MetaStorm: A Public Resource for Customizable Metagenomics Annotation

Bacterial diversity and resistome analysis of drinking water stored in cisterns from two First Nations communities in Manitoba, Canada

The microbiological content of water is an ongoing concern in First Nations communities in Canada. Many communities lack water treatment plants and continue to be under drinking water advisories. However, lack of access to treatment plants is only a part of the problem as poor water distribution systems also contribute to the failure to provide safe drinking water. Here, we studied the microbial diversity and antibiotic resistome from water stored in cisterns from two First Nations communities in Manitoba, Canada. We found that the cistern water contained a high number of bacteria and showed the presence of diverse antimicrobial resistance genes. Interestingly, the bacterial diversity and antimicrobial resistance genes varied considerably from that of the untreated source water, indicating that the origin of contamination in the cistern water came from within the treatment plant or along the delivery route to the homes. Our study highlights the importance of proper maintenance of the water distribution system in addition to access to water treatment facilities to ensure a supply of safe water to First Nations communities in Canada.IMPORTANCEThe work described addresses a critical issue in First Nations communities in Canada-the microbiological content of water. Many of these communities lack access to water treatment plants and frequently experience drinking water advisories. This study focused on the microbial diversity and antibiotic resistome in water stored in cisterns within two First Nations communities in Manitoba, Canada. These findings reveal that cistern water, a common source of drinking water in these communities, contains a high number of bacteria and a wide range of antimicrobial resistance genes. This highlights a serious health risk as exposure to such water can lead to the spread of drug-resistant infections, posing a threat to the well-being of the residents.

ARGem: a new metagenomics pipeline for antibiotic resistance genes: metadata, analysis, and visualization

Antibiotic resistance is of crucial interest to both human and animal medicine. It has been recognized that increased environmental monitoring of antibiotic resistance is needed. Metagenomic DNA sequencing is becoming an attractive method to profile antibiotic resistance genes (ARGs), including a special focus on pathogens. A number of computational pipelines are available and under development to support environmental ARG monitoring; the pipeline we present here is promising for general adoption for the purpose of harmonized global monitoring. Specifically, ARGem is a user-friendly pipeline that provides full-service analysis, from the initial DNA short reads to the final visualization of results. The capture of extensive metadata is also facilitated to support comparability across projects and broader monitoring goals. The ARGem pipeline offers efficient analysis of a modest number of samples along with affordable computational components, though the throughput could be increased through cloud resources, based on the user's configuration. The pipeline components were carefully assessed and selected to satisfy tradeoffs, balancing efficiency and flexibility. It was essential to provide a step to perform short read assembly in a reasonable time frame to ensure accurate annotation of identified ARGs. Comprehensive ARG and mobile genetic element databases are included in ARGem for annotation support. ARGem further includes an expandable set of analysis tools that include statistical and network analysis and supports various useful visualization techniques, including Cytoscape visualization of co-occurrence and correlation networks. The performance and flexibility of the ARGem pipeline is demonstrated with analysis of aquatic metagenomes. The pipeline is freely available at https://github.com/xlxlxlx/ARGem.

Shotgun Metagenomics Reveals Impacts of Copper and Water Heater Anodes on Pathogens and Microbiomes in Hot Water Plumbing Systems

Hot water building plumbing systems are vulnerable to the proliferation of opportunistic pathogens (OPs), including Legionella pneumophila and Mycobacterium avium. Implementation of copper as a disinfectant could help reduce OPs, but a mechanistic understanding of the effects on the microbial community under real-world plumbing conditions is lacking. Here, we carried out a controlled pilot-scale study of hot water systems and applied shotgun metagenomic sequencing to examine the effects of copper dose (0-2 mg/L), orthophosphate corrosion control agent, and water heater anode materials (aluminum vs magnesium vs powered anode) on the bulk water and biofilm microbiome composition. Metagenomic analysis revealed that, even though a copper dose of 1.2 mg/L was required to reduce Legionella and Mycobacterium numbers, lower doses (e.g., ≤0.6 mg/L) measurably impacted the broader microbial community, indicating that the OP strains colonizing these systems were highly copper tolerant. Orthophosphate addition reduced bioavailability of copper, both to OPs and to the broader microbiome. Functional gene analysis indicated that both membrane damage and interruption of nucleic acid replication are likely at play in copper inactivation mechanisms. This study identifies key factors (e.g., orthophosphate, copper resistance, and anode materials) that can confound the efficacy of copper for controlling OPs in hot water plumbing.

Anthropogenic pollution drives the bacterial resistome in a complex freshwater ecosystem

Aquatic ecosystems in anthropogenically impacted areas are important reservoirs of antibiotic resistance genes (ARGs) of allochthonous origin. However, the dynamics of the different ARGs within the bacterial communities of lakes and rivers, as well as the factors that drive their selection, are not completely understood. In this study, we analysed the fate of the bacterial resistome (total content of ARGs and of metal resistance genes, MRGs) for a period of six months (summer-winter) in a continuum lake-river-lake system (Lake Varese, River Bardello, Lake Maggiore) in Northern Italy, by shotgun metagenomics. The metagenomic data were then compared with chemical, physical and microbiological data, to infer the role of anthropogenic pressure in the different sampling stations. ARGs and MRGs were more abundant and diverse in the River Bardello, characterised by the highest anthropogenic pollution. The date of sampling influenced ARGs and MRGs, with higher abundances in summer (August) than in fall or in winter, when the impact of the treated wastewater discharge in the river was limited by a higher water flow from Lake Varese. ARG and MRG abundances were significantly correlated and they co-occurred in the main network analysis modules with potential pathogenic bacteria. Different levels of anthropogenic impact selectively promoted specific ARGs while others, generally abundant in waters, were not affected by anthropogenic pressure. Reducing the level of anthropogenic pressure resulted in a rapid decrease of most ARGs. From our results, the role of anthropogenic pressure in promoting the spread of specific antibiotic resistances and of potential pathogens in aquatic ecosystem becomes clear. Finally we highlight the strict correlation between ARGs and MRGs suggesting their potential co-selection in stressed aquatic bacterial communities.

Analysis and Interpretation of metagenomics data: an approach

Advances in next-generation sequencing technologies have accelerated the momentum of metagenomic studies, which is increasing yearly. The metagenomics field is one of the versatile applications in microbiology, where any interaction in the environment involving microorganisms can be the topic of study. Due to this versatility, the number of applications of this omics technology reached its horizons. Agriculture is a crucial sector involving crop plants and microorganisms interacting together. Hence, studying these interactions through the lenses of metagenomics would completely disclose a new meaning to crop health and development. The rhizosphere is an essential reservoir of the microbial community for agricultural soil. Hence, we focus on the R&D of metagenomic studies on the rhizosphere of crops such as rice, wheat, legumes, chickpea, and sorghum. These recent developments are impossible without the continuous advancement seen in the next-generation sequencing platforms; thus, a brief introduction and analysis of the available sequencing platforms are presented here to have a clear picture of the workflow. Concluding the topic is the discussion about different pipelines applied to analyze data produced by sequencing techniques and have a significant role in interpreting the outcome of a particular experiment. A plethora of different software and tools are incorporated in the automated pipelines or individually available to perform manual metagenomic analysis. Here we describe 8-10 advanced, efficient pipelines used for analysis that explain their respective workflows to simplify the whole analysis process.

