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

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.