Collaborative Research: URoL:ASC: Using the Rules of Antibiotic Resistance Development to Inform Wastewater Mitigation Strategies

合作研究：URoL:ASC：利用抗生素耐药性发展规则为废水减排策略提供信息

• 批准号: 2319522
• 负责人: Liqing Zhang
• 金额: $ 65万
• 依托单位: Virginia Polytechnic Institute and 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: 2023-11-01 至 2028-10-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2319522&HistoricalAwards=false
• 关键词: Collaborative; Research; URoL; ASC; Using

The increased prevalence among bacteria of resistance to antimicrobial drugs (antimicrobial resistance, or AMR) is a critical societal challenge that threatens human, environmental and agricultural health. When antibiotics used to treat bacterial infections are no longer effective, infections last longer and there is increased risk of death. Municipal wastewater treatment plants (WWTPs) are “hotspots” for AMR spread due to the enriched presence of antibiotic residues, antibiotic resistance genes, and antibiotic resistant bacteria. Therefore, WWTPs are a unique system for mitigating AMR spread in the environment. This project investigates the role of different environmental factors, such as temperature, heavy metals, and other contaminants in the development of AMR. The convergent research will conduct field, laboratory, and computational studies to determine when and how susceptible bacterial strains are replaced by more antibiotic-tolerant resistant populations in the natural environment. Knowledge from these studies will facilitate development of predictive models and cost-effective strategies to prevent AMR proliferation in the environment. This project also emphasizes the role of education, poverty, and environmental pollution in AMR spread. Activities will include dissemination of co-produced knowledge beyond the scientific community, through trust-based partnership with farmers, K-12 students, and stakeholders. The minimal selective concentrations (MSC) for antibiotics, at which a resistant strain acquires competitive advantage in growth relative to its susceptible progenitor, are challenging to determine under dynamic natural environmental systems such as WWTPs. In this project, integrated studies using metagenomics, non-target chemical analysis, and machine learning approaches will be conducted to characterize emergence of AMR genotypes and phenotypes within WWTPs. Engineered resistant strains of E. coli will be developed to determine how variations in chemical contaminants affect de novo resistance development and horizontal transfer of resistance genes. To control input of AMR drivers from WWTPs, knowledge is needed to establish appropriate endpoints for mitigating prevalence of AMR. The overall objective is to develop predictive models that describe how AMR emerges and spreads in WWTP activated sludge systems. Machine learning approaches will be used to determine MSC for two test antibiotics, azithromycin and ciprofloxacin, in WWTP activated sludge under varying environmental conditions. The central hypothesis is that temperature, heavy metals, and other contaminants influence the selection of AMR at sub-inhibitory antibiotic concentrations. Our research team will work closely with WWTP engineers and utility workers to ensure that the knowledge gained in this research can be translated into practice effectively.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.

抗菌药物耐药性（抗菌素耐药性或 AMR）细菌普遍存在，是威胁人类、环境和农业健康的一项重大社会挑战。当用于治疗细菌感染的抗生素不再有效时，感染会持续更长时间，并且感染率也会增加。由于抗生素残留、抗生素抗性基因和抗生素抗性细菌丰富，城市污水处理厂 (WWTP) 是抗生素耐药性传播的“热点”。该研究预测了不同环境因素（例如温度、重金属和其他污染物）在抗菌素耐药性发展中的作用。该融合研究将进行现场、实验室和计算研究，以确定细菌何时以及如何传播。从这些易感研究中获得的知识将有助于开发预测模型和具有成本效益的策略，以防止环境中的抗菌素耐药性扩散。和环境污染活动将包括通过与农民、K-12 学生和利益相关者基于信任的伙伴关系，在科学界之外传播共同产生的知识。相对于其易受影响的祖先，在动态自然环境系统（例如污水处理厂）下的生长竞争优势很难确定。在该项目中，将使用宏基因组学、非目标化学分析和机器学习方法进行综合研究来描述其出现的特征。将开发污水处理厂内的 AMR 基因型和表型，以确定化学污染物的变化如何影响抗性基因的从头发展和水平转移。为了控制来自污水处理厂的 AMR 驱动因素的输入，需要建立知识。减少 AMR 流行的适当终点是开发预测模型，描述 AMR 在污水处理厂活性污泥系统中如何出现和传播，将用于确定两个的 MSC。在不同环境条件下测试污水处理厂活性污泥中的抗生素、阿奇霉素和环丙沙星。核心假设是温度、重金属和其他污染物会影响亚抑制抗生素浓度下 AMR 的选择。和公用事业工作者，以确保在这项研究中获得的知识能够有效地转化为实践。该奖项反映了 NSF 的法定使命，并通过使用基金会的智力优点和能力进行评估，被认为值得支持。更广泛的影响审查标准。