Subsurface Manure Injection Reduces Surface Transport of Antibiotic Resistance Genes but May Create Antibiotic Resistance Hotspots in Soils

Compared to surface application, manure subsurface injection reduces surface runoff of nutrients, antibiotic resistant microorganisms, and emerging contaminants. Less is known regarding the impact of both manure application methods on surface transport of antibiotic resistance genes (ARGs) in manure-amended fields. We applied liquid dairy manure to field plots by surface application and subsurface injection and simulated rainfall on the first or seventh day following application. The ARG richness, relative abundance (normalized to 16s rRNA), and ARG profiles in soil and surface runoff were monitored using shotgun metagenomic sequencing. Within 1 day of manure application, compared to unamended soils, soils treated with manure had 32.5-70.5% greater ARG richness and higher relative abundances of sulfonamide (6.5-129%) and tetracycline (752-3766%) resistance genes (p ≤ 0.05). On day 7, soil ARG profiles in the surface-applied plots were similar to, whereas subsurface injection profiles were different from, that of the unamended soils. Forty-six days after manure application, the soil ARG profiles in manure injection slits were 37% more diverse than that of the unamended plots. The abundance of manure-associated ARGs were lower in surface runoff from manure subsurface injected plots and carried a lower resistome risk score in comparison to surface-applied plots. This study demonstrated, for the first time, that although manure subsurface injection reduces ARGs in the runoff, it can create potential long-term hotspots for elevated ARGs within injection slits.

Antibiotic resistance genes correlate with metal resistances and accumulate in the deep water layers of the Black Sea

Seas and oceans are a global reservoir of antibiotic resistance genes (ARGs). Only a few studies investigated the dynamics of ARGs along the water column of the Black Sea, a unique environment, with a peculiar geology, biology and history of anthropogenic pollution. In this study, we analyzed metagenomic data from two sampling campaigns (2013 and 2019) collected across three different sites in the Western Black Sea at depths ranging from 5 to 2000 m. The data were processed to annotate ARGs, metal resistance genes (MRGs) and integron integrase genes. The ARG abundance was significantly higher in the deep water layers and depth was the main driver of beta-diversity both for ARGs and MRGs. Moreover, ARG and MRG abundances strongly correlated (r = 0.95). The integron integrase gene abundances and composition were not influenced by the water depth and did not correlate with ARGs. The analysis of the obtained MAGs showed that some of them harbored intI gene together with several ARGs and MRGs, suggesting the presence of multidrug resistant bacteria and that MRGs and integrons could be involved in the selection of ARGs. These results demonstrate that the Black Sea is not only an important reservoir of ARGs, but also that they accumulate in the deep water layers where co-selection with MRGs could be assumed as a relevant mechanism of their persistence.

Demonstrating a Comprehensive Wastewater-Based Surveillance Approach That Differentiates Globally Sourced Resistomes

Wastewater-based surveillance (WBS) for disease monitoring is highly promising but requires consistent methodologies that incorporate predetermined objectives, targets, and metrics. Herein, we describe a comprehensive metagenomics-based approach for global surveillance of antibiotic resistance in sewage that enables assessment of 1) which antibiotic resistance genes (ARGs) are shared across regions/communities; 2) which ARGs are discriminatory; and 3) factors associated with overall trends in ARGs, such as antibiotic concentrations. Across an internationally sourced transect of sewage samples collected using a centralized, standardized protocol, ARG relative abundances (16S rRNA gene-normalized) were highest in Hong Kong and India and lowest in Sweden and Switzerland, reflecting national policy, measured antibiotic concentrations, and metal resistance genes. Asian versus European/US resistomes were distinct, with macrolide-lincosamide-streptogramin, phenicol, quinolone, and tetracycline versus multidrug resistance ARGs being discriminatory, respectively. Regional trends in measured antibiotic concentrations differed from trends expected from public sales data. This could reflect unaccounted uses, captured only by the WBS approach. If properly benchmarked, antibiotic WBS might complement public sales and consumption statistics in the future. The WBS approach defined herein demonstrates multisite comparability and sensitivity to local/regional factors.

Metagenome Analysis Reveals a Response of the Antibiotic Resistome to Mars-like Extraterrestrial Conditions

The spread of antibiotic resistance is becoming a serious global health concern. Numerous studies have been done to investigate the dynamics of antibiotic resistance genes (ARGs) in both indoor and outdoor environments. Nonetheless, few studies are available about the dynamics of the antibiotic resistome (total content of ARGs in the microbial cultures or communities) under stress in outer space environments. In this study, we aimed to experimentally investigate the dynamics of ARGs and metal resistance genes (MRGs) in Kombucha Mutualistic Community (KMC) samples exposed to Mars-like conditions simulated during the BIOMEX experiment outside the International Space Station with analysis of the metagenomics data previously produced. Thus, we compared them with those of the respective non-exposed KMC samples. The antibiotic resistome responded to the Mars-like conditions by enriching its diversity with ARGs after exposure, which were not found in non-exposed samples (i.e., tet and van genes against tetracycline and vancomycin, respectively). Furthermore, ARGs and MRGs were correlated; therefore, their co-selection could be assumed as a mechanism for maintaining antibiotic resistance in Mars-like environments. Overall, these results highlight the high plasticity of the antibiotic resistome in response to extraterrestrial conditions and in the absence of anthropogenic stresses.

Inhalable Antibiotic Resistome from Wastewater Treatment Plants to Urban Areas: Bacterial Hosts, Dissemination Risks, and Source Contributions

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 (PM_2.5) 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. PM_2.5 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 PM_2.5, 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 PM_2.5 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.

Bacterial Genes Encoding Resistance Against Antibiotics and Metals in Well-Maintained Drinking Water Distribution Systems in Finland

Information on the co-occurrence of antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) among bacterial communities in drinking water distribution systems (DWDSs) is scarce. This study characterized ARGs and MRGs in five well-maintained DWDSs in Finland. The studied DWDSs had different raw water sources and treatment methods. Two of the waterworks employed artificially recharged groundwater (ARGW) and used no disinfection in the treatment process. The other three waterworks (two surface and one groundwater source) used UV light and chlorine during the treatment process. Ten bulk water samples (two from each DWDS) were collected, and environmental DNA was extracted and then sequenced using the Illumina HiSeq platform for high-throughput shotgun metagenome sequencing. A total of 430 ARGs were characterized among all samples with the highest diversity of ARGs identified from samples collected from non-disinfected DWDSs. Furthermore, non-disinfected DWDSs contained the highest diversity of bacterial communities. However, samples from DWDSs using disinfectants contained over double the ratio of ARG reads to 16S rRNA gene reads and most of the MRG (namely mercury and arsenic resistance genes). The total reads and types of ARGs conferring genes associated with antibiotic groups namely multidrug resistance, and bacitracin, beta-lactam, and aminoglycoside and mercury resistance genes increased in waterworks treating surface water with disinfection. The findings of this study contribute toward a comprehensive understanding of ARGs and MRGs in DWDSs. The occurrence of bacteria carrying antibiotic or metal resistance genes in drinking water causes direct exposure to people, and thus, more systematic investigation is needed to decipher the potential effect of these resistomes on human health.

Bioplastic accumulates antibiotic and metal resistance genes in coastal marine sediments

The oceans are increasingly polluted with plastic debris, and several studies have implicated plastic as a reservoir for antibiotic resistance genes and a potential vector for antibiotic-resistant bacteria. Bioplastic is widely regarded as an environmentally friendly replacement to conventional petroleum-based plastic, but the effects of bioplastic pollution on marine environments remain largely unknown. Here, we present the first evidence that bioplastic accumulates antibiotic resistance genes (ARGs) and metal resistance genes (MRGs) in marine sediments. Biofilms fouling ceramic, polyethylene terephthalate (PET), and polyhydroxyalkanoate (PHA) were investigated by shotgun metagenomic sequencing. Four ARG groups were more abundant in PHA: trimethoprim resistance (TMP), multidrug resistance (MDR), macrolide-lincosamide-streptogramin resistance (MLS), and polymyxin resistance (PMR). One MRG group was more abundant in PHA: multimetal resistance (MMR). The relative abundance of ARGs and MRGs were strongly correlated based on a Mantel test between the Bray-Curtis dissimilarity matrices (R = 0.97, p < 0.05) and a Pearson's analysis (R = 0.96, p < 0.05). ARGs were detected in more than 40% of the 57 metagenome-assembled genomes (MAGs) while MRGs were detected in more than 90% of the MAGs. Further investigation (e.g., culturing, genome sequencing, antibiotic susceptibility testing) revealed that PHA biofilms were colonized by hemolytic Bacillus cereus group bacteria that were resistant to beta-lactams, vancomycin, and bacitracin. Taken together, our findings indicate that bioplastic, like conventional petroleum-based plastic, is a reservoir for resistance genes and a potential vector for antibiotic-resistant bacteria in coastal marine sediments.

