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Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework

INTRODUCTION: Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined. METHODS: To identify the sources of environmental AMR, the novel m...

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Autores principales: Wiesner-Friedman, Corinne, Beattie, Rachelle E., Stewart, Jill R., Hristova, Krassimira R., Serre, Marc L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10508347/
https://www.ncbi.nlm.nih.gov/pubmed/37731922
http://dx.doi.org/10.3389/fmicb.2023.1223876
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author Wiesner-Friedman, Corinne
Beattie, Rachelle E.
Stewart, Jill R.
Hristova, Krassimira R.
Serre, Marc L.
author_facet Wiesner-Friedman, Corinne
Beattie, Rachelle E.
Stewart, Jill R.
Hristova, Krassimira R.
Serre, Marc L.
author_sort Wiesner-Friedman, Corinne
collection PubMed
description INTRODUCTION: Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined. METHODS: To identify the sources of environmental AMR, the novel microbial Find, Inform, and Test (FIT) model was applied to a panel of five antibiotic resistance-associated genes (ARGs), namely, erm(B), tet(W), qnrA, sul1, and intI1, quantified from riverbed sediment and surface water from a mixed-use region. RESULTS: A one standard deviation increase in the modeled contributions of elevated AMR from bovine sources or land-applied waste sources [land application of biosolids, sludge, and industrial wastewater (i.e., food processing) and domestic (i.e., municipal and septage)] was associated with 34–80% and 33–77% increases in the relative abundances of the ARGs in riverbed sediment and surface water, respectively. Sources influenced environmental AMR at overland distances of up to 13 km. DISCUSSION: Our study corroborates previous evidence of offsite migration of microbial pollution from bovine sources and newly suggests offsite migration from land-applied waste. With FIT, we estimated the distance-based influence range overland and downstream around sources to model the impact these sources may have on AMR at unsampled sites. This modeling supports targeted monitoring of AMR from sources for future exposure and risk mitigation efforts.
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spelling pubmed-105083472023-09-20 Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework Wiesner-Friedman, Corinne Beattie, Rachelle E. Stewart, Jill R. Hristova, Krassimira R. Serre, Marc L. Front Microbiol Microbiology INTRODUCTION: Antimicrobial resistance (AMR) is an increasing public health concern for humans, animals, and the environment. However, the contributions of spatially distributed sources of AMR in the environment are not well defined. METHODS: To identify the sources of environmental AMR, the novel microbial Find, Inform, and Test (FIT) model was applied to a panel of five antibiotic resistance-associated genes (ARGs), namely, erm(B), tet(W), qnrA, sul1, and intI1, quantified from riverbed sediment and surface water from a mixed-use region. RESULTS: A one standard deviation increase in the modeled contributions of elevated AMR from bovine sources or land-applied waste sources [land application of biosolids, sludge, and industrial wastewater (i.e., food processing) and domestic (i.e., municipal and septage)] was associated with 34–80% and 33–77% increases in the relative abundances of the ARGs in riverbed sediment and surface water, respectively. Sources influenced environmental AMR at overland distances of up to 13 km. DISCUSSION: Our study corroborates previous evidence of offsite migration of microbial pollution from bovine sources and newly suggests offsite migration from land-applied waste. With FIT, we estimated the distance-based influence range overland and downstream around sources to model the impact these sources may have on AMR at unsampled sites. This modeling supports targeted monitoring of AMR from sources for future exposure and risk mitigation efforts. Frontiers Media S.A. 2023-09-05 /pmc/articles/PMC10508347/ /pubmed/37731922 http://dx.doi.org/10.3389/fmicb.2023.1223876 Text en Copyright © 2023 Wiesner-Friedman, Beattie, Stewart, Hristova and Serre. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Microbiology
Wiesner-Friedman, Corinne
Beattie, Rachelle E.
Stewart, Jill R.
Hristova, Krassimira R.
Serre, Marc L.
Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework
title Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework
title_full Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework
title_fullStr Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework
title_full_unstemmed Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework
title_short Identifying sources of antibiotic resistance genes in the environment using the microbial Find, Inform, and Test framework
title_sort identifying sources of antibiotic resistance genes in the environment using the microbial find, inform, and test framework
topic Microbiology
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10508347/
https://www.ncbi.nlm.nih.gov/pubmed/37731922
http://dx.doi.org/10.3389/fmicb.2023.1223876
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