A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences
A novel method to discover fluoroquinolone antibiotic resistance (qnr) genes in fragmented nucleotide sequences
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DOI：
10.1186/1471-2164-13-695
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发表时间：
2012-12-11
期刊：
影响因子：
4.4
通讯作者：
Kristiansson, Erik
中科院分区：
文献类型：
--
作者：
Boulund, Fredrik;Johnning, Anna;Kristiansson, Erik
背景：广谱氟喹诺酮类抗生素是现代医疗保健的核心，用于治疗和预防广泛的细菌感染。最近发现的qnr基因提供了一种抗性机制，有可能通过水平基因转移在细菌之间迅速传播。对于目前病原菌中存在的许多抗生素耐药基因，qnr基因被假设为起源于环境细菌。因此，猎枪元基因组学产生的海量数据可以用来更详细地探索qnr基因的多样性。结果：本文描述了一种从核苷酸序列数据中识别qnr基因的新方法。我们使用交叉验证表明，该方法具有从新类别的qnr基因中正确分类序列的高统计能力，即使对于短至100个核苷酸的片段也是如此。基于公共文库中的序列，该方法能够识别所有先前报道的质粒介导的qnr基因。此外，在元基因组中还发现了几个新的可能的qnr基因片段。该方法还能够标注39个染色体变异，其中11个以前没有文献报道。结论：本文所描述的方法显著提高了核苷酸序列数据中qnr基因识别和标注的敏感性和特异性。在元基因组数据中预测的新的假定的qnr基因支持这样的假设，即在环境细菌群落中，这个抗性基因家族中存在着巨大的和未被表征的多样性。该方法的实现可在http://bioinformatics.math.chalmers.se/qnr/.上免费获得
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/.