity hazard of unknown compounds with the UKN1 test system.

No MeSH data available.


Related in: MedlinePlus

Validation of the transcriptome-based classifier to identify HDACi. a Differentiating cells were treated as indicated in Fig. 1 and transcriptome changes of neurally differentiating stem cells induced by HDACi and mercurials are plotted in a PCA (as in Fig. 1e) together with samples treated with 25, 150, 350, 450, 550, 650, 800 and 1 mM valproic acid (VPA) obtained from Waldmann et al. (2014). Each point represents one experiment (=data from one microarray), and the colour coding indicates the compound used in the experiment, mercurials (blue shades), HDACi (red shades) and VPA legacy data (green). The four samples from the present study (VPA classifier) have been encircled for better visualisation. The purple arrow indicates the track of transcriptional changes after exposure to increasing concentrations of VPA in the Waldmann et al. (2014) data set. The SVM classifier was applied to this (green) data set, and the prediction of VPA, at indicated concentrations (25 µM–1 mM) acting on stem cell differentiation like an HDACi, is shown in the table as a mean of four replicate samples. The lower row of the table indicates whether the respective sample triggered developmental toxicity (+) or not (−), according to Waldmann et al. (2014). b The diagram shows various schedules of drug exposure. Grey bars indicate the period of drug exposure with 600 µM VPA or 10 nM TSA, and whiteopen bars indicate culture periods in medium without HDACi. The samples were analysed at the times indicated. Exposures of a limited duration relative to the overall experiment were termed ‘pulsed’ treatments, and these were distinguished as early, medium and late pulse according to the exposure scheme. c, d The tables indicate the calculated probability of VPA or TSA acting like an HDACi when used as described in b. Probabilities >0.5 are defined as HDACi classification (green), and p < 0.5 indicates that the experimental condition did not show a canonical HDAC effect (colour figure online)
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Fig6: Validation of the transcriptome-based classifier to identify HDACi. a Differentiating cells were treated as indicated in Fig. 1 and transcriptome changes of neurally differentiating stem cells induced by HDACi and mercurials are plotted in a PCA (as in Fig. 1e) together with samples treated with 25, 150, 350, 450, 550, 650, 800 and 1 mM valproic acid (VPA) obtained from Waldmann et al. (2014). Each point represents one experiment (=data from one microarray), and the colour coding indicates the compound used in the experiment, mercurials (blue shades), HDACi (red shades) and VPA legacy data (green). The four samples from the present study (VPA classifier) have been encircled for better visualisation. The purple arrow indicates the track of transcriptional changes after exposure to increasing concentrations of VPA in the Waldmann et al. (2014) data set. The SVM classifier was applied to this (green) data set, and the prediction of VPA, at indicated concentrations (25 µM–1 mM) acting on stem cell differentiation like an HDACi, is shown in the table as a mean of four replicate samples. The lower row of the table indicates whether the respective sample triggered developmental toxicity (+) or not (−), according to Waldmann et al. (2014). b The diagram shows various schedules of drug exposure. Grey bars indicate the period of drug exposure with 600 µM VPA or 10 nM TSA, and whiteopen bars indicate culture periods in medium without HDACi. The samples were analysed at the times indicated. Exposures of a limited duration relative to the overall experiment were termed ‘pulsed’ treatments, and these were distinguished as early, medium and late pulse according to the exposure scheme. c, d The tables indicate the calculated probability of VPA or TSA acting like an HDACi when used as described in b. Probabilities >0.5 are defined as HDACi classification (green), and p < 0.5 indicates that the experimental condition did not show a canonical HDAC effect (colour figure online)

