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Screening currency notes for microbial pathogens and antibiotic resistance genes using a shotgun metagenomic approach.

ABSTRACT: Fomites are a well-known source of microbial infections and previous studies have provided insights into the sojourning microbiome of fomites from various sources. Paper currency notes are one of the most commonly exchanged objects and its potential to transmit pathogenic organisms has been well recognized. Approaches to identify the microbiome associated with paper currency notes have been largely limited to culture dependent approaches. Subsequent studies portrayed the use of 16S ribosomal RNA based approaches which provided insights into the taxonomical distribution of the microbiome. However, recent techniques including shotgun sequencing provides resolution at gene level and enable estimation of their copy numbers in the metagenome. We investigated the microbiome of Indian paper currency notes using a shotgun metagenome sequencing approach. Metagenomic DNA isolated from samples of frequently circulated denominations of Indian currency notes were sequenced using Illumina Hiseq sequencer. Analysis of the data revealed presence of species belonging to both eukaryotic and prokaryotic genera. The taxonomic distribution at kingdom level revealed contigs mapping to eukaryota (70%), bacteria (9%), viruses and archae (~1%). We identified 78 pathogens including Staphylococcus aureus, Corynebacterium glutamicum, Enterococcus faecalis, and 75 cellulose degrading organisms including Acidothermus cellulolyticus, Cellulomonas flavigena and Ruminococcus albus. Additionally, 78 antibiotic resistance genes were identified and 18 of these were found in all the samples. Furthermore, six out of 78 pathogens harbored at least one of the 18 common antibiotic resistance genes. To the best of our knowledge, this is the first report of shotgun metagenome sequence dataset of paper currency notes, which can be useful for future applications including as bio-surveillance of exchangeable fomites for infectious agents.

SUBMITTER: Jalali S

PROVIDER: S-EPMC4452720 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Human-health relevant impacts on marine ecosystems are increasing on both spatial and temporal scales. Traditional indicators for environmental health monitoring and microbial risk assessment have relied primarily on single species analyses and have provided only limited spatial and temporal information. More high-throughput, broad-scale approaches to evaluate these impacts are therefore needed to provide a platform for informing public health. This study uses shotgun metagenomics to survey the taxonomic composition and antibiotic resistance determinant content of surface water bacterial communities in the Puget Sound estuary. Metagenomic DNA was collected at six sites in Puget Sound in addition to one wastewater treatment plant (WWTP) that discharges into the Sound and pyrosequenced. A total of ~550 Mbp (1.4 million reads) were obtained, 22 Mbp of which could be assembled into contigs. While the taxonomic and resistance determinant profiles across the open Sound samples were similar, unique signatures were identified when comparing these profiles across the open Sound, a nearshore marina and WWTP effluent. The open Sound was dominated by ?-Proteobacteria (in particular Rhodobacterales sp.), ?-Proteobacteria and Bacteroidetes while the marina and effluent had increased abundances of Actinobacteria, ?-Proteobacteria and Firmicutes. There was a significant increase in the antibiotic resistance gene signal from the open Sound to marina to WWTP effluent, suggestive of a potential link to human impacts. Mobile genetic elements associated with environmental and pathogenic bacteria were also differentially abundant across the samples. This study is the first comparative metagenomic survey of Puget Sound and provides baseline data for further assessments of community composition and antibiotic resistance determinants in the environment using next generation sequencing technologies. In addition, these genomic signals of potential human impact can be used to guide initial public health monitoring as well as more targeted and functionally-based investigations.

Project description: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 .

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Screening currency notes for microbial pathogens and antibiotic resistance genes using a shotgun metagenomic approach.

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