Please use this identifier to cite or link to this item:
|Title:||Prospective Whole-Genome Sequencing Enhances National Surveillance of Listeria monocytogenes.|
|Authors:||Kwong, Jason C;Mercoulia, Karolina;Tomita, Takehiro;Easton, Marion;Li, Hua Y;Bulach, Dieter M;Stinear, Timothy P;Seemann, Torsten;Howden, Benjamin P|
|Affiliation:||Doherty Applied Microbial Genomics, Department of Microbiology and Immunology, University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia|
Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology and Immunology, University of Melbourne at the Doherty Institute for Infection & Immunity, Melbourne, Victoria, Australia
Infectious Diseases Department, Austin Health, Heidelberg, Victoria, Australia
Victorian Life Sciences Computation Initiative, University of Melbourne, Victoria, Australia
|Citation:||Journal of clinical microbiology 2016; 54(2): 333-42|
|Abstract:||Whole-genome sequencing (WGS) has emerged as a powerful tool for comparing bacterial isolates in outbreak detection and investigation. Here we demonstrate that WGS performed prospectively for national epidemiologic surveillance of Listeria monocytogenes has the capacity to be superior to our current approaches using pulsed-field gel electrophoresis (PFGE), multilocus sequence typing (MLST), multilocus variable-number tandem-repeat analysis (MLVA), binary typing, and serotyping. Initially 423 L. monocytogenes isolates underwent WGS, and comparisons uncovered a diverse genetic population structure derived from three distinct lineages. MLST, binary typing, and serotyping results inferred in silico from the WGS data were highly concordant (>99%) with laboratory typing performed in parallel. However, WGS was able to identify distinct nested clusters within groups of isolates that were otherwise indistinguishable using our current typing methods. Routine WGS was then used for prospective epidemiologic surveillance on a further 97 L. monocytogenes isolates over a 12-month period, which provided a greater level of discrimination than that of conventional typing for inferring linkage to point source outbreaks. A risk-based alert system based on WGS similarity was used to inform epidemiologists required to act on the data. Our experience shows that WGS can be adopted for prospective L. monocytogenes surveillance and investigated for other pathogens relevant to public health.|
Research Support, Non-U.S. Gov't
|Appears in Collections:||Journal articles|
Files in This Item:
There are no files associated with this item.
Items in AHRO are protected by copyright, with all rights reserved, unless otherwise indicated.