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Identification and reconstruction of novel antibiotic resistance genes from metagenomes

Journal article
Authors Fanny Berglund
Tobias Österlund
Fredrik Boulund
Nachiket Marathe
D. G. Joakim Larsson
Erik Kristiansson
Published in Microbiome
Volume 7
Publication year 2019
Published at Department of Mathematical Sciences
Department of Mathematical Sciences, Applied Mathematics and Statistics
Centre for antibiotic resistance research, CARe
Institute of Biomedicine, Department of Infectious Medicine
Language en
Subject categories Microbiology in the medical area, Bioinformatics and Systems Biology, Biochemistry and Molecular Biology


Background Environmental and commensal bacteria maintain a diverse and largely unknown collection of antibiotic resistance genes (ARGs) that, over time, may be mobilized and transferred to pathogens. Metagenomics enables cultivation-independent characterization of bacterial communities but the resulting data is noisy and highly fragmented, severely hampering the identification of previously undescribed ARGs. We have therefore developed fARGene, a method for identification and reconstruction of ARGs directly from shotgun metagenomic data. Results fARGene uses optimized gene models and can therefore with high accuracy identify previously uncharacterized resistance genes, even if their sequence similarity to known ARGs is low. By performing the analysis directly on the metagenomic fragments, fARGene also circumvents the need for a high-quality assembly. To demonstrate the applicability of fARGene, we reconstructed β-lactamases from five billion metagenomic reads, resulting in 221 ARGs, of which 58 were previously not reported. Based on 38 ARGs reconstructed by fARGene, experimental verification showed that 81% provided a resistance phenotype in Escherichia coli. Compared to other methods for detecting ARGs in metagenomic data, fARGene has superior sensitivity and the ability to reconstruct previously unknown genes directly from the sequence reads. Conclusions We conclude that fARGene provides an efficient and reliable way to explore the unknown resistome in bacterial communities. The method is applicable to any type of ARGs and is freely available via GitHub under the MIT license.

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