Mentions: First, we used a data set on the effect of different VPA concentrations, for which we had previously identified the range at which developmental toxicity is observed in the UKN1 test (Waldmann et al. 2014). The eight concentrations ranged from 25 to 1000 µM, and the different conditions mapped to largely different positions on the PCA plot used above to show the 12 test compounds of the present study (Fig. 6a). The classifier did not recognize VPA as an HDACi at the two lowest tested concentrations of 25 and 150 µM. At concentrations of 350 µM and higher, classification was excellent, with probabilities close to 100 %. This classification correlated with clinical observations on concentrations that trigger developmental toxicity and with our previous results suggesting that VPA is not affecting neurodevelopment of UKN1 at these low concentrations (Fig. 6a). Thus, the classifier appeared to be specific for concentrations of an HDACi relevant for developmental toxicity and not just any HDACi concentration. Good sensitivity of the classifier was suggested by the fact that VPA concentrations of 350 µM were classified with a probability of 97 % as HDACi, although such a concentration triggered a much smaller transcriptome effect than, e.g. 600 or 800 µM of the compound (Waldmann et al. 2014).Fig. 6


A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors.

Rempel E, Hoelting L, Waldmann T, Balmer NV, Schildknecht S, Grinberg M, Das Gaspar JA, Shinde V, Stöber R, Marchan R, van Thriel C, Liebing J, Meisig J, Blüthgen N, Sachinidis A, Rahnenführer J, Hengstler JG, Leist M - Arch. Toxicol. (2015)

Validation of the transcriptome-based classifier to identify HDACi. a Differentiating cells were treated as indicated in Fig. 1 and transcriptome changes of neurally differentiating stem cells induced by HDACi and mercurials are plotted in a PCA (as in Fig. 1e) together with samples treated with 25, 150, 350, 450, 550, 650, 800 and 1 mM valproic acid (VPA) obtained from Waldmann et al. (2014). Each point represents one experiment (=data from one microarray), and the colour coding indicates the compound used in the experiment, mercurials (blue shades), HDACi (red shades) and VPA legacy data (green). The four samples from the present study (VPA classifier) have been encircled for better visualisation. The purple arrow indicates the track of transcriptional changes after exposure to increasing concentrations of VPA in the Waldmann et al. (2014) data set. The SVM classifier was applied to this (green) data set, and the prediction of VPA, at indicated concentrations (25 µM–1 mM) acting on stem cell differentiation like an HDACi, is shown in the table as a mean of four replicate samples. The lower row of the table indicates whether the respective sample triggered developmental toxicity (+) or not (−), according to Waldmann et al. (2014). b The diagram shows various schedules of drug exposure. Grey bars indicate the period of drug exposure with 600 µM VPA or 10 nM TSA, and whiteopen bars indicate culture periods in medium without HDACi. The samples were analysed at the times indicated. Exposures of a limited duration relative to the overall experiment were termed ‘pulsed’ treatments, and these were distinguished as early, medium and late pulse according to the exposure scheme. c, d The tables indicate the calculated probability of VPA or TSA acting like an HDACi when used as described in b. Probabilities >0.5 are defined as HDACi classification (green), and p < 0.5 indicates that the experimental condition did not show a canonical HDAC effect (colour figure online)
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Fig6: Validation of the transcriptome-based classifier to identify HDACi. a Differentiating cells were treated as indicated in Fig. 1 and transcriptome changes of neurally differentiating stem cells induced by HDACi and mercurials are plotted in a PCA (as in Fig. 1e) together with samples treated with 25, 150, 350, 450, 550, 650, 800 and 1 mM valproic acid (VPA) obtained from Waldmann et al. (2014). Each point represents one experiment (=data from one microarray), and the colour coding indicates the compound used in the experiment, mercurials (blue shades), HDACi (red shades) and VPA legacy data (green). The four samples from the present study (VPA classifier) have been encircled for better visualisation. The purple arrow indicates the track of transcriptional changes after exposure to increasing concentrations of VPA in the Waldmann et al. (2014) data set. The SVM classifier was applied to this (green) data set, and the prediction of VPA, at indicated concentrations (25 µM–1 mM) acting on stem cell differentiation like an HDACi, is shown in the table as a mean of four replicate samples. The lower row of the table indicates whether the respective sample triggered developmental toxicity (+) or not (−), according to Waldmann et al. (2014). b The diagram shows various schedules of drug exposure. Grey bars indicate the period of drug exposure with 600 µM VPA or 10 nM TSA, and whiteopen bars indicate culture periods in medium without HDACi. The samples were analysed at the times indicated. Exposures of a limited duration relative to the overall experiment were termed ‘pulsed’ treatments, and these were distinguished as early, medium and late pulse according to the exposure scheme. c, d The tables indicate the calculated probability of VPA or TSA acting like an HDACi when used as described in b. Probabilities >0.5 are defined as HDACi classification (green), and p < 0.5 indicates that the experimental condition did not show a canonical HDAC effect (colour figure online)
Mentions: First, we used a data set on the effect of different VPA concentrations, for which we had previously identified the range at which developmental toxicity is observed in the UKN1 test (Waldmann et al. 2014). The eight concentrations ranged from 25 to 1000 µM, and the different conditions mapped to largely different positions on the PCA plot used above to show the 12 test compounds of the present study (Fig. 6a). The classifier did not recognize VPA as an HDACi at the two lowest tested concentrations of 25 and 150 µM. At concentrations of 350 µM and higher, classification was excellent, with probabilities close to 100 %. This classification correlated with clinical observations on concentrations that trigger developmental toxicity and with our previous results suggesting that VPA is not affecting neurodevelopment of UKN1 at these low concentrations (Fig. 6a). Thus, the classifier appeared to be specific for concentrations of an HDACi relevant for developmental toxicity and not just any HDACi concentration. Good sensitivity of the classifier was suggested by the fact that VPA concentrations of 350 µM were classified with a probability of 97 % as HDACi, although such a concentration triggered a much smaller transcriptome effect than, e.g. 600 or 800 µM of the compound (Waldmann et al. 2014).Fig. 6

