A real-time antibody-based field assay to predict containment bioavailability in

描述(由申请人提供)：NIEHS-SRP研究项目将使用最新开发的生物传感器技术来快速预测暴露在受污染沉积物中的海产品中多环芳烃(PAH)的积累。疏水性污染物，如多环芳烃，很容易在贝类中积累，在食用时会对人类健康构成重大风险。在动态的自然系统中，脂质分配驱动贝类的生物积累，但多种化学、物理和环境因素影响生物利用度和组织浓度。由于测量生物群中的污染物吸收既耗时又昂贵，已开发出预测污染物去向和处置的模型。然而，自然生境的时间变异性和异质性使得根据测量的沉积物浓度可靠地预测生物累积以进行风险评估是困难的。归根结底，特定地点的测量对于准确预测污染物的生物有效性和评估沉积物修复工作的有效性至关重要。生物传感器技术的最新进展现在可以对十亿分之一以下浓度的污染物进行近实时测量。该项目将评估、改进和验证一种自动的、定量的、基于单抗的传感器来测量与沉积物相关的水中的多环芳烃。我们将验证生物传感器作为贝类中多环芳烃组织负荷的预测因子，这是人类从受污染的沉积物中接触多环芳烃的重要途径。这将通过在实验室控制牡蛎剂量来实现。然后，该生物传感器将被应用于弗吉尼亚州诺福克市高污染的伊丽莎白河，以评估正在采用的补救策略的有效性，以减少与通过食物网暴露的多环芳烃相关的人类健康风险。假设1.实时生物传感器对水样(沉积物、孔隙水、地表水)中多环芳烃浓度的估计快速和特异地预测栖息在多环芳烃污染场所的本地牡蛎组织中的脂归一化多环芳烃浓度。2.生物传感器对水中多环芳烃浓度的测量是特异的、剂量反应的，与投药牡蛎中多环芳烃的组织浓度直接相关，因此是组织生物积累的预测指标。3.对于不同的多环芳烃类别，结合具有不同抗体特异性的混合分析床将有助于更准确地辨别这些不同的多环芳烃在现场和实验室中的相对贡献。

DESCRIPTION (provided by applicant): This NIEHS-SRP research project will use newly developed biosensor technology to rapidly predict how polycyclic aromatic hydrocarbons (PAH) accumulate in seafood exposed to contaminated sediments. Hydrophobic contaminants such as PAH readily accumulate in shellfish, where they pose a significant human health risk when consumed. Lipid partitioning drives bioaccumulation in shellfish but multiple chemical, physical and environmental factors influence bioavailability and tissue concentrations in dynamic natural systems. Because measuring contaminant uptake in biota is time consuming and expensive, models have been developed to predict contaminant fate and disposition. However, temporal variability and heterogeneity of natural habitats make it difficult to reliably predict bioaccumulation for risk assessments from measured sediment concentrations. Ultimately, site-specific measurements are vital to accurately predict contaminant bioavailability and to evaluate the effectiveness of sediment remediation efforts. Recent advances in biosensor technology now allow near real-time measurement of contaminants at sub part per billion concentrations. This project will evaluate, refine and validate an automated, quantitative, monoclonal antibody-based sensor to measure PAH in sediment-associated water. We will validate the biosensor as a predictor of PAH tissue burdens in shellfish, an important route for PAH exposure to humans from contaminated sediments. This will be accomplished through controlled laboratory dosing of oysters. The biosensor will be then be applied in the highly contaminated Elizabeth River, Norfolk, Virginia to assess the effectiveness of ongoing remediation strategies being employed to reduce the human health risks associated with PAH exposure through the food web. Hypotheses 1. Real-time bio-sensor estimates of PAH concentration in aqueous samples (sediment pore water, surface water) rapidly and specifically predict lipid-normalized PAH concentrations in the tissues of native oysters inhabiting PAH-contaminated sites. 2. Biosensor measurements of aqueous PAH concentrations are specific, dose-responsive, correlate directly with tissue concentrations of PAH in dosed oysters and are therefore predictive surrogates of tissue bioaccumulation. 3. Incorporation of mixed analyte beds with differentative antibody specificities for different PAH classes will provide for more accurate discernment of the relative contribution of these different PAHs in the field and laboratory.