Resistance indicators in sewage microbiota mirror clinical antibiotic use with temporal lag linked to host
7 affiliations
- 1Center For Microbial Communities, Department of Chemistry and Biosciences, Aalborg University, Aalborg, Denmark
- 2Department of Civil and Environmental Engineering, Virginia Tech, Blacksburg, VA, USA
- 3Department of Computer Science, Virginia Tech, Blacksburg, VA, USA
- 4Carilion Roanoke Memorial Hospital, Department of Pharmacy Services, Roanoke, VA, USA
- 5Department of Genetics, Bioinformatics, and Computational Biology, Virginia Tech, Blacksburg, VA, USA
- 6US Environmental Protection Agency, Cincinnati, Ohio, USA
- 7Carilion Roanoke Memorial Hospital/Virginia Tech Carilion School of Medicine, Division of Infectious Diseases, Roanoke, VA, USA
Abstract
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.
Related
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BibTeX
@article{brown2026resistance,
title = {{Resistance indicators in sewage microbiota mirror clinical antibiotic use with temporal lag linked to host}},
author = {Connor Brown and MA. Rumi and Lauren F. McDaniel and Ayella Maile-Moskowitz and Justin Sein and L. Nguyen and M. Choi and Fadi Hindi and James I Mullet and M. Emon and Nazifa Ahmed Moumi and M. F. Blair and B. C. Davis and J. Rao and Anthony W. Baffoe-Bonnie and Peter J. Vikesland and Amy J. Pruden and L. Zhang},
journal = {Nature Communications},
year = {2026},
month = oct,
doi = {10.1038/s41467-026-78142-3},
url = {https://www.nature.com/articles/s41467-026-78142-3},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Resistance indicators in sewage microbiota mirror clinical antibiotic use with temporal lag linked to host
AU - Connor Brown
AU - MA. Rumi
AU - Lauren F. McDaniel
AU - Ayella Maile-Moskowitz
AU - Justin Sein
AU - L. Nguyen
AU - M. Choi
AU - Fadi Hindi
AU - James I Mullet
AU - M. Emon
AU - Nazifa Ahmed Moumi
AU - M. F. Blair
AU - B. C. Davis
AU - J. Rao
AU - Anthony W. Baffoe-Bonnie
AU - Peter J. Vikesland
AU - Amy J. Pruden
AU - L. Zhang
PY - 2026
DA - 2026/10/09
JO - Nature Communications
DO - 10.1038/s41467-026-78142-3
UR - https://www.nature.com/articles/s41467-026-78142-3
AB - 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.
SN - 2041-1723
ER -