A proposed framework for the identification of indicator genes for monitoring antibiotic resistance in wastewater: Insights from metagenomic sequencing

The risk of pathogenicity and antibiotic resistance in deep-sea cold seep microorganisms

Deep-sea cold seeps host high microbial biomass and biodiversity that thrive on hydrocarbon and inorganic compound seepage, exhibiting diverse ecological functions and unique genetic resources. However, potential health risks from pathogenic or antibiotic-resistant microorganisms in these environments remain largely overlooked, especially during resource exploitation and laboratory research. Here, we analyzed 165 metagenomes and 33 metatranscriptomes from 16 global cold seep sites to investigate the diversity and distribution of virulence factors (VFs), antibiotic resistance genes (ARGs), and mobile genetic elements (MGEs). A total of 2,353 VFs are retrieved in 689 metagenome-assembled genomes (MAGs), primarily associated with indirect pathogenesis like adherence. In addition, cold seeps harbor nearly 100,000 ARGs, as important reservoirs, with high-risk ARGs (11.22%) presenting at low abundance. Compared to other environments, microorganisms in cold seeps exhibit substantial differences in VF and ARG counts, with potential horizontal gene transfer facilitating their spread. These virulome and resistome profiles provide valuable insights into the evolutionary and ecological implications of pathogenicity and antibiotic resistance in extreme deep-sea ecosystems. Collectively, these results indicate that cold seep sediments pose minimal public health risks, shedding light on environmental safety in deep-sea resource exploitation and research.

IMPORTANCE

In the "One Health" era, understanding pathogenicity and antibiotic resistance in vast and largely unexplored regions like deep-sea cold seeps is critical for assessing public health risks. These environments serve as critical reservoirs where resistant and virulent bacteria can persist, adapt, and undergo genetic evolution. The increasing scope of human activities, such as deep-sea mining, is disrupting these previously isolated ecosystems, heightening the potential for microbial exchange between deep-sea communities and human or animal populations. This interaction poses a significant risk for the dissemination of resistance and virulence genes, with potential consequences for global public health and ecosystem stability. This study offers the first comprehensive analysis of virulome, resistome, and mobilome profiles in cold seep microbial communities. While cold seeps act as reservoirs for diverse ARGs, high-risk ARGs are rare, and most VFs were low risk that contribute to ecological functions. These results provide a reference for monitoring the spread of pathogenicity and resistance in extreme ecosystems, informing environmental safety assessments during deep-sea resource exploitation.

Evaluating Quantitative Metagenomics for Environmental Monitoring of Antibiotic Resistance and Establishing Detection Limits

Metagenomics holds promise as a comprehensive, nontargeted tool for environmental monitoring. However, one key limitation is that the quantitative capacity of metagenomics is not well-defined. Here, we demonstrated a quantitative metagenomic technique and benchmarked the approach for wastewater-based surveillance of antibiotic resistance genes. To assess the variability of low-abundance oligonucleotide detection across sample matrices, we spiked DNA reference standards (meta sequins) into replicate wastewater DNA extracts at logarithmically decreasing mass-to-mass percentages (m/m%). Meta sequin ladders exhibited strong linearity at input concentrations as low as 2 × 10-3 m/m% (R2 > 0.95), with little to no reference length or GC bias. At a mean sequencing depth of 94 Gb, the limits of quantification (LoQ) and detection were calculated to be 1.3 × 103 and 1 gene copy per μL DNA extract, respectively. In wastewater influent, activated sludge, and secondary effluent samples, 27.3, 47.7, and 44.3% of detected genes were ≤LoQ, respectively. Volumetric gene concentrations and log removal values were statistically equivalent between quantitative metagenomics and ddPCR for 16S rRNA, intI1, sul1, CTX-M-1, and vanA. The quantitative metagenomics benchmark here is a key step toward establishing metagenomics for high-throughput, nontargeted, and quantitative environmental monitoring.

