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Functional verification of computationally predicted qnr genes
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 recombi...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2013
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4222258/ https://www.ncbi.nlm.nih.gov/pubmed/24257207 http://dx.doi.org/10.1186/1476-0711-12-34 |
Sumario: | 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. |
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