The endophytome (plant-associated microbiome): methodological approaches, biological aspects, and biotech applications

Similar to other organisms, plants establish interactions with a variety of microorganisms in their natural environment. The plant microbiome occupies the host plant's tissues, either internally or on its surfaces, showing interactions that can assist in its growth, development, and adaptation to face environmental stresses. The advance of metagenomics and metatranscriptomics approaches has strongly driven the study and recognition of plant microbiome impacts. Research in this regard provides comprehensive information about the taxonomic and functional aspects of microbial plant communities, contributing to a better understanding of their dynamics. Evidence of the plant microbiome's functional potential has boosted its exploitation to develop more ecological and sustainable agricultural practices that impact human health. Although microbial inoculants' development and use are promising to revolutionize crop production, interdisciplinary studies are needed to identify new candidates and promote effective practical applications. On the other hand, there are challenges in understanding and analyzing complex data generated within a plant microbiome project's scope. This review presents aspects about the complex structuring and assembly of the microbiome in the host plant's tissues, metagenomics, and metatranscriptomics approaches for its understanding, covering descriptions of recent studies concerning metagenomics to characterize the microbiome of non-model plants under different aspects. Studies involving bio-inoculants, isolated from plant microbial communities, capable of assisting in crops' productivity, are also reviewed.

Music of metagenomics-a review of its applications, analysis pipeline, and associated tools

This humble effort highlights the intricate details of metagenomics in a simple, poetic, and rhythmic way. The paper enforces the significance of the research area, provides details about major analytical methods, examines the taxonomy and assembly of genomes, emphasizes some tools, and concludes by celebrating the richness of the ecosystem populated by the "metagenome."

Mapping the Terrain for Pathogen Persistence and Proliferation in Non-potable Reuse Distribution Systems: Interactive Effects of Biofiltration, Disinfection, and Water Age

Diverse pathogens can potentially persist and proliferate in reclaimed water distribution systems (RWDSs). The goal of this study was to evaluate interactive effects of reclaimed water treatments and water age on persistence and proliferation of multiple fecal (e.g., Klebsiella, Enterobacter) and non-fecal (e.g., Legionella, mycobacteria) gene markers in RWDSs. Six laboratory-scale RWDSs were operated in parallel receiving the influent with or without biologically active carbon (BAC) filtration + chlorination, chloramination, or no disinfectant residual. After 3 years of operation, the RWDSs were subject to sacrificial sampling and shotgun metagenomic sequencing. We developed an in-house metagenome-derived pathogen quantification pipeline, validated by quantitative polymerase chain reaction and mock community analysis, to estimate changes in abundance of ∼30 genera containing waterborne pathogens. Microbial community composition in the RWDS bulk water, biofilm, and sediments was clearly shaped by BAC filtration, disinfectant conditions, and water age. Key commonalities were noted in the ecological niches occupied by fecal pathogen markers in the RWDSs, while non-fecal pathogen markers were more varied in their distribution. BAC-filtration + chlorine was found to most effectively control the widest range of target genera. However, filtration alone or chlorine secondary disinfection alone resulted in proliferation of some of these genera containing waterborne pathogens.

Integrated Metagenomic Assessment of Multiple Pre-harvest Control Points on Lettuce Resistomes at Field-Scale

An integrated understanding of factors influencing the occurrence, distribution, and fate of antibiotic resistance genes (ARGs) in vegetable production systems is needed to inform the design and development of strategies for mitigating the potential for antibiotic resistance propagation in the food chain. The goal of the present study was to holistically track antibiotic resistance and associated microbiomes at three distinct pre-harvest control points in an agroecosystem in order to identify the potential impacts of key agricultural management strategies. Samples were collected over the course of a single growing season (67 days) from field-scale plots amended with various organic and inorganic amendments at agronomic rates. Dairy-derived manure and compost amendment samples (n = 14), soil samples (n = 27), and lettuce samples (n = 12) were analyzed via shotgun metagenomics to assess multiple pre-harvest factors as hypothetical control points that shape lettuce resistomes. Pre-harvest factors of interest included manure collection during/post antibiotic use, manure composting, and soil amended with organic (stockpiled manure/compost) versus chemical fertilizer. Microbial community resistome and taxonomic compositions were unique from amendment to soil to lettuce surface according to dissimilarity analysis. The highest resistome alpha diversity (i.e., unique ARGs, n = 642) was detected in amendment samples prior to soil application, while the composted manure had the lowest total ARG relative abundance (i.e., 16S rRNA gene-normalized). Regardless of amendment type, soils acted as an apparent ecological buffer, i.e., soil resistome and taxonomic profiles returned to background conditions 67 d-post amendment application. Effects of amendment conditions surprisingly re-emerged in lettuce phyllosphere resistomes, with the highest total ARG relative abundances recovered on the surface of lettuce plants grown in organically-fertilized soils (i.e., compost- and manure-amended soils). Co-occurrence analysis identified 55 unique ARGs found both in the soil amendments and on lettuce surfaces. Among these, arnA and pmrF were the most abundant ARGs co-occurring with mobile genetic elements (MGE). Other prominent ARG-MGE co-occurrences throughout this pre-harvest lettuce production chain included: TetM to transposon (Clostridiodies difficile) in the manure amendment and TriC to plasmid (Ralstonia solanacearum) on the lettuce surfaces. This suggests that, even with imposing manure management and post-amendment wait periods in agricultural systems, ARGs originating from manure can still be found on crop surfaces. This study demonstrates a comprehensive approach to identifying key control points for the propagation of ARGs in vegetable production systems, identifying potential ARG-MGE combinations that could inform future surveillance. The findings suggest that additional pre-harvest and potentially post-harvest interventions may be warranted to minimize risk of propagating antibiotic resistance in the food chain.

Interplay of Biologically Active Carbon Filtration and Chlorine-Based Disinfection in Mitigating the Dissemination of Antibiotic Resistance Genes in Water Reuse Distribution Systems

Appropriate management approaches are needed to minimize the proliferation of antibiotic resistance genes (ARGs) in reclaimed water distribution systems (RWDSs). Six laboratory-scale RWDSs were operated over 3 years receiving influent with or without biologically active carbon (BAC) filtration + chlorination, chloramination, or no disinfectant residual. Shotgun metagenomic sequencing was applied toward comprehensive characterization of resistomes, focusing on total ARGs, ARG mobility, and specific ARGs of clinical concern. ARGs such as aadA, bacA, bla_OXA, mphE, msrE, sul1, and sul2 were found to be particularly sensitive to varying RWDS conditions. BAC filtration with chlorination most effectively achieved and maintained the lowest levels of nearly all metagenomically derived antibiotic resistance indicators. However, BAC filtration or addition of residual disinfectants alone tended to increase these indicators. Biofilm and sediment compartments harbored ARGs in disinfected systems, presenting a concern for their release to bulk water. Relative and absolute abundances of most ARGs tended to decrease with water age (up to 5 days), with notable exceptions in BAC-filtered chloraminated and no residual systems. Superchlorination of unfiltered water especially raised concerns in terms of elevation of clinically relevant and mobile ARGs. This study revealed that BAC filtration and disinfection must be carefully coordinated in order to effectively mitigate ARG dissemination via RWDSs.

