Rogers Jesse D, Bundy Joseph L, Harrill Joshua A, Judson Richard J, Paul-Friedman Katie, Everett Logan J
Center for Computational Toxicology and Exposure, Office of Research and Development, US Environmental Protection Agency, Durham, North Carolina, USA.
Oak Ridge Institute for Science and Education, Oak Ridge, Tennessee, USA.
Environ Health Perspect. 2025 Jun;133(6):67013. doi: 10.1289/EHP16024. Epub 2025 Jun 13.
With thousands of chemicals in commerce and the environment, rapid identification of potential hazards is a critical need. Combining broad molecular profiling with targeted assays, such as high-throughput transcriptomics (HTTr) and receptor screening assays, could improve identification of chemicals that perturb key molecular targets associated with adverse outcomes.
We aimed to link transcriptomic readouts to individual molecular targets and integrate transcriptomic predictions with orthogonal receptor-level assays in a proof-of-concept framework for chemical hazard prioritization.
Transcriptomic profiles generated via TempO-Seq in U-2 OS and HepaRG cell lines were used to develop signatures composed of genes uniquely responsive to reference chemicals for distinct molecular targets. These signatures were applied to 75 reference and 1,126 nonreference chemicals screened via HTTr in both cell lines. Selective bioactivity toward each signature was determined by comparing potency estimates against the bulk of transcriptomic bioactivity for each chemical. Chemicals predicted by transcriptomics were confirmed for target bioactivity and selectivity using available orthogonal assay data from the US Environmental Protection Agency ToxCast program. A subset of 37 selectively acting chemicals from HTTr that did not have sufficient orthogonal data were prospectively tested using one of five receptor-level assays.
Of the 1,126 nonreference chemicals screened, 201 demonstrated selective bioactivity in at least one transcriptomic signature and 57 were confirmed as selective nuclear receptor agonists. Chemicals bioactive for each signature were significantly associated with orthogonal assay bioactivity, and signature-based points-of-departure were equally or more sensitive than biological pathway altering concentrations in 95.4% of signature-prioritized chemicals. Prospective profiling found that 18 of 37 (49%) chemicals without prior orthogonal assay data were bioactive against the predicted receptor.
Our work demonstrates that integrating transcriptomics with targeted orthogonal assays in a tiered framework can support Next Generation Risk Assessment by informing putative molecular targets and prioritizing chemicals for further testing. https://doi.org/10.1289/EHP16024.
鉴于商业和环境中存在数千种化学物质，快速识别潜在危害至关重要。将广泛的分子谱分析与靶向检测相结合，如高通量转录组学（HTTr）和受体筛选检测，可改进对扰乱与不良后果相关关键分子靶点的化学物质的识别。
我们旨在将转录组学读数与单个分子靶点联系起来，并在一个概念验证框架中，将转录组学预测与正交受体水平检测相结合，以对化学危害进行优先级排序。
通过TempO-Seq在U-2 OS和HepaRG细胞系中生成的转录组谱，用于开发由对不同分子靶点的参考化学物质有独特反应的基因组成的特征。这些特征应用于通过HTTr在两种细胞系中筛选的75种参考化学物质和1126种非参考化学物质。通过将效力估计值与每种化学物质的整体转录组生物活性进行比较，确定对每个特征的选择性生物活性。利用美国环境保护局ToxCast计划的现有正交检测数据，对转录组学预测的化学物质的靶点生物活性和选择性进行确认。对HTTr中37种没有足够正交数据的选择性作用化学物质的子集，使用五种受体水平检测方法之一进行前瞻性测试。
在筛选的1126种非参考化学物质中，201种在至少一种转录组特征中表现出选择性生物活性，57种被确认为选择性核受体激动剂。对每个特征有生物活性的化学物质与正交检测生物活性显著相关，基于特征的起始点在95.4%的按特征优先级排序的化学物质中，与改变生物途径的浓度同样敏感或更敏感。前瞻性分析发现，37种没有先前正交检测数据的化学物质中有18种（49%）对预测的受体有生物活性。
我们的工作表明，在分层框架中将转录组学与靶向正交检测相结合，可通过告知假定的分子靶点并对化学物质进行优先级排序以进行进一步测试，来支持下一代风险评估。https://doi.org/10.1289/EHP16024 。
