Advancing antimicrobial resistance (AMR) surveillance: Characterizing AMR mobility and improving AMR prediction from metagenomics data

Antimicrobial resistance (AMR) is a global health crisis, with drug resistant infections killing an estimated 700,000 people annually. Advances in genomics have transformed infectious disease surveillance, including tracking AMR. Metagenomics, or the direct sequencing of DNA from an environmental sample, has expanded genomics approaches further, enabling the characterization of microbial communities without time-consuming and bias-inducing culturing. Nevertheless, limitations still exist, including understanding the mobility of resistance genes, which is needed for risk assessment of AMR emergence and spread in a One Health context. To date, some AMR genes have been empirically observed to be highly mobile, contributing to clinically relevant circulation of resistance. Despite these observations, no large-scale, systematic study has investigated the degree in which certain classes of AMR genes are strongly associated with certain mobile genetic elements (MGEs). Furthermore, the recovery accuracy of these MGEs by common metagenomic analysis paradigms has not been well characterized. This thesis dissertation therefore aims to advance AMR surveillance efforts and our understanding of AMR transmission dynamics by systematically characterizing predictions of AMR mobility and assessing and improving metagenomics detection of AMR genes. First, a large-scale analysis was performed to gain insight into which classes of AMR genes are more likely to be mobile, and thus present a higher risk for environmental transmission. This includes the separate analysis of plasmids and genomic islands (GIs). Secondly, I highlighted the limitations of current metagenomic methods with respect to the recovery of MGEs, and then developed a new read-based AMR prediction tool from metagenomic datasets. This tool can be used in combination with current methods to provide a more comprehensive prediction of the environmental resistome and aid investigations of the transmission of AMR genes. Lastly, I analyzed the microbiome of St. Lawrence Estuary beluga skin and surrounding water for potential contaminant biomarkers and characterized their resistome. Collectively, this thesis led to the discovery of patterns and associations between AMR genes and MGEs, plus the development and application of a metagenomic AMR detection tool that can further aid the study of AMR emergence and transmission in a One Health context.