A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors
A transcriptome-based classifier to identify developmental toxicants by stem cell testing: design, validation and optimization for histone deacetylase inhibitors
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DOI：
10.1007/s00204-015-1573-y
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发表时间：
2015-09-01
影响因子：
6.1
通讯作者：
Leist, Marcel
中科院分区：
文献类型：
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作者：
Rempel, Eugen;Hoelting, Lisa;Leist, Marcel
鉴定发育毒性物质的测试系统是迫切需要的。人类干细胞技术和转录组分析的结合提供了一个概念的证明，具有相关作用模式的毒物可以被识别和分组以进行读取。我们选择了一个与多能干细胞（UKN1）产生神经外胚层有关的发育毒性测试系统，并将细胞暴露于组蛋白去乙酰化酶抑制剂（HDACi）丙戊酸、trichostatin a、vorinostat、belinostat、panobinostat和entinostat中6天。为了深入了解它们的毒性作用，我们确定了HDACi共识基因，将它们分配到高级生物过程中，并将它们映射到由数百个转录组数据集构建的人类转录因子网络中。我们还测试了异质组的“汞”（甲基汞、硫柳汞、氯化汞（II）、溴化汞（II）、4-氯汞苯甲酸、苯汞酸）。在所有12种毒物的最高非细胞毒性浓度下比较微阵列数据。基于支持向量机（SVM）的分类器可以正确预测所有HDACi。为了验证，将分类器应用于hdac的遗留数据集，对于每种暴露情况，SVM预测与发育毒性相关。最后，基于100个探针集对分类器进行优化，发现F2RL2、TFAP2B、EDNRA、FOXD3、SIX3、MT1E、ETS1和LHX2 8个基因足以分离hdac和汞。我们的数据表明，人类干细胞和转录组分析可以结合起来进行机制分组和毒物预测。将这一概念扩展到HDACi以外的机制，将允许使用UKN1测试系统预测未知化合物的人类发育毒性危害。
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.