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Abstract BackgroundWhile there is increasing recognition of numerous environmental contributions to the spread of antibiotic resistance, quantifying the relative contributions of various sources remains a fundamental challenge. Similarly, there is a need to differentiate acute human health risks corresponding to exposure to a given environment, versus broader ecological risk of evolution and spread of antibiotic resistance genes (ARGs) across microbial taxa. Recent studies have proposed various methods of harnessing the rich information housed by metagenomic data for achieving such aims. Here, we introduce MetaCompare 2.0, which improves upon the original MetaCompare pipeline by differentiating indicators of human health resistome risk (i.e., potential for human pathogens to acquire ARGs) from ecological resistome risk (i.e., overall mobility of ARGs across a given microbiome). ResultsTo demonstrate the sensitivity of the MetaCompare 2.0 pipeline, we analyzed publicly available metagenomes representing a broad array of environments, including wastewater, surface water, soil, sediment, and human gut. We also assessed the effect of sequence assembly methods on the risk scores. We further evaluated the robustness of the pipeline to sequencing depth, contig count, and metagenomic library coverage bias through comparative analysis of a range of subsamples extracted from a set of deeply sequenced wastewater metagenomes. The analysis utilizing samples from different environments demonstrated that MetaCompare 2.0 consistently produces lower risk scores for environments with little human influence and higher risk scores for human contaminated environments affected by pollution or other stressors. We found that the ranks of risk scores were not measurably affected by different assemblers employed. The Meta-Compare 2.0 risk scores were remarkably consistent despite varying sequencing depth, contig count, and coverage. ConclusionMetaCompare 2.0 successfully ranked a wide array of environments according to both human health and ecological resistome risks, with both scores being strongly impacted by anthropogenic stress. We packaged the improved pipeline into a publicly-available web service that provides an easy-to-use interface for computing resistome risk scores and visualizing results. The web service is available athttp://metacompare.cs.vt.edu/ 

Awareness of the need for surveillance of antimicrobial resistance (AMR) in water environments is growing, but there is uncertainty regarding appropriate monitoring targets. Adapting culture-based fecal indicator monitoring to include antibiotics in the media provides a potentially low-tech and accessible option, while quantitative polymerase chain reaction (qPCR) targeting key genes of interest provides a broad, quantitative measure across the microbial community. The purpose of this study was to compare findings obtained from the culture of cefotaxime-resistant (cefR) Escherichia coli with two qPCR methods for quantification of antibiotic resistance genes across wastewater, recycled water, and surface waters. The culture method was a modification of US EPA Method 1603 for E. coli, in which cefotaxime is included in the medium to capture cefR strains, while qPCR methods quantified sul1 and intI1. A common standard operating procedure for each target was applied to samples collected by six water utilities across the United States and processed by two laboratories. The methods performed consistently, and all three measures reflected the same overarching trends across water types. The qPCR detection of sul1 yielded the widest dynamic range of measurement as an AMR indicator (7-log versus 3.5-log for cefR E. coli), while intI1 was the most frequently detected target (99% versus 96.5% and 50.8% for sul1 and cefR E. coli, respectively). All methods produced comparable measurements between labs (p < 0.05, Kruskal–Wallis). Further study is needed to consider how relevant each measure is to capturing hot spots for the evolution and dissemination of AMR in the environment and as indicators of AMR-associated human health risk. 

Abstract Purpose of ReviewMounting evidence indicates that habitats such as wastewater and environmental waters are pathways for the spread of antibiotic-resistant bacteria (ARB) and mobile antibiotic resistance genes (ARGs). We identified antibiotic-resistant members of the generaAcinetobacter,Aeromonas, andPseudomonasas key opportunistic pathogens that grow or persist in built (e.g., wastewater) or natural aquatic environments. Effective methods for monitoring these ARB in the environment are needed to understand their influence on dissemination of ARB and ARGs, but standard methods have not been developed. This systematic review considers peer-reviewed papers where the ARB above were cultured from wastewater or surface water, focusing on the accuracy of current methodologies. Recent FindingsRecent studies suggest that many clinically important ARGs were originally acquired from environmental microorganisms.Acinetobacter,Aeromonas,andPseudomonasspecies are of interest because their ability to persist and grow in the environment provides opportunities to engage in horizontal gene transfer with other environmental bacteria. Pathogenic strains of these organisms resistant to multiple, clinically relevant drug classes have been identified as an urgent threat. However, culture methods for these bacteria were generally developed for clinical samples and are not well-vetted for environmental samples. SummaryThe search criteria yielded 60 peer-reviewed articles over the past 20 years, which reported a wide variety of methods for isolation, confirmation, and antibiotic resistance assays. Based on a systematic comparison of the reported methods, we suggest a path forward for standardizing methodologies for monitoring antibiotic resistant strains of these bacteria in water environments. 

Wastewater treatment plants (WWTPs) receive a confluence of sewage containing antimicrobials, antibiotic resistant bacteria, antibiotic resistance genes (ARGs), and pathogens and thus are a key point of interest for antibiotic resistance surveillance. WWTP monitoring has the potential to inform with respect to the antibiotic resistance status of the community served as well as the potential for ARGs to escape treatment. However, there is lack of agreement regarding suitable sampling frequencies and monitoring targets to facilitate comparison within and among individual WWTPs. The objective of this study was to comprehensively evaluate patterns in metagenomic-derived indicators of antibiotic resistance through various stages of treatment at a conventional WWTP for the purpose of informing local monitoring approaches that are also informative for global comparison. Relative abundance of total ARGs decreased by ∼50% from the influent to the effluent, with each sampling location defined by a unique resistome (i.e., total ARG) composition. However, 90% of the ARGs found in the effluent were also detected in the influent, while the effluent ARG-pathogen taxonomic linkage patterns identified in assembled metagenomes were more similar to patterns in regional clinical surveillance data than the patterns identified in the influent. Analysis of core and discriminatory resistomes and general ARG trends across the eight sampling events (i.e., tendency to be removed, increase, decrease, or be found in the effluent only), along with quantification of ARGs of clinical concern, aided in identifying candidate ARGs for surveillance. Relative resistome risk characterization further provided a comprehensive metric for predicting the relative mobility of ARGs and likelihood of being carried in pathogens and can help to prioritize where to focus future monitoring and mitigation. Most antibiotics that were subject to regional resistance testing were also found in the WWTP, with the total antibiotic load decreasing by ∼40–50%, but no strong correlations were found between antibiotics and corresponding ARGs. Overall, this study provides insight into how metagenomic data can be collected and analyzed for surveillance of antibiotic resistance at WWTPs, suggesting that effluent is a beneficial monitoring point with relevance both to the local clinical condition and for assessing efficacy of wastewater treatment in reducing risk of disseminating antibiotic resistance.