Improving the risk assessment of antimicrobial resistance (AMR) along the food/feed chain and from environmental reservoirs using qMRA and probabilistic modelling.

Efficient risk assessment of antimicrobial resistance (AMR) in environmental reservoirs, particularly agroecosystems, is critical for predicting threats to animal and human health due to infections unresponsive to antibiotic therapy. However, approaches currently employed for the risk assessment of AMR along the human food chain rarely rely on antibiotic resistance gene (ARG) environmental pathways connected to food production and related quantitative data. The present project aimed at improving the risk assessment related to the spread of AMR along the food/feed chain based on ARG quantification in agroecosystems and interconnected environments. The fellow received training and worked in close cooperation with the team on two ongoing research projects which involved: (i) the monitoring of ARGs in field soils and surface waters to identify and characterise food/feed chain-associated environmental reservoirs of AMR relevant at the national level; (ii) the evaluation of ARG dynamics in relation to agricultural practice within an international project assessing biodiversity as an ecological barrier for the spread of clinically relevant ARGs in the environment. ARG quantification was performed using single/multiplex real-time polymerase chain reaction (PCR) with tailor-made primers/probe sets according to in-house optimised and validated conditions. The assessment was completed by a comprehensive revision of available literature data for risk-ranking of ARGs along with a literature review exploring AMR quantitative knowledge gaps and the role of certain AMR determinants encoded on free extracellular DNA (exDNA) in their environmental spread.