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

对抗菌素药物产生耐药性的细菌（抗菌素耐药性或AMR）日益普遍，是威胁人类、环境和农业健康的重大社会挑战。当用于治疗细菌感染的抗生素不再有效时，感染持续的时间会更长，死亡的风险也会增加。城市污水处理厂（WWTPs）是AMR传播的“热点”，因为抗生素残留、抗生素耐药基因和抗生素耐药细菌的丰富存在。因此，污水处理厂是减轻抗菌素耐药性在环境中传播的独特系统。本项目探讨了不同的环境因素，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。融合研究将进行实地、实验室和计算研究，以确定在自然环境中，易感细菌菌株何时以及如何被更耐抗生素的耐药菌群所取代。来自这些研究的知识将有助于开发预测模型和具有成本效益的策略，以防止抗菌素耐药性在环境中扩散。该项目还强调了教育、贫困和环境污染在抗生素耐药性传播中的作用。活动将包括通过与农民、K-12学生和利益相关者建立基于信任的伙伴关系，在科学界之外传播共同生产的知识。抗生素的最小选择浓度（MSC），在这个浓度下，耐药菌株在生长方面相对于敏感的前体细胞获得竞争优势，在动态的自然环境系统（如污水处理厂）下是具有挑战性的。在本项目中，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，以表征污水处理厂内AMR基因型和表型的出现。将开发大肠杆菌的工程抗性菌株，以确定化学污染物的变化如何影响新抗性的发展和抗性基因的水平转移。为了控制来自污水处理厂的AMR驱动因素的输入，需要知识来建立适当的端点以减轻AMR的流行。总体目标是开发预测模型，描述AMR如何在污水处理厂活性污泥系统中出现和传播。机器学习方法将用于在不同环境条件下确定污水处理厂活性污泥中两种测试抗生素阿奇霉素和环丙沙星的MSC。中心假设是温度、重金属和其他污染物影响亚抑制抗生素浓度下抗菌素耐药性的选择。我们的研究团队将与污水处理厂的工程师和公用事业人员密切合作，确保在这项研究中获得的知识能够有效地转化为实践。该奖项反映了美国国家科学基金会的法定使命，并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，被认为值得支持。

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.