Structural, functional, resistome and pathogenicity profiling of the Cooum river

The microbial community's structure and functions determine the health, quality, and anthropogenic conditions of the river ecosystems. The presence of Bacteria such as Arcobacter spp, Escherichia spp, and Campylobacters spp, have been shown to reflect the poor water quality of rivers. Apprehension of the microbial community in polluted water bodies is significant because it affects human health and the environment. Culture-independent metagenomic and metatranscriptomic approaches employed in the current study of the Cooum river unraveled the taxonomic classification of diverse microbes, including archaea, bacteria, viruses, and phages. The presence of abundant Macellibacteroides fermentans, Arcobacter bivolvorium, Arcobacter butzleri, Methanothrix soenhngeii, and Bacteroides graminisolvens were noted. Viruses and phages like Caudovirales, Human mastadenovirus C, Siphoviridae, Escherichia phage, Erwinia phage, Synechoccus phage, and Vibrio phage were relatively predominant. Various metabolic pathways like methane, sulfur, and nitrogen metabolism adopted by the microbiome confer dangerous gases. Mechanisms such as secretory systems, signal transduction, Chemotaxis, quorum sensing, transportation of chemicals and ions were significantly enriched. The microbes expressed antimicrobial resistance mechanisms as identified from the genes encoding beta-lactamase enzymes and aminoglycoside phosphotransferase enzymes. Metal resistance mechanisms against copper, tellurium, chromium, and cadmium were plentiful. Presence of human pathogens interactions with Yersinia pestis, Campylobacter jejuni, Escherichia coli, Helicobacter pylori, and Francisella tularensis subsp. tularensis suggested the possibilities of transmission of pathogenesis to humans. The current study is the first to apprehend the detailed microbiome composition of one of the highly polluted rivers in South India. The study elaborated the microbiome's structure, functions, and metabolic potential at a specific site of the polluted river.

Evaluation of Metagenomic-Enabled Antibiotic Resistance Surveillance at a Conventional Wastewater Treatment Plant

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.

Cross-comparison of methods for quantifying antibiotic resistance in agricultural soils amended with dairy manure and compost

Agricultural soils are often amended with livestock manure, making them a key reservoir of antibiotic resistance genes (ARGs). Given that soils are among the most microbially-diverse environments on the planet; effective characterization and quantification of the effects of manure-derived amendments on soil resistomes is a major challenge. This study examined the effects of dairy manure-derived amendments on agricultural soils via two strategies: quantification of anthropogenic ARG markers via qPCR and shotgun metagenomic resistome profiling; and these strategies were compared to a previously published antibiotic resistant fecal coliform dataset. Soil samples were collected throughout a 120 day complete block field experiment to compare the effects of amendment type on antibiotic resistance. Results of all three measurements were consistent with the hypothesis that the application of composted manure reduced antibiotic resistance in soil relative to the application of raw manure, although some differences were noted in comparing the patterns of the three measurements with time. Raw dairy manure-amended soils yielded high sul1 and tet(W) relative abundances on Day 0 (following amendment application), but significantly decreased to background levels by Day 67 (harvest) and Day 120 (study completion). Shotgun metagenomics similarly detected a decrease in the relative abundances of sulfonamide and tetracycline-associated ARGs over time in the raw manure- and compost-amended soils; however, these levels were significantly lower than those estimated by qPCR. Interestingly, although patterns of sulfonamide and tetracycline resistance among culturable fecal coliforms echoed those observed via qPCR and metagenomics; erythromycin resistant coliforms were directly recovered by culture in amended soils, but corresponding ARGs were not detected by qPCR or metagenomics. This study supports both composting and time restrictions as means of reducing the potential for antibiotic resistance in manure to spread via soil application. Results suggest some differences in finer conclusions drawn depending on which antibiotic resistance monitoring target is selected.

AgroSeek: a system for computational analysis of environmental metagenomic data and associated metadata

BACKGROUND

Metagenomics is gaining attention as a powerful tool for identifying how agricultural management practices influence human and animal health, especially in terms of potential to contribute to the spread of antibiotic resistance. However, the ability to compare the distribution and prevalence of antibiotic resistance genes (ARGs) across multiple studies and environments is currently impossible without a complete re-analysis of published datasets. This challenge must be addressed for metagenomics to realize its potential for helping guide effective policy and practice measures relevant to agricultural ecosystems, for example, identifying critical control points for mitigating the spread of antibiotic resistance.

RESULTS

Here we introduce AgroSeek, a centralized web-based system that provides computational tools for analysis and comparison of metagenomic data sets tailored specifically to researchers and other users in the agricultural sector interested in tracking and mitigating the spread of ARGs. AgroSeek draws from rich, user-provided metagenomic data and metadata to facilitate analysis, comparison, and prediction in a user-friendly fashion. Further, AgroSeek draws from publicly-contributed data sets to provide a point of comparison and context for data analysis. To incorporate metadata into our analysis and comparison procedures, we provide flexible metadata templates, including user-customized metadata attributes to facilitate data sharing, while maintaining the metadata in a comparable fashion for the broader user community and to support large-scale comparative and predictive analysis.

CONCLUSION

AgroSeek provides an easy-to-use tool for environmental metagenomic analysis and comparison, based on both gene annotations and associated metadata, with this initial demonstration focusing on control of antibiotic resistance in agricultural ecosystems. Agroseek creates a space for metagenomic data sharing and collaboration to assist policy makers, stakeholders, and the public in decision-making. AgroSeek is publicly-available at https://agroseek.cs.vt.edu/ .

Biodiversity of magnetotactic bacteria in the tropical marine environment of Singapore revealed by metagenomic analysis

Most studies on the diversity of magnetotactic bacteria (MTB) have been conducted on samples obtained from the Northern or the Southern hemispheres. The diversity of MTB in tropical Asia near the geo-equator, with a close-to-zero geomagnetic inclination, weak magnetic field and constantly high seawater temperature has never been explored. This study aims to decipher the diversity of MTB in the marine environment of Singapore through shotgun metagenomics. Although MTB has been acknowledged to be ubiquitous in aquatic environments, we did not observe magnetotactic behaviour in the samples. However, we detected the presence and determined the diversity of MTB through bioinformatic analyses. Metagenomic analysis suggested majority of the MTB in the seafloor sediments represents novel MTB taxa that cannot be classified at the species level. The relative abundance of MTB (~0.2-1.69%) in the samples collected from the marine environment of Singapore was found to be substantially lower than studies for other regions. In contrast to other studies, the genera Magnetovibrio and Desulfamplus, but not Magnetococcus, were the dominant MTB. Additionally, we recovered 3 MTB genomic bins that are unclassified at the species level, with Magnetovibrio blakemorei being the closest-associated genome. All the recovered genomic bins contain homologs of at least 5 of the 7 mam genes but lack homologs for mamI, a membrane protein suggested to take part in the magenetosome invagination. This study fills in the knowledge gap of MTB biodiversity in the tropical marine environment near the geo-equator. Our findings will facilitate future research efforts aiming to unravel the ecological roles of MTB in the tropical marine environments as well as to bioprospecting novel MTB that have been adapted to tropical marine environments for biotechnological applications.

CAMAMED: a pipeline for composition-aware mapping-based analysis of metagenomic data

Metagenomics is the study of genomic DNA recovered from a microbial community. Both assembly-based and mapping-based methods have been used to analyze metagenomic data. When appropriate gene catalogs are available, mapping-based methods are preferred over assembly based approaches, especially for analyzing the data at the functional level. In this study, we introduce CAMAMED as a composition-aware mapping-based metagenomic data analysis pipeline. This pipeline can analyze metagenomic samples at both taxonomic and functional profiling levels. Using this pipeline, metagenome sequences can be mapped to non-redundant gene catalogs and the gene frequency in the samples are obtained. Due to the highly compositional nature of metagenomic data, the cumulative sum-scaling method is used at both taxa and gene levels for compositional data analysis in our pipeline. Additionally, by mapping the genes to the KEGG database, annotations related to each gene can be extracted at different functional levels such as KEGG ortholog groups, enzyme commission numbers and reactions. Furthermore, the pipeline enables the user to identify potential biomarkers in case-control metagenomic samples by investigating functional differences. The source code for this software is available from https://github.com/mhnb/camamed. Also, the ready to use Docker images are available at https://hub.docker.com.

