Antimicrobial resistance (AMR) is considered a critical threat to public health, and genomic/metagenomic investigations featuring high-throughput analysis of sequence data are increasingly common and important. We previously introduced MEGARes, a comprehensive AMR database with an acyclic hierarchical annotation structure that facilitates high-throughput computational analysis, as well as AMR++, a customized bioinformatic pipeline specifically designed to use MEGARes in high-throughput analysis for characterizing AMR genes (ARGs) in metagenomic sequence data. Here, we present MEGARes v3.0, a comprehensive database of published ARG sequences for antimicrobial drugs, biocides, and metals, and AMR++ v3.0, an update to our customized bioinformatic pipeline for high-throughput analysis of metagenomic data (available at MEGLab.org). Database annotations have been expanded to include information regarding specific genomic locations for single-nucleotide polymorphisms (SNPs) and insertions and/or deletions (indels) when required by specific ARGs for resistance expression, and the updated AMR++ pipeline uses this information to check for presence of resistance-conferring genetic variants in metagenomic sequenced reads. This new information encompasses 337 ARGs, whose resistance-conferring variants could not previously be confirmed in such a manner. In MEGARes 3.0, the nodes of the acyclic hierarchical ontology include 4 antimicrobial compound types, 59 resistance classes, 233 mechanisms and 1448 gene groups that classify the 8733 accessions.

The National Antimicrobial Resistance Monitoring System (NARMS) has monitored antimicrobial resistance (AMR) associated with pathogens of humans and animals since 1996. In alignment with One Health strategic planning, NARMS is currently exploring the inclusion of surface waters as an environmental modality for monitoring AMR. From a One Health perspective, surface waters function as key environmental integrators between humans, animals, agriculture, and the environment. Surface waters however, due to their dilute nature present a unique challenge for monitoring critically important antimicrobial resistance. Selective enrichments from water paired with genomic sequencing effectively describe AMR for single genomes but do not provide data to describe a broader environmental resistome. Metagenomic data effectively describe a broad range of AMR from certain matrices however, depth of coverage is usually insufficient to describe clinically significant AMR from aquatic matrices. Thus, the coupling of biological enrichments of surface water with shotgun NGS sequencing has been shown to greatly enhance the capacity to report an expansive profile of clinically significant antimicrobial resistance genes. Here we demonstrate, using water samples from distinct sites (a creek in close proximity to a hospital and a reservoir used for recreation and municipal water), that the AMR portfolio provided by enriched (quasimetagenomic) data is capable of describing almost 30% of NARMS surveillance targets contrasted to only 1% by metagenomic data. Additionally, the quasimetagenomic data supported reporting of statistically significant (P< 0.05) differential abundance of specific AMR genes between sites. A single time-point for two sites is a small pilot, but the robust results describing critically important AMR determinants from each water source, provide proof of concept that quasimetagenomics can be applied to aquatic AMR surveillance efforts for local, national, and global monitoring.

Surface waters present a unique challenge for the monitoring of critically important antimicrobial resistance. Metagenomic approaches provide unbiased descriptions of taxonomy and antimicrobial resistance genes in many environments, but for surface water, culture independent data is insufficient to describe critically important resistance. To address this challenge and expand resistome reporting capacity of antimicrobial resistance in surface waters, we apply metagenomic and quasimetagenomic (enriched microbiome) data to examine and contrast water from two sites, a creek near a hospital, and a reservoir used for recreation and municipal water. Approximately 30% of the National Antimicrobial Resistance Monitoring System’s critically important resistance gene targets were identified in enriched data contrasted to only 1% in culture independent data. Four different analytical approaches consistently reported substantially more antimicrobial resistance genes in quasimetagenomic data compared to culture independent data across most classes of antimicrobial resistance. Statistically significant differential fold changes (p<0.05) of resistance determinants were used to infer microbiological differences in the waters. Important pathogens associated with critical antimicrobial resistance were described for each water source. While the single time-point for only two sites represents a small pilot project, the successful reporting of critically important resistance determinants is proof of concept that the quasimetagenomic approach is robust and can be expanded to multiple sites and timepoints for national and global monitoring and surveillance of antimicrobial resistance in surface waters.