Optimization of the TeraTox assay for preclinical teratogenicity assessment
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ABSTRACT:
PROVIDER: PRJNA761146 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| SRR15732500.fastq.gz | Fastqsanger.gz | |||
| SRR15732600.fastq.gz | Fastqsanger.gz | |||
| SRR15732700.fastq.gz | Fastqsanger.gz | |||
| SRR15732800.fastq.gz | Fastqsanger.gz | |||
| SRR15732900.fastq.gz | Fastqsanger.gz |
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Project description:Stem-cell-based in vitro systems are promising tools to predict human teratogenicity. However, current in vitro assays are limited because they either capture effects on a certain germ layer or focus on a subset of predictive markers. Here we report the characterization and critical assessment of TeraTox, a newly developed multi-lineage differentiation assay using 3D human induced pluripotent stem cells. TeraTox probes stem-cell-derived embryoid bodies with two endpoints, one quantifying cytotoxicity and the other inferring the teratogenicity potential with gene expression as a molecular phenotypic readout. To derive teratogenicity potential from gene expression profiles, we applied both unsupervised machine-learning tools including factor analysis, and supervised tools including classification and regression. To identify the best predictive model for the teratogenicity potential that is explainable, we systematically tested 64 machine-learning model architectures and identified the optimal model, which uses the expression of 77 representative germ-layer genes, summarized by 10 latent germ-layer factors, as input for random-forest regression. We combined measured cytotoxicity and inferred teratogenicity potential to predict concentration-dependent teratogenicity profiles of 33 approved pharmaceuticals and 12 proprietary drug candidates with known in vivo data. Compared with the mouse embryonic stem cell test, which has been in routine use for more than a decade, the TeraTox assay shows higher sensitivity, particularly towards teratogens impairing ectodermal development or stem-cell renewal, and a more balanced prediction performance. We envision that further refinement and development of TeraTox has the potential to reduce and replace animal research in drug discovery and to improve preclinical assessment of teratogenicity.
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Project description:Current animal-free methods to assess teratogenicity of drugs under development still deliver high numbers of false negatives. To improve the sensitivity of human teratogenicity prediction, we characterized the TeraTox test, a newly developed multilineage differentiation assay using 3D human-induced pluripotent stem cells. TeraTox produces primary output concentration-dependent cytotoxicity and altered gene expression induced by each test compound. These data are fed into an interpretable machine-learning model to perform prediction, which relates to the concentration-dependent human teratogenicity potential of drug candidates. We applied TeraTox to profile 33 approved pharmaceuticals and 12 proprietary drug candidates with known in vivo data. Comparing TeraTox predictions with known human or animal toxicity, we report an accuracy of 69% (specificity: 53%, sensitivity: 79%). TeraTox performed better than 2 quantitative structure-activity relationship models and had a higher sensitivity than the murine embryonic stem cell test (accuracy: 58%, specificity: 76%, and sensitivity: 46%) run in the same laboratory. The overall prediction accuracy could be further improved by combining TeraTox and mouse embryonic stem cell test results. Furthermore, patterns of altered gene expression revealed by TeraTox may help grouping toxicologically similar compounds and possibly deducing common modes of action. The TeraTox assay and the dataset described here therefore represent a new tool and a valuable resource for drug teratogenicity assessment.
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Project description:Diffuse midline glioma (DMG) is a uniformly fatal pediatric, adolescent, and young adult cancer diagnosed in the midline structures of the brain. PI3K/Akt signaling is an overarching contributor to the poor outcomes of DMG patients due to their dependance on insulin and recurring mutations and amplifications in PI3K/Akt genes. Paxalisib (GDC-0084) is a potent PI3K/Akt inhibitor developed to penetrate the brain’s protective blood-brain barrier (BBB). We performed paxalisib regimen optimization by assessment of blood glucose levels, analysis in vivo pharmacokinetics/pharmacodynamics properties, and employed DMG patient derived xenograft (PDX) mouse models to determine preclinical efficacy. To identify novel combination strategies, we conducted high-resolution quantitative phosphoproteomics of DMG cells treated with paxalisib. Elevated blood glucose levels promoting hyperglycemia was seen in mice using paxalisib 10 mg/kg/day (~mouse equivalent human maximum tolerated dose- NCT03696355). Whereas 5 mg/kg/day, or 5mg/kg/b.i.d. (twice daily) non-significantly increased blood glucose levels compared to controls. Pharmacokinetic analysis of mouse tissues showed 5 mg/kg/b.i.d. increased paxalisib acumination in the brainstem, suppressing PI3K/Akt signaling to significantly extend the survival of DMG PDX models compared to all other regimens; a survival benefit amplified when combined with the anti-glycemic therapy metformin. Phosphoproteomic profiling identified increased Protein kinase C (PKC) signaling following paxalisib treatment. The combination of paxalisib and enzastaurin (PKC inhibitor) synergistically extended the survival of PDX models. Our optimized dosing regimen increased the preclinical benefits of paxalisib, with the combination of paxalisib with metformin, or paxalisib with enzastaurin, heralding rationally designed combination strategies for the treatment of DMG.
