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

描述（由申请人提供）：本NIEHS-SRP研究项目将使用新开发的生物传感器技术快速预测暴露于污染沉积物的海产品中多环芳烃（PAH）的积累情况。疏水污染物如多环芳烃很容易在贝类中积累，食用后对人体健康构成重大风险。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动态自然系统中的生物利用度和组织浓度。由于测量生物群中污染物的吸收既耗时又昂贵，所以人们开发了模型来预测污染物的命运和处置。然而，由于自然生境的时间变异性和异质性，很难根据测量的沉积物浓度可靠地预测用于风险评估的生物积累。最终，特定地点的测量对于准确预测污染物的生物利用度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接近实时地测量十亿分之一浓度的污染物。该项目将评估、完善和验证一种自动化、定量、基于单克隆抗体的传感器，以测量沉积物相关水中的多环芳烃。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器，这是人类从污染沉积物中接触多环芳烃的重要途径。这将通过控制实验室牡蛎的剂量来完成。然后，生物传感器将被应用于弗吉尼亚州诺福克市高度污染的伊丽莎白河，以评估正在采取的补救策略的有效性，这些策略旨在通过食物网减少与多环芳烃暴露相关的人类健康风险。假设1。实时生物传感器估算含水样品（沉积物孔隙水、地表水）中多环芳烃的浓度，快速而具体地预测生活在多环芳烃污染地点的本地牡蛎组织中脂质标准化的多环芳烃浓度。2. 水中多环芳烃浓度的生物传感器测量具有特异性，剂量响应性，与剂量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性替代品。3. 将不同多环芳烃类别的不同抗体特异性的混合分析物混合在一起，将在现场和实验室中更准确地识别这些不同多环芳烃的相对贡献。

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.