Genomic Resolution of Outbreak-Associated Legionella pneumophila Serogroup 1 Isolates from New York State

Abstract

A total of 30 Legionella pneumophila serogroup 1 isolates representing 10 separate legionellosis laboratory investigations ("outbreaks") that occurred in New York State between 2004 and 2012 were selected for evaluation of whole-genome sequencing (WGS) approaches for molecular subtyping of this organism. Clinical and environmental isolates were available for each outbreak and were initially examined by pulsed-field gel electrophoresis (PFGE). Sequence-based typing alleles were extracted from WGS data yielding complete sequence types (ST) for isolates representing 8 out of the 10 outbreaks evaluated in this study. Isolates from separate outbreaks sharing the same ST also contained the fewest differences in core genome single nucleotide polymorphisms (SNPs) and the greatest proportion of identical allele sequences in a whole-genome multilocus sequence typing (wgMLST) scheme. Both core SNP and wgMLST analyses distinguished isolates from separate outbreaks, including those from two outbreaks sharing indistinguishable PFGE profiles. Isolates from a hospital-associated outbreak spanning multiple years shared indistinguishable PFGE profiles but displayed differences in their genome sequences, suggesting the presence of multiple environmental sources. Finally, the rtx gene demonstrated differences in the repeat region sequence among ST1 isolates from different outbreaks, suggesting that variation in this gene may be useful for targeted molecular subtyping approaches for L. pneumophila. This study demonstrates the utility of various genome sequence analysis approaches for L. pneumophila for environmental source attribution studies while furthering the understanding of Legionella ecology. IMPORTANCEWe demonstrate that whole-genome sequencing helps to improve resolution of Legionella pneumophila isolated during laboratory investigations of legionellosis compared to traditional subtyping methods. These data can be important in confirming the environmental sources of legionellosis outbreaks. Moreover, we evaluated various methods to analyze genome sequence data to help resolve outbreak-related isolates.

Construction of a Pan-Genome Allele Database of Salmonella enterica Serovar Enteritidis for Molecular Subtyping and Disease Cluster Identification

“…wgMLST uses the same principle as the SNP-based approach; it converts SNPs in target genes into a standardized and portable allele numbering system. Our study and others indicate that the performance of wgMLST is equivalent to that of the SNP-based approach (de Been et al, 2015; Raphael et al, 2016). One great advantage is that the wgMLST profile consists of serial digital numbers in numeric order that represent alleles of the target genes.…”

“…The wgMLST approach has been used as a subtyping tool in the epidemiological study of various bacterial pathogens (Cody et al, 2013; Bratcher et al, 2014; Kohl et al, 2014; Leopold et al, 2014; Antwerpen et al, 2015; de Been et al, 2015; Jackson et al, 2016; Lindsey et al, 2016; Raphael et al, 2016). This gene-by-gene comparison method is able to provide high-resolution typing results to allow accurate discrimination between epidemiologically related isolates and unrelated isolates.…”

The sporadic nature of Legionella pneumophila, Legionella pneumophila Sg1 and Mycobacterium avium occurrence within residences and office buildings across 36 states in the United States

“…Recent outbreak events have been reported in Flint, MI ( Legionella ) (June, 2014–November, 2015) (MDHHS, 2015), the Bronx, NY ( Legionella ) (Raphael et al . ) and Munich, Germany (MAC) (Haller et al . ).…”

Commentary: Next-Generation Epidemiology: Using Real-Time Core Genome Multilocus Sequence Typing To Support Infection Control Policy

“…The cgMLST methods address this problem by comparing only genes common to a set of bacterial isolates (2). These methods are now mature and have been applied successfully to the analysis of a number of bacterial outbreaks (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21).Given the developments in WGS-based strain typing described above, there is much excitement about applying these methods to real-time hospital epidemiology, but the actual cost-benefit equation, the feasibility of achieving results on an actionable time scale, and the technical requirements for implementation in a "real world" clinical microbiology laboratory are difficult to study. In work published in this issue of the Journal of Clinical Microbiology, …”

“…The cgMLST methods address this problem by comparing only genes common to a set of bacterial isolates (2). These methods are now mature and have been applied successfully to the analysis of a number of bacterial outbreaks (8)(9)(10)(11)(12)(13)(14)(15)(16)(17)(18)(19)(20)(21).…”