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Genomic comparative analysis and gene function prediction in infectious diseases: application to the investigation of a meningitis outbreak.

Authors :
Lavezzo E
Toppo S
Franchin E
Di Camillo B
Finotello F
Falda M
Manganelli R
Palù G
Barzon L
Source :
BMC infectious diseases [BMC Infect Dis] 2013 Nov 19; Vol. 13, pp. 554. Date of Electronic Publication: 2013 Nov 19.
Publication Year :
2013

Abstract

Background: Next generation sequencing (NGS) is being increasingly used for the detection and characterization of pathogens during outbreaks. This technology allows rapid sequencing of pathogen full genomes, useful not only for accurate genotyping and molecular epidemiology, but also for identification of drug resistance and virulence traits.<br />Methods: In this study, an approach based on whole genome sequencing by NGS, comparative genomics, and gene function prediction was set up and retrospectively applied for the investigation of two N. meningitidis serogroup C isolates collected from a cluster of meningococcal disease, characterized by a high fatality rate.<br />Results: According to conventional molecular typing methods, all the isolates had the same typing results and were classified as outbreak isolates within the same N. meningitidis sequence type ST-11, while full genome sequencing demonstrated subtle genetic differences between the isolates. Looking for these specific regions by means of 9 PCR and cycle sequencing assays in other 7 isolates allowed distinguishing outbreak cases from unrelated cases. Comparative genomics and gene function prediction analyses between outbreak isolates and a set of reference N. meningitidis genomes led to the identification of differences in gene content that could be relevant for pathogenesis. Most genetic changes occurred in the capsule locus and were consistent with recombination and horizontal acquisition of a set of genes involved in capsule biosynthesis.<br />Conclusions: This study showed the added value given by whole genome sequencing by NGS over conventional sequence-based typing methods in the investigation of an outbreak. Routine application of this technology in clinical microbiology will significantly improve methods for molecular epidemiology and surveillance of infectious disease and provide a bulk of data useful to improve our understanding of pathogens biology.

Details

Language :
English
ISSN :
1471-2334
Volume :
13
Database :
MEDLINE
Journal :
BMC infectious diseases
Publication Type :
Academic Journal
Accession number :
24252229
Full Text :
https://doi.org/10.1186/1471-2334-13-554