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Modelling metallothionein induction in the liver of Sparus aurata exposed to metal-contaminated sediments

机译:模拟暴露于金属污染沉积物的金头鸥肝脏中金属硫蛋白的诱导

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摘要

Metallothionein (MT) in the liver of gilthead seabreams (Sparus aurata L., 1758) exposed to Sado estuary (Portugal) sediments was quantified to assess the MT induction potential as a biomarker of sediment-based contamination by copper (Cu), cadmium (Cd), lead (Pb) and arsenic (As). Sediments were collected from two control sites and four sites with different levels of contamination. Sediment Cu, Cd, Pb, As, total organic matter (TOM) and fine fraction (FF) levels were determined. Generalized linear models (GLM) allowed integration of sediment parameters with liver Cu, Cd, Pb, As and MT concentrations. Although sediment metal levels were lower than expected, we relate MT with liver Cd and also with interactions between liver and sediment Cu and between liver Cu and TOM. We suggest integrating biomarkers and environmental parameters using statistical models such as GLM as a more sensitive and reliable technique for sediment risk assessment than traditional isolated biomarker approaches.
机译:对暴露于佐渡河口(葡萄牙)沉积物的金头鲷(Sparus aurata L.,1758)的肝脏中的金属硫蛋白(MT)进行定量,以评估MT诱导潜力,作为铜(Cu),镉( Cd),铅(Pb)和砷(As)。从两个控制地点和四个污染程度不同的地点收集沉积物。确定了沉积物中的铜,镉,铅,砷,总有机质(TOM)和细级分(FF)的水平。通用线性模型(GLM)允许将沉积物参数与肝脏的Cu,Cd,Pb,As和MT浓度进行积分。尽管沉积物中的金属含量低于预期水平,但我们将MT与肝脏Cd以及肝脏与沉积物Cu之间以及肝脏Cu与TOM之间的相互作用相关联。我们建议使用统计模型(例如GLM)整合生物标志物和环境参数,这是一种比传统的分离生物标志物方法更为敏感和可靠的沉积物风险评估技术。

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