Classification of developmental toxicants in a human iPSC transcriptomics-based test
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PROVIDER: PRJNA777253 | ENA |
REPOSITORIES: ENA
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Project description:There is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we used the hiPSC-based UKK-24h-test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. All compounds were analyzed at the maximal plasma concentration (Cmax) and the 20-fold Cmax for a 24 h incubation period in three independent experiments. Using the 1,000 probe sets with the highest variability and including information on cytotoxicity, a penalized logistic regression based classifier (top-1,000 classifier) reached an AUC, accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96 and 0.88, respectively to correctly classify the compounds as teratogens or non-teratogens. This top-1,000 classifier reached a higher AUC, accuracy and sensitivity than a second classifier (SPS-classifier), which used the number of significant probe sets to classify the compounds. At least, the SPS-classifier had a specificity of 1. Classification at 20-fold Cmax did not lead to better performance metrics than testing at 1-fold Cmax. Finally, inclusion of cytotoxicity information into the classifier clearly improved the performance metrics. In conclusion, although further optimization by additional readouts and inclusion of cell systems that model different developmental processes is required, the UKK-24h-test will support the early discovery-phase detection of human developmental toxicants already in its present form.
2022-06-24 | GSE187001 | GEO
Project description:Despite the progress made in developmental toxicology, there is a great need for in vitro tests that identify developmental toxicants in relation to human oral doses and blood concentrations. In the present study, we established the hiPSC-based UKK2 in vitro test and analyzed genome-wide expression profiles of 23 known teratogens and 16 non-teratogens. Compounds were analyzed at the maximal plasma concentration (Cmax) and at 20-fold Cmax for a 24 h incubation period in three independent experiments. Based on the 1000 probe sets with the highest variance and including information on cytotoxicity, penalized logistic regression with leave-one-out cross-validation was used to classify the compounds as test-positive or test-negative, reaching an area under the curve (AUC), accuracy, sensitivity, and specificity of 0.96, 0.92, 0.96, and 0.88, respectively. Omitting the cytotoxicity information reduced the test performance to an AUC of 0.94, an accuracy of 0.79, and a sensitivity of 0.74. A second method, which used the number of significantly deregulated probe sets to classify the compounds, resulted in a specificity of 1; however, the AUC (0.90), accuracy (0.90), and sensitivity (0.83) were inferior compared to those of the logistic regression-based procedure. Finally, no increased performance was achieved when the high test concentrations (20-fold Cmax) were used, in comparison to testing within the realistic clinical range (1-fold Cmax). In conclusion, although further optimization is required, for example, by including additional readouts and cell systems that model different developmental processes, the UKK2-test in its present form can support the early discovery-phase detection of human developmental toxicants.
| S-EPMC9377669 | biostudies-literature
Project description:Stem cell-based tests for developmental toxicity are currently intensively studied. Here, we used a protocol that differentiates human induced pluripotent stem cells (hiPSCs) to neural ectodermal progenitor cells (UKN1) and exposed them to 23 teratogens and 16 non-teratogens at human relevant concentrations. Substance-induced, genome-wide expression profiles were analyzed by two procedures that classified the compounds either based on the number of significantly deregulated genes or on a classification technique with cross-validation. Both procedures demonstrated a high performance with accuracy, sensitivity and specificity of 0.90, 0.83 and 1.0, as well as 0.87, 0.92 and 0.80, respectively. Comparing the present findings with the UKN1 test to those from our recently published UKK2 test that instead recapitulates cardiomyogenic differentiation showed a high congruence in compound classification. This may be explained by the fact that the same pathways, such as PI3K-Akt, P53, TGF-beta, MAPK, EGFR and Hippo were overrepresented among the differentially expressed genes for both tests. However, for some teratogens discrepant results were obtained, such as atorvastatin and thalidomide. In conclusion, UKN1 identifies teratogens with high sensitivity and specificity, and combined interpretation analysis with other tests that differentiate hiPSCs into otherdifferent cell types may further improve the results in the future.
2022-11-03 | GSE209962 | GEO
Project description:High accuracy classification of developmental toxicants by in vitro tests of human neuroepithelial and cardiomyoblast differentiation
| PRJNA863236 | ENA
Project 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.
2017-02-07 | GSE94521 | GEO
Project description:Identification of cardiac developmental toxicants in differentiated human induced pluripotent stem cells [Toxicants]
| PRJNA978589 | ENA
Project description:DNA methylation-based classification of sinonasal tumors [test set]
| PRJNA803928 | ENA
Project description:Test systems to identify developmental toxicants are urgently needed. A combination of human stem cell technology and transcriptome analysis was used here to provide 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 six days to benchmark concentration (BMC) of 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) (BMCs). 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, 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.
2015-07-21 | GSE71127 | GEO
Project description:To develop an in vitro model for developmental toxicity testing, we characterized gene expression changes during mouse embryonic stem cell (mESC) differentiation and their modulation by developmental toxicants. C57BL/6 mESCs were allowed to differentiate spontaneously and global gene expression changes were studied using microarrays at at different time points after embryoid body (EB) formation. Develpmental toxicants tested include 2.0 mM monobutyl phthalate (MBP), 0.25 mM thalidomide (THD), and 1.0 mM valproic acid (VPA).
2014-09-09 | GSE60174 | GEO