Bottom Line: Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants.Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1 and LHX2) are sufficient to separate HDACi from mercurials.Our data demonstrate how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants.

View Article: PubMed Central - PubMed

Affiliation: Department of Statistics, TU Dortmund University, 44139, Dortmund, Germany.

ABSTRACT
Test systems to identify developmental toxicants are urgently needed. A combination of human stem cell technology and transcriptome analysis was to provide a proof of concept that toxicants with a related mode of action can be identified and grouped for read-across. We chose a test system of developmental toxicity, related to the generation of neuroectoderm from pluripotent stem cells (UKN1), and exposed cells for 6 days to the histone deacetylase inhibitors (HDACi) valproic acid, trichostatin A, vorinostat, belinostat, panobinostat and entinostat. To provide insight into their toxic action, we identified HDACi consensus genes, assigned them to superordinate biological processes and mapped them to a human transcription factor network constructed from hundreds of transcriptome data sets. We also tested a heterogeneous group of 'mercurials' (methylmercury, thimerosal, mercury(II)chloride, mercury(II)bromide, 4-chloromercuribenzoic acid, phenylmercuric acid). Microarray data were compared at the highest non-cytotoxic concentration for all 12 toxicants. A support vector machine (SVM)-based classifier predicted all HDACi correctly. For validation, the classifier was applied to legacy data sets of HDACi, and for each exposure situation, the SVM predictions correlated with the developmental toxicity. Finally, optimization of the classifier based on 100 probe sets showed that eight genes (F2RL2, TFAP2B, EDNRA, FOXD3, SIX3, MT1E, ETS1 and LHX2) are sufficient to separate HDACi from mercurials. Our data demonstrate how human stem cells and transcriptome analysis can be combined for mechanistic grouping and prediction of toxicants. Extension of this concept to mechanisms beyond HDACi would allow prediction of human developmental toxicity hazard of unknown compounds with the UKN1 test system.

No MeSH data available.


Related in: MedlinePlus