Investigation of next‐generation sequencing data ofKlebsiella pneumoniaeusing web‐based tools. Issue 11 (November 2017)

• Record Type: Journal Article
• Authors: Brhelova, Eva; Antonova, Mariya; Pardy, Filip; Kocmanova, Iva; Mayer, Jiri; Racil, Zdenek; Lengerova, Martina
• Is Part Of: Journal of medical microbiology. Volume 66:Issue 11(2017)
• Publication Date: 2017-11
• Subjects: next generation sequencing -- Klebsiella pneumoniae -- MLST -- ResFinder -- PlasmidFinder -- BIGSdb‐Kp; Medical microbiology -- Periodicals; 616.9041
• DOI: 10.1099/jmm.0.000624
• Languages: English
• ISSNs: 0022-2615

Abstract

Abstract : Purpose. : Rapid identification and characterization of multidrug‐resistant Klebsiella pneumoniae strains is necessary due to the increasing frequency of severe infections in patients. The decreasing cost of next‐generation sequencing enables us to obtain a comprehensive overview of genetic information in one step. The aim of this study is to demonstrate and evaluate the utility and scope of the application of web‐based databases to next‐generation sequenced (NGS) data. Methodology. : The whole genomes of 11 clinical Klebsiella pneumoniae isolates were sequenced using Illumina MiSeq. Selected web‐based tools were used to identify a variety of genetic characteristics, such as acquired antimicrobial resistance genes, multilocus sequence types, plasmid replicons, and identify virulence factors, such as virulence genes, cps clusters, urease‐nickel clusters and efflux systems. Results. : Using web‐based tools hosted by the Center for Genomic Epidemiology, we detected resistance to 8 main antimicrobial groups with at least 11 acquired resistance genes. The isolates were divided into eight sequence types (ST11, 23, 37, 323, 433, 495 and 562, and a new one, ST1646). All of the isolates carried replicons of large plasmids. Capsular types, virulence factors and genes coding AcrAB and OqxAB efflux pumps were detected using BIGSdb‐Kp, whereas the selected virulence genes, identified in almost all of the isolates, were detected using CLC Genomic Workbench software. Conclusion. : Applying appropriate web‐based online tools to NGS data enables the rapid extraction of comprehensive information that can be used for more efficient diagnosis and treatment of patients, while data processing is free of charge, easy and time‐efficient.