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Antibiotic-resistance emergence and selection have become major public health issues globally. The presence of antibiotic resistant bacteria (ARB) in natural and anthroposophical environments threatens to compromise the sustainability of care in human and animal populations. This study was undertaken to develop a simple model formalizing the selective impact of antibiotics and pollutants on the dynamics of bacterial resistance in water and use the model to analyze longitudinal spatiotemporal data collected in hospital and urban wastewaters. Longitudinal-sampling data were collected between 2012 and 2015 in four different locations in Haute-Savoie, France: hospital and urban wastewaters, before and after water-treatment plants. Concentration in three different types of compounds: 1) heavy metals 2) antibiotics and 3) surfactants; and abundance of 88 individual genes and mobile genetic elements, mostly conferring resistance to antibiotics, were simultaneously collected. A simple hypothesis-driven model describing the weekly ARB dynamics was proposed to fit available data by assuming normalized gene abundance to be proportional to ARB populations in water. Compounds impacts on the dynamics of 17 genes found in multiple sites were estimated. We found that while mercury and vancomycin had relevant effects on ARB dynamics, respectively positively affecting the dynamics of 10 and 12 identified genes, surfactants antagonistically affected genes dynamics (identified for three genes). This simple model enables analyzing the relationship between resistance-gene persistence in aquatic environments and specific compounds inherent to human activities. Applying our model to longitudinal data, we identified compounds that act as co-selectors for antibiotic resistance.HighlightsWe analyzed longitudinal wastewater resistance genes and environmental dataWe developed a simple hypothesis-driven model to assess resistance selectionMercury and vancomycin were key drivers of antibiotic resistance in wastewater |
