An optimized gene set for transcriptomics based neurodevelopmental toxicity prediction in the neural embryonic stem cell test

• Published in: Toxicology (Amsterdam) Vol. 300; no. 3; pp. 158 - 167
• Main Authors: Pennings, Jeroen L.A., Theunissen, Peter T., Piersma, Aldert H.
• Format: Journal Article
• Language: English
• Published: Kidlington Elsevier Ireland Ltd 28.10.2012; Elsevier
• Subjects: Algorithms; Animals; Biological and medical sciences; Cell Culture Techniques; Cell Differentiation - drug effects; Cell Line; Databases, Genetic; Embryonic stem cells; Embryonic Stem Cells - drug effects; Embryonic Stem Cells - metabolism; Emergency; gene expression; Gene Expression Profiling - methods; Gene Expression Regulation, Developmental; genes; Medical sciences; Mice; Neural development; Neural Stem Cells - drug effects; Neural Stem Cells - metabolism; Oligonucleotide Array Sequence Analysis; prediction; Predictive Value of Tests; Principal Component Analysis; risk assessment; toxicity; toxicity testing; Toxicity Tests - methods; Toxicogenomics; Toxicology; transcriptomics; Embryonic stem cells; Toxicogenomics; Principal Component Analysis; Neural development; Embryo; Toxicity; Stem cell; Prediction; Transcriptome; Neural stem cell; Nervous system; Gene; Embryonic cell; Development; Genetics; Predictive factor; Principal component analysis
• ISSN: 0300-483X; 1879-3185; 1879-3185
• DOI: 10.1016/j.tox.2012.06.016

► The neural embryonic stem cell test is an in vitro model for toxicity testing. ► We combined existing gene expression data to identify the best predictive genes. ► A set of 29 functionally relevant genes was obtained. ► This set gave 84% prediction accuracy for neurodevelopmental toxicity. The murine neural embryonic stem cell test (ESTn) is an in vitro model for neurodevelopmental toxicity testing. Recent studies have shown that application of transcriptomics analyses in the ESTn is useful for obtaining more accurate predictions as well as mechanistic insights. Gene expression responses due to stem cell neural differentiation versus toxicant exposure could be distinguished using the Principal Component Analysis based differentiation track algorithm. In this study, we performed a de novo analysis on combined raw data (10 compounds, 19 exposures) from three previous transcriptomics studies to identify an optimized gene set for neurodevelopmental toxicity prediction in the ESTn. By evaluating predictions of 200,000 randomly selected gene sets, we identified genes which significantly contributed to the prediction reliability. A set of 100 genes was obtained, predominantly involved in (neural) development. Further stringency restrictions resulted in a set of 29 genes that allowed for 84% prediction accuracy (area under the curve 94%). We anticipate these gene sets will contribute to further improve ESTn transcriptomics studies aimed at compound risk assessment.