| Autor: |
Amos GC; School of Life Sciences, University of Warwick, Coventry, UK., Gozzard E; NERC Centre for Ecology & Hydrology, Wallingford, UK., Carter CE; School of Life Sciences, University of Warwick, Coventry, UK., Mead A; 1] School of Life Sciences, University of Warwick, Coventry, UK [2] Applied Statistics Group, Department of Computational and Systems Biology, Rothamsted Research, Hertfordshire, UK., Bowes MJ; NERC Centre for Ecology & Hydrology, Wallingford, UK., Hawkey PM; 1] Health Protection Agency, West Midlands Public Health Laboratory, Heart of England NHS Foundation Trust, Birmingham, UK [2] Institute of Microbiology and Infection, Biosciences, University of Birmingham, Birmingham, UK., Zhang L; School of Life Sciences, University of Warwick, Coventry, UK., Singer AC; NERC Centre for Ecology & Hydrology, Wallingford, UK., Gaze WH; European Centre for Environment and Human Health, University of Exeter Medical School, Knowledge Spa, Royal Cornwall Hospital, Truro, UK., Wellington EM; School of Life Sciences, University of Warwick, Coventry, UK. |
| Abstrakt: |
Multi-drug-resistant bacteria pose a significant threat to public health. The role of the environment in the overall rise in antibiotic-resistant infections and risk to humans is largely unknown. This study aimed to evaluate drivers of antibiotic-resistance levels across the River Thames catchment, model key biotic, spatial and chemical variables and produce predictive models for future risk assessment. Sediment samples from 13 sites across the River Thames basin were taken at four time points across 2011 and 2012. Samples were analysed for class 1 integron prevalence and enumeration of third-generation cephalosporin-resistant bacteria. Class 1 integron prevalence was validated as a molecular marker of antibiotic resistance; levels of resistance showed significant geospatial and temporal variation. The main explanatory variables of resistance levels at each sample site were the number, proximity, size and type of surrounding wastewater-treatment plants. Model 1 revealed treatment plants accounted for 49.5% of the variance in resistance levels. Other contributing factors were extent of different surrounding land cover types (for example, Neutral Grassland), temporal patterns and prior rainfall; when modelling all variables the resulting model (Model 2) could explain 82.9% of variations in resistance levels in the whole catchment. Chemical analyses correlated with key indicators of treatment plant effluent and a model (Model 3) was generated based on water quality parameters (contaminant and macro- and micro-nutrient levels). Model 2 was beta tested on independent sites and explained over 78% of the variation in integron prevalence showing a significant predictive ability. We believe all models in this study are highly useful tools for informing and prioritising mitigation strategies to reduce the environmental resistome. |
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