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Open Access Methodology article

A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences

Fredrik Boulund1, Anna Johnning2, Mariana Buongermino Pereira1, DG Joakim Larsson3 and Erik Kristiansson1*

Author Affiliations

1 Department of Mathematical Sciences, Chalmers University of Technology and University of Gothenburg, Göteborg, SE-412 96, Sweden

2 Institute of Neuroscience and Physiology, the Sahlgrenska Academy at the University of Gothenburg, Box 434, Göteborg, SE-405 30, Sweden

3 Department of Infectious Diseases, Institute of Biomedicine, the Sahlgrenska Academy at the University of Gothenburg, Box 434, Göteborg, SE-405 30, Sweden

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BMC Genomics 2012, 13:695  doi:10.1186/1471-2164-13-695

Published: 11 December 2012

Abstract

Background

Broad-spectrum fluoroquinolone antibiotics are central in modern health care and are used to treat and prevent a wide range of bacterial infections. The recently discovered qnr genes provide a mechanism of resistance with the potential to rapidly spread between bacteria using horizontal gene transfer. As for many antibiotic resistance genes present in pathogens today, qnr genes are hypothesized to originate from environmental bacteria. The vast amount of data generated by shotgun metagenomics can therefore be used to explore the diversity of qnr genes in more detail.

Results

In this paper we describe a new method to identify qnr genes in nucleotide sequence data. We show, using cross-validation, that the method has a high statistical power of correctly classifying sequences from novel classes of qnr genes, even for fragments as short as 100 nucleotides. Based on sequences from public repositories, the method was able to identify all previously reported plasmid-mediated qnr genes. In addition, several fragments from novel putative qnr genes were identified in metagenomes. The method was also able to annotate 39 chromosomal variants of which 11 have previously not been reported in literature.

Conclusions

The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of qnr genes in nucleotide sequence data. The predicted novel putative qnr genes in the metagenomic data support the hypothesis of a large and uncharacterized diversity within this family of resistance genes in environmental bacterial communities. An implementation of the method is freely available at http://bioinformatics.math.chalmers.se/qnr/ webcite.

Keywords:
Metagenomics; Antibiotic resistance; Fluoroquinolones; PMQR; Qnr; Hidden markov models