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

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 and Benjamin P. Howden ()
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Charlie Higgs: The University of Melbourne
Norelle L. Sherry: The University of Melbourne
Torsten Seemann: The University of Melbourne
Kristy Horan: The University of Melbourne
Hasini Walpola: The University of Melbourne
Paul Kinsella: Royal Melbourne Hospital
Katherine Bond: Royal Melbourne Hospital
Deborah A. Williamson: The University of Melbourne
Caroline Marshall: The Peter Doherty Institute for Infection and Immunity
Jason C. Kwong: The University of Melbourne
M. Lindsay Grayson: Austin Health
Timothy P. Stinear: The University of Melbourne
Claire L. Gorrie: The University of Melbourne
Benjamin P. Howden: The University of Melbourne

Nature Communications, 2022, vol. 13, issue 1, 1-11

Abstract: Abstract 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.

Date: 2022
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DOI: 10.1038/s41467-022-28156-4

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