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

抗菌素耐药性（AMR）是指包括细菌在内的微生物不再被抗生素等抗菌素有效治疗。环境不断受到各种直接和间接来源的抗菌剂的污染，在那里它们被严重稀释。然而，有令人信服的证据表明，即使这些非常低的抗生素浓度也会增加抗菌素耐药性。很少有研究调查抗生素污染环境，特别是人类和动物粪便中存在的复杂抗生素混合物如何选择抗菌素耐药性。迫切需要这些数据来设计有效的环境缓解战略，以减少受污染的自然环境产生抗菌素耐药性的可能性。此外，围绕低环境浓度下抗菌素耐药性演变的几个基本问题仍未得到解答。这些知识差距妨碍了我们理解将环境污染降低到给定的选择性抗生素浓度以下是否将是限制抗菌素耐药性演变的有效策略。该项目将产生最大的、公开可用的最低抗生素浓度数据库，这些最低抗生素浓度增加了单个化合物和抗生素混合物的抗菌素耐药性，填补了一个重大的研究空白。以前对抗生素混合物的研究侧重于治疗浓度和简单混合物（即临床抗生素组合），因此与环境无关。本项目将采用自下而上和自上而下的方法，在对照实验中探索暴露于与环境相关的抗生素混合物和浓度的环境细菌群落的抗菌素耐药性演变。还将讨论抗菌素耐药性发展中尚未探索的方面。例如，如果将来再次暴露于抗生素，可能影响细菌群落长期携带AMR及其进化AMR能力的关键因素是什么？了解这些动态对于预测旨在减少或消除不同环境中抗生素污染的缓解策略的效果非常重要。这个项目将产生各种各样的经验数据，为一个模型提供信息，该模型将探索支撑这些动态的重要进化机制。结合成熟的实验进化微观世界、稳健的化学分析、创新的模型和可靠的分子微生物学技术，如下一代测序，将用于增加对抗菌素耐药性进化的理解。这些数据将有助于制定适当和强有力的抗生素环境质量标准，并将通过现有和新的关键利益攸关方合作广泛共享。最终，这些发现将通过限制环境中抗生素耐药性的发展来改善对环境、人类健康、全球经济和粮食安全的保护。

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.