Cooking with Gas: Can Anaerobic Digestion Reduce the Prevalence of Antimicrobial Resistance in Manures and Slurries?

Lead Research Organisation: University of Nottingham
Department Name: Sch of Biosciences


Cattle dung is usually stored in slurry tanks or lagoons until spread onto fields as a fertiliser. Within the slurry are millions of bacteria and residual antibiotics excreted by treated cows. This organic 'soup' is the ideal setting for horizontal gene transfer between bacteria, resulting in possible proliferation of antibiotic resistance. When slurry is spread onto soil, antibiotic residues and antibiotic resistant bacteria may enter the agroecosystem and possibly the food chain. Many farmers use their cattle slurry to produce bioenergy (methane) via anaerobic digestion prior to adding it to the soil. We have already shown that methane production and microbial diversity are altered by antibiotic residues present within anaerobic digesters. Alterations in microbial diversity are likely to drive changes in antibiotic resistance genes. Little is known about the effect of anaerobic digestion on antibiotic resistant bacteria, although recent work (elsewhere) suggests that anaerobic digestion of swine waste lowers presence of resistance genes. We hypothesise that anaerobic digestion will reduce antibiotic resistance, but the degree will depend on the absence of drivers of resistance within the digester. The aim of this project is to determine if anaerobic digestion reduces the number of antibiotic resistant bacteria and antibiotic resistance genes within cattle slurry. Laboratory-scale anaerobic digestion trials will be carried out and the slurry tested for antibiotic resistant bacteria and resistance genes before and after digestion. Laboratory digestions will allow for changes in the process (such as retention time, temperature and stressors) to be readily made. We will also sample 'real' on-farm digesters. The data obtained from classical and molecular microbiological techniques will be used to fit a mathematical model to infer rates of change with their uncertainty to inform slurry management options and reduce inputs of antibiotic resistant bacteria and/or genes into areas of food production.


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Studentship Projects

Project Reference Relationship Related To Start End Student Name
BB/M008770/1 01/10/2015 30/09/2023
2274921 Studentship BB/M008770/1 01/10/2019 30/09/2023 Sarah Guest