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Project description:Pre-Amplification Assay Optimization for Low RNA Inputs and Single Cells Low Input Total RNA
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Project description:Diffuse midline glioma (DMG) is a uniformly fatal pediatric, adolescent, and young adult cancer diagnosed in the midline structures of the brain. PI3K/Akt signaling is an overarching contributor to the poor outcomes of DMG patients due to their dependance on insulin and recurring mutations and amplifications in PI3K/Akt genes. Paxalisib (GDC-0084) is a potent PI3K/Akt inhibitor developed to penetrate the brain’s protective blood-brain barrier (BBB). We performed paxalisib regimen optimization by assessment of blood glucose levels, analysis in vivo pharmacokinetics/pharmacodynamics properties, and employed DMG patient derived xenograft (PDX) mouse models to determine preclinical efficacy. To identify novel combination strategies, we conducted high-resolution quantitative phosphoproteomics of DMG cells treated with paxalisib. Elevated blood glucose levels promoting hyperglycemia was seen in mice using paxalisib 10 mg/kg/day (~mouse equivalent human maximum tolerated dose- NCT03696355). Whereas 5 mg/kg/day, or 5mg/kg/b.i.d. (twice daily) non-significantly increased blood glucose levels compared to controls. Pharmacokinetic analysis of mouse tissues showed 5 mg/kg/b.i.d. increased paxalisib acumination in the brainstem, suppressing PI3K/Akt signaling to significantly extend the survival of DMG PDX models compared to all other regimens; a survival benefit amplified when combined with the anti-glycemic therapy metformin. Phosphoproteomic profiling identified increased Protein kinase C (PKC) signaling following paxalisib treatment. The combination of paxalisib and enzastaurin (PKC inhibitor) synergistically extended the survival of PDX models. Our optimized dosing regimen increased the preclinical benefits of paxalisib, with the combination of paxalisib with metformin, or paxalisib with enzastaurin, heralding rationally designed combination strategies for the treatment of DMG
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Project description:Background: With the growing availability of entire genome sequences, an increasing number of scientists can exploit oligonucleotide microarrays for genome-scale expression studies. While probe-design is a major research area, relatively little work has been reported on the optimization of microarray protocols. Results: As shown in this study, suboptimal conditions can have considerable impact on biologically relevant observations. For example, deviation from the optimal temperature by one degree Celsius lead to a loss of 44% of differentially expressed genes identified. While genes from thousands of Gene Ontology categories were affected, transcription factors and other low-copy-number regulators were disproportionately lost. Calibrated protocols are thus required in order to take full advantage of the large dynamic range of microarrays. For an objective optimization of protocols we introduce an approach that maximizes the amount of information obtained per experiment. A comparison of two typical samples is sufficient for this calibration. We ensure, however, that optimization results are independent of the samples and the specific measures used for calibration. Both simulations and spike-in experiments confirm an unbiased determination of generally optimal experimental conditions. Conclusions: Well calibrated hybridization conditions are thus easily achieved and necessary for the efficient detection of differential expression. They are essential for the sensitive profiling of low-copy-number molecules. This is particularly critical for studies of transcription factor expression, or the inference and study of regulatory networks. Supporting material, including source code and data, is available at http://bioinf.boku.ac.at/pub/optMA2010/. Optimization of hybridization temperature via an assessment of differential expression between two samples (male vs female Drosophila melanogaster) in 6 technical replicates (3 regular + 3 dye-swaps) for hybridizations at different temperatures (in two batches of 50, 52, 54, and 56; and 47, 49, 50, and 51 degree Celsius, with the repeated hybridization at 50 degree Celsius serving to demonstrate batch-to-batch stability).
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