github link
Accession IconGSE94521

Identification of transcriptome signatures and biomarkers specific for potential developmental toxicants inhibiting human neural crest cell migration

Organism Icon Homo sapiens
Sample Icon 57 Downloadable Samples
Technology Badge Icon Affymetrix Human Genome U133 Plus 2.0 Array (hgu133plus2)

Submitter Supplied Information

Description
The in vitro test battery of the European research consortium ESNATS (novel stem cell-based test systems) has been used to screen for potential human developmental toxicants. As part of this effort, the migration of neural crest (MINC) assay has been used to evaluate chemical effects on neural crest function. It identified some drug-like compounds in addition to known environmental toxicants. The hits included the HSP90 inhibitor geldanamycin, the chemotherapeutic arsenic trioxide, the flame-retardant PBDE-99, the pesticide triadimefon and the histone deacetylase inhibitors valproic acid and trichostatin A. Transcriptome changes triggered by these substances in human neural crest cells were recorded and analysed here to answer three questions: (1) can toxicants be individually identified based on their transcript profile; (2) how can the toxicity pattern reflected by transcript changes be compacted/ dimensionality-reduced for practical regulatory use; (3) how can a reduced set of biomarkers be selected for large-scale follow up? Transcript profiling allowed clear separation of different toxicants and the identification of toxicant types in a blinded test study. We also developed a diagrammatic system to visualize and compare toxicity patterns of a group of chemicals by giving a quantitative overview of altered superordinate biological processes (e.g. activation of KEGG pathways or overrepresentation of gene ontology terms). The transcript data were mined for potential markers of toxicity, and 39 transcripts were selected to either indicate general developmental toxicity or distinguish compounds with different modes-of-action in read-across. In summary, we found inclusion of transcriptome data to largely increase the information from the MINC phenotypic test.
PubMed ID
Total Samples
60
Submitter’s Institution

Samples

Show of 0 Total Samples
Filter
Add/Remove
Accession Code
Title
Sex
Specimen part
Processing Information
Additional Metadata
No rows found
Loading...