Li Xiaojing, Zhou Jiarui, Bai Yaohui, Qiao Meng, Xiong Wei, Schulze Tobias, Krauss Martin, Williams Timothy D, Brown Ben, Orsini Luisa, Guo Liang-Hong, Colbourne John K
Centre for Environmental Research and Justice (CERJ), School of Biosciences, The University of Birmingham, Birmingham B15 2TT, U.K.
Research Centre for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, P. R. China.
Environ Sci Technol. 2025 Jan 14;59(1):291-301. doi: 10.1021/acs.est.4c11095. Epub 2024 Dec 20.
The assessment and regulation of chemical toxicity to protect human health and the environment are done one chemical at a time and seldom at environmentally relevant concentrations. However, chemicals are found in the environment as mixtures, and their toxicity is largely unknown. Understanding the hazard posed by chemicals within the mixture is critical to enforce protective measures. Here, we demonstrate the application of bioactivity profiling of environmental water samples using the sentinel and ecotoxicology model species to reveal the biomolecular response induced by exposure to real-world mixtures. We exposed a strain to 30 sampled waters of the Chaobai River and measured the gene expression response profiles. Using a multiblock correlation analysis, we establish correlations between chemical mixtures identified in 30 water samples with gene expression patterns induced by these chemical mixtures. We identified 80 metabolic pathways putatively activated by mixtures of inorganic ions, heavy metals, polycyclic aromatic hydrocarbons, industrial chemicals, and a set of biocides, pesticides, and pharmacologically active substances. Our data-driven approach discovered both known bioactivity signatures with previously described modes of action and new pathways linked to undiscovered potential hazards. This study demonstrates the feasibility of reducing the complexity of real-world mixture toxicity to characterize the biomolecular effects of a defined number of chemical components based on gene expression monitoring of the sentinel species .
为保护人类健康和环境而进行的化学毒性评估与监管,每次只针对一种化学物质,而且很少在与环境相关的浓度下进行。然而,环境中发现的化学物质是以混合物形式存在的,其毒性在很大程度上尚不清楚。了解混合物中化学物质所构成的危害对于实施保护措施至关重要。在此,我们展示了利用哨兵物种和生态毒理学模型物种对环境水样进行生物活性分析,以揭示暴露于实际混合物中所诱导的生物分子反应。我们将一个菌株暴露于潮白河的30个采样水体中,并测量基因表达反应谱。通过多模块相关性分析,我们建立了30个水样中鉴定出的化学混合物与这些化学混合物所诱导的基因表达模式之间的相关性。我们确定了80条代谢途径,推测这些途径被无机离子、重金属、多环芳烃、工业化学品以及一组杀菌剂、杀虫剂和药理活性物质的混合物激活。我们的数据驱动方法既发现了具有先前描述作用模式的已知生物活性特征,也发现了与未发现的潜在危害相关的新途径。这项研究证明了基于哨兵物种的基因表达监测来降低实际混合物毒性复杂性,以表征特定数量化学成分的生物分子效应的可行性。