ArticleArchives of toxicology2015
A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors.
Article in Archives of toxicology, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 37 papers, 1 of them a synthesis that pooled it.
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Who cites it
37 citing papers in PubMed, 1 synthesis or guideline pooled it, 94 citations in OpenAlex.
- DNA methylation changes associated with prenatal mercury exposure: A meta-analysis of prospective cohort studies from PACE consortium.Environmental research · 2022Pooled it
- Multi-omics data-based modeling reveals tumorigenesis- and prognosis-associated genes with clinical potential in lung adenocarcinoma.BMC cancer · 2025Article
- Pluripotent stem cells for target organ developmental toxicity testing.Toxicological sciences : an official journal of the Society of Toxicology · 2024Review
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- Definition of the Neurotoxicity-Associated Metabolic Signature Triggered by Berberine and Other Respiratory Chain Inhibitors.Antioxidants (Basel, Switzerland) · 2023Article
- Identification and validation of core genes for type 2 diabetes mellitus by integrated analysis of single-cell and bulk RNA-sequencing.European journal of medical research · 2023Article
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- Optimization of the TeraTox Assay for Preclinical Teratogenicity Assessment.Toxicological sciences : an official journal of the Society of Toxicology · 2022Article
- Classification of Developmental Toxicants in a Human iPSC Transcriptomics-Based Test.Chemical research in toxicology · 2022Article
- New Insights on Gene by Environmental Effects of Drugs of Abuse in Animal Models Using GeneNetwork.Genes · 2022Article
- Article title: Transcriptional profiling efficacy to define biological activity similarity for cosmetic ingredients' safety assessment based on next-generation read-across.Frontiers in toxicology · 2022Article
- Development and validation of the TGx-HDACi transcriptomic biomarker to detect histone deacetylase inhibitors in human TK6 cells.Archives of toxicology · 2021Article
- Kinetic modeling of stem cell transcriptome dynamics to identify regulatory modules of normal and disturbed neuroectodermal differentiation.Nucleic acids research · 2020Article
- Roadmap for the development of alternative test methods.Archives of toxicology · 2020Article
- The EU-ToxRisk method documentation, data processing and chemical testing pipeline for the regulatory use of new approach methods.Archives of toxicology · 2020Article
- Methylmercury Impact on Adult Neurogenesis: Is the Worst Yet to Come From Recent Brazilian Environmental Disasters?Frontiers in aging neuroscience · 2020Article
- Internationalization of read-across as a validated new approach method (NAM) for regulatory toxicology.ALTEX · 2020Article
- Reproductive toxicity of boron.EXCLI journal · 2020Article
- Genome-scale network model of metabolism and histone acetylation reveals metabolic dependencies of histone deacetylase inhibitors.Genome biology · 2019Article
- Performance metrics ofEXCLI journal · 2019Article
Corrections and comments
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Authors and funding
18 authors at 5 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
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