Demonstrating an Integrated Antibiotic Resistance Gene Surveillance Approach in Puerto Rican Watersheds Post-Hurricane Maria

Comprehensive surveillance approaches are needed to assess sources, clinical relevance, and mobility of antibiotic resistance genes (ARGs) in watersheds. Here, we examined metrics derived from shotgun metagenomic sequencing and relationship to human fecal markers (HFMs; crAssphage, enterococci) and anthropogenic antibiotic resistance markers (AARMs; intI1, sul1) in three distinct Puerto Rican watersheds as a function of adjacent land use and wastewater treatment plant (WWTP) input 6 months after Hurricane Maria, a category V storm. Relative abundance and diversity of total ARGs increased markedly downstream of WWTP inputs, with ARGs unique to WWTP and WWTP-impacted river samples predominantly belonging to the aminoglycoside and β-lactam resistance classes. WWTP and other anthropogenic inputs were similarly associated with elevated resistome risk scores and mobility incidence (M%). Contig analysis indicated a wide variety of mobile β-lactam ARGs associated with pathogens downstream of WWTP discharge that were consistent with regional clinical concern, e.g., Klebsiella pneumoniae contigs containing KPC-2 within an ISKpn6-like transposase. HFMs and AARMs correlated strongly with the absolute abundance of total ARGs, but AARMs better predicted the majority of ARGs in general (85.4 versus <2%) and β-lactam ARGs in particular. This study reveals sensitive, quantitative, mobile, clinically relevant, and comprehensive targets for antibiotic resistance surveillance in watersheds.

Homogeneous selection drives antibiotic resistome in two adjacent sub-watersheds, China

Rivers are a significant reservoir of antibiotic resistance genes (ARGs), yet the biogeographic pattern of riverine ARGs and its underlying driving forces remain poorly understood. Here, we used metagenomic approach to investigate the spatio-temporal variation of ARGs in two adjacent sub-watersheds viz. North River (NR) and West River (WR), China. The results demonstrated that Bacitracin (22.8 % of the total ARGs), multidrug (20.7 %), sulfonamide (15.2 %) and tetracycline (10.9 %) were the dominant ARG types. SourceTracker analysis indicated that sewage treatment plants as the main source of ARGs, while animal feces mainly contributed in spreading the ARGs in the upstream of NR. Random forest and network analyses confirmed that NR was under the influence of fecal pollution. PCoA analysis demonstrated that the composition of ARGs changed along with the anthropogenic gradients, while the Raup-Crick null model showed that homogenizing selection mediated by class 1 integron intI1 resulted in stable ARG communities at whole watershed scale. Structural equation models revealed that microbial community, grassland and several non-antibiotic micropollutants may also play certain roles in influencing the distribution of ARGs. Overall, the observed deterministic formation of ARGs in riverine systems calls effective management strategies to mitigate the risks of antibiotic resistance on public health.

Effect of mesophilic anaerobic digestion on the resistome profile of dairy manure

The effect of mesophilic anaerobic digestion (AD) on the resistome profile of manures from two different dairy farms was evaluated using a metagenomic approach. A total of 187 unique Antibiotic resistance genes (ARGs) for 17 different classes of antibiotics were detected in raw (undigested) manures. The results indicate that regardless of the origin of the dairy manure, mesophilic AD was capable of reducing or enriching the relative abundance of some ARGs. The main driver of these changes was strongly correlated with the evolution of the microbial community during the AD process. Putative ARG hosts were suggested by analyses of the co-occurrence of microbial groups and ARGs. Finally, network analyses revealed that mesophilic AD could also reduce the co-occurrence of different groups of ARGs potentially located in the same genetic elements. Our results provide valuable insights into the microbial mechanisms driving the diversity and abundance of ARGs during mesophilic AD.

Comparative metagenomics and characterization of antimicrobial resistance genes in pasteurized and homemade fermented Arabian laban

The aim of this study was to investigate bacterial diversity and function in a fermented milk drink called laban, which is traditionally served in the Middle East, Africa, and Indian subcontinent. Pasteurized laban (LBP) and unpasteurized, homemade, raw laban (LBR) underwent 16S rRNA gene amplicon and shotgun sequencing to analyze their bacterial community, presence of antimicrobial resistance genes (ARGs), and metabolic pathways. This study highlighted relatively greater diversity in LBR bacterial populations compared to LBP, despite containing similar major taxa that consisted primarily of Firmicutes followed by Proteobacteria, Bacteroidetes, and Actinobacteria. The dominant species, Streptococcus thermophilus, was relatively more abundant in LBP (80.7%) compared to LBR (47.9%). LBR had increased diversity and higher relative abundance of several known probiotic bacteria, such as Streptococcus salivarius and Lactococcus lactis, whereas Lactobacillus acidophilus was detected at a higher abundance in LBP. Pathogens like Acinetobacter baumannii, Streptococcus pneumoniae, Streptococcus pyogenes, and Escherichia coli had lower abundance in LBP compared to LBR. Thirty-three ARGs were detected in LBR compared to nine in LBP and are responsible for resistance to 11 classes of antibiotics. A significant proportion of the metagenomes from both types of laban were assigned to housekeeping functions, such as amino acid metabolism, translation, membrane transport, and carbohydrate metabolism. LBR demonstrated increased diversity in probiotics and metabolic functions compared to LBP. However, the relatively high diversity of pathogenic and opportunistic bacteria and ARGs in LBR raises safety concerns and highlights the need for a more hygienic environment for the processing of homemade fermented dairy foods.

Metagenomic approaches in microbial ecology: an update on whole-genome and marker gene sequencing analyses

Metagenomics and marker gene approaches, coupled with high-throughput sequencing technologies, have revolutionized the field of microbial ecology. Metagenomics is a culture-independent method that allows the identification and characterization of organisms from all kinds of samples. Whole-genome shotgun sequencing analyses the total DNA of a chosen sample to determine the presence of micro-organisms from all domains of life and their genomic content. Importantly, the whole-genome shotgun sequencing approach reveals the genomic diversity present, but can also give insights into the functional potential of the micro-organisms identified. The marker gene approach is based on the sequencing of a specific gene region. It allows one to describe the microbial composition based on the taxonomic groups present in the sample. It is frequently used to analyse the biodiversity of microbial ecosystems. Despite its importance, the analysis of metagenomic sequencing and marker gene data is quite a challenge. Here we review the primary workflows and software used for both approaches and discuss the current challenges in the field.

Environmental resistome risks of wastewaters and aquatic environments deciphered by shotgun metagenomic assembly

In this paper, we deciphered the core resistome disseminating from hospital wastewater to the aquatic environment by characterising the resistome, plasmidome, mobilome and virulome using metagenomic analysis. This study also elucidated different environmental resistome risks using shotgun-metagenomic assembly. The results showed that clinically relevant taxa were found in assessed matrices (Salmonella spp., Acinetobacter spp, Escherichia-Shigella spp., Pseudomonas spp., Staphylococcus spp. and Vibrio spp.). For the plasmidome, we found 249 core plasmidome sequences that were shared among all assessed matrices. The core mobilome of 2424 mobile genetic elements shared among all assessed matrices was found. Regarding the virulome, we found 148 core virulence factors shared among all assessed samples, and the core virulome content was consistently shared across the most abundant bacterial genera. Although influent of wastewater showed considerable higher relative bacterial abundance (P = 0.008), hospital wastewater showed significant higher environmental resistome risk scores against all other assessed matrices, with an average of 46.34% (P = 0.001). These results suggest hospital wastewater, effluent and sewage sludge should be subjected to stringent mitigating measures to minimise such dissemination.

