Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, North Carolina 27709, USA.
Oak Ridge Institute for Science and Education (ORISE), Oak Ridge, Tennessee, USA.
Toxicol Sci. 2021 Apr 27;181(1):68-89. doi: 10.1093/toxsci/kfab009.
New approach methodologies (NAMs) that efficiently provide information about chemical hazard without using whole animals are needed to accelerate the pace of chemical risk assessments. Technological advancements in gene expression assays have made in vitro high-throughput transcriptomics (HTTr) a feasible option for NAMs-based hazard characterization of environmental chemicals. In this study, we evaluated the Templated Oligo with Sequencing Readout (TempO-Seq) assay for HTTr concentration-response screening of a small set of chemicals in the human-derived MCF7 cell model. Our experimental design included a variety of reference samples and reference chemical treatments in order to objectively evaluate TempO-Seq assay performance. To facilitate analysis of these data, we developed a robust and scalable bioinformatics pipeline using open-source tools. We also developed a novel gene expression signature-based concentration-response modeling approach and compared the results to a previously implemented workflow for concentration-response analysis of transcriptomics data using BMDExpress. Analysis of reference samples and reference chemical treatments demonstrated highly reproducible differential gene expression signatures. In addition, we found that aggregating signals from individual genes into gene signatures prior to concentration-response modeling yielded in vitro transcriptional biological pathway altering concentrations (BPACs) that were closely aligned with previous ToxCast high-throughput screening assays. Often these identified signatures were associated with the known molecular target of the chemicals in our test set as the most sensitive components of the overall transcriptional response. This work has resulted in a novel and scalable in vitro HTTr workflow that is suitable for high-throughput hazard evaluation of environmental chemicals.
需要新的方法学（NAMs）来高效地提供有关化学危害的信息，而无需使用整个动物，以加速化学风险评估的步伐。基因表达分析技术的进步使得体外高通量转录组学（HTTr）成为基于 NAMs 的环境化学危害特征描述的可行选择。在这项研究中，我们评估了用于在人源 MCF7 细胞模型中对一小部分化学物质进行 HTTr 浓度反应筛选的基于模板寡核苷酸测序（TempO-Seq）测定法。我们的实验设计包括各种参考样品和参考化学处理，以便客观地评估 TempO-Seq 测定法的性能。为了便于分析这些数据，我们使用开源工具开发了一个强大且可扩展的生物信息学管道。我们还开发了一种基于基因表达特征的新型浓度反应建模方法，并将结果与之前使用 BMDExpress 进行转录组学数据浓度反应分析的工作流程进行了比较。参考样品和参考化学处理的分析表明，差异基因表达特征具有高度可重复性。此外，我们发现，在进行浓度反应建模之前，将来自单个基因的信号聚合到基因特征中，可产生与之前的 ToxCast 高通量筛选测定法紧密一致的体外转录生物学途径改变浓度（BPACs）。通常，这些确定的特征与我们测试集中化学物质的已知分子靶标相关，是整体转录反应的最敏感成分。这项工作产生了一种新颖且可扩展的体外 HTTr 工作流程，适用于环境化学物的高通量危害评估。
