Mixture toxicity and gene inductions: Can we predict the outcome?
Mixture toxicity and gene inductions: Can we predict the outcome?
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DOI：
10.1897/07-303.1
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发表时间：
2008-03-01
影响因子：
4.1
通讯作者：
Blust, Ronny
中科院分区：
文献类型：
--
作者：
Dardenne, Freddy;Nobels, Ingrid;Blust, Ronny
由于大多数现实生活中暴露场景的性质，过去十年的生态毒理学研究对混合物生态毒理学的评估越来越感兴趣。通常，混合物被认为遵循两种模型之一：浓度加成 (CA) 或响应加成 (RA)，这两种模型均已在文献中进行了描述。然而，存在偏离其中一个或两个模型的混合物；它们通常表现出协同作用、比例或浓度依赖性或抑制等现象。此外，CA和RA都主要针对相对较高水平的生物组织的急性反应（例如整个生物体死亡率）而受到挑战和评估，并且其对遗传反应的适用性尚未受到太多关注。遗传反应被认为是接触有毒物质时的主要反应，并携带有价值的机制信息。基因表达水平的影响是毒物和混合物作用模式的核心。在此主要响应水平上预测混合物响应的能力对于预测和理解生物组织不同层面的混合物效应而言是一项重要资产。本研究使用具有已知作用模式的二元组合模型毒物，评估了混合物模型对大肠杆菌应激基因诱导的适用性。结果表明，即使剂量反应曲线的最大值未知，使得经典的 ECx（引起 x% 效应的浓度）方法不可能，混合物模型也可以根据单一毒物反应曲线预测对二元混合物的反应。在大多数情况下，毒物的作用模式并不能决定模型的最佳选择（即 CA、RA 或其偏差）。
As a consequence of the nature of most real-life exposure scenarios, the last decade of ecotoxicological research has seen increasing interest in the assessment of mixture ecotoxicology. Often, mixtures are considered to follow one of two models, concentration addition (CA) or response addition (RA), both of which have been described in the literature. Nevertheless, mixtures that deviate from either or both models exist; they typically exhibit phenomena like synergism, ratio or concentration dependency, or inhibition. Moreover, both CA and RA have been challenged and evaluated mainly for acute responses at relatively high levels of biological organization (e.g., whole-organism mortality), and applicability to genetic responses has not received much attention. Genetic responses are considered to be the primary reaction in case of toxicant exposure and carry valuable mechanistic information. Effects at the gene-expression level are at the heart of the mode of action by toxicants and mixtures. The ability to predict mixture responses at this primary response level is an important asset in predicting and understanding mixture effects at different levels of biological organization. The present study evaluated the applicability of mixture models to stress gene inductions in Escherichia coli employing model toxicants with known modes of action in binary combinations. The results showed that even if the maximum of the dose-response curve is not known, making a classical ECx (concentration causing x% effect) approach impossible, mixture models can predict responses to the binary mixtures based on the single-toxicant response curves. In most cases, the mode of action of the toxicants does not determine the optimal choice of model (i.e., CA, RA, or a deviation thereof).