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Mounting 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 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.

The 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.

 

We conducted a critical review to establish what is known about the sources, characteristics, and dissemination of ARGs in the atmosphere. We identified 52 papers that reported direct measurements of bacterial ARGs in air samples and met other inclusion criteria. The settings of the studies fell into the following categories: urban, rural, hospital, industrial, wastewater treatment plants (WWTPs), composting and landfill sites, and indoor environments. Certain genes were commonly studied and generally abundant: sul1 , intI1 , β-lactam ARGs, and tetracycline ARGs. Abundances of total ARGs varied by season and setting, with air in urban areas having higher ARG abundance than rural areas during the summer and vice versa during the winter. There was greater consistency in the types and abundances of ARGs throughout the seasons in urban areas. Human activity within indoor environments was also linked to increased ARG content (abundance, diversity, and concentration) in the air. Several studies found that human exposure to ARGs through inhalation was comparable to exposure through drinking water or ingesting soil. Detection of ARGs in air is a developing field, and differences in sampling and analysis methods reflect the many possible approaches to studying ARGs in air and make direct comparisons between studies difficult. Methodologies need to be standardized to facilitate identification of the dominant ARGs in the air, determine their major sources, and quantify the role of atmospheric transport in dissemination of ARGs in the environment. With such knowledge we can develop better policies and guidelines to limit the spread of antimicrobial resistance. 

ABSTRACT Bacterial mobile genetic elements (MGEs) encode functional modules that perform both core and accessory functions for the element, the latter of which are often only transiently associated with the element. The presence of these accessory genes, which are often close homologs to primarily immobile genes, incur high rates of false positives and, therefore, limits the usability of these databases for MGE annotation. To overcome this limitation, we analyzed 10,776,849 protein sequences derived from eight MGE databases to compile a comprehensive set of 6,140 manually curated protein families that are linked to the “life cycle” (integration/excision, replication/recombination/repair, transfer, stability/transfer/defense, and phage-specific processes) of plasmids, phages, integrative, transposable, and conjugative elements. We overlay experimental information where available to create a tiered annotation scheme of high-quality annotations and annotations inferred exclusively through bioinformatic evidence. We additionally provide an MGE-class label for each entry (e.g., plasmid or integrative element), and assign to each entry a major and minor category. The resulting database, mobileOG-db (for mobile orthologous groups), comprises over 700,000 deduplicated sequences encompassing five major mobileOG categories and more than 50 minor categories, providing a structured language and interpretable basis for an array of MGE-centered analyses. mobileOG-db can be accessed at mobileogdb.flsi.cloud.vt.edu/, where users can select, refine, and analyze custom subsets of the dynamic mobilome. IMPORTANCE The analysis of bacterial mobile genetic elements (MGEs) in genomic data is a critical step toward profiling the root causes of antibiotic resistance, phenotypic or metabolic diversity, and the evolution of bacterial genera. Existing methods for MGE annotation pose high barriers of biological and computational expertise to properly harness. To bridge this gap, we systematically analyzed 10,776,849 proteins derived from eight databases of MGEs to identify 6,140 MGE protein families that can serve as candidate hallmarks, i.e., proteins that can be used as “signatures” of MGEs to aid annotation. The resulting resource, mobileOG-db, provides a multilevel classification scheme that encompasses plasmid, phage, integrative, and transposable element protein families categorized into five major mobileOG categories and more than 50 minor categories. mobileOG-db thus provides a rich resource for simple and intuitive element annotation that can be integrated seamlessly into existing MGE detection pipelines and colocalization analyses. 

Antibiotic resistance is a continually rising threat to global health. A primary driver of the evolution of new strains of resistant pathogens is the horizontal gene transfer (HGT) of antibiotic resistance genes (ARGs). However, identifying and quantifying ARGs subject to HGT remains a significant challenge. Here, we introduce HT-ARGfinder (horizontally transferred ARG finder), a pipeline that detects and enumerates horizontally transferred ARGs in metagenomic data while also estimating the directionality of transfer. To demonstrate the pipeline, we applied it to an array of publicly-available wastewater metagenomes, including hospital sewage. We compare the horizontally transferred ARGs detected across various sample types and estimate their directionality of transfer among donors and recipients. This study introduces a comprehensive tool to track mobile ARGs in wastewater and other aquatic environments. 

Antibiotic resistance genes (ARGs) are commonly detected in the atmosphere, but questions remain regarding their sources and relative contributions, bacterial hosts, and corresponding human health risks. Here, we conducted a qPCR- and metagenomics-based investigation of inhalable fine particulate matter (PM2.5) at a large wastewater treatment plant (WWTP) and in the ambient air of Hong Kong, together with an in-depth analysis of published data of other potential sources in the area. PM2.5 was observed with increasing enrichment of total ARGs along the coastal–urban–WWTP gradient and clinically relevant ARGs commonly identified in urban and WWTP sites, illustrating anthropogenic impacts on the atmospheric accumulation of ARGs. With certain kinds of putative antibiotic-resistant pathogens detected in urban and WWTP PM2.5, a comparable proportion of ARGs that co-occurred with MGEs was found between the atmosphere and WWTP matrices. Despite similar emission rates of bacteria and ARGs within each WWTP matrix, about 11–13% of the bacteria and >57% of the relevant ARGs in urban and WWTP PM2.5 were attributable to WWTPs. Our study highlights the importance of WWTPs in disseminating bacteria and ARGs to the ambient air from a quantitative perspective and, thus, the need to control potential sources of inhalation exposure to protect the health of urban populations. 

To evaluate the use of wastewater-based surveillance and epidemiology to monitor and predict SARS-CoV-2 virus trends, over the 2020–2021 academic year we collected wastewater samples twice weekly from 17 manholes across Virginia Tech’s main campus. We used data from external door swipe card readers and student isolation/quarantine status to estimate building-specific occupancy and COVID-19 case counts at a daily resolution. After analyzing 673 wastewater samples using reverse transcription quantitative polymerase chain reaction (RT-qPCR), we reanalyzed 329 samples from isolation and nonisolation dormitories and the campus sewage outflow using reverse transcription digital droplet polymerase chain reaction (RT-ddPCR). Population-adjusted viral copy means from isolation dormitory wastewater were 48% and 66% higher than unadjusted viral copy means for N and E genes (1846/100 mL to 2733/100 mL/100 people and 2312/100 mL to 3828/100 mL/100 people, respectively; n = 46). Prespecified analyses with random-effects Poisson regression and dormitory/cluster-robust standard errors showed that the detection of N and E genes were associated with increases of 85% and 99% in the likelihood of COVID-19 cases 8 days later (incident–rate ratio (IRR) = 1.845, p = 0.013 and IRR = 1.994, p = 0.007, respectively; n = 215), and one-log increases in swipe card normalized viral copies (copies/100 mL/100 people) for N and E were associated with increases of 21% and 27% in the likelihood of observing COVID-19 cases 8 days following sample collection (IRR = 1.206, p < 0.001, n = 211 for N; IRR = 1.265, p < 0.001, n = 211 for E). One-log increases in swipe normalized copies were also associated with 40% and 43% increases in the likelihood of observing COVID-19 cases 5 days after sample collection (IRR = 1.403, p = 0.002, n = 212 for N; IRR = 1.426, p < 0.001, n = 212 for E). Our findings highlight the use of building-specific occupancy data and add to the evidence for the potential of wastewater-based epidemiology to predict COVID-19 trends at subsewershed scales.