Identification of discriminatory antibiotic resistance genes among environmental resistomes using extremely randomized tree algorithm

BACKGROUND

The interconnectivities of built and natural environments can serve as conduits for the proliferation and dissemination of antibiotic resistance genes (ARGs). Several studies have compared the broad spectrum of ARGs (i.e., "resistomes") in various environmental compartments, but there is a need to identify unique ARG occurrence patterns (i.e., "discriminatory ARGs"), characteristic of each environment. Such an approach will help to identify factors influencing ARG proliferation, facilitate development of relative comparisons of the ARGs distinguishing various environments, and help pave the way towards ranking environments based on their likelihood of contributing to the spread of clinically relevant antibiotic resistance. Here we formulate and demonstrate an approach using an extremely randomized tree (ERT) algorithm combined with a Bayesian optimization technique to capture ARG variability in environmental samples and identify the discriminatory ARGs. The potential of ERT for identifying discriminatory ARGs was first evaluated using in silico metagenomic datasets (simulated metagenomic Illumina sequencing data) with known variability. The application of ERT was then demonstrated through analyses using publicly available and in-house metagenomic datasets associated with (1) different aquatic habitats (e.g., river, wastewater influent, hospital effluent, and dairy farm effluent) to compare resistomes between distinct environments and (2) different river samples (i.e., Amazon, Kalamas, and Cam Rivers) to compare resistome characteristics of similar environments.

RESULTS

The approach was found to readily identify discriminatory ARGs in the in silico datasets. Also, it was not found to be biased towards ARGs with high relative abundance, which is a common limitation of feature projection methods, and instead only captured those ARGs that elicited significant profiles. Analyses of publicly available metagenomic datasets further demonstrated that the ERT approach can effectively differentiate real-world environmental samples and identify discriminatory ARGs based on pre-defined categorizing schemes.

CONCLUSIONS

Here a new methodology was formulated to characterize and compare variances in ARG profiles between metagenomic data sets derived from similar/dissimilar environments. Specifically, identification of discriminatory ARGs among samples representing various environments can be identified based on factors of interest. The methodology could prove to be a particularly useful tool for ARG surveillance and the assessment of the effectiveness of strategies for mitigating the spread of antibiotic resistance. The python package is hosted in the Git repository: https://github.com/gaarangoa/ExtrARG.

Effect of antibiotic use and composting on antibiotic resistance gene abundance and resistome risks of soils receiving manure-derived amendments

Manure-derived amendments are commonly applied to soil, raising questions about whether antibiotic use in livestock could influence the soil resistome (collective antibiotic resistance genes (ARGs)) and ultimately contribute to the spread of antibiotic resistance to humans during food production. Here, we examined the metagenomes of soils amended with raw or composted manure generated from dairy cows administered pirlimycin and cephapirin (antibiotic) or no antibiotics (control) relative to unamended soils. Initial amendment (Day 1) with manure or compost significantly increased the diversity (richness) of ARGs in soils (p < 0.01) and resulted in distinct abundances of individual ARG types. Notably, initial amendment with antibiotic-manure significantly increased the total ARG relative abundances (per 16S rRNA gene) in the soils (2.21 × unamended soils, p < 0.001). After incubating 120 days, to simulate a wait period before crop harvest, 282 ARGs reduced 4.33-fold (median) up to 307-fold while 210 ARGs increased 2.89-fold (median) up to 76-fold in the antibiotic-manure-amended soils, resulting in reduced total ARG relative abundances equivalent to those of the unamended soils. We further assembled the metagenomic data and calculated resistome risk scores, which was recently defined as a relative index comparing co-occurrence of sequences corresponding to ARGs, mobile genetic elements, and putative pathogens on the same scaffold. Initial amendment of manure significantly increased the soil resistome risk scores, especially when generated by cows administered antibiotics, while composting reduced the effects and resulted in soil resistomes more similar to the background. The risk scores of manure-amended soils reduced to levels comparable to the unamended soils after 120 days. Overall, this study provides an integrated, high-resolution examination of the effects of prior antibiotic use, composting, and a 120-day wait period on soil resistomes following manure-derived amendment, demonstrating that all three management practices have measurable effects and should be taken into consideration in the development of policy and practice for mitigating the spread of antibiotic resistance.

Water and sanitation: an essential battlefront in the war on antimicrobial resistance

Water and sanitation represent a key battlefront in combatting the spread of antimicrobial resistance (AMR). Basic water sanitation infrastructure is an essential first step towards protecting public health, thereby limiting the spread of pathogens and the need for antibiotics. AMR presents unique human health risks, meriting new risk assessment frameworks specifically adapted to water and sanitation-borne AMR. There are numerous exposure routes to AMR originating from human waste, each of which must be quantified for its relative risk to human health. Wastewater treatment plants play a vital role in centralized collection and treatment of human sewage, but there are numerous unresolved issues in terms of the microbial ecological processes occurring within them and the extent to which they attenuate or amplify AMR. Research is needed to advance understanding of the fate of resistant bacteria and antibiotic resistance genes in various waste management systems, depending on the local constraints and intended reuse applications. World Health Organization and national AMR action plans would benefit from a more holistic 'One Water' understanding. In this article we provide a framework for research, policy, practice and public engagement aimed at limiting the spread of AMR from water and sanitation in low-, medium- and high-income countries.

NanoARG: a web service for detecting and contextualizing antimicrobial resistance genes from nanopore-derived metagenomes

BACKGROUND

Direct and indirect selection pressures imposed by antibiotics and co-selective agents and horizontal gene transfer are fundamental drivers of the evolution and spread of antibiotic resistance. Therefore, effective environmental monitoring tools should ideally capture not only antibiotic resistance genes (ARGs), but also mobile genetic elements (MGEs) and indicators of co-selective forces, such as metal resistance genes (MRGs). A major challenge towards characterizing the potential human health risk of antibiotic resistance is the ability to identify ARG-carrying microorganisms, of which human pathogens are arguably of greatest risk. Historically, short reads produced by next-generation sequencing technologies have hampered confidence in assemblies for achieving these purposes.

RESULTS

Here, we introduce NanoARG, an online computational resource that takes advantage of the long reads produced by nanopore sequencing technology. Specifically, long nanopore reads enable identification of ARGs in the context of relevant neighboring genes, thus providing valuable insight into mobility, co-selection, and pathogenicity. NanoARG was applied to study a variety of nanopore sequencing data to demonstrate its functionality. NanoARG was further validated through characterizing its ability to correctly identify ARGs in sequences of varying lengths and a range of sequencing error rates.

CONCLUSIONS

NanoARG allows users to upload sequence data online and provides various means to analyze and visualize the data, including quantitative and simultaneous profiling of ARGs, MRGs, MGEs, and putative pathogens. A user-friendly interface allows users the analysis of long DNA sequences (including assembled contigs), facilitating data processing, analysis, and visualization. NanoARG is publicly available and freely accessible at https://bench.cs.vt.edu/nanoarg .

