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

抗生素耐药性（AMR）是指微生物（包括细菌）不再有效地用抗生素（如抗生素）治疗。环境不断受到来自各种直接和间接来源的抗菌剂的污染，这些抗菌剂在这些来源中被严重稀释。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AMR。很少有研究调查抗生素对环境的污染，特别是人类和动物粪便中存在的抗生素的复杂混合物，如何选择AMR。迫切需要这些数据来设计有效的环境缓解策略，以减少受污染的自然环境中出现AMR的可能性。此外，围绕AMR在低环境浓度下的演变的几个基本问题仍然没有答案。这些知识差距阻碍了对将环境污染降低到低于给定的选择性抗生素浓度是否是限制AMR演变的有效策略的理解。该项目将产生最大的、公开可用的数据库，其中包含增加AMR的最低抗生素浓度，包括单个化合物和抗生素混合物，填补了一个重大的研究空白。以前对抗生素混合物的研究集中在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。该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环境相关抗生素混合物和浓度的环境细菌群落中的AMR演变。还将讨论AMR演变的未探索方面。例如，什么是可能影响细菌群落长期携带AMR及其在未来再次暴露于抗生素时进化AMR的能力的关键因素。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影响非常重要。该项目将产生各种经验数据，为探索支撑这些动态的重要进化机制的模型提供信息。完善的实验进化微观世界，强大的化学分析，创新的建模和可靠的分子微生物学技术（如下一代测序）的组合将用于增加对AMR进化的理解。这些数据将有助于制定适当和可靠的抗生素环境质量标准，并将通过现有和新的主要利益相关者合作广泛共享。最终，这些发现将通过限制环境中AMR的发展来改善环境保护，人类健康，全球经济和粮食安全。

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.