BackgroundGrowing 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.ResultsEvaluation 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.ConclusionsThe 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.Electronic supplementary materialThe online version of this article (10.1186/s40168-018-0401-z) contains supplementary material, which is available to authorized users.
Growing concerns regarding increasing rates of antibiotic resistance call for global monitoring efforts. Monitoring of environmental media (e.g., wastewater, agricultural waste, food, and water) is of particular interest as these media can serve as sources of potential novel antibiotic resistance genes (ARGs), as hot spots for ARG exchange, and as pathways for the spread of ARGs and human exposure. Next-generation sequencebased monitoring has recently enabled direct access and profiling of the total metagenomic DNA pool, where ARGs are identified or predicted based on the "best hits" of homology searches against existing databases. Unfortunately, this approach tends to produce high rates of false negatives. To address such limitations, we propose here a deep leaning approach, taking into account a dissimilarity matrix created using all known categories of ARGs. Two models, deepARG-SS and deepARG-LS, were constructed for short read sequences and full gene length sequences, respectively.Performance evaluation of the deep learning models over 30 classes of antibiotics demonstrates that the deepARG models can predict ARGs with both high precision (>0.97) and recall (>0.90) for most of the antibiotic resistance categories. The models show advantage over the traditional best hit approach by having consistently much lower false negative rates and thus higher overall recall (>0.9). As more data become available for under-represented antibiotic resistance categories, the deepARG models' performance can be expected to be further enhanced due to the nature of the . CC-BY-NC 4.0 International license peer-reviewed) is the author/funder. It is made available under a The copyright holder for this preprint (which was not . http://dx.doi.org/10.1101/149328 doi: bioRxiv preprint first posted online Jun. 12, 2017; underlying neural networks. The deepARG models are available both in command line version and via a Web server at http://bench.cs.vt.edu/deeparg. Our newly developed ARG database, deepARG-DB, containing predicted ARGs with high confidence and high degree of manual curation, greatly expands the current ARG repository.DeepARG-DB can be downloaded freely to benefit community research and future development of antibiotic resistance-related resources.
Conspectus 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 ...
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, 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, qnrA; p ≤ 0.0145) reclaimed POU samples compared to corresponding potable samples. Bacterial community composition was weakly correlated with ARGs (Adonis, R = 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.
We hypothesized that the interrupted corrosion control and associated release of iron, nutrients, and depleted chlorine residual in the distribution system would lead to high levels of Legionella. A tap water survey conducted throughout Flint in August and October 2015 confirmed Legionella pneumophila in two hospitals (mean of 1890 ± 2220 gene copy numbers/mL, 48% positivity), but not small single-story buildings. The hospitals frequently had optimal Legionella growth temperatures and were located in high-water age zones of the distribution system (3 to >6 days). Relatively high concentrations of iron were present (mean of 51.0 ± 37.2 ppb), and Cl 2 residual was sporadic (mean of 0.700 ± 0.775 mg/L) throughout the Flint distribution system. This study addresses knowledge gaps linking legionellosis outbreaks to changes in municipal water quality and distribution system operation.
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