Department of Environmental Toxicology, Texas Tech University, Lubbock, Texas 79409, United States.
Department of Civil and Environmental Engineering, University of Washington, Seattle, Washington 98195, United States.
Environ Sci Technol. 2021 Mar 2;55(5):3070-3080. doi: 10.1021/acs.est.0c05546. Epub 2021 Feb 18.
Current aquatic toxicity assessments usually focus on targeted analyses coupled with toxicity testing to determine the impacts of complex mixtures on aquatic organisms. However, based on this approach alone, it is sometimes difficult to explain observed toxicity from the selected chemical analytes. Recent analytical advances such as high-resolution mass spectrometry (HRMS) can improve the characterizations of the chemical composition of complex mixtures, but the intensive labor required to produce confident identifications limits its utility in high-throughput screening. In the present study, we evaluated a rapid workflow to predict potential toxicity signatures of complex water samples based on high-throughput, tentative HRMS identifications derived from database matching, followed by identification of chemical-ligand interactions and pathway identification. We tested the workflow with water samples from the effluent-dominated Lubbock Canyon Lake System (LCLS). Results across all sites showed that predicted toxicity signatures had little variation when correcting for HRMS false-positive rates. The most common pathways across sites were gonadotropin-releasing hormone receptor and α-adrenergic receptor signaling. Alterations to the predicted pathways were successfully observed in larval zebrafish exposures to LCLS water samples. These results may allow researchers to better utilize rapid assessments of HRMS data for the assessment of adverse impacts on aquatic organisms.
目前的水生毒性评估通常侧重于目标分析和毒性测试相结合,以确定复杂混合物对水生生物的影响。然而,仅基于这种方法,有时很难从选定的化学分析物中解释观察到的毒性。最近的分析进展,如高分辨率质谱(HRMS),可以提高复杂混合物化学成分的表征,但产生可靠鉴定所需的密集劳动限制了其在高通量筛选中的应用。在本研究中,我们评估了一种基于高通量、基于数据库匹配的暂定 HRMS 鉴定的快速工作流程,以预测复杂水样的潜在毒性特征,然后进行化学配体相互作用和途径识别。我们用来自废水主导的拉伯克峡谷湖系统(LCLS)的水样测试了该工作流程。所有站点的结果均表明,在纠正 HRMS 假阳性率后,预测的毒性特征变化不大。最常见的途径是促性腺激素释放激素受体和α-肾上腺素能受体信号。在对 LCLS 水样进行幼鱼暴露的实验中,成功地观察到了预测途径的改变。这些结果可能使研究人员能够更好地利用 HRMS 数据的快速评估来评估对水生生物的不利影响。