Functional verification of computationally predicted qnr genes
Functional verification of computationally predicted qnr genes
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DOI：
10.1186/1476-0711-12-34
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发表时间：
2013-11-21
影响因子：
5.7
通讯作者：
Larsson, D. G. Joakim
中科院分区：
文献类型：
--
作者：
Flach, Carl-Fredrik;Boulund, Fredrik;Larsson, D. G. Joakim
背景：喹诺酮类药物耐药基因广泛分布于细菌中。我们最近开发并应用了概率模型，在包括碎片化宏基因组在内的大量公共DNA序列数据中鉴定暂定的新qnr基因。结果：通过诱导重组表达系统，鉴定出的4个候选qnr在大肠杆菌中的功能得到了评价。几个已知qnr基因的表达以及两个新的候选基因提供了氟喹诺酮类药物耐药性，随着诱导剂浓度的升高而增加。这两个新的功能验证的qnr基因被命名为Vfuqnr和组装的qnr 1。两个qnr基因的共表达提示无协同作用。结论：计算模型和重组表达系统的结合为在基因组和宏基因组数据集中探索和鉴定新的抗生素耐药基因提供了机会。
Background: The quinolone resistance (qnr) genes are widely distributed among bacteria. We recently developed and applied probabilistic models to identify tentative novel qnr genes in large public collections of DNA sequence data including fragmented metagenomes.Findings: By using inducible recombinant expressions systems the functionality of four identified qnr candidates were evaluated in Escherichia coli. Expression of several known qnr genes as well as two novel candidates provided fluoroquinolone resistance that increased with elevated inducer concentrations. The two novel, functionally verified qnr genes are termed Vfuqnr and assembled qnr 1. Co-expression of two qnr genes suggested non-synergistic action.Conclusion: The combination of a computational model and recombinant expression systems provides opportunities to explore and identify novel antibiotic resistance genes in both genomic and metagenomic datasets.