Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater

ABSTRACT Although wastewater and sewage systems are known to be significant reservoirs of antibiotic-resistant bacterial populations and periodic outbreaks of drug-resistant infection, there is little quantitative understanding of the drivers behind resistant population growth in these settings. In...

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Autores principales:	Indorica Sutradhar, Carly Ching, Darash Desai, Mark Suprenant, Emma Briars, Zachary Heins, Ahmad S. Khalil, Muhammad H. Zaman
Formato:	article
Lenguaje:	EN
Publicado:	American Society for Microbiology 2021
Materias:	antibiotic resistance mathematical modeling wastewater Microbiology QR1-502
Acceso en línea:	https://doaj.org/article/334c6ff811e5432986ffefef41b24d87
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spelling	oai:doaj.org-article:334c6ff811e5432986ffefef41b24d872021-12-02T18:11:52ZComputational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater10.1128/mSystems.00360-212379-5077https://doaj.org/article/334c6ff811e5432986ffefef41b24d872021-06-01T00:00:00Zhttps://journals.asm.org/doi/10.1128/mSystems.00360-21https://doaj.org/toc/2379-5077ABSTRACT Although wastewater and sewage systems are known to be significant reservoirs of antibiotic-resistant bacterial populations and periodic outbreaks of drug-resistant infection, there is little quantitative understanding of the drivers behind resistant population growth in these settings. In order to fill this gap in quantitative understanding of the development of antibiotic-resistant infections in wastewater, we have developed a mathematical model synthesizing many known drivers of antibiotic resistance in these settings to help predict the growth of resistant populations in different environmental scenarios. A number of these drivers of drug-resistant infection outbreak, including antibiotic residue concentration, antibiotic interaction, chromosomal mutation, and horizontal gene transfer, have not previously been integrated into a single computational model. We validated the outputs of the model with quantitative studies conducted on the eVOLVER continuous culture platform. Our integrated model shows that low levels of antibiotic residues present in wastewater can lead to increased development of resistant populations and that the dominant mechanism of resistance acquisition in these populations is horizontal gene transfer rather than acquisition of chromosomal mutations. Additionally, we found that synergistic antibiotics at low concentrations lead to increased resistant population growth. These findings, consistent with recent experimental and field studies, provide new quantitative knowledge on the evolution of antibiotic-resistant bacterial reservoirs, and the model developed herein can be adapted for use as a prediction tool in public health policy making, particularly in low-income settings where water sanitation issues remain widespread and disease outbreaks continue to undermine public health efforts. IMPORTANCE The rate at which antimicrobial resistance (AMR) has developed and spread throughout the world has increased in recent years, and according to the Review on Antimicrobial Resistance in 2014, it is suggested that the current rate will lead to AMR-related deaths of several million people by 2050 (Review on Antimicrobial Resistance, Tackling a Crisis for the Health and Wealth of Nations, 2014). One major reservoir of resistant bacterial populations that has been linked to outbreaks of drug-resistant bacterial infections but is not well understood is in wastewater settings, where antibiotic pollution is often present. Using ordinary differential equations incorporating several known drivers of resistance in wastewater, we find that interactions between antibiotic residues and horizontal gene transfer significantly affect the growth of resistant bacterial reservoirs.Indorica SutradharCarly ChingDarash DesaiMark SuprenantEmma BriarsZachary HeinsAhmad S. KhalilMuhammad H. ZamanAmerican Society for Microbiologyarticleantibiotic resistancemathematical modelingwastewaterMicrobiologyQR1-502ENmSystems, Vol 6, Iss 3 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	antibiotic resistance mathematical modeling wastewater Microbiology QR1-502
spellingShingle	antibiotic resistance mathematical modeling wastewater Microbiology QR1-502 Indorica Sutradhar Carly Ching Darash Desai Mark Suprenant Emma Briars Zachary Heins Ahmad S. Khalil Muhammad H. Zaman Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater
description	ABSTRACT Although wastewater and sewage systems are known to be significant reservoirs of antibiotic-resistant bacterial populations and periodic outbreaks of drug-resistant infection, there is little quantitative understanding of the drivers behind resistant population growth in these settings. In order to fill this gap in quantitative understanding of the development of antibiotic-resistant infections in wastewater, we have developed a mathematical model synthesizing many known drivers of antibiotic resistance in these settings to help predict the growth of resistant populations in different environmental scenarios. A number of these drivers of drug-resistant infection outbreak, including antibiotic residue concentration, antibiotic interaction, chromosomal mutation, and horizontal gene transfer, have not previously been integrated into a single computational model. We validated the outputs of the model with quantitative studies conducted on the eVOLVER continuous culture platform. Our integrated model shows that low levels of antibiotic residues present in wastewater can lead to increased development of resistant populations and that the dominant mechanism of resistance acquisition in these populations is horizontal gene transfer rather than acquisition of chromosomal mutations. Additionally, we found that synergistic antibiotics at low concentrations lead to increased resistant population growth. These findings, consistent with recent experimental and field studies, provide new quantitative knowledge on the evolution of antibiotic-resistant bacterial reservoirs, and the model developed herein can be adapted for use as a prediction tool in public health policy making, particularly in low-income settings where water sanitation issues remain widespread and disease outbreaks continue to undermine public health efforts. IMPORTANCE The rate at which antimicrobial resistance (AMR) has developed and spread throughout the world has increased in recent years, and according to the Review on Antimicrobial Resistance in 2014, it is suggested that the current rate will lead to AMR-related deaths of several million people by 2050 (Review on Antimicrobial Resistance, Tackling a Crisis for the Health and Wealth of Nations, 2014). One major reservoir of resistant bacterial populations that has been linked to outbreaks of drug-resistant bacterial infections but is not well understood is in wastewater settings, where antibiotic pollution is often present. Using ordinary differential equations incorporating several known drivers of resistance in wastewater, we find that interactions between antibiotic residues and horizontal gene transfer significantly affect the growth of resistant bacterial reservoirs.
format	article
author	Indorica Sutradhar Carly Ching Darash Desai Mark Suprenant Emma Briars Zachary Heins Ahmad S. Khalil Muhammad H. Zaman
author_facet	Indorica Sutradhar Carly Ching Darash Desai Mark Suprenant Emma Briars Zachary Heins Ahmad S. Khalil Muhammad H. Zaman
author_sort	Indorica Sutradhar
title	Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater
title_short	Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater
title_full	Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater
title_fullStr	Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater
title_full_unstemmed	Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater
title_sort	computational model to quantify the growth of antibiotic-resistant bacteria in wastewater
publisher	American Society for Microbiology
publishDate	2021
url	https://doaj.org/article/334c6ff811e5432986ffefef41b24d87
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Computational Model To Quantify the Growth of Antibiotic-Resistant Bacteria in Wastewater

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