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ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes.

ABSTRACT: ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) is a new bioinformatic tool that was created to detect existing and putative new antibiotic resistance (AR) genes in bacterial genomes. ARG-ANNOT uses a local BLAST program in Bio-Edit software that allows the user to analyze sequences without a Web interface. All AR genetic determinants were collected from published works and online resources; nucleotide and protein sequences were retrieved from the NCBI GenBank database. After building a database that includes 1,689 antibiotic resistance genes, the software was tested in a blind manner using 100 random sequences selected from the database to verify that the sensitivity and specificity were at 100% even when partial sequences were queried. Notably, BLAST analysis results obtained using the rmtF gene sequence (a new aminoglycoside-modifying enzyme gene sequence that is not included in the database) as a query revealed that the tool was able to link this sequence to short sequences (17 to 40 bp) found in other genes of the rmt family with significant E values. Finally, the analysis of 178 Acinetobacter baumannii and 20 Staphylococcus aureus genomes allowed the detection of a significantly higher number of AR genes than the Resfinder gene analyzer and 11 point mutations in target genes known to be associated with AR. The average time for the analysis of a genome was 3.35 ± 0.13 min. We have created a concise database for BLAST using a Bio-Edit interface that can detect AR genetic determinants in bacterial genomes and can rapidly and easily discover putative new AR genetic determinants.

SUBMITTER: Gupta SK

PROVIDER: S-EPMC3910750 | biostudies-literature | 2014

REPOSITORIES: biostudies-literature

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ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes.

Gupta Sushim Kumar SK Padmanabhan Babu Roshan BR Diene Seydina M SM Lopez-Rojas Rafael R Kempf Marie M Landraud Luce L Rolain Jean-Marc JM

Antimicrobial agents and chemotherapy 20131021 1

ARG-ANNOT (Antibiotic Resistance Gene-ANNOTation) is a new bioinformatic tool that was created to detect existing and putative new antibiotic resistance (AR) genes in bacterial genomes. ARG-ANNOT uses a local BLAST program in Bio-Edit software that allows the user to analyze sequences without a Web interface. All AR genetic determinants were collected from published works and online resources; nucleotide and protein sequences were retrieved from the NCBI GenBank database. After building a databa ...[more]

PMID: 24145532

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Project description:BackgroundThe spread of antibiotic resistance has become one of the most urgent threats to global health, which is estimated to cause 700,000 deaths each year globally. Its surrogates, antibiotic resistance genes (ARGs), are highly transmittable between food, water, animal, and human to mitigate the efficacy of antibiotics. Accurately identifying ARGs is thus an indispensable step to understanding the ecology, and transmission of ARGs between environmental and human-associated reservoirs. Unfortunately, the previous computational methods for identifying ARGs are mostly based on sequence alignment, which cannot identify novel ARGs, and their applications are limited by currently incomplete knowledge about ARGs.ResultsHere, we propose an end-to-end Hierarchical Multi-task Deep learning framework for ARG annotation (HMD-ARG). Taking raw sequence encoding as input, HMD-ARG can identify, without querying against existing sequence databases, multiple ARG properties simultaneously, including if the input protein sequence is an ARG, and if so, what antibiotic family it is resistant to, what resistant mechanism the ARG takes, and if the ARG is an intrinsic one or acquired one. In addition, if the predicted antibiotic family is beta-lactamase, HMD-ARG further predicts the subclass of beta-lactamase that the ARG is resistant to. Comprehensive experiments, including cross-fold validation, third-party dataset validation in human gut microbiota, wet-experimental functional validation, and structural investigation of predicted conserved sites, demonstrate not only the superior performance of our method over the state-of-art methods, but also the effectiveness and robustness of the proposed method.ConclusionsWe propose a hierarchical multi-task method, HMD-ARG, which is based on deep learning and can provide detailed annotations of ARGs from three important aspects: resistant antibiotic class, resistant mechanism, and gene mobility. We believe that HMD-ARG can serve as a powerful tool to identify antibiotic resistance genes and, therefore mitigate their global threat. Our method and the constructed database are available at http://www.cbrc.kaust.edu.sa/HMDARG/ . Video abstract (MP4 50984 kb).

Project description:Bacterial resistance to antibiotics is a pressing health issue around the world, not only in health care settings but also in the community and environment, particularly in crowded urban populations. The aim of our work was to characterize the microbial populations in sewage and the spread of antibiotic resistance within New York City (NYC). Here, we investigated the structure of the microbiome and the prevalence of antibiotic resistance genes in raw sewage samples collected from the fourteen NYC Department of Environmental Protection wastewater treatment plants, distributed across the five NYC boroughs. Sewage, a direct output of anthropogenic activity and a reservoir of microbes, provides an ecological niche to examine the spread of antibiotic resistance. Taxonomic diversity analysis revealed a largely similar and stable bacterial population structure across all the samples, which was found to be similar over three time points in an annual cycle, as well as in the five NYC boroughs. All samples were positive for the presence of the seven antibiotic resistance genes tested, based on real-time quantitative PCR assays, with higher levels observed for tetracycline resistance genes at all time points. For five of the seven genes, abundances were significantly higher in May than in February and August. This study provides characteristics of the NYC sewage resistome in the context of the overall bacterial populations.IMPORTANCE Urban sewage or wastewater is a diverse source of bacterial growth, as well as a hot spot for the development of environmental antibiotic resistance, which can in turn influence the health of the residents of the city. As part of a larger study to characterize the urban New York City microbial metagenome, we collected raw sewage samples representing three seasonal time points spanning the five boroughs of NYC and went on to characterize the microbiome and the presence of a range of antibiotic resistance genes. Through this study, we have established a baseline microbial population and antibiotic resistance abundance in NYC sewage which can prove to be very useful in studying the load of antibiotic usage, as well as for developing effective measures in antibiotic stewardship.

Dataset Information

ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes.

Publications

ARG-ANNOT, a new bioinformatic tool to discover antibiotic resistance genes in bacterial genomes.

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