Advancing antimicrobial resistance monitoring in surface waters with metagenomic and quasimetagenomic methods

Abstract

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.

Metagenomic survey of antimicrobial resistance (AMR) in Maryland surface waters differentiated by high and low human impact

“…From the metagenomic (culture independent) sequence data, only 5 critically important ARGs were detected in 20% (6/30) of sampling sites (Table 1). These results are consistent with previous work that demonstrated that for detection of critically important ARGs from surface water [8] or sediment [10], it is important to include either microbiological enrichment or molecular enrichment. Interestingly, β-lactam resistance genes were prevalent in twice as many of the high impact sites compared to low impact sites (Figure 1).…”

“…DNA was extracted from replicates of microbiologically enriched and culture-independent samples and sequenced on the Illumina Next Seq 2000 (P3 300 Cycle). Approximately 20 million reads per sample were used with taxonomic and AMR annotation tools (AMR++, AMRFinderPlus, CARD, FDA Kmer [1][2][3][4][5]) to describe bacterial taxa and antimicrobial resistance genes (ARGs) of public health importance to NARMS and veterinary monitoring efforts according to previously described methods [6][7][8][9].…”

Paired metagenomic and chemical evaluation of an aflatoxin contaminated dog kibble

“…Currently, genomic and metagenomic data are used for food safety applications such as source tracking pathogens [1][2][3][4][5][6], identification of adulterants, contaminants, toxins, allergens [7], and detection and prediction of antimicrobial resistance(AMR) [8]. A new frontier of utility for metagenomic data includes support for chemical toxicity evaluations through provision of wholistic compositional DNA-based profiles of macro (plant and animal) and micro (bacterial, fungal, viral, AMR) components of both unprocessed and highly processed human and animal foods.…”

MEGARes and AMR++, v3.0: an updated comprehensive database of antimicrobial resistance determinants and an improved software pipeline for classification using high-throughput sequencing

“…While there are a variety of resources for verifying the presence of critical variants within individual sequences, prior to release of MEGARes and AMR++ v3.0, there were few efficient, high-throughput methods for confirming resistance-conferring variants (i.e. SNPs and other genetic variants) within unassembled, untranslated metagenomic data, which led many researchers to perform meticulous validation of individual sequence reads, which is highly tedious and time consuming ( 19 ). Creating a suitable high-throughput computational approach for automating this validation process was a high priority in developing updates for MEGARes and AMR++.…”