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

• Main Authors: Boulund Fredrik, Johnning Anna, Pereira Mariana Buongermino, Larsson DG Joakim, Kristiansson Erik
• Format: Article
• Language: English
• Published: BMC 2012-12-01
• Series: BMC Genomics
• Subjects: Metagenomics; Antibiotic resistance; Fluoroquinolones; PMQR; Qnr; Hidden markov models
• Online Access: http://www.biomedcentral.com/1471-2164/13/695

<p>Abstract</p> <p>Background</p> <p>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 <it>qnr</it> 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, <it>qnr</it> 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 <it>qnr</it> genes in more detail.</p> <p>Results</p> <p>In this paper we describe a new method to identify <it>qnr</it> 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 <it>qnr</it> 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 <it>qnr</it> genes. In addition, several fragments from novel putative <it>qnr</it> 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.</p> <p>Conclusions</p> <p>The method described in this paper significantly improves the sensitivity and specificity of identification and annotation of <it>qnr</it> genes in nucleotide sequence data. The predicted novel putative <it>qnr</it> 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 <url>http://bioinformatics.math.chalmers.se/qnr/</url>.</p>