Dataset Information

A pilot study of rapid whole-genome sequencing for the investigation of a Legionella outbreak.

ABSTRACT: Epidemiological investigations of Legionnaires' disease outbreaks rely on the rapid identification and typing of clinical and environmental Legionella isolates in order to identify and control the source of infection. Rapid bacterial whole-genome sequencing (WGS) is an emerging technology that has the potential to rapidly discriminate outbreak from non-outbreak isolates in a clinically relevant time frame.We performed a pilot study to determine the feasibility of using bacterial WGS to differentiate outbreak from non-outbreak isolates collected during an outbreak of Legionnaires' disease. Seven Legionella isolates (three clinical and four environmental) were obtained from the reference laboratory and sequenced using the Illumina MiSeq platform at Addenbrooke's Hospital, Cambridge. Bioinformatic analysis was performed blinded to the epidemiological data at the Wellcome Trust Sanger Institute.We were able to distinguish outbreak from non-outbreak isolates using bacterial WGS, and to confirm the probable environmental source. Our analysis also highlighted constraints, which were the small number of Legionella pneumophila isolates available for sequencing, and the limited number of published genomes for comparison.We have demonstrated the feasibility of using rapid WGS to investigate an outbreak of Legionnaires' disease. Future work includes building larger genomic databases of L pneumophila from both clinical and environmental sources, developing automated data interpretation software, and conducting a cost-benefit analysis of WGS versus current typing methods.

SUBMITTER: Reuter S

PROVIDER: S-EPMC3553392 | biostudies-literature | 2013 Jan

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:Whole genome sequencing (WGS) has become the new gold standard for bacterial outbreak investigation, due to the high resolution available for typing. While sequencing is currently predominantly performed on Illumina devices, the preceding library preparation can be performed using various protocols. Enzymatic fragmentation library preparation protocols are fast, have minimal hands-on time, and work with small quantities of DNA. The aim of our study was to compare three library preparation protocols for molecular typing: Nextera XT (Illumina); Nextera Flex (Illumina); and QIAseq FX (Qiagen). We selected 12 ATCC strains from human Gram-positive and Gram-negative pathogens with %G+C-content ranging from 27% (Fusobacterium nucleatum) to 73% (Micrococcus luteus), each having a high quality complete genome assembly available, to allow in-depth analysis of the resulting Illumina sequence data quality. Additionally, we selected isolates from previously analyzed cases of vancomycin-resistant Enterococcus faecium (VRE) (n = 7) and a local outbreak of Klebsiella aerogenes (n = 5). The number of protocol steps and time required were compared, in order to test the suitability for routine laboratory work. Data analyses were performed with standard tools commonly used in outbreak situations: Ridom SeqSphere+ for cgMLST; CLC genomics workbench for SNP analysis; and open source programs. Nextera Flex and QIAseq FX were found to be less sensitive than Nextera XT to variable %G+C-content, resulting in an almost uniform distribution of read-depth. Therefore, low coverage regions are reduced to a minimum resulting in a more complete representation of the genome. Thus, with these two protocols, more alleles were detected in the cgMLST analysis, producing a higher resolution of closely related isolates. Furthermore, they result in a more complete representation of accessory genes. In particular, the high data quality and relative simplicity of the workflow of Nextera Flex stood out in this comparison. This thorough comparison within an ISO/IEC 17025 accredited environment will be of interest to those aiming to optimize their clinical microbiological genome sequencing.

Dataset Information

A pilot study of rapid whole-genome sequencing for the investigation of a Legionella outbreak.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets