Abstract
BackgroundThe 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.FindingsBy 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.ConclusionThe 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.
Highlights
The quinolone resistance genes are widely distributed among bacteria
Chromosomal qnr alleles have been found in several bacterial species, alleles that may serve as progenitors of plasmid-borne qnr genes [8,9,10]
Identifications of the first representative of each qnr family relied on screening of clinical isolates and resistance transfer assays [1,3,4,5,7,11], whereas subsequent studies have identified related qnr genes mainly by using PCR-based strategies [5,9,10,12]
Summary
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. 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. In this study we have experimentally evaluated four of these novel candidates (NC), nc1 – nc4 (Additional file 1), in recombinant Escherichia coli expression systems, an approach that has been used with success to characterize qnr genes previously [3,4,5,7,8,13,15].
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