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Prior knowledge-based approach for associating contaminants with biological effects: A case study in the St. Croix River basin, MN, WI, USA

机译:基于知识的先验方法将污染物与生物效应相关联:以美国威斯康星州明尼苏达州圣克罗伊河流域为例

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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, 17 beta-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. Published by Elsevier Ltd.
机译:评估环境中复杂化学混合物的潜在不利影响具有挑战性。解决这一挑战的一种方法是通过对化学监测和生物效应数据进行更全面的分析。在本研究中,分析了位于美国明尼苏达州和威斯康星州边界的圣克鲁瓦河流域两个市政污水处理厂附近五个地点的水样,分析了127种有机污染物。已知的化学-基因相互作用用于开发特定地点的知识组装模型(KAM),并提出与可能从每个位置的水样本中检测到的化学物质相关的生物效应的假设。此外,还收集了在每个位置连续暴露12天的黑头P鱼(Pimephales promelas)的肝基因表达数据。分析来自寡核苷酸微阵列的表达数据以鉴定在差异表达探针中富集的功能注释术语。许多术语的一般性质使得仅基于转录组水平的反应就很难提出假设。但是,在特定位点KAM的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有助于观察到的生物学反应的可能性。十三种化学品(阿特拉津,卡马西平,二甲双胍,噻苯达唑,地西epa,胆固醇,对甲酚,苯妥英钠,奥美拉唑,乙霉素,17β-雌二醇,西咪替丁和雌酮)在现场与预期发生之间具有统计学上的显着一致性。确定了以KAM为代表的生物学反应。该方法虽然不确定,但为评估复杂污染物混合物的成分与生物效应数据之间的潜在因果关系提供了一系列证据,可为后续的监测和调查提供依据。由Elsevier Ltd.发布

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