Li Heng-Hong, Hyduke Daniel R, Chen Renxiang, Heard Pamela, Yauk Carole L, Aubrecht Jiri, Fornace Albert J
Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC.
Department of Oncology, Georgetown University Medical Center, Washington, DC.
Environ Mol Mutagen. 2015 Jul;56(6):505-19. doi: 10.1002/em.21941. Epub 2015 Mar 2.
The development of in vitro molecular biomarkers to accurately predict toxicological effects has become a priority to advance testing strategies for human health risk assessment. The application of in vitro transcriptomic biomarkers promises increased throughput as well as a reduction in animal use. However, the existing protocols for predictive transcriptional signatures do not establish appropriate guidelines for dose selection or account for the fact that toxic agents may have pleiotropic effects. Therefore, comparison of transcriptome profiles across agents and studies has been difficult. Here we present a dataset of transcriptional profiles for TK6 cells exposed to a battery of well-characterized genotoxic and nongenotoxic chemicals. The experimental conditions applied a new dose optimization protocol that was based on evaluating expression changes in several well-characterized stress-response genes using quantitative real-time PCR in preliminary dose-finding studies. The subsequent microarray-based transcriptomic analyses at the optimized dose revealed responses to the test chemicals that were typically complex, often exhibiting substantial overlap in the transcriptional responses between a variety of the agents making analysis challenging. Using the nearest shrunken centroids method we identified a panel of 65 genes that could accurately classify toxicants as genotoxic or nongenotoxic. To validate the 65-gene panel as a genomic biomarker of genotoxicity, the gene expression profiles of an additional three well-characterized model agents were analyzed and a case study demonstrating the practical application of this genomic biomarker-based approach in risk assessment was performed to demonstrate its utility in genotoxicity risk assessment.
开发能够准确预测毒理学效应的体外分子生物标志物，已成为推进人类健康风险评估测试策略的首要任务。体外转录组生物标志物的应用有望提高通量并减少动物使用。然而，现有的预测转录特征方案并未为剂量选择制定适当的指导方针，也未考虑到有毒物质可能具有多效性这一事实。因此，跨试剂和研究比较转录组图谱一直很困难。在此，我们展示了一组TK6细胞暴露于一系列特征明确的遗传毒性和非遗传毒性化学物质后的转录图谱数据集。实验条件采用了一种新的剂量优化方案，该方案基于在初步剂量探索研究中使用定量实时PCR评估几个特征明确的应激反应基因的表达变化。随后在优化剂量下基于微阵列的转录组分析揭示了对测试化学物质的反应通常很复杂，不同试剂之间的转录反应往往存在大量重叠，这使得分析具有挑战性。使用最近收缩质心法，我们鉴定出一组65个基因，它们可以准确地将有毒物质分类为遗传毒性或非遗传毒性。为了验证这65个基因组成的基因集作为遗传毒性的基因组生物标志物，我们分析了另外三种特征明确的模型试剂的基因表达谱，并进行了一个案例研究，以证明这种基于基因组生物标志物的方法在风险评估中的实际应用，从而展示其在遗传毒性风险评估中的效用。
