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Validated predictive modelling of the environmental resistome

Amos, Gregory C.A.; Gozzard, Emma; Carter, Charlotte E.; Mead, Andrew; Bowes, Mike J.; Hawkey, Peter M.; Zhang, Lihong; Singer, Andrew C.; Gaze, William H.; Wellington, Elizabeth M.H.. 2015 Validated predictive modelling of the environmental resistome. ISME Journal, 9 (6). 1467-1476. https://doi.org/10.1038/ismej.2014.237

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Abstract/Summary

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.

Item Type: Publication - Article
Digital Object Identifier (DOI): https://doi.org/10.1038/ismej.2014.237
CEH Sections/Science Areas: Rees (from October 2014)
ISSN: 1751-7362
Additional Information. Not used in RCUK Gateway to Research.: Open Access paper - full text available via Official URL link
NORA Subject Terms: Ecology and Environment
Health
Biology and Microbiology
Date made live: 16 Feb 2015 11:36 +0 (UTC)
URI: http://nora.nerc.ac.uk/id/eprint/509737

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