Wastewater-based surveillance (WBS) is proving to be a valuable source of information regarding pathogens circulating in the community, but complex microbial ecological processes that underlie antibiotic resistance (AR) complicate the prospect of extending WBS for AR monitoring. The epidemiological significance of observed relative abundances of antibiotic resistance genes (ARGs) in sewage is unclear, in part due to multiple sources and in-sewer processes that shape the ARG signal at the entry to the wastewater treatment plant (WWTP). Differentiating between human-derived signals of resistance and those associated with downstream physical and ecological processes could help amplify public health value of WBS of AR by removing noise. In particular, autochthonous sewage microbiota, i.e., microbes stably associated with sewage collection networks independent of human/fecal input, could influence profiles of antibiotic resistance via seasonality, temperature, or other factors that alter human community-level AR signals at a given time point. Here we address this fundamental challenge by differentiating distinct associations between sewage-borne antibiotic resistant bacteria and outpatient antibiotic use in the community served by the sewershed. This was made possible using a unique dataset of outpatient antibiotic prescription rates encompassing the majority of antibiotic use over a 5-year period. Leveraging a yearlong 2x weekly sampling of a conventional WWTP with deep metagenomic sequencing (average 29 Gbp/sample) and extensive bioinformatics analysis, we identify striking associations between sewage-borne ARGs and antibiotic usage depending on the putative bacterial host and the presumed environmental stability of the antibiotic. It was found that a subset of ARGs, predominantly associated with Enterobacteriaceae, displayed a direct correlation with antibiotic usage, while ARGs predominantly associated with Pseudomonadaceae displayed a lagged relationship with antibiotic usage (between 1-3 months). Nested statistical modeling was applied to model the relationship between Pseudomonas metagenome assembled genomes and lagged sulfamethoxazole/trimethoprim use while jointly considering sewage characteristics and seasonality. This effort demonstrates the utility of WBS for understanding epidemiological dimensions of AR and provides a framework for accomplishing this purpose by considering microbial ecological factors that contribute to the corresponding signals in sewage.

The authors have declared no competing interest.

This study was supported by NSF CSSI Award 2004751, NSF NRT Award 2125798, and NSF PIRE award 1545756.

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The study was deemed exempt by the Carilion Clinic Institutional Review Board (IRB-22-1775) as it is not categorized as human subjects research as it included only de-identifiable prescription data.

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All sequencing data are available via BioProject PRJNA1083020.