We are delighted that our second paper – and first modelling paper – on antimicrobial resistance in slurry has been pubished, also in FEMS Microbial Ecology.
Baker M, Hobman JL, Dodd CER, Ramsden SJ and Stekel DJ (2016). Mathematical modelling of antimicrobial resistance in agricultural waste highlights importance of gene transfer rate. FEMS Microbial Ecology DOI:10.1093/femsec/fiw040.
The work came from the very short post that Michelle spent with us – funded by pump prime money from the school. Both the experimental paper (led by Jon Hobman) and the modelling paper have been accepted for the Virtual Issue of FEMS Microbial Ecology: Environmental Dimension of Antibiotic Resistance associated with the EDAR 2015 conference we attended last year. These papers can show the value and importance of timely institutional pump prime support.
Antimicrobial resistance is of global concern. Most antimicrobial use is in agriculture; manures and slurry are especially important because they contain a mix of bacteria, including potential pathogens, antimicrobial resistance genes and antimicrobials. In many countries, manures and slurry are stored, especially over winter, before spreading onto fields as organic fertilizer. Thus these are a potential location for gene exchange and selection for resistance. We develop and analyze a mathematical model to quantify the spread of antimicrobial resistance in stored agricultural waste. We use parameters from a slurry tank on a UK dairy farm as an exemplar. We show that the spread of resistance depends in a subtle way on the rates of gene transfer and antibiotic inflow. If the gene transfer rate is high, then its reduction controls resistance, while cutting antibiotic inflow has little impact. If the gene transfer rate is low, then reducing antibiotic inflow controls resistance. Reducing length of storage can also control spread of resistance. Bacterial growth rate, fitness costs of carrying antimicrobial resistance and proportion of resistant bacteria in animal faeces have little impact on spread of resistance. Therefore effective treatment strategies depend critically on knowledge of gene transfer rates.