Costa P M, Repolho T, Caeiro S, Diniz M E, Moura I, Costa M H
IMAR-Instituto do Mar, DCEA, Faculdade de Ciências e Tecnologia da Universidade Nova de Lisboa, 2829-516 Monte de Caparica, Portugal.
Ecotoxicol Environ Saf. 2008 Sep;71(1):117-24. doi: 10.1016/j.ecoenv.2007.05.012. Epub 2007 Jul 6.
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.
对暴露于葡萄牙萨多河口沉积物中的金头鲷（Sparus aurata L.，1758）肝脏中的金属硫蛋白（MT）进行了定量分析，以评估MT作为铜（Cu）、镉（Cd）、铅（Pb）和砷（As）所致沉积物污染生物标志物的诱导潜力。沉积物取自两个对照地点和四个污染程度不同的地点。测定了沉积物中铜、镉、铅、砷、总有机质（TOM）和细颗粒（FF）的含量。广义线性模型（GLM）能够将沉积物参数与肝脏中铜、镉、铅、砷和MT的浓度进行整合。尽管沉积物中的金属含量低于预期，但我们发现MT与肝脏中的镉有关，也与肝脏和沉积物中铜之间以及肝脏铜和TOM之间的相互作用有关。我们建议使用GLM等统计模型将生物标志物和环境参数整合起来，作为一种比传统单一生物标志物方法更敏感、更可靠的沉积物风险评估技术。
