Predicting fate and transport of antibiotic resistance genes in streams

抗菌素耐药性已成为全球公共卫生威胁。在美国，疾病控制和预防中心(CDC)估计，每年发生的抗菌素耐药性感染超过280万例。当病原微生物对抗生素等药物不再有反应时，就会出现抗菌素耐药性(AMR)，这使得治疗感染和控制传染病和疾病传播变得非常困难。随着微生物在空气、水、土壤和沉积物等环境介质中交换遗传物质(例如抗生素抗性基因)，AMR自然发生并随着时间的推移而发展。地表水系统和污水处理厂作为抗生素耐药基因(ARGs)的储存库和来源的作用日益受到关注。然而，能够准确预测ARGs在地表水系统中的去向和迁移的有效模型仍然难以获得。该项目的总体目标是开发和实验验证一个计算模型，以预测ARGs在地表水系统中的命运和传输，以爱荷华州中部的一条河流为模型系统。为了推进这一目标，首席调查员(PI)建议将实验室实验、现场测量和基于物理的计算模型结合起来，以准确预测ARGs在包括溪流和河流在内的地表水系统中的命运和迁移。该项目的成功完成将使社会受益，因为它将开发一种新的有效模型，以提高预测和评估ARG在地表水系统中传播和传播的风险的能力。还将通过学生教育和培训为社会带来更多好处，包括对爱荷华州立大学的两名研究生和一名本科生进行指导。由于涉及大量过程，预测抗生素耐药性基因(ARGs)在河流中的命运和运输变得复杂。与河流中的所有水质模型一样，ARG传输模型必须考虑河流流量的平流和扩散，以及横向流入河流的情况，包括雨水/农业径流和废水处理厂污水的排放。此外，ARGs在地表水系统中可以表现出不同的形态因子，包括胞内DNA(IDNA)、游离胞外DNA(EDNA)和颗粒相关DNA。控制ARG在河流中的去向和运输的潜在重要过程包括对颗粒的吸附、向活细胞的基因转移、基因复制和动员。由于河流沉积物中可能发生复制、水平基因转移和腐烂，因此河流中ARG命运的准确模型需要考虑河流水柱和沉积物床之间的传输。基于对ARGs在模型河流系统中去向和迁移的初步模拟调查结果，该项目的首席调查员(PI)建议检验这样一种假设，即包括解释沉积物床和Edna交换的过程将提高他们的模型的预测能力。这项研究的具体目标是(1)利用河流现场测量来评估PIS修订的ARG传输模型的预测能力，并确定需要进一步研究的参数；(2)在批量和中观实验中测量这些参数；以及(3)通过更多的现场测量和分析来测试模型的稳健性。目标1将重点放在艾姆斯(爱荷华州)污水处理厂(WWTP)下游的现场测量，这些测量旨在评估沉积物和EDNA对模型预测能力的贡献。目标2将包括针对目标1中确定的参数的实验室实验，目的是确定模型参数的实际取值范围。在目标3中，来自目标2的估计参数将被用作模型的输入，以预测目标1中污水处理厂下游的ARG浓度，然后进行现场测量，以评估模型预测能力及其参数化的稳健性。为了实现这个项目的教育和培训目标，PIS计划将研究成果整合到一个基于项目的课程“研究导论”中，这是爱荷华州立大学新推出的环境工程学士学位课程的必修课。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

Antimicrobial resistance has become a global public health threat. In the United States, the Centers for Disease Control and Prevention (CDC) estimates that more than 2.8 million antimicrobial-resistant infections occur each year. Antimicrobial resistance (AMR) occurs when pathogenic microorganisms no longer respond to drugs such as antibiotics making it very difficult to treat infections and control contagion and disease spread. AMR occurs naturally and develops over time as microorganisms exchange genetic materials (e.g., antibiotic resistance genes) in environmental media including air, water, soils, and sediments. There is growing concern about the roles of surface water systems and wastewater treatment plants as reservoirs and sources for antibiotic resistance genes (ARGs). However, the availability of validated models that can accurately predict the fate and transport of ARGs in surface water systems has remained elusive. The overarching goal of this project is to develop and experimentally validate a computational model to predict the fate and transport of ARGs in surface water systems using a river in Central Iowa as a model system. To advance this goal, the Principal Investigators (PIs) propose to combine and integrate laboratory experiments, field measurements, and physics-based computational modeling to accurately predict the fate and transport of ARGs in surface water systems including streams and rivers. The successful completion of this project will benefit society through the development of a new and validated model to improve the ability to predict and assess the risk of ARG spread and transmission in surface water systems. Additional benefits to society will be achieved through student education and training including the mentoring of two graduate students and one undergraduate student at Iowa State University.Predicting the fate and transport of antibiotic resistance genes (ARGs) in rivers is complicated by the large number of processes involved. As with all models of water quality in rivers, a model for ARG transport must account for advection and dispersion by the river’s flow, as well as lateral inflows into the river including stormwater/agricultural runoffs and the discharges of wastewater treatment plant effluents. In addition, ARGs can exhibit different form factors in surface water systems including intracellular DNA (iDNA), free extracellular DNA (eDNA), and particle-associated DNA. Potentially important processes that control the fate and transport of ARGs in rivers include sorption to particles, gene transfer to live cells, gene replication, and mobilization. Because replication, horizontal gene transfer, and decay can occur in river sediments, an accurate model of ARG fate in rivers needs to account for the transport between the water column and the sediment bed of a river. Building the results of preliminary modeling investigations of the fate and transport of ARGs in a model river system, the Principal Investigators (PIs) of this project propose to test the hypothesis that the inclusion of processes that account for sediment bed and eDNA exchanges will improve the predictive capability of their model. The specific objectives of the research are to (1) evaluate the predictive capability of the PIs’ revised ARG transport model using field measurements in a river and identify parameters requiring further study; (2) measure these parameters in batch and mesocosm experiments; and (3) test the robustness of the model with additional field measurements and analyses. Objective 1 will focus on field measurements downstream of a wastewater treatment plant (WWTP) in Ames (Iowa) that are designed to evaluate the sediment and eDNA contributions to the model predictive capability. Objective 2 will consist of laboratory experiments targeting the parameters identified in Objective 1 with the goal of determining realistic ranges of values for the model parameters. In Objective 3, the estimated parameters from Objective 2 will be used as inputs to the model to predict concentrations of ARGs downstream of the WWTP in Objective 1 followed by field measurements designed to evaluate the model predictive capability and the robustness of its parametrization. To implement the education and training goals of this project, the PIs plan to integrate the research findings into a project-based course entitled “Introduction to Research” which is a required course for the newly launched B.S. degree program in environmental engineering at Iowa State University.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.