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Computational discovery and functional validation of novel fluoroquinolone resistance genes in public metagenomic data sets

Artikel i vetenskaplig tidskrift
Författare Fredrik Boulund
Fanny Berglund
Carl-Fredrik Flach
Johan Bengtsson-Palme
Nachiket Marathe
D. G. Joakim Larsson
Erik Kristiansson
Publicerad i BMC Genomics
Volym 18
Publiceringsår 2017
Publicerad vid Institutionen för matematiska vetenskaper
Institutionen för biomedicin, avdelningen för infektionssjukdomar
CARe - Centrum för antibiotikaresistensforskning
Språk en
Länkar https://doi.org/10.1186/s12864-017-...
Ämnesord Fluroquinolone resistance, Hidden Markov models, Horizontal gene transfer, Metagenomics, Qnr
Ämneskategorier Cellbiologi och genomforskning, Infektionsmedicin

Sammanfattning

© 2017 The Author(s). Background: Fluoroquinolones are broad-spectrum antibiotics used to prevent and treat a wide range of bacterial infections. Plasmid-mediated qnr genes provide resistance to fluoroquinolones in many bacterial species and are increasingly encountered in clinical settings. Over the last decade, several families of qnr genes have been discovered and characterized, but their true prevalence and diversity still remain unclear. In particular, environmental and host-associated bacterial communities have been hypothesized to maintain a large and unknown collection of qnr genes that could be mobilized into pathogens. Results: In this study we used computational methods to screen genomes and metagenomes for novel qnr genes. In contrast to previous studies, we analyzed an almost 20-fold larger dataset comprising almost 13 terabases of sequence data. In total, 362,843 potential qnr gene fragments were identified, from which 611 putative qnr genes were reconstructed. These gene sequences included all previously described plasmid-mediated qnr gene families. Fifty-two of the 611 identified qnr genes were reconstructed from metagenomes, and 20 of these were previously undescribed. All of the novel qnr genes were assembled from metagenomes associated with aquatic environments. Nine of the novel genes were selected for validation, and six of the tested genes conferred consistently decreased susceptibility to ciprofloxacin when expressed in Escherichia coli. Conclusions: The results presented in this study provide additional evidence for the ubiquitous presence of qnr genes in environmental microbial communities, expand the number of known qnr gene variants and further elucidate the diversity of this class of resistance genes. This study also strengthens the hypothesis that environmental bacterial communities act as sources of previously uncharacterized qnr genes.

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