*Biomolecular Screening Branch, Division of National Toxicology Program, National Institute of Environmental Health Sciences of National Institutes of Health, Durham, North Carolina 27709.
Sciome, LLC, Research Triangle Park, Durham, North Carolina 27709.
Toxicol Sci. 2019 Jun 1;169(2):553-566. doi: 10.1093/toxsci/kfz065.
Prediction of human response to chemical exposures is a major challenge in both pharmaceutical and toxicological research. Transcriptomics has been a powerful tool to explore chemical-biological interactions, however, limited throughput, high-costs, and complexity of transcriptomic interpretations have yielded numerous studies lacking sufficient experimental context for predictive application. To address these challenges, we have utilized a novel high-throughput transcriptomics (HTT) platform, TempO-Seq, to apply the interpretive power of concentration-response modeling with exposures to 24 reference compounds in both differentiated and non-differentiated human HepaRG cell cultures. Our goals were to (1) explore transcriptomic characteristics distinguishing liver injury compounds, (2) assess impacts of differentiation state of HepaRG cells on baseline and compound-induced responses (eg, metabolically-activated), and (3) identify and resolve reference biological-response pathways through benchmark concentration (BMC) modeling. Study data revealed the predictive utility of this approach to identify human liver injury compounds by their respective BMCs in relation to human internal exposure plasma concentrations, and effectively distinguished drug analogs with varied associations of human liver injury (eg, withdrawn therapeutics trovafloxacin and troglitazone). Impacts of cellular differentiation state (proliferated vs differentiated) were revealed on baseline drug metabolizing enzyme expression, hepatic receptor signaling, and responsiveness to metabolically-activated toxicants (eg, cyclophosphamide, benzo(a)pyrene, and aflatoxin B1). Finally, concentration-response modeling enabled efficient identification and resolution of plausibly-relevant biological-response pathways through their respective pathway-level BMCs. Taken together, these findings revealed HTT paired with differentiated in vitro liver models as an effective tool to model, explore, and interpret toxicological and pharmacological interactions.
预测人类对化学暴露的反应是药物和毒理学研究中的一个主要挑战。转录组学一直是探索化学-生物学相互作用的有力工具，然而，由于通量有限、成本高和转录组解释的复杂性，导致许多研究缺乏足够的实验背景来进行预测应用。为了解决这些挑战，我们利用了一种新型的高通量转录组学（HTT）平台 TempO-Seq，应用浓度-反应建模的解释能力，对分化和非分化的人 HepaRG 细胞培养物中的 24 种参考化合物进行暴露。我们的目标是：（1）探索区分肝损伤化合物的转录组特征；（2）评估 HepaRG 细胞分化状态对基线和化合物诱导反应（如代谢激活）的影响；（3）通过基准浓度（BMC）建模识别和解决参考生物学反应途径。研究数据显示，这种方法具有预测能力，可以根据与人类内部暴露血浆浓度相关的各自 BMC 识别人类肝损伤化合物，并有效地区分与人类肝损伤有不同关联的药物类似物（例如，撤回的治疗药物 trovafloxacin 和 troglitazone）。细胞分化状态（增殖与分化）的影响体现在基线药物代谢酶表达、肝受体信号和对代谢激活毒物（如环磷酰胺、苯并[a]芘和黄曲霉毒素 B1）的反应性上。最后，浓度-反应建模通过各自的途径水平 BMC 实现了对可能相关生物学反应途径的有效识别和解决。总之，这些发现揭示了高通量转录组学与分化的体外肝脏模型相结合，是一种用于建模、探索和解释毒理学和药理学相互作用的有效工具。
