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高通量转录组筛选数据的特征分析用于机制推断和化学分组。

Signature analysis of high-throughput transcriptomics screening data for mechanistic inference and chemical grouping.

机构信息

Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States.

Oak Ridge Associated Universities (ORAU) National Student Services Contractor, Oak Ridge, TN 37831, United States.

出版信息

Toxicol Sci. 2024 Nov 1;202(1):103-122. doi: 10.1093/toxsci/kfae108.

Abstract

High-throughput transcriptomics (HTTr) uses gene expression profiling to characterize the biological activity of chemicals in in vitro cell-based test systems. As an extension of a previous study testing 44 chemicals, HTTr was used to screen an additional 1,751 unique chemicals from the EPA's ToxCast collection in MCF7 cells using 8 concentrations and an exposure duration of 6 h. We hypothesized that concentration-response modeling of signature scores could be used to identify putative molecular targets and cluster chemicals with similar bioactivity. Clustering and enrichment analyses were conducted based on signature catalog annotations and ToxPrint chemotypes to facilitate molecular target prediction and grouping of chemicals with similar bioactivity profiles. Enrichment analysis based on signature catalog annotation identified known mechanisms of action (MeOAs) associated with well-studied chemicals and generated putative MeOAs for other active chemicals. Chemicals with predicted MeOAs included those targeting estrogen receptor (ER), glucocorticoid receptor (GR), retinoic acid receptor (RAR), the NRF2/KEAP/ARE pathway, AP-1 activation, and others. Using reference chemicals for ER modulation, the study demonstrated that HTTr in MCF7 cells was able to stratify chemicals in terms of agonist potency, distinguish ER agonists from antagonists, and cluster chemicals with similar activities as predicted by the ToxCast ER Pathway model. Uniform manifold approximation and projection (UMAP) embedding of signature-level results identified novel ER modulators with no ToxCast ER Pathway model predictions. Finally, UMAP combined with ToxPrint chemotype enrichment was used to explore the biological activity of structurally related chemicals. The study demonstrates that HTTr can be used to inform chemical risk assessment by determining in vitro points of departure, predicting chemicals' MeOA and grouping chemicals with similar bioactivity profiles.

摘要

高通量转录组学 (HTTr) 使用基因表达谱来描述体外基于细胞的测试系统中化学物质的生物学活性。作为先前测试 44 种化学物质的研究的扩展,HTTr 用于在 MCF7 细胞中使用 8 个浓度和 6 小时的暴露时间筛选 EPA 的 ToxCast 收藏中另外 1751 种独特的化学物质。我们假设可以使用特征分数的浓度反应模型来识别潜在的分子靶标,并对具有相似生物活性的化学物质进行聚类。基于特征目录注释和 ToxPrint 化学型进行聚类和富集分析,以促进分子靶标预测和具有相似生物活性谱的化学物质的分组。基于特征目录注释的富集分析确定了与研究充分的化学物质相关的已知作用机制 (MeOAs),并为其他活性化学物质生成了潜在的 MeOAs。具有预测 MeOAs 的化学物质包括针对雌激素受体 (ER)、糖皮质激素受体 (GR)、视黄酸受体 (RAR)、NRF2/KEAP/ARE 途径、AP-1 激活等的化学物质。使用 ER 调节的参考化学物质,该研究表明 MCF7 细胞中的 HTTr 能够根据激动剂效力对化学物质进行分层,区分 ER 激动剂和拮抗剂,并根据 ToxCast ER 途径模型预测的化学物质的相似活性进行聚类。特征水平结果的统一流形逼近和投影 (UMAP) 嵌入确定了没有 ToxCast ER 途径模型预测的新型 ER 调节剂。最后,使用 UMAP 结合 ToxPrint 化学型富集来探索结构相关化学物质的生物学活性。该研究表明,HTTr 可用于通过确定体外起始点、预测化学物质的 MeOA 和对具有相似生物活性谱的化学物质进行分组来告知化学风险评估。

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