Introduction
The rapid spread of antimicrobial resistance (AMR) in humans, animals, and the environment poses growing challenges to global health. Rapid, cost-effective methods for routine resistome surveillance in dairy herds are increasingly needed to support antimicrobial stewardship and implement risk management strategies. High-resolution resistome profiling could be a possible solution to better understand the antibiotic resistance landscape in herds related to the dairy industry chain. This study aimed to provide a proof-of-concept for a quantitative analysis of the resistome retrieved from milking filters in six dairy herds in Lombardy (Italy).
Materials and Methods
Milking filters (n=6, one from each herd), replaced after each milking session, were collected. DNA was extracted using the PowerWater DNA kit (Qiagen) and quantified with the NanoReady Touch series Micro Volume Spectrophotometer (Aurogene, Italy). DNA libraries were prepared using the Rapid Barcoding Kit (Oxford Nanopore). Sequencing was performed on a MinION MK1C device.
Microbial communities and resistomes were analyzed through a customized bioinformatic pipeline for taxonomic classification (Centrifuge) and antibiotic resistance gene identification (ABRicate); finally, results were visualized using R statistical software.
Results
The two most abundant families of resistant bacteria were Mycobacteriaceae and Staphylococcaceae for herd 1 and Moraxellaceae and Methylococcaceae for herd 5. Two out of six dairy herds showed high levels of resistance, specifically herd 1 and herd 5 with 335 and 465 genomic copies/Gigabase (gc/Gb) when compared to the other four herds, which showed values lower than 20 gc/Gb. The most predominant levels of resistance were against gentamicin (17.6%) and kanamycin (15.1%) for herd 1 and spectinomycin (37.1%) and tetracyclines (18.8%) for herd 5.
Discussion and Conclusions
This study shows a relatively quick and easy method to quantitatively analyse the resistome of milking filters based on a long-read sequencing approach. This approach can determine precisely the levels of antibiotic resistance present in the sample and associate them with the bacterial families harboring them.