Metagenomic Insights in Antimicrobial Resistance Threats in Sludge from Aerobic and Anaerobic Membrane Bioreactors

Sludge is a biohazardous solid waste that is produced during wastewater treatment. It contains antibiotic resistance genes (ARGs) that pose significant antimicrobial resistance (AMR) threats. Herein, aerobic and anaerobic membrane bioreactors (AeMBRs and AnMBRs, respectively) were compared in terms of the volume of waste sludge generated by them, the presence of ARGs in the sludge, and the potential for horizontal gene transfer (HGT) events using metagenomics to determine which treatment process can better address AMR concerns associated with the generation of waste sludge. The estimated abundance of ARGs in the suspended sludge generated by the AnMBR per treated volume is, on average, 5-55 times lower than that of sludge generated by the AeMBR. Additionally, the ratio of potential HGT in the two independent runs was lower in the anaerobic sludge (0.6 and 0.9) compared with that in the aerobic sludge (2.4 and 1.6). The AnMBR sludge exhibited reduced HGT of ARGs involving potential opportunistic pathogens (0.09) compared with the AeMBR sludge (0.27). Conversely, the AeMBR sludge displayed higher diversity and more transfer events, encompassing genes that confer resistance to quinolones, rifamycin, multidrug, aminoglycosides, and tetracycline. A significant portion of these ARGs were transferred to Burkholderia sp. By contrast, the AnMBR showed a lower abundance of mobile genetic elements associated with conjugation and exhibited less favorable conditions for natural transformation. Our findings suggest that the risk of potential HGT to opportunistic pathogens is greater in the AeMBR sludge than in AnMBR sludge.

Riverine antibiotic resistome along an anthropogenic gradient

The introduction of antibiotic-resistant bacteria into riverine systems through the discharge of wastewater treatment plant (WWTP) effluent and agricultural waste poses significant health risks. Even when not pathogenic, these bacteria can act as reservoirs for antibiotic resistance genes (ARGs), transferring them to pathogens that infect humans and animals. In this study, we used fluorescence in situ hybridization, qPCR, and metagenomics to investigate how anthropogenic activities affect microbial abundance and the resistome along the Holtemme River, a small river in Germany, from near-pristine to human-impacted sites. Our results showed higher bacterial abundance, a greater absolute and relative abundance of ARGs, and a more diverse ARG profile at the impacted sites. Overall, the ARG profiles at these sites reflected antibiotic usage in Germany, with genes conferring resistance to drug classes such as beta-lactams, aminoglycosides, folate biosynthesis inhibitors, and tetracyclines. There were also variations in the ARG profiles of the impacted sites. Notably, there was a high abundance of the oxacillin resistance gene OXA-4 at the downstream site in the river. In the metagenome assembly, this gene was associated with a contig homologous to small plasmids previously identified in members of the Thiotrichaceae. The likely in-situ host of the putative plasmid was a close relative of Thiolinea (also known as Thiothrix) eikelboomii, a prominent member of WWTP microbiomes worldwide. Our results show that the effluent from WWTPs can introduce bacteria into the environment that act as shuttle systems for clinically relevant ARG.

Metagenomic insights into resistome, mobilome and virulome in different fecal waste

Fecal waste is a significant source of antimicrobial resistance (AMR) pollution and provides valuable insights into the AMR development in animal and human populations within the "One health" framework. Various genetic elements, including antibiotic resistance genes (ARGs), biocide and metal resistance genes (BMGs), mobile genetic elements (MGEs), and virulence factor genes (VFGs), are crucial AMR risk determinants (ARDs). However, few studies focused on compositional characteristics of ARDs in different feces. Here, we analyzed 753 public metagenomes from human, pig, chicken, and cattle feces, revealing significant differences in ARD richness and abundance across fecal types, notably lowest in cattle samples. Tetracycline, multi-metal, and -biocide resistance genes were dominant resistome. A few core genes contributed to 25.6%-91.1% of gene abundance, and their correlations were stronger in cattle samples. Procrustes analysis showed that microbial composition had higher correlations with ARGs (M2 = 0.579) and BMGs (M2 = 0.519). Gammaproteobacteria was identified as major ARD-hosts especially in human and pig feces, and they mainly carried multi-resistance genes. MGEs exhibited direct positive effects on ARGs and BMGs, indirectly impacting VFGs. Utilizing random forest methods, we identified 42 indicator genes for tracking AMR pollution originating from fecal sources in the environments. This study offers new insights into understanding and controlling the AMR pollution of fecal waste from human and food animals.

