Schroeder Anthony L, Martinović-Weigelt Dalma, Ankley Gerald T, Lee Kathy E, Garcia-Reyero Natalia, Perkins Edward J, Schoenfuss Heiko L, Villeneuve Daniel L
University of Minnesota - Twin Cities, Water Resources Center, 1985 Lower Buford Circle, St. Paul, MN 55108, USA; U.S. Environmental Protection Agency, National Health and Environmental Effects Research Laboratory, Duluth, MN 55804, USA.
University of St. Thomas, Department of Biology, Mail OWS 390, 2115 Summit Ave, Saint Paul, MN 55105, USA.
Environ Pollut. 2017 Feb;221:427-436. doi: 10.1016/j.envpol.2016.12.005. Epub 2016 Dec 8.
Evaluating potential adverse effects of complex chemical mixtures in the environment is challenging. One way to address that challenge is through more integrated analysis of chemical monitoring and biological effects data. In the present study, water samples from five locations near two municipal wastewater treatment plants in the St. Croix River basin, on the border of MN and WI, USA, were analyzed for 127 organic contaminants. Known chemical-gene interactions were used to develop site-specific knowledge assembly models (KAMs) and formulate hypotheses concerning possible biological effects associated with chemicals detected in water samples from each location. Additionally, hepatic gene expression data were collected for fathead minnows (Pimephales promelas) exposed in situ, for 12 d, at each location. Expression data from oligonucleotide microarrays were analyzed to identify functional annotation terms enriched among the differentially-expressed probes. The general nature of many of the terms made hypothesis formulation on the basis of the transcriptome-level response alone difficult. However, integrated analysis of the transcriptome data in the context of the site-specific KAMs allowed for evaluation of the likelihood of specific chemicals contributing to observed biological responses. Thirteen chemicals (atrazine, carbamazepine, metformin, thiabendazole, diazepam, cholesterol, p-cresol, phenytoin, omeprazole, ethyromycin, 17β-estradiol, cimetidine, and estrone), for which there was statistically significant concordance between occurrence at a site and expected biological response as represented in the KAM, were identified. While not definitive, the approach provides a line of evidence for evaluating potential cause-effect relationships between components of a complex mixture of contaminants and biological effects data, which can inform subsequent monitoring and investigation.
评估环境中复杂化学混合物的潜在不利影响具有挑战性。应对这一挑战的一种方法是对化学监测和生物效应数据进行更综合的分析。在本研究中，对美国明尼苏达州（MN）和威斯康星州（WI）边境的圣克罗伊河流域两座城市污水处理厂附近五个地点的水样进行了127种有机污染物分析。利用已知的化学-基因相互作用建立特定地点的知识组装模型（KAMs），并就与每个地点水样中检测到的化学物质相关的可能生物效应提出假设。此外，还收集了在每个地点原位暴露12天的黑头呆鱼（Pimephales promelas）的肝脏基因表达数据。对寡核苷酸微阵列的表达数据进行分析，以识别差异表达探针中富集的功能注释术语。许多术语具有一般性，这使得仅基于转录组水平的反应来制定假设变得困难。然而，在特定地点的KAMs背景下对转录组数据进行综合分析，可以评估特定化学物质导致观察到的生物反应的可能性。确定了13种化学物质（阿特拉津、卡马西平、二甲双胍、噻苯达唑、地西泮、胆固醇、对甲酚、苯妥英、奥美拉唑、红霉素、17β-雌二醇、西咪替丁和雌酮），在这些化学物质的存在与KAM中所代表的预期生物反应之间存在统计学上的显著一致性。虽然不是决定性的，但该方法为评估复杂污染物混合物成分与生物效应数据之间的潜在因果关系提供了一条证据链，可为后续监测和调查提供参考。
