Escaping the Fate of Sisyphus: Assessing Resistome Hybridization Baits for Antimicrobial Resistance Gene Capture

Abstract

SummaryFinding, characterizing, and monitoring reservoirs for antimicrobial resistance (AMR) is vital to protecting public health. Hybridization capture baits are an accurate, sensitive, and cost-effective technique used to enrich and characterize DNA sequences of interest, including antimicrobial resistance genes (ARGs), in complex environmental samples. We demonstrate the continued utility of a set of 19,933 hybridization capture baits designed from the Comprehensive Antibiotic Resistance Database (CARD)v1.1.2 and Pathogenicity Island Database (PAIDB)v2.0, targeting 3,565 unique nucleotide sequences that confer resistance. We demonstrate the efficiency of our bait set on a custom-made resistance mock community and complex environmental samples to increase the proportion of on-target reads as much as >200-fold. However, keeping pace with newly discovered ARGs poses a challenge when studying AMR, because novel ARGs are continually being identified and would not be included in bait sets designed prior to discovery. We provide imperative information on how our bait set performs against CARDv3.3.1, as well as a generalizable approach for deciding when and how to update hybridization capture bait sets. This research encapsulates the full life cycle of baits for hybridization capture of the resistome from design and validation (both in silico and in vitro) to utilization and forecasting updates and retirement.Originality-Significance StatementThis work is applicable to a wide range of research. It helps to define conditions under which hybridization capture is useful regarding not only antimicrobial resistance specifically, but also more generally how to assess the ongoing utility of existing bait sets - giving objective criteria for when and by what strategies baits should be updated. We also provide a method for quantifying and comparing antimicrobial resistance genes (ARGs) similar to what is used for RNAseq experiments. This approach improves comparison of ARGs across environments. Thus, the work provides an improved foundation for ARG future studies, while cutting across traditional areas of microbiology and extending beyond.