Exploring understudied aspects of antimicrobial resistance evolution to improve environmental pollution policy

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Antimicrobial resistance (AMR) is when microorganisms, including bacteria, are no longer effectively treated with antimicrobials, such as antibiotics. The environment is continually polluted with antimicrobials from a variety of direct and indirect sources, where they become heavily diluted. However, there is compelling evidence that even these very low antibiotic concentrations can increase AMR. Little research has investigated how contamination of the environment with antibiotics, particularly complex mixtures of antibiotics present in human and animal waste, can select for AMR. These data are urgently needed to design effective environmental mitigation strategies to reduce the probability of AMR emerging from polluted natural environments. Further, several fundamental questions surrounding AMR evolution at low, environmental concentrations remain unanswered. These knowledge gaps preclude understanding of whether reducing environmental contamination to below a given selective antibiotic concentration will be an effective strategy to constrain AMR evolution. This project will generate the largest, publicly available database of the lowest antibiotic concentrations that increase AMR, both for individual compounds and antibiotic mixtures, filling a significant research gap. Previous research on antibiotic mixtures has focused on therapeutic concentrations and simple mixtures (i.e., clinical antibiotic combinations) and so is not environmentally relevant. This project will use bottom-up and top-down approaches to explore AMR evolution in environmental bacterial communities exposed to environmentally relevant antibiotic mixtures and concentrations in controlled experiments. Unexplored aspects of AMR evolution will also be addressed. For example, what are the key factors that might impact a bacterial community's long-term carriage of AMR and its ability to evolve AMR if exposed to antibiotics again in the future. Understanding these dynamics is important for predicting effects of mitigation strategies that aim to reduce or remove antibiotic pollution in different environments. This project will generate a variety of empirical data to inform a model that will explore important evolutionary mechanisms that underpin these dynamics. A combination of well-established experimental evolution microcosms, robust chemical analyses, innovative modelling, and reliable molecular microbiology techniques such as next generation sequencing will be used to increase understanding of AMR evolution. These data will contribute to development of appropriate and robust environmental quality standards for antibiotics and will be shared widely through existing and new key stakeholder collaborations. Ultimately, these findings will improve protection of the environment, human health, the global economy, and food security by limiting the development of AMR in the environment.