RESUMEN
Heavy metals occur naturally in the environment, and their concentration varies in soil across different regions. However, the presence of heavy metals may influence the antimicrobial resistance (AMR) in bacterial populations. Therefore, the objective of this study was to investigate and characterise the antimicrobial resistance profiles of Enterobacterales in soil and bovine milk filters from high and low zinc-containing regions in Ireland. In total, 50 soil samples and 29 milk filters were collected from two geographic locations with varying soil zinc concentrations. Samples were cultured for the enumeration and detection of Enterobacterales. Specifically, extended-spectrum beta-lactamase-producing Enterobacterales, carbapenem-resistant Enterobacterales and ciprofloxacin-resistant Enterobacterales were isolated using selective media. Species identification was performed using MALDI-TOF. The phenotypic resistance profiles of selected Enterobacterales were determined by disk diffusion testing, following EUCAST and CLSI criteria; while, the genotypic resistance profiles of the same isolates were determined by whole genome sequencing (WGS). Heavy metal concentrations were also measured for all soil samples. A total of 40 antimicrobial resistant Enterobacterales were identified in soil (n = 31) and milk filters (n = 9). The predominant species detected in the high zinc-containing region was Escherichia coli in both sample types (soil n = 10, milk filters n = 2), while in the low zinc-containing region Serratia fonticola was predominant in soil samples (n = 8) and E. coli in milk filters (n = 4). Ten E. coli isolates identified from soil samples in the high zinc-containing region were multidrug resistant, showing resistance to all the antimicrobials tested, except for carbapenems. The WGS findings confirmed the phenotypic resistance results. Moreover, zinc resistance-associated genes and genes encoding for efflux pumps were identified. The current study revealed distinct phenotypic resistance profiles of Enterobacterales in low and high zinc-containing regions, and highlighted the benefit of utilising milk filters for AMR surveillance in dairy production.
RESUMEN
Bacterial antimicrobial resistance (AMR) is considered to be very alarming following an upward trend and thus posing a primary threat to public health. AMR has tremendous adverse effects on humans, farm animals, healthcare, the environment, agriculture and, thus, on national economies. Several tools have been proposed and adopted by numerous countries after comprehending the need for antimicrobial stewardship and for a rational use of antibiotics. These tools include diagnostics for infections or AMR detection, for measuring and monitoring antibiotic consumption (e.g. surveillance tools) and for guiding medical doctors and veterinarians in selecting suitable antibiotics. In addition, it has been known that the food chain represents a leading vector for the transmission of pathogens to humans via various routes (direct or indirect). Considerable efforts have been made and are still in progress both at international and national levels in order to control and mitigate the spread of pathogens and thus ensure food safety. During the last decades, a new concern has risen regarding the food chain playing a potential major role in the transmission of resistant bacteria as well as resistance genes from the animal kingdom to humans. Several recent studies highlight the role of food processing environments as potential AMR hotspots contributing to this spread phenomenon. Next-generation sequencing (NGS) technologies are becoming broadly used in the AMR field, since they allow the surveillance of resistant microorganisms, AMR determinants and mobile genetic elements. Moreover, NGS is capable of providing information on the mechanisms driving and spreading AMR throughout the food chain. In the current work programme, the aim was to acquire knowledge and skills to track AMR genes and mobile genetic elements in the food chain through NGS methodologies in order to implement a quantitative risk assessment and identify hotspots and routes of transmission of AMR along the food chain.