Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America.
Center for Computational Toxicology & Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, NC 27711, United States of America.
Toxicol Appl Pharmacol. 2024 Oct;491:117073. doi: 10.1016/j.taap.2024.117073. Epub 2024 Aug 17.
New approach methodologies (NAMs) aim to accelerate the pace of chemical risk assessment while simultaneously reducing cost and dependency on animal studies. High Throughput Transcriptomics (HTTr) is an emerging NAM in the field of chemical hazard evaluation for establishing in vitro points-of-departure and providing mechanistic insight. In the current study, 1201 test chemicals were screened for bioactivity at eight concentrations using a 24-h exposure duration in the human- derived U-2 OS osteosarcoma cell line with HTTr. Assay reproducibility was assessed using three reference chemicals that were screened on every assay plate. The resulting transcriptomics data were analyzed by aggregating signal from genes into signature scores using gene set enrichment analysis, followed by concentration-response modeling of signatures scores. Signature scores were used to predict putative mechanisms of action, and to identify biological pathway altering concentrations (BPACs). BPACs were consistent across replicates for each reference chemical, with replicate BPAC standard deviations as low as 5.6 × 10 μM, demonstrating the internal reproducibility of HTTr-derived potency estimates. BPACs of test chemicals showed modest agreement (R = 0.55) with existing phenotype altering concentrations from high throughput phenotypic profiling using Cell Painting of the same chemicals in the same cell line. Altogether, this HTTr based chemical screen contributes to an accumulating pool of publicly available transcriptomic data relevant for chemical hazard evaluation and reinforces the utility of cell based molecular profiling methods in estimating chemical potency and predicting mechanism of action across a diverse set of chemicals.
新方法学（NAMs）旨在加快化学风险评估的步伐，同时降低成本并减少对动物研究的依赖。高通量转录组学（HTTr）是化学危害评估领域中的一种新兴 NAM，可用于建立体外起始点，并提供机制见解。在当前的研究中，使用 HTTr 在人源性 U-2 OS 骨肉瘤细胞系中以 24 小时暴露时间在八个浓度下筛选了 1201 种测试化学品的生物活性。使用三种在每个测定板上筛选的参考化学品评估了测定重现性。通过将基因信号聚集到签名分数中，使用基因集富集分析对转录组学数据进行分析，然后对签名分数进行浓度反应建模。使用签名分数来预测潜在的作用机制，并确定改变生物途径的浓度（BPACs）。对于每个参考化学品，BPAC 在重复实验中是一致的，每个参考化学品的重复 BPAC 标准偏差低至 5.6×10μM，表明 HTTr 衍生的效力估计值具有内部重现性。测试化学品的 BPACs 与使用相同细胞系中的 Cell Painting 对相同化学品进行高通量表型分析得出的现有表型改变浓度具有适度的一致性（R=0.55）。总的来说，这种基于 HTTr 的化学筛选为化学危害评估提供了一组不断增加的公共转录组学数据，并加强了基于细胞的分子分析方法在估计化学效力和预测作用机制方面的实用性，涵盖了一系列不同的化学品。