MetaCompare 2.0: differential ranking of ecological and human health resistome risks

While numerous environmental factors contribute to the spread of antibiotic resistance genes (ARGs), quantifying their relative contributions remains a fundamental challenge. Similarly, it is important to differentiate acute human health risks from environmental exposure, versus broader ecological risk of ARG evolution and spread across microbial taxa. Recent studies have proposed various methods for achieving such aims. Here, we introduce MetaCompare 2.0, which improves upon original MetaCompare pipeline by differentiating indicators of human health resistome risk (potential for human pathogens of acute resistance concern to acquire ARGs) from ecological resistome risk (overall mobility of ARGs and potential for pathogen acquisition). The updated pipeline's sensitivity was demonstrated by analyzing diverse publicly-available metagenomes from wastewater, surface water, soil, sediment, human gut, and synthetic microbial communities. MetaCompare 2.0 provided distinct rankings of the metagenomes according to both human health resistome risk and ecological resistome risk, with both scores trending higher when influenced by anthropogenic impact or other stress. We evaluated the robustness of the pipeline to sequence assembly methods, sequencing depth, contig count, and metagenomic library coverage bias. The risk scores were remarkably consistent despite variations in these technological aspects. We packaged the improved pipeline into a publicly-available web service (http://metacompare.cs.vt.edu/) that provides an easy-to-use interface for computing resistome risk scores and visualizing results.

Metagenomic Profiling of Internationally Sourced Sewage Influents and Effluents Yields Insight into Selecting Targets for Antibiotic Resistance Monitoring

It has been debated whether wastewater treatment plants (WWTPs) primarily act to attenuate or amplify antibiotic resistance genes (ARGs). However, ARGs are highly diverse with respect to their resistance mechanisms, mobilities, and taxonomic hosts and therefore their behavior in WWTPs should not be expected to be universally conserved. We applied metagenomic sequencing to wastewater influent and effluent samples from 12 international WWTPs to classify the behavior of specific ARGs entering and exiting WWTPs. In total, 1079 different ARGs originating from a variety of bacteria were detected. This included ARGs that could be mapped to assembled scaffolds corresponding to nine human pathogens. While the relative abundance (per 16S rRNA gene) of ARGs decreased during treatment at 11 of the 12 WWTPs sampled and absolute abundance (per mL) decreased at all 12 WWTPs, increases in relative abundance were observed for 40% of the ARGs detected at the 12th WWTP. Also, the relative abundance of mobile genetic elements (MGE) increased during treatment, but the fraction of ARGs known to be transmissible between species decreased, thus demonstrating that increased MGE prevalence may not be generally indicative of an increase in ARGs. A distinct conserved resistome was documented in both influent and effluent across samples, suggesting that well-functioning WWTPs generally attenuate influent antibiotic resistance loads. This work helps inform strategies for wastewater surveillance of antibiotic resistance, highlighting the utility of tracking ARGs as indicators of treatment performance and relative risk reduction.

Towards the definition of an antibiotic resistome signature in wastewater and downstream environments

Domestic wastewater is a significant reservoir of antibiotic resistance genes, which pose environmental and public health risks. We aimed to define an antibiotic resistome signature, represented by core genes, i.e., shared by ≥ 90% of the metagenomes of each of three conceptual environmental compartments - wastewater (influent, sludge, effluent), freshwater, and agricultural soil. The definition of resistome signatures would support the proposal of a framework for monitoring treatment efficacy and assessing the impact of treated wastewater discharge into the environment, such as freshwater and agricultural soil. Metagenomic data from 163 samples originating from wastewater (n = 81), freshwater (n = 58), and agricultural soils (n = 24) across different regions (29 countries, 5 continents), were analysed regarding antibiotic resistance diversity, based on annotation against a database that merged CARD and ResFinder databases. The relative abundance of the total antibiotic resistance genes (corresponding to the ratio between the antibiotic resistance genes and total reads number) was not statistically different between raw and treated wastewater, being significantly higher than in freshwater or agricultural soils. The latter had the significantly lowest relative abundance of antibiotic resistance genes. Genes conferring resistance to aminoglycosides, beta-lactams, and tetracyclines were among the most abundant in wastewater environments, while multidrug resistance was equally distributed across all environments. The wastewater resistome signature included 27 antibiotic resistance genes that were detected in at least 90% of the wastewater resistomes, and that were not frequent in freshwater or agricultural soil resistomes. Among these were genes responsible for resistance to tetracyclines (n = 8), macrolide-lincosamide-streptogramin B (n = 7), aminoglycosides (n = 4), beta-lactams (n = 3), multidrug (n = 2), sulphonamides (n = 2), and polypeptides (n = 1). This comprehensive assessment provides valuable insights into the dynamics of antibiotic resistance in urban wastewater systems and their potential ecological implications in diverse environmental settings. Furthermore, provides guidance for the implementation of One Health monitoring approaches.

