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.

Age-related macular degeneration (AMD) is the world’s most important age-related blinding disorder. The current proposal utilises epidemiological data describing clinical phenotype, molecular genetics, lifestyle, nutrition, and in-depth retinal imaging derived from existing longitudinal European epidemiological cohorts and biobanks to provide three major insights needed for long-lasting prevention and therapy for AMD: (a) the development of robust algorithms utilising genetic and non-genetic risk factors to identify personalised risks of developing advanced wet and dry AMD; (b) the identification of novel biomarkers for further stratification of disease risks. New insights from (a)+(b) will be used to elaborate preventive medical recommendations for highrisk subgroups of AMD patients; and (c) the identification of molecular drivers/biological pathways relevant for onset and progression of advanced AMD that will be used to identify and validate new therapeutic targets. Key deliverables are: 1. Determination of AMD frequency in Europe, and assessment of AMD risk for phenotypical, genetic, environmental, and biochemical risk factors and their interaction. (WP1-3) 2. Development of a web-based prediction model for personalised risk assessment of AMD based on integration of risk profiles derived from retinal imaging, molecular genetics, assessment of lifestyle, and biochemical testing. (WP4) 3. Modelling and functional characterisation of pathophysiological pathways identified from integrated analysis of current knowledge and the above risk profiles. (WP5) 4. Experimental testing and interpretation of pathophysiological consequences of risks at the molecular level. (WP6) 5. An extension and refinement of the prediction model (WP4) based on work in WP5 and WP6 to generate clinical guidelines for the medical management of high-risk subgroups of patients with AMD. (WP7) 6. Promotion and dissemination of newly gained knowledge towards AMD prevention and therapy development