Validated predictive modelling of the environmental resistome

Abstract

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.

Antibiotics and Antibiotic Resistance in Agroecosystems: State of the Science

“…Recent mathematical models have been used to evaluate the combined influence of numerous anthropogenic sources on observed levels of ARGs found in river systems (Pruden et al, 2012;Amos et al, 2015). Amos et al (2015) constructed a multilevel model to predict the prevalence of the Class 1 integron gene, int1, in the Thames River basin as a function of wastewater treatment plant outputs, land cover type, and climatic variables. The model had fairly good predictive capability at another site in the United Kingdom.…”

Antibiotic resistance in urban aquatic environments: can it be controlled?

“…This implies that for each ARG, it would be important to explore preferential bacterial hosts and propagation pathways, including in what concerns the interaction between the environmental and human microbiome resistome. In parallel, the proposal of models capable of predicting antibiotic resistance fate, fitness, and major constraints under different environmental conditions is also important to prevent and control antibiotic dissemination (Nguyen et al 2014;Amos et al 2015). In both cases, such developments are strongly associated with the existence of reliable and systematically acquired data of ARB&ARG in different environmental compartments and its interface with humans.…”

Abundance of Class 1 Integron-Integrase and Sulfonamide Resistance Genes in River Water and Sediment Is Affected by Anthropogenic Pressure and Environmental Factors

“…However, culture-based methods do not take into account nonculturable microorganisms which constitute the vast majority of environmental microorganisms [13]. The prevalence of integrons is proposed to serve as a marker of antibiotic resistance level [14] and anthropogenic pollution in the environment [15]. Investigations into the levels of intI1 and sul genes along a gradient of anthropogenic impact not only can give insights into the spread and proliferation of ARGs in surface water but also can identify critical point sources of ARGs in river water [15].…”