A Tiered Approach for Screening and Assessment of Environmental Mixtures by Omics and <i>In Vitro</i> Assays

New methodology approaches with a broad coverage of the biological effects are urgently needed to evaluate the safety of the universe of environmentally relevant chemicals. Here, we propose a tiered approach incorporating transcriptomics and in vitro bioassays to assess environmental mixtures. The mixture samples and the perturbed biological pathways are prioritized by concentration-dependent transcriptome (CDT) and then used to guide the selection of in vitro bioassays for toxicant identification. To evaluate omics’ screening capability, we first applied a CDT technique to test mixture samples by HepG2 and MCF7 cells. The effect recoveries of large-volume solid-phase extraction on the overall bioactivity of the mixture were 48.9% in HepG2 and 58.3% in MCF7. The overall bioactivity potencies obtained by transcriptomics were positively correlated with the panel of 8 bioassays among 14 mixture samples combined with the previous data. Transcriptomics could predict their activation status (AUC = 0.783) and the relative potency (p < 0.05) of bioassays for four of the eight receptors (AhR, ER, AR, and Nrf2). Furthermore, the CDT identified other biological pathways perturbated by mixture samples, such as the pathway related to TP53, CAR, FXR, HIF, THRA, etc. Overall, this study demonstrates the potential of concentration-dependent omics for effect-based water quality assessment.当前亟需可覆盖广泛生物效应的新型方法学体系，以评估各类环境相关化学品的全品类安全风险。在此，我们提出一种整合转录组学与体外生物检测的分级策略，用于环境混合物的安全评估。本策略先通过浓度依赖性转录组（concentration-dependent transcriptome, CDT）对混合物样本及受扰动的生物通路进行优先级排序，以此指导体外生物检测的筛选，进而实现有毒成分的识别。为验证组学技术的筛选效能，我们首先采用CDT技术，分别利用HepG2与MCF7细胞对混合物样本开展检测。大体积固相萃取对混合物整体生物活性的回收率在HepG2细胞体系中为48.9%，在MCF7细胞体系中为58.3%。结合既往数据，在14份混合物样本中，通过转录组学获得的整体生物活性效价与8种生物检测组合的结果呈显著正相关。转录组学可预测8种受体中4种（芳香烃受体（AhR）、雌激素受体（ER）、雄激素受体（AR）以及核因子红细胞2相关因子2（Nrf2））对应生物检测的激活状态（曲线下面积AUC=0.783）与相对效价（p<0.05）。此外，CDT技术还识别出了混合物样本诱导扰动的其他生物通路，例如与TP53、组成型雄烷受体（CAR）、法尼醇X受体（FXR）、缺氧诱导因子（HIF）以及甲状腺激素受体α（THRA）等相关的通路。综上，本研究证实了浓度依赖性组学技术在基于效应的水质评估中的应用潜力。