DNA Traffic in the Environment and Antimicrobial Resistance

Hooton, Steven P.T.; Millard, Andrew; Baker, Michelle; Stekel, Dov J.; Hobman, Jon L.

doi:10.1007/978-981-13-3411-5_10

Cited by 4 publications

(4 citation statements)

References 126 publications

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“…The deterministic version shows that loss of resistance genes or resistant bacteria is only possible in low toxicity (lower death rate ratio) environment, or, in the rare case of cost prohibitive plasmids (high fitness cost). Most AMR phenomena are observed in large scale environments such as guts, tanks, fields, farms, hospitals [54], and the deterministic model can provide a reasonable approximation for prediction of the behaviour of large scale populations if they are, or can be considered to be, well-mixed. However, most environments are comprised of smaller, diverse, microscopic bacterial communities, and so deterministic models may be less realistic; thus stochasticity can play an important role [32].…”

Section: Discussionmentioning

confidence: 99%

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

Arya

Williams

Reina

et al. 2021

Environmental Pollution

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Many antibiotic resistance genes co-occur with resistance genes for transition metals, such as copper, zinc, or mercury. In some environments, a positive correlation between high metal concentration and high abundance of antibiotic resistance genes has been observed, suggesting co-selection due to metal presence. Of particular concern is the use of copper and zinc in animal husbandry, leading to potential co-selection for antibiotic resistance in animal gut microbiomes, slurry, manure, or amended soils. For antibiotics, predicted no effect concentrations have been derived from laboratory measured minimum inhibitory concentrations and some minimal selective concentrations have been investigated in environmental settings. However, minimal co-selection concentrations for metals are difficult to identify. Here, we use mathematical modelling to provide a general mechanistic framework to predict minimal co-selective concentrations for metals, given knowledge of their toxicity at different concentrations.We apply the method to copper (Cu), Zinc (Zn), mercury (Hg), lead (Pb) and silver (Ag), predicting their minimum co-selective concentrations in mg/L (Cu: 5.5, Zn: 1.6, Hg: 0.0156, Pb: 21.5, Ag: 0.152). To exemplify use of these thresholds, we consider metal concentrations from slurry and slurry-amended soil from a UK dairy farm that uses copper and zinc as additives for feed and antimicrobial footbath: the slurry is predicted to be co-selective, but not the slurry-amended soil. This modelling framework could be used as the basis for defining standards to mitigate risks of antimicrobial resistance applicable to a wide range of environments, including manure, slurry and other waste streams..

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Section: Discussionmentioning

confidence: 99%

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

Arya

Williams

Reina

et al. 2021

Environmental Pollution

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show abstract

“…The deterministic version shows that loss of resistance genes or resistant bacteria is only possible in low toxicity (lower death rate ratio) environment, or, in the rare case of cost prohibitive plasmids (high fitness cost). Most AMR September 10, 2020 14/31 phenomena are observed in large scale environments such as guts, tanks, fields, farms, hospitals [54], and the deterministic model can provide a reasonable approximation for prediction of the behaviour of large scale populations if they are, or can be considered to be, well-mixed. However, most environments are comprised of smaller, diverse, microscopic bacterial communities, and so deterministic models may be less realistic;…”

Section: Discussionmentioning

confidence: 99%

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

Arya¹,

Williams²,

Reina

et al. 2020

Preprint

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Many antibiotic resistance genes co-occur with resistance genes for transition metals, such as copper, zinc, or mercury. In some environments, a positive correlation between high metal concentration and high abundance of antibiotic resistance genes has been observed, suggesting co-selection due to metal presence. Of particular concern is the use of copper and zinc in animal husbandry, leading to potential co-selection for antibiotic resistance in animal gut microbiomes, slurry, manure, or amended soils. For antibiotics, predicted no effect concentrations have been derived from laboratory measured minimum inhibitory concentrations and some minimal selective concentrations have been investigated in environmental settings. However, minimal co-selection concentrations for metals are difficult to identify. Here, we use mathematical modelling to provide a general mechanistic framework to predict minimal co-selective concentrations for metals, given knowledge of their toxicity at different concentrations. We apply the method to copper (Cu), Zinc (Zn), mercury (Hg), lead (Pb) and silver (Ag), predicting their minimum co-selective concentrations in mg/L (Cu: 5.5, Zn: 1.6, Hg: 0.0156, Pb: 21.5, Ag: 0.152). To exemplify use of these thresholds, we consider metal concentrations from slurry and slurry-amended soil from a UK dairy farm that uses copper and zinc as additives for feed and antimicrobial footbath: the slurry is predicted to be co-selective, but not the slurry-amended soil. This modelling framework could be used as the basis for defining standards to mitigate risks of antimicrobial resistance applicable to a wide range of environments, including manure, slurry and other waste streams.

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“…The higher concentration of pharmaceuticals and personal care products, chemicals, drugs, and heavy metals in sewage and wastewater has increased opportunities for genetic exchange between environmental bacteria and human pathogens leading to the evolution of multidrug-resistant pathogens [ [40] , [41] , [42] ]. Horizontal gene transfer (HGT) acts as a principal mechanism for the dissemination of antibiotic resistance in the natural and clinical environment [ 43 , 44 ]. It accounts for over 75 percent resistance genes exchange between environmental microbes and clinical pathogens ( Fig.…”

Section: Critical Knowledge Gaps Associated With Environmental Antimimentioning

confidence: 99%

Antibiotic resistance: Global health crisis and metagenomics

Yadav

Kapley

2021

Biotechnology Reports

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Highlights Antimicrobial resistance is global risk to both humans & environment health. Aquatic system serves as hotspot for dissemination of AMR. Metagenomics can be used for monitoring abundance of ARGs in environment. Application of metagenomics has potential to yield novel secondary metabolites having antimicrobial potential.

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DNA Traffic in the Environment and Antimicrobial Resistance

Cited by 4 publications

References 126 publications

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

Towards a general model for predicting minimal metal concentrations co-selecting for antibiotic resistance plasmids

Antibiotic resistance: Global health crisis and metagenomics

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