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: 2319520
• 负责人: Diana Aga
• 金额: $ 140万
• 依托单位: SUNY at Buffalo
• 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=2319520&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.

对抗菌药物抗药性（抗菌素耐药性或AMR）的耐药性细菌的患病率增加是威胁人类，环境和农业健康的关键社会挑战。当用来治疗细菌感染的抗生素不再有效，感染持续时间更长，死亡风险增加。市政废水处理厂（WWTPS）是AMR扩散的“热点”，这是因为抗生素保留率丰富，抗生素耐药性基因和抗生素耐药细菌。因此，WWTP是缓解环境中AMR传播的独特系统。该项目研究了不同环境因素的作用，例如温度，重金属和其他污染物在AMR的发展中。收敛研究将进行现场，实验室和计算研究，以确定易感细菌菌株何时以及如何被自然环境中更多抗生素耐药性种群取代。这些研究的知识将开发主动模型和具有成本效益的策略，以防止环境中的AMR扩散。该项目还强调了教育，贫困和环境污染在AMR传播中的作用。活动将包括通过与农民，K-12学生和利益相关者的信任伙伴关系来传播科学界之外的共同生产的知识。抗生素的最低选择性浓度（MSC），在该抗生素中，抗性菌株相对于其易感祖细胞获得了竞争优势，在易感祖细胞中获得竞争优势，以确定在动态自然环境系统（例如WWTPS）下。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，以表征WWTPS中AMR基因型和表型的出现。将开发出工程性的大肠杆菌抗性菌株，以确定化学污染物的变化如何影响从头耐药性的发展和耐药基因的水平转移。为了控制WWTP的AMR驱动程序的输入，需要知识来建立适当的终点以减轻AMR的流行。总体目标是开发预测模型，以描述AMR在WWTP激活的污泥系统中的出现和传播。机器学习方法的中心假设是，温度，重金属和其他污染物会影响亚抑制性抗生素下AMR的选择。我们的研究团队将与WWTP工程师和公用事业人员紧密合作，以确保可以有效地将本研究中获得的知识转化为实践。该奖项反映了NSF的法定任务，并通过使用基金会的智力优点和更广泛影响的评估标准通过评估来诚实地支持支持。