Predicting fate and transport of antibiotic resistance genes in streams

预测河流中抗生素抗性基因的命运和运输

• 批准号: 2241853
• 负责人: Chris Rehmann
• 金额: $ 41.9万
• 依托单位: Iowa State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-01 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2241853&HistoricalAwards=false
• 关键词: Predicting; fate; transport; antibiotic; resistance

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.

抗生素耐药性已成为全球公共卫生威胁。在美国，疾病控制与预防中心 (CDC) 估计每年发生超过 280 万例抗菌药物耐药性感染。当病原微生物不再对抗生素等药物产生反应时，就会出现抗菌素耐药性 (AMR)，这使得治疗感染、控制传染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），抗菌素耐药性自然发生并随着时间的推移而发展。人们越来越关注地表水系统和废水处理厂作为抗生素抗性基因（ARG）的储存库和来源的作用。然而，能够准确预测地表水系统中 ARG 的命运和运输的经过验证的模型仍然难以实现。该项目的总体目标是开发并通过实验验证一个计算模型，以爱荷华州中部的一条河流作为模型系统来预测地表水系统中 ARG 的命运和运输。为了推进这一目标，主要研究者 (PI) 建议结合和整合实验室实验、现场测量和基于物理的计算模型，以准确预测 ARG 在包括溪流和河流在内的地表水系统中的命运和传输。该项目的成功完成将通过开发新的经过验证的模型来提高预测和评估地表水系统中 ARG 传播和传播风险的能力，从而使社会受益。通过学生教育和培训，包括指导爱荷华州立大学的两名研究生和一名本科生，将给社会带来额外的好处。由于涉及大量过程，预测河流中抗生素抗性基因（ARG）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水流的平流和扩散，以及流入河流的横向流入，包括雨水/农业径流和废水处理厂废水的排放。此外，ARG 在地表水系统中可以表现出不同的形态因素，包括细胞内 DNA (iDNA)、游离细胞外 DNA (eDNA) 和颗粒相关 DNA。控制河流中 ARG 的命运和运输的潜在重要过程包括颗粒吸附、基因转移到活细胞、基因复制和动员。由于河流沉积物中可能发生复制、水平基因转移和衰变，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。该项目的主要研究人员 (PI) 建立了模型河流系统中 ARG 的命运和运输的初步模型研究结果，提出检验以下假设：纳入考虑沉积物床和 eDNA 交换的过程将提高模型的预测能力。研究的具体目标是（1）利用河流现场测量来评估 PI 修订后的 ARG 输运模型的预测能力，并确定需要进一步研究的参数； (2) 在批量和介观实验中测量这些参数； (3) 通过额外的现场测量和分析来测试模型的稳健性。目标 1 将重点关注艾姆斯（爱荷华州）废水处理厂 (WWTP) 下游的现场测量，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值的实际范围。在目标 3 中，目标 2 中的估计参数将用作模型的输入，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，旨在评估模型的预测能力及其参数化的稳健性。为实现该项目的教育和培训目标，PI计划将研究成果整合到名为“研究概论”的项目课程中，该课程是新开办的理学学士课程的必修课。爱荷华州立大学环境工程学位课程。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。