Meier Matthew J, Harrill Joshua, Johnson Kamin, Thomas Russell S, Tong Weida, Rager Julia E, Yauk Carole L
Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, Canada.
Center for Computational Toxicology and Exposure, Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, Durham, NC, USA.
Nat Rev Genet. 2025 Feb;26(2):105-122. doi: 10.1038/s41576-024-00767-1. Epub 2024 Sep 2.
Toxicogenomics measures molecular features, such as transcripts, proteins, metabolites and epigenomic modifications, to understand and predict the toxicological effects of environmental and pharmaceutical exposures. Transcriptomics has become an integral tool in contemporary toxicology research owing to innovations in gene expression profiling that can provide mechanistic and quantitative information at scale. These data can be used to predict toxicological hazards through the use of transcriptomic biomarkers, network inference analyses, pattern-matching approaches and artificial intelligence. Furthermore, emerging approaches, such as high-throughput dose-response modelling, can leverage toxicogenomic data for human health protection even in the absence of predicting specific hazards. Finally, single-cell transcriptomics and multi-omics provide detailed insights into toxicological mechanisms. Here, we review the progress since the inception of toxicogenomics in applying transcriptomics towards toxicology testing and highlight advances that are transforming risk assessment.
毒理基因组学通过测量分子特征，如转录本、蛋白质、代谢物和表观基因组修饰，来理解和预测环境暴露及药物暴露的毒理学效应。由于基因表达谱分析技术的创新，转录组学已成为当代毒理学研究中不可或缺的工具，这种创新能够大规模地提供机制和定量信息。这些数据可通过使用转录组学生物标志物、网络推断分析、模式匹配方法和人工智能来预测毒理学危害。此外，即使在无法预测特定危害的情况下，新兴方法，如高通量剂量反应建模，也可利用毒理基因组数据来保护人类健康。最后，单细胞转录组学和多组学为毒理学机制提供了详细的见解。在此，我们回顾了自毒理基因组学诞生以来在将转录组学应用于毒理学测试方面取得的进展，并强调了正在改变风险评估的进展。