Utilizing co-abundances of antimicrobial resistance genes to identify potential co-selection in the resistome

The rapid spread of antimicrobial resistance (AMR) is a threat to global health, and the nature of co-occurring antimicrobial resistance genes (ARGs) may cause collateral AMR effects once antimicrobial agents are used. Therefore, it is essential to identify which pairs of ARGs co-occur. Given the wealth of next-generation sequencing data available in public repositories, we have investigated the correlation between ARG abundances in a collection of 214,095 metagenomic data sets. Using more than 6.76∙108 read fragments aligned to acquired ARGs to infer pairwise correlation coefficients, we found that more ARGs correlated with each other in human and animal sampling origins than in soil and water environments. Furthermore, we argued that the correlations could serve as risk profiles of resistance co-occurring to critically important antimicrobials (CIAs). Using these profiles, we found evidence of several ARGs conferring resistance for CIAs being co-abundant, such as tetracycline ARGs correlating with most other forms of resistance. In conclusion, this study highlights the important ARG players indirectly involved in shaping the resistomes of various environments that can serve as monitoring targets in AMR surveillance programs.

IMPORTANCE

Understanding the collateral effects happening in a resistome can reveal previously unknown links between antimicrobial resistance genes (ARGs). Through the analysis of pairwise ARG abundances in 214K metagenomic samples, we observed that the co-abundance is highly dependent on the environmental context and argue that these correlations can be used to show the risk of co-selection occurring in different settings.

Environmentally Relevant Antibiotic Concentrations Exert Stronger Selection Pressure on River Biofilm Resistomes than AMR-Reservoir Effluents

Freshwater environments are primary receiving systems of wastewater and effluents, which carry low concentrations of antibiotics and antimicrobial-resistant (AMR) bacteria and genes. Aquatic microbial communities are thus exposed to environmentally relevant concentrations of antibiotics (ERCA) that presumably influence the acquisition and spread of environmental AMR. Here, we analyzed ERCA exposure with and without the additional presence of municipal wastewater treatment plant effluent (W) and swine manure run-off (M) on aquatic biofilm resistomes. Microscopic analyses revealed decreased taxonomic diversity and biofilm structural integrity, while metagenomic analysis revealed an increased abundance of resistance, virulence, and mobile element-related genes at the highest ERCA exposure levels, with less notable impacts observed when solely exposed to W or M effluents. Microbial function predictions indicated increased gene abundance associated with energy and cell membrane metabolism and heavy metal resistance under ERCA conditions. In silico predictions of increased resistance mechanisms did not correlate with observed phenotypic resistance patterns when whole communities were exposed to antimicrobial susceptibility testing. This reveals important insight into the complexity of whole-community coordination of physical and genetic responses to selective pressures. Lastly, the environmental AMR risk assessment of metagenomic data revealed a higher risk score for biofilms grown at sub-MIC antibiotic conditions.

Application of machine learning for antibiotic resistance in water and wastewater: A systematic review

Antibiotic resistance (AR) is considered one of the greatest global threats in the current century, which can only be overcome if all interconnected areas of humans, animals and the environment are taken into account as part of the One Health concept proposed by the World Health Organization (WHO). Water and wastewater are among the most important environmental media of AR sources, where the phenomena are generally non-linear. Therefore, the aim of this study was to investigate the application of machine learning-based methods (MLMs) to solve AR-induced problems in water and wastewater. For this purpose, most relevant databases were searched in the period between 1987 and 2023 to systematically analyze and categorize the applications. Accordingly, the results showed that out of 12 applications, 11 (91.6%) were for shallow learning and 1 (8.3%) for deep learning. In shallow learning category, n = 6, 50% of the applications were regression and n = 4, 33.3% were classification, mainly using artificial neural networks, decision trees and Bayesian methods for the following objectives: Predicting the survival of antibiotic-resistant bacteria (ARB), determining the order of influencing parameters on AR-based scores, and identifying the major sources of antibiotic resistance genes (ARGs). In addition, only one study (8.3%) was found for clustering and no study for association. Surprisingly, deep learning had been used in only one study (8.3%) to predict ARGs sequences. Therefore, working on the knowledge gaps of AR, especially using clustering, association and deep learning methods, would be a promising option to analyze more aspects of the related problems. However, there is still a long way to go to consider and apply MLMs as unique approaches to study different aspects of AR in water and wastewater.

