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，疾病控制和预防中心（CDC）估计每年发生超过280万例抗生素耐药性感染。当病原微生物不再对抗生素等药物产生反应时，就会发生抗菌素耐药性（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。AMR自然发生并随着时间的推移随着微生物交换遗传物质（例如，抗生素抗性基因）在包括空气、水、土壤和沉积物在内的环境介质中。地表水系统和污水处理厂作为抗生素耐药基因（ARG）的储存库和来源的作用越来越受到关注。然而，仍然难以获得能够准确预测阿根廷温室气体在地表水系统中的归宿和迁移的经过验证的模型。该项目的总体目标是开发和实验验证一个计算模型，以预测在地表水系统中的命运和运输的ARGs使用在中央爱荷华州的河流作为一个模型系统。为了推进这一目标，主要研究者（PI）建议将实验室实验、现场测量和基于物理的计算建模联合收割机结合起来，以准确预测ARGs在地表水系统（包括溪流和河流）中的命运和运输。该项目的成功完成将使社会受益，因为它将开发一个新的有效模型，以提高预测和评估地表水系统中的ARG扩散和传播风险的能力。通过学生教育和培训，包括指导爱荷华州州立大学的两名研究生和一名本科生，将为社会带来额外的好处。预测河流中抗生素耐药基因（ARG）的命运和运输是复杂的，涉及大量的过程。与所有河流水质模型一样，ARG运输模型必须考虑河流的平流和扩散，以及横向流入河流，包括雨水/农业径流和废水处理厂废水的排放。此外，ARG可以在地表水系统中表现出不同的形状因子，包括细胞内DNA（iDNA），游离细胞外DNA（eDNA）和颗粒相关DNA。控制ARGs在河流中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。由于复制，水平基因转移和衰变可以发生在河流沉积物中，一个准确的ARG在河流中的命运模型需要考虑到水体和河流沉积物床之间的传输。建立在一个模型河流系统的命运和运输的ARGs的初步建模调查的结果，该项目的主要研究者（PI）建议测试的假设，包括过程，占沉积床和eDNA交换将提高其模型的预测能力。这项研究的具体目标是：（1）利用河流中的实地测量结果，评估项目研究员修订的ARG传输模型的预测能力，并确定需要进一步研究的参数;（2）在批量和围隔实验中测量这些参数;（3）利用额外的实地测量和分析，测试模型的稳健性。目标1将侧重于现场测量下游的污水处理厂（WWTP）在艾姆斯（爱荷华州），旨在评估沉积物和eDNA的贡献模型的预测能力。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ARGs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。为落实这项计划的教育及培训目的，研究所所长计划将研究结果纳入一个名为“研究导论”的项目为本课程，该课程是新开办的理学士学位课程的必修课程。爱荷华州州立大学的环境工程学位课程。该奖项反映了NSF的法定使命，并被认为是值得通过使用基金会的知识价值和更广泛的影响审查标准进行评估的支持。