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Functional metagenomic approach to identify overlooked antibiotic resistance mutations in bacterial rRNA.

ABSTRACT: Our knowledge as to how bacteria acquire antibiotic resistance is still fragmented, especially for the ribosome-targeting drugs. In this study, with the aim of finding novel mechanisms that render bacteria resistant to the ribosome-targeting antibiotics, we developed a general method to systematically screen for antibiotic resistant 16?S ribosomal RNAs (rRNAs), which are the major target for multiple antibiotics (e.g. spectinomycin, tetracycline, and aminoglycosides), and identify point mutations therein. We used Escherichia coli ?7, a null mutant of the rrn (ribosomal RNA) operons, as a surrogate host organism to construct a metagenomic library of 16?S rRNA genes from the natural (non-clinical) environment. The library was screened for spectinomycin resistance to obtain four resistant 16?S rRNA genes from non-E. coli bacterial species. Bioinformatic analysis and site-directed mutagenesis identified three novel mutations - U1183C (the first mutation discovered in a region other than helix 34), and C1063U and U1189C in helix 34 - as well as three well-described mutations (C1066U, C1192G, and G1193A). These results strongly suggest that uncharacterized antibiotic resistance mutations still exist, even for traditional antibiotics.

SUBMITTER: Miyazaki K

PROVIDER: S-EPMC5882664 | biostudies-literature | 2018 Apr

REPOSITORIES: biostudies-literature

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Functional metagenomic approach to identify overlooked antibiotic resistance mutations in bacterial rRNA.

Miyazaki Kentaro K Kitahara Kei K

Scientific reports 20180403 1

Our knowledge as to how bacteria acquire antibiotic resistance is still fragmented, especially for the ribosome-targeting drugs. In this study, with the aim of finding novel mechanisms that render bacteria resistant to the ribosome-targeting antibiotics, we developed a general method to systematically screen for antibiotic resistant 16 S ribosomal RNAs (rRNAs), which are the major target for multiple antibiotics (e.g. spectinomycin, tetracycline, and aminoglycosides), and identify point mutation ...[more]

PMID: 29615654

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Project description:Traditionally slurry is used as source of nitrogen, phosphorous, and potassium in bio fertilizers to improve crop production. However, poorly managed slurry causes a hazardous effect to the environment by producing greenhouse gases, causing the eutrophication of water bodies, and polluting the groundwater. It has been largely reported that the microbial presence in slurry causing a diverse effect on its storage and disposal system. However, the diversity of bacterial populations in pig slurries remains largely unexplored. Here we report the bacterial diversity present in the slurry from slurry pits, and the effect of storage time on bacterial population. We collected 42 samples from three different pig slurry pits, as three replicates from each one until the 14th week. We used the 16S rRNA, Quantitative Insights Into Microbial Ecology (QIIME) and Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) protocols for the metagenomic downstream analysis. Taxonomic annotation using the Greengenes metagenomic database indicated that on an average 76.2% Firmicutes, 14.4% Bacteroidetes, 4.9% Proteobacteria, etc. microbial populations were present. Comparative microbial analysis showed that the population of Firmicutes decreased from the first to the 14th week, whereas the population of Bacteroidetes increased from the first to the 14th week. Through principal coordinate analysis (PCoA), (linear discriminant analysis effect size (LEfSe), and Pearson's correlation analysis, we found microbial biomarkers according to the storage time point. All bacterial populations were well clustered according to the early, middle, and last weeks of storage. LEfSe showed that Actinobacteria, Lachnospiraceae, Ruminococcaceae, and Bacteroidia are dominantly present in first, seventh, ninth, and 14th week, respectively. Lachnospiraceae and Ruminococcaceae are ubiquitous gastrointestinal non-pathogenic bacteria. KEGG pathways, such as membrane transport, carbohydrate and amino acid metabolism, genetic replication and repair, were significant among all samples. Such a KEGG pathway may indicate the association between the host organism's metabolic activity and the microbes present in the gastro intestinal tract (GIT).

Project description: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 .

Dataset Information

Functional metagenomic approach to identify overlooked antibiotic resistance mutations in bacterial rRNA.

Publications

Functional metagenomic approach to identify overlooked antibiotic resistance mutations in bacterial rRNA.

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