Effects of Dairy Manure-Based Amendments and Soil Texture on Lettuce- and Radish-Associated Microbiota and Resistomes

Dairy cattle are routinely treated with antibiotics, and the resulting manure or composted manure is commonly used as a soil amendment for crop production, raising questions regarding the potential for antibiotic resistance to propagate from "farm to fork." The objective of this study was to compare the microbiota and "resistomes" (i.e., carriage of antibiotic resistance genes [ARGs]) associated with lettuce leaf and radish taproot surfaces grown in different soils amended with dairy manure, compost, or chemical fertilizer only (control). Manure was collected from antibiotic-free dairy cattle (DC) or antibiotic-treated dairy cattle (DA), with a portion composted for parallel comparison. Amendments were applied to loamy sand or silty clay loam, and lettuce and radishes were cultivated to maturity in a greenhouse. Metagenomes were profiled via shotgun Illumina sequencing. Radishes carried a distinct ARG composition compared to that of lettuce, with greater relative abundance of total ARGs. Taxonomic species richness was also greater for radishes by 1.5-fold. The resistomes of lettuce grown with DC compost were distinct from those grown with DA compost, DC manure, or fertilizer only. Further, compost applied to loamy sand resulted in twofold-greater relative abundance of total ARGs on lettuce than when applied to silty clay loam. The resistomes of radishes grown with biological amendments were distinct from the corresponding fertilizer controls, but effects of composting or antibiotic use were not measureable. Cultivation in loamy sand resulted in higher species richness for both lettuce and radishes than when grown in silty clay loam by 2.2-fold and 1.2-fold, respectively, when amended with compost.IMPORTANCE A controlled, integrated, and replicated greenhouse study, along with comprehensive metagenomic analysis, revealed that multiple preharvest factors, including antibiotic use during manure collection, composting, biological soil amendment, and soil type, influence vegetable-borne resistomes. Here, radishes, a root vegetable, carried a greater load of ARGs and species richness than lettuce, a leafy vegetable. However, the lettuce resistome was more noticeably influenced by upstream antibiotic use and composting. Network analysis indicated that cooccurring ARGs and mobile genetic elements were almost exclusively associated with conditions receiving raw manure amendments, suggesting that composting could alleviate the mobility of manure-derived resistance traits. Effects of preharvest factors on associated microbiota and resistomes of vegetables eaten raw are worthy of further examination in terms of potential influence on human microbiomes and spread of antibiotic resistance. This research takes a step toward identifying on-farm management practices that can help mitigate the spread of agricultural sources of antibiotic resistance.

Differential Drivers of Antimicrobial Resistance across the World

Antimicrobial resistance (AMR) is one of the greatest threats faced by humankind. The development of resistance in clinical and hospital settings has been well documented ever since the initial discovery of penicillin and the subsequent introduction of sulfonamides as clinical antibiotics. In contrast, the environmental (i.e., community-acquired) dimensions of resistance dissemination have been only more recently delineated. The global spread of antibiotic resistant bacteria (ARB) and antibiotic resistance genes (ARGs) between air, water, soil, and food is now well documented, while the factors that affect ARB and ARG dissemination (e.g., water and air quality, antibiotic fluxes, urbanization, sanitation practices) in these and other environmental matrices are just now beginning to be more fully appreciated. In this Account, we discuss how the global perpetuation of resistance is dictated by highly interconnected socioeconomic risk factors and illustrate that development status should be more fully considered when developing global strategies to address AMR. We first differentiate low to middle income countries (LMICs) and high-income countries (HICs), then we summarize the modes of action of commercially available antibiotics, and then discuss the four primary mechanisms by which bacteria develop resistance to those antibiotics. Resistance is disseminated via both vertical gene transfer (VGT; parent to offspring) as well as by horizontal gene transfer (HGT; cell to cell transference of genetic material). A key challenge hindering attempts to control resistance dissemination is the presence of native, environmental bacteria that can harbor ARGs. Such environmental "resistomes" have potential to transfer resistance to pathogens via HGT. Of particular concern is the development of resistance to antibiotics of last-resort such as the cephalosporins, carbapenems, and polymyxins. We then illustrate how antibiotic use differs in LMICs relative to HICs in terms of the volumes of antibiotics used and their fate within local environments. Antibiotic use in HICs has remained flat over the past 15 years, while in LMICs use over the same period has increased substantially as a result of economic improvements and changes in diet. These use and fate differences impact local citizens and thus the local dissemination of AMR. Various physical, social, and economic circumstances within LMICs potentially favor AMR dissemination. We focus on three physical factors: changing population density, sanitation infrastructure, and solid-waste disposal. We show that high population densities in cities within LMICs that suffer from poor sanitation and solid-waste disposal can potentially impact the dissemination of resistance. In the final section, we discuss potential monitoring approaches to quantify the spread of resistance both within LMICs as well as in HICs. We posit that culture-based approaches, molecular approaches, and cutting-edge nanotechnology-based methods for monitoring ARB and ARGs should be considered both within HICs and, as appropriate, within LMICs.

Gamma Irradiation Influences the Survival and Regrowth of Antibiotic-Resistant Bacteria and Antibiotic-Resistance Genes on Romaine Lettuce

Contamination of romaine lettuce with human pathogens, antibiotic-resistant bacteria (ARB), and antibiotic resistance genes (ARGs) occurs during production. Post-harvest interventions are emplaced to mitigate pathogens, but could also mitigate ARB and ARGs on vegetables. The objective of this research was to determine changes to lettuce phyllosphere microbiota, inoculated ARB, and the resistome (profile of ARGs) following washing with a sanitizer, gamma irradiation, and cold storage. To simulate potential sources of pre-harvest contamination, romaine lettuce leaves were inoculated with compost slurry containing antibiotic-resistant strains of pathogenic (Escherichia coli O157:H7) and representative of spoilage bacteria (Pseudomonas aeruginosa). Various combinations of washing with sodium hypochlorite (50 ppm free chlorine), packaging under modified atmosphere (98% nitrogen), irradiating (1.0 kGy) and storing at 4°C for 1 day versus 14 days were compared. Effects of post-harvest treatments on the resistome were profiled by shotgun metagenomic sequencing. Bacterial 16S rRNA gene amplicon sequencing was performed to determine changes to the phyllosphere microbiota. Survival and regrowth of inoculated ARB were evaluated by enumeration on selective media. Washing lettuce in water containing sanitizer was associated with reduced abundance of ARG classes that confer resistance to glycopeptides, β-lactams, phenicols, and sulfonamides (Wilcoxon, p < 0.05). Washing followed by irradiation resulted in a different resistome chiefly due to reductions in multidrug, triclosan, polymyxin, β-lactam, and quinolone ARG classes (Wilcoxon, p < 0.05). Irradiation followed by storage at 4°C for 14 days led to distinct changes to the β-diversity of the host bacteria of ARGs compared to 1 day after treatment (ANOSIM, R = 0.331; p = 0.003). Storage of washed and irradiated lettuce at 4°C for 14 days increased the relative abundance of Pseudomonadaceae and Carnobacteriaceae (Wilcoxon, p < 0.05), two groups whose presence correlated with detection of 10 ARG classes on the lettuce phyllosphere (p < 0.05). Irradiation resulted in a significant reduction (∼3.5 log CFU/g) of inoculated strains of E. coli O157:H7 and P. aeruginosa (ANOVA, p < 0.05). Results indicate that washing, irradiation and storage of modified atmosphere packaged lettuce at 4°C are effective strategies to reduce antibiotic-resistant E. coli O157:H7 and P. aeruginosa and relative abundance of various ARG classes.

DirtyGenes: testing for significant changes in gene or bacterial population compositions from a small number of samples

High throughput genomics technologies are applied widely to microbiomes in humans, animals, soil and water, to detect changes in bacterial communities or the genes they carry, between different environments or treatments. We describe a method to test the statistical significance of differences in bacterial population or gene composition, applicable to metagenomic or quantitative polymerase chain reaction data. Our method goes beyond previous published work in being universally most powerful, thus better able to detect statistically significant differences, and through being more reliable for smaller sample sizes. It can also be used for experimental design, to estimate how many samples to use in future experiments, again with the advantage of being universally most powerful. We present three example analyses in the area of antimicrobial resistance. The first is to published data on bacterial communities and antimicrobial resistance genes (ARGs) in the environment; we show that there are significant changes in both ARG and community composition. The second is to new data on seasonality in bacterial communities and ARGs in hooves from four sheep. While the observed differences are not significant, we show that a minimum group size of eight sheep would provide sufficient power to observe significance of similar changes in further experiments. The third is to published data on bacterial communities surrounding rice crops. This is a much larger data set and is used to verify the new method. Our method has broad uses for statistical testing and experimental design in research on changing microbiomes, including studies on antimicrobial resistance.

