Optimising genomic approaches for identifying vancomycin-resistant Enterococcus faecium transmission in healthcare settings

被引:0
作者
Charlie Higgs
Norelle L. Sherry
Torsten Seemann
Kristy Horan
Hasini Walpola
Paul Kinsella
Katherine Bond
Deborah A. Williamson
Caroline Marshall
Jason C. Kwong
M. Lindsay Grayson
Timothy P. Stinear
Claire L. Gorrie
Benjamin P. Howden
机构
[1] The University of Melbourne,Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity
[2] The University of Melbourne,Microbiological Diagnostic Unit Public Health Laboratory, Department of Microbiology & Immunology, The Peter Doherty Institute for Infection and Immunity
[3] Austin Health,Department of Infectious Diseases
[4] Royal Melbourne Hospital,Department of Microbiology
[5] The Peter Doherty Institute for Infection and Immunity,Victorian Infectious Diseases Reference Laboratory, Royal Melbourne Hospital
[6] The Peter Doherty Institute for Infection and Immunity,Victorian Infectious Diseases Service
[7] The University of Melbourne,Department of Medicine, Austin Health
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Nature Communications | / 13卷
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摘要
Vancomycin-resistant Enterococcus faecium (VREfm) is a major nosocomial pathogen. Identifying VREfm transmission dynamics permits targeted interventions, and while genomics is increasingly being utilised, methods are not yet standardised or optimised for accuracy. We aimed to develop a standardized genomic method for identifying putative VREfm transmission links. Using comprehensive genomic and epidemiological data from a cohort of 308 VREfm infection or colonization cases, we compared multiple approaches for quantifying genetic relatedness. We showed that clustering by core genome multilocus sequence type (cgMLST) was more informative of population structure than traditional MLST. Pairwise genome comparisons using split k-mer analysis (SKA) provided the high-level resolution needed to infer patient-to-patient transmission. The more common mapping to a reference genome was not sufficiently discriminatory, defining more than three times more genomic transmission events than SKA (3729 compared to 1079 events). Here, we show a standardized genomic framework for inferring VREfm transmission that can be the basis for global deployment of VREfm genomics into routine outbreak detection and investigation.
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