Variation in conjugation frequencies of wastewater-derived multidrug-resistant E. coli influences predicted dynamics...

Within a One Health framework, sewage-derived extended-spectrum β-lactamase-producing Escherichia coli (ESBL-EC) are an increasing epidemiological threat due to their potential to transmit conjugative multidrug-resistance (MDR) plasmids to environmental and gut-associated bacteria. However, quantitative horizontal gene transfer (HGT) data of wastewater-derived ESBL-EC plasmids under environmentally relevant conditions is restricted, limiting the capacity of Quantitative Microbial Risk Assessment (QMRA) frameworks to evaluate plasmid transfer risks. We evaluated the capacity of seven genomically-characterized sewage-derived ESBL-EC strains to transfer MDR-plasmids to non-resistant recipients under environmentally-relevant conditions, and assessed the stability of the resulting transconjugants. These data were used to inform a population dynamics model simulating the behavior of bacteria in the gut following recreational water exposure. Conjugation efficiencies of the different strains varied by several orders of magnitude, and the effect of oxygen, turbulence and biofilm on these frequencies was strain- and plasmid-dependent. We focused on strain 19, characterized by high conjugation efficiency and the unique capacity of transferring its plasmid to a Salmonella recipient, and strain 68, which exhibited significantly lower conjugation efficiency but a higher growth rate. Experimentally-parameterized single species simulations using these two strains in a nutrient-rich environment demonstrated that resistant strain dominance would not be expected under tested conditions, resulting only if conjugation rates exceeded ∼10^-11 - 10^-12 mL CFU^-1 h^-1. The model further showed the higher influence of conjugation-mediated gut dynamics compared with differences in the environmental exposure dose from highly effluent-impacted water. These findings emphasize the need to quantify AMR ecological dynamics within risk assessment frameworks.