Shotgun Metagenomics Reveals Taxonomic and Functional Shifts in Hot Water Microbiome Due to Temperature Setting and Stagnation

Hot water premise plumbing has emerged as a critical nexus of energy, water, and public health. The composition of hot water microbiomes is of special interest given daily human exposure to resident flora, especially opportunistic pathogens (OPs), which rely on complex microbial ecological interactions for their proliferation. Here, we applied shotgun metagenomic sequencing to characterize taxonomic and functional shifts in microbiomes as a function of water heater temperature setting, stagnation in distal pipes, and associated shifts in water chemistry. A cross-section of samples from controlled, replicated, pilot-scale hot water plumbing rigs representing different temperature settings (39, 42, and 51°C), stagnation periods (8 h vs. 7 days), and time-points, were analyzed. Temperature setting exhibited an overarching impact on taxonomic and functional gene composition. Further, distinct taxa were selectively enriched by specific temperature settings (e.g., Legionella at 39°C vs. Deinococcus at 51°C), while relative abundances of genes encoding corresponding cellular functions were highly consistent with expectations based on the taxa driving these shifts. Stagnation in distal taps diminished taxonomic and functional differences induced by heating the cold influent water to hot water in recirculating line. In distal taps relative to recirculating hot water, reads annotated as being involved in metabolism and growth decreased, while annotations corresponding to stress response (e.g., virulence disease and defense, and specifically antibiotic resistance) increased. Reads corresponding to OPs were readily identified by metagenomic analysis, with L. pneumophila reads in particular correlating remarkably well with gene copy numbers measured by quantitative polymerase chain reaction. Positive correlations between L. pneumophila reads and those of known protozoan hosts were also identified. Elevated proportions of genes encoding metal resistance and hydrogen metabolism were noted, which was consistent with elevated corrosion-induced metal concentrations and hydrogen generation. This study provided new insights into real-world factors influencing taxonomic and functional compositions of hot water microbiomes. Here metagenomics is demonstrated as an effective tool for screening for potential presence, and even quantities, of pathogens, while also providing diagnostic capabilities for assessing functional responses of microbiomes to various operational conditions. These findings can aid in informing future monitoring and intentional control of hot water microbiomes.

MetaCompare: a computational pipeline for prioritizing environmental resistome risk

The spread of antibiotic resistance is a growing public health concern. While numerous studies have highlighted the importance of environmental sources and pathways of the spread of antibiotic resistance, a systematic means of comparing and prioritizing risks represented by various environmental compartments is lacking. Here, we introduce MetaCompare, a publicly available tool for ranking 'resistome risk', which we define as the potential for antibiotic resistance genes (ARGs) to be associated with mobile genetic elements (MGEs) and mobilize to pathogens based on metagenomic data. A computational pipeline was developed in which each ARG is evaluated based on relative abundance, mobility, and presence within a pathogen. This is determined through the assembly of shotgun sequencing data and analysis of contigs containing ARGs to determine if they contain sequence similarity to MGEs or human pathogens. Based on the assembled metagenomes, samples are projected into a 3-dimensionalhazard space and assigned resistome risk scores. To validate, we tested previously published metagenomic data derived from distinct aquatic environments. Based on unsupervised machine learning, the test samples clustered in the hazard space in a manner consistent with their origin. The derived scores produced a well-resolved ascending resistome risk ranking of: wastewater treatment plant effluent, dairy lagoon, and hospital sewage.

Metagenomic Characterization of Antibiotic Resistance Genes in Full-Scale Reclaimed Water Distribution Systems and Corresponding Potable Systems

Water reclamation provides a valuable resource for meeting nonpotable water demands. However, little is known about the potential for wastewater reuse to disseminate antibiotic resistance genes (ARGs). Here, samples were collected seasonally in 2014-2015 from four U.S. utilities' reclaimed and potable water distribution systems before treatment, after treatment, and at five points of use (POU). Shotgun metagenomic sequencing was used to profile the resistome (i.e., full contingent of ARGs) of a subset ( n = 38) of samples. Four ARGs ( qnrA, bla_TEM, vanA, sul1) were quantified by quantitative polymerase chain reaction. Bacterial community composition (via 16S rRNA gene amplicon sequencing), horizontal gene transfer (via quantification of intI1 integrase and plasmid genes), and selection pressure (via detection of metals and antibiotics) were investigated as potential factors governing the presence of ARGs. Certain ARGs were elevated in all ( sul1; p ≤ 0.0011) or some ( bla_TEM, qnrA; p ≤ 0.0145) reclaimed POU samples compared to corresponding potable samples. Bacterial community composition was weakly correlated with ARGs (Adonis, R² = 0.1424-0.1734) and associations were noted between 193 ARGs and plasmid-associated genes. This study establishes that reclaimed water could convey greater abundances of certain ARGs than potable waters and provides observations regarding factors that likely control ARG occurrence in reclaimed water systems.

Metagenomic analysis of microbial communities yields insight into impacts of nanoparticle design

Next-generation DNA sequencing and metagenomic analysis provide powerful tools for the environmentally friendly design of nanoparticles. Herein we demonstrate this approach using a model community of environmental microbes (that is, wastewater-activated sludge) dosed with gold nanoparticles of varying surface coatings and morphologies. Metagenomic analysis was highly sensitive in detecting the microbial community response to gold nanospheres and nanorods with either cetyltrimethylammonium bromide or polyacrylic acid surface coatings. We observed that the gold-nanoparticle morphology imposes a stronger force in shaping the microbial community structure than does the surface coating. Trends were consistent in terms of the compositions of both taxonomic and functional genes, which include antibiotic resistance genes, metal resistance genes and gene-transfer elements associated with cell stress that are relevant to public health. Given that nanoparticle morphology remained constant, the potential influence of gold dissolution was minimal. Surface coating governed the nanoparticle partitioning between the bioparticulate and aqueous phases.

DeepARG: a deep learning approach for predicting antibiotic resistance genes from metagenomic data

BACKGROUND

Growing concerns about increasing rates of antibiotic resistance call for expanded and comprehensive global monitoring. Advancing methods for monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is especially needed for identifying potential resources of novel antibiotic resistance genes (ARGs), hot spots for gene exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencing now enables direct access and profiling of the total metagenomic DNA pool, where ARGs are typically identified or predicted based on the "best hits" of sequence searches against existing databases. Unfortunately, this approach produces a high rate of false negatives. To address such limitations, we propose here a deep learning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two deep learning models, DeepARG-SS and DeepARG-LS, were constructed for short read sequences and full gene length sequences, respectively.

RESULTS

Evaluation of the deep learning models over 30 antibiotic resistance categories demonstrates that the DeepARG models can predict ARGs with both high precision (> 0.97) and recall (> 0.90). The models displayed an advantage over the typical best hit approach, yielding consistently lower false negative rates and thus higher overall recall (> 0.9). As more data become available for under-represented ARG categories, the DeepARG models' performance can be expected to be further enhanced due to the nature of the underlying neural networks. Our newly developed ARG database, DeepARG-DB, encompasses ARGs predicted with a high degree of confidence and extensive manual inspection, greatly expanding current ARG repositories.

CONCLUSIONS

The deep learning models developed here offer more accurate antimicrobial resistance annotation relative to current bioinformatics practice. DeepARG does not require strict cutoffs, which enables identification of a much broader diversity of ARGs. The DeepARG models and database are available as a command line version and as a Web service at http://bench.cs.vt.edu/deeparg .