From disinfection to pathogenicity: Occurrence, resistome risks and assembly mechanism of biocide and metal resistance genes in hospital wastewaters

Hospital wastewaters (HWWs) represent critical reservoir for the accumulation and propagation of resistance genes. However, studies on biocide and metal resistance genes (BMRGs) and their associated resistome risks and driving mechanisms in HWWs are still in their infancy. Here, metagenomic assembly was firstly used to investigate host pathogenicity and transferability profiles of BMGRs in a typical HWWs system. As a result, genes conferring resistance to Ethidium Bromide, Benzylkonium Chloride, and Cetylpyridinium Chloride dominated biocide resistance genes (BRGs), whereas Cu resistance gene was the largest contributor of metal resistance genes (MRGs). Most BMRGs experienced significant reduction from anoxic-aerobic treatment to sedimentation stages but exhibited enrichment after chlorine disinfection. Network analysis indicated intense interactions between BMRGs and virulence factors (VFs). Polar_flagella, belonging to the adherence was identified to play important role in the network. Contig-based analysis further revealed noteworthy shifts in host associations along the treatment processes, with Pseudomonadota emerging as the primary carrier, hosting 91.1% and 85.3% of the BRGs and MRGs. A total of 199 opportunistic pathogens were identified to carry 285 BMRG subtypes, which mainly included Pseudomonas alcaligenes, Pseudomonas lundensis, and Escherichia coli. Notably, ruvB conferring resistance to Cr, Cetylpyridinium Chloride, and Dodine were characterized with the highest frequency carried by pathogens. Diverse co-occurrence patterns between BMRGs and mobile genetic elements (MGEs) were found from the raw influent to final effluent. Overall, 10.5% BRGs and 8.84% MRGs were mobile and among the 4 MGEs, transposase exhibited the greatest potential for the BMRGs dissemination. Furthermore, deterministic processes played a dominant role in bacterial communities and BMRGs assembly in HWWs. Bacterial communities contributed more than MGEs in shaping the resistome. Taken together, this work demonstrated widespread BMRGs pollution throughout the HWWs treatment system, emphasizing the potential for informing resistome risk and ecological mechanism in medical practice.

Development of a High-Throughput Analytical Method for Antimicrobials in Wastewater Using an Automated Pipetting and Solid-Phase Extraction System

Antimicrobial resistance (AMR) has emerged and spread globally. Recent studies have also reported the presence of antimicrobials in a wide variety of aquatic environments. Conducting a nationwide monitoring survey of AMR in the environment to elucidate its status and to assess its impact on ecosystems and human health is of social importance. In this study, we developed a novel high-throughput analysis (HTA) system based on a 96-well plate solid-phase extraction (SPE), using automated pipetting and an SPE pre-treatment system. The effectiveness of the system as an HTA for antimicrobials in environmental water was verified by comparing it with a conventional manual analytical system in a domestic hospital over a period of two years and four months. The results of the manual analysis and HTA using a combination of automated pipetting and SPE systems were generally consistent, and no statistically significant difference was observed (p > 0.05) between the two systems. The agreement ratios between the measured concentrations based on the conventional and HTA methods were positively correlated with a correlation coefficient of r = 0.99. These results indicate that HTA, which combines automated pipetting and an SPE pre-treatment system for rapid, high-volume analysis, can be used as an effective approach for understanding the environmental contamination of antimicrobials at multiple sites. To the best of our knowledge, this is the first report to present the accuracy and agreement between concentrations based on a manual analysis and those measured using HTA in hospital wastewater. These findings contribute to a comprehensive understanding of antimicrobials in aquatic environments and assess the ecological and human health risks associated with antimicrobials and antimicrobial-resistant bacteria to maintain the safety of aquatic environments.

