Abstract
Abstract Multidrug-resistant bacteria are on the increase and threaten human and animal health. Livestock commensal bacteria are a reservoir of antimicrobial resistance genes that can transmit to foodborne pathogens via plasmids and other mobile genetic elements. Assessing the relative risk of resistance genes in a culture-independent manner is a critical goal of antimicrobial resistance surveillance; however, determining the genomic context of a resistance gene remains difficult as mobile genetic elements often fail to assemble and bin accurately in metagenomic studies. To address this issue and better understand the transmission of antimicrobial resistance genes, we developed a machine learning classifier to identify plasmid genomic segments in metagenomic and long-read datasets. We further demonstrate that our plasmid classifier can be applied to identify plasmid bins with low contamination following metagenomic binning. Our findings highlight that metagenomic binning combined with accurate plasmid classification can better resolve the genomic context of antimicrobial resistance genes in culture-independent studies. Lastly, we successfully applied our plasmid prediction tool with droplet digital PCR to determine the absolute abundance of plasmid-encoded antimicrobial resistance genes in bacterial populations. Overall, we demonstrate how our machine learning approach for classifying plasmids can improve the surveillance of mobile antimicrobial resistance genes over current quasi-metagenomic approaches based on culture enrichments. In the future, this research can be extended to predict and better understand the risk of antimicrobial resistance gene transmission in animal agriculture environments.
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