Streambed immobilization controls the transport of antibiotic resistance genes in flowing water

Abstract

Antibiotic resistance is a serious global health issue, resulting in at least 1.2 million deaths in 2019. The environment is a potentially important reservoir of antibiotic resistance; however, the fate of Antibiotic Resistance Genes (ARGs) in the environment remains poorly characterized. One important environmental source of ARGs is manure used as a soil amendment. ARGs from manure may then enter nearby flowing waterbodies, where the factors governing their downstream transport remain unknown. To address this, we conducted experiments by spiking cattle manure in an artificial stream to estimate removal rates (k; m−1) for three ARGs (mefA, tetQ, and tetW) and a ruminant fecal marker (bacR). We then used a Stochastic Mobile-Immobile Model (SMIM) to separate the overall removal into two components, rs, and rh, corresponding to immobilizations in the surface (i.e., water column) and subsurface (i.e., streambed), respectively. Finally, we applied the SMIM across four model streams to predict the downstream travel distance of ARGs and bacR. Our results showed measurable removal for all targets in all experimental replicates (n = 3) and no differences were found in the removal rates among replicates for any target (ANCOVA; p > 0.05). We found that the removal of bacR was significantly lower than tetW (p < 0.05) and slightly lower than mefA (p = 0.088), while tetQ removal was slightly different from tetW’s (p = 0.072). We also found that rh values were orders of magnitude larger than rs for ARGs and bacR (t-test; p < 0.05). These findings suggest that ARGs and bacR are being removed from the water column through immobilization reactions occurring in the streambed. Additionally, we predicted that the 90 % removal (or D90) of targets occurs within the first 500 m in all model streams except in a slow-flow pastoral stream, which required 1400 m of downstream transport for 90 % removal. Our findings and model stand out as promising tools to predict the fate of ARGs in streams and will contribute to improving and managing agricultural practices that employ animal manure.