Jurgelewicz Amanda, Breaux Kristen, Willis Clinton M, Harris Felix R, Byrd Gabrielle, Witten Joshua, Haggard Derik E, Bundy Joseph L, Everett Logan J, Deisenroth Chad, Harrill Joshua A
Center for Computational Toxicology and Exposure (CCTE), US EPA, Durham, NC, 27709.
Oak Ridge Institute of Science and Education (ORISE), Oak Ridge, TN, 37830.
Toxicol Sci. 2025 May 2. doi: 10.1093/toxsci/kfaf061.
High-throughput profiling assays such as high-throughput phenotypic profiling (HTPP) with Cell Painting and high-throughput transcriptomics (HTTr) with TempO-SeqTM have been used to characterize the bioactivity and potential hazards associated with large inventories of chemicals. Although both methods offer broad coverage of molecular targets, a limitation is that the cell types used in these in vitro assays typically lack the xenobiotic metabolism capabilities of humans or laboratory animals used for in vivo testing. To address this limitation, this proof-of-concept study coupled the Alginate Immobilization of Metabolic Enzymes (AIME) platform to both assays and evaluated the impact of metabolism on chemical bioactivity in a breast cancer cell line, VM7Luc4E2. HTPP detected concentration-dependent increases in chemical bioactivity corresponding to increased estrogen receptor (ER) activation measured using an ER transactivation assay (ERTA) that had been previously coupled to the AIME platform in VM7Luc4E2 cells. Additionally, HTTr detected a greater number of active genes in the metabolic condition associated with increased ER activation. This corresponded to a greater number of active ER high-confidence (ERHC) gene signatures and/or metabolism-induced shifts in ERHC signature enrichment as a transcriptomic readout of ER activity. This study demonstrates that the high-throughput profiling assays can detect changes in chemical bioactivity between parent compounds and metabolites generated using the AIME platform in a reproducible way. Incorporating metabolic competence into high-throughput profiling assays will better inform next generation risk assessment by capturing potential metabolite-based changes in bioactivity of test chemicals that may be missed by current screening approaches.
高通量分析方法，如采用细胞绘画技术的高通量表型分析（HTPP）和采用TempO-SeqTM技术的高通量转录组学（HTTr），已被用于表征与大量化学物质库存相关的生物活性和潜在危害。尽管这两种方法都能广泛覆盖分子靶点，但一个局限性在于，这些体外分析中使用的细胞类型通常缺乏用于体内测试的人类或实验动物的异源生物代谢能力。为了解决这一局限性，本概念验证研究将代谢酶的海藻酸盐固定化（AIME）平台与这两种分析方法相结合，并评估了代谢对乳腺癌细胞系VM7Luc4E2中化学生物活性的影响。HTPP检测到化学生物活性呈浓度依赖性增加，这与使用雌激素受体（ER）反式激活分析（ERTA）测量的ER激活增加相对应，该分析先前已与VM7Luc4E2细胞中的AIME平台相结合。此外，HTTr在与ER激活增加相关的代谢条件下检测到更多的活性基因。这对应于更多的活性ER高可信度（ERHC）基因特征和/或ERHC特征富集的代谢诱导变化，作为ER活性的转录组学读数。本研究表明，高通量分析方法能够以可重复方式检测母体化合物与使用AIME平台生成的代谢物之间化学生物活性的变化。将代谢能力纳入高通量分析方法将通过捕捉当前筛选方法可能遗漏的测试化学品生物活性中基于潜在代谢物的变化，更好地为下一代风险评估提供信息。
