Plugging the leaks: antibiotic resistance at human-animal interfaces in low-resource settings.

Antibiotic resistance is one of the greatest public health challenges of our time. International efforts to curb resistance have largely focused on drug development and limiting unnecessary antibiotic use. However, in areas where water, sanitation, and hygiene infrastructure is lacking, we propose that bacterial flow between humans and animals can exacerbate the emergence and spread of resistant pathogens. Here, we describe the consequences of poor environmental controls by comparing mobile resistance elements among Escherichia coli recovered from humans and meat in Cambodia, a middle-income country with substantial human-animal connectivity and unregulated antibiotic use. We identified identical mobile resistance elements and a conserved transposon region that were widely dispersed in both humans and animals, a phenomenon rarely observed in high-income settings. Our findings indicate that plugging leaks at human-animal interfaces should be a critical part of addressing antibiotic resistance in low- and especially middle-income countries.

Towards facilitated interpretation of shotgun metagenomics long-read sequencing data analyzed with KMA for the detection of bacterial pathogens and their antimicrobial resistance genes.

Metagenomic sequencing is a promising method that has the potential to revolutionize the world of pathogen detection and antimicrobial resistance (AMR) surveillance in food-producing environments. However, the analysis of the huge amount of data obtained requires performant bioinformatics tools and databases, with intuitive and straightforward interpretation. In this study, based on long-read metagenomics data of chicken fecal samples with a spike-in mock community, we proposed confidence levels for taxonomic identification and AMR gene detection, with interpretation guidelines, to help with the analysis of the output data generated by KMA, a popular k-mer read alignment tool. Additionally, we demonstrated that the completeness and diversity of the genomes present in the reference databases are key parameters for accurate and easy interpretation of the sequencing data. Finally, we explored whether KMA, in a two-step procedure, can be used to link the detected AMR genes to their bacterial host chromosome, both detected within the same long-reads. The confidence levels were successfully tested on 28 metagenomics datasets which were obtained with sequencing of real and spiked samples from fecal (chicken, pig, and buffalo) or food (minced beef and food enzyme products) origin. The methodology proposed in this study will facilitate the analysis of metagenomics sequencing datasets for KMA users. Ultimately, this will contribute to improvements in the rapid diagnosis and surveillance of pathogens and AMR genes in food-producing environments, as prioritized by the EU.