Tracking the source of antibiotic resistome in the stormwater network drainage in the presence of sewage illicit connections

Stormwater pipes are illicitly connected with sewage in many countries, which means that sewage enters stormwater pipes and the drainage is discharged to surface water without any treatment. Sewage contains more pathogens and highly risky antibiotic resistance genes (ARGs) than surface runoff. Therefore, sewage may alter the microbial and ARG compositions in stormwater pipe drainage, which in turn leads to an increased risk of resistance in surface water. However, the effects of sewage on ARGs in the drainage of stormwater networks have not been systematically studied. This study characterized the microbial and ARG composition of several environmental compartments of a typical stormwater network and quantified their contributions to those in the drainage. This network transported ARGs and microorganisms from sewage, sediments in stormwater pipes, and surface runoff into the drainage and thus into the river. According to metagenomic analysis, multidrug resistance genes were most abundant in all samples and the numbers and relative abundance of ARGs in the drainage collected during wet weather were comparable to that of sewage. The results of SourceTracker showed that the relative contribution of sewage was double that of rainwater and surface runoff in the drainage during wet weather for both microorganisms and ARGs. Desulfovibrio, Azoarcus, and Sulfuritalea were connected with the greatest number of ARGs and were most abundant in the sediments of stormwater pipes. Furthermore, stochastic processes were found to dominate ARG and microbial assembly, as the effects of high hydrodynamic intensity outweighed the effects of environmental filtration and species interactions. The findings of this study can increase our understanding of ARGs in stormwater pipe drainage, a crucial medium linking ARGs in sewage to environmental ARGs.

A Potential Indicator Gene, tetM, to Assess Contamination by Antibiotic Resistance Genes in Greenhouses in South Korea

Antibiotic resistance genes (ARGs) have been emerging as a concerning threat to both environment and public health. The continuous input of manure, irrigation water, and fertilizers increases the abundance of ARGs in agricultural environments. However, current risk assessments have focused on clinical settings, which are not applicable to environmental settings. Therefore, we herein aimed to identify and assess indicator genes to reduce the time and effort required for ARG surveillance. A nationwide ana-lysis of 322 ARGs and 58 mobile genetic elements (MGEs) was performed on 42 greenhouse and 19 control soil samples. The chemical properties and pH of soil were also investigated to characterize differences between greenhouse and control soil samples. The results obtained showed that the abundance of ARGS was significantly higher and ion concentrations were higher in greenhouse samples than in control samples. These results indicate that agricultural activities increased the abundance of ARGs. Furthermore, the abundance of core genes was significantly higher in greenhouse samples than in control samples, and the chemical characteristics of soil significantly differed between these samples. Among the discriminatory genes selected, tetM was identified as an ARG surveillance indicator gene based on its clinical relevance, prevalence in the soil resistome, and relationship with mobile genetic elements. The present results will contribute to the continuous and rapid surveillance of antibiotic resistance dissemination and proliferation in greenhouses in South Korea.

Wastewater treatment plants: The missing link in global One-Health surveillance and management of antibiotic resistance

INTRODUCTION

As a global public health crisis, antibiotic resistance (AR) should be monitored and managed under the One-Health concept according to the World Health Organization (WHO), considering the interconnection between humans, animals, and the environment. But this approach often remains focused on human health and rarely on the environment and its compartments, especially wastewater as the main AR receptor. Wastewater treatment plants (WWTPs) not only are not designed for reliving AR but also provide appropriate conditions for enhancing AR through different mechanisms.

METHODS

By reviewing the research-based statistics on the inclusion of WWTPs in the One-Health/AR program crisis, this paper highlights the importance of paying attention to these hotspots, at first. Also, the importance and technical roadmap for the application of WWTPs in both surveillance and management of AR were provided. The current position of these facilities was also evaluated using strengths, weaknesses, opportunities, and threats (SWOT) analysis. In the end, the concluding knowledge gaps and research needs for future investigations were presented.

RESULTS

Despite the fact that wastewater matrices are the hotspot for AR dissemination, WWTPs appear under-represented in One-Health/AR literature. So, of the 414434 articles retrieved for One-Health only 1.5% (n = 6321) focused on AR and about 0.04% (n = 158) on WWTPs. The potential of WWTPs inclusion in AR surveillance has been confirmed by several studies, however, when it comes to its inclusion for management of AR, more evidence should be presented, which confirmed by SWOT results.

DISCUSSION

As such, WWTPs simultaneously provide opportunities for AR surveillance as it is assumed that this medium can reflect the reality of the corresponding society, and for managing unexpected crises which could impact the public. Nonetheless, there are still numerous considerations to change WWTPs role from Achilles' heel to Ajax' shield, including strengthening the research-based knowledge and conducting both surveillance and management strategies of AR under One-Health concept (One-Health/AR) in a clear straightforward framework.

Comparison of Cefotaxime-Resistant Escherichia coli and sul1 and intI1 by qPCR for Monitoring of Antibiotic Resistance of Wastewater, Surface Water, and Recycled Water

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.