TY - JOUR
T1 - One day in Denmark
T2 - whole-genome sequence-based analysis of Escherichia coli isolates from clinical settings
AU - Rebelo, Ana Rita
AU - Bortolaia, Valeria
AU - Leekitcharoenphon, Pimlapas
AU - Hansen, Dennis Schrøder
AU - Nielsen, Hans Linde
AU - Ellermann-Eriksen, Svend
AU - Kemp, Michael
AU - Røder, Bent Løwe
AU - Frimodt-Møller, Niels
AU - Søndergaard, Turid Snekloth
AU - Coia, John Eugenio
AU - Østergaard, Claus
AU - Westh, Henrik
AU - Aarestrup, Frank M
N1 - © The Author(s) 2025. Published by Oxford University Press on behalf of British Society for Antimicrobial Chemotherapy.
PY - 2025/1/30
Y1 - 2025/1/30
N2 - BACKGROUND: WGS can potentially be routinely used in clinical microbiology settings, especially with the increase in sequencing accuracy and decrease in cost. Escherichia coli is the most common bacterial species analysed in those settings, thus fast and accurate diagnostics can lead to reductions in morbidity, mortality and healthcare costs.OBJECTIVES: To evaluate WGS for diagnostics and surveillance in a collection of clinical E. coli; to examine the pool of antimicrobial resistance (AMR) determinants circulating in Denmark and the most frequent STs; and to evaluate core-genome MLST (cgMLST) and SNP-based clustering approaches for detecting genetically related isolates.METHODS: We analysed the genomes of 699 E. coli isolates collected throughout all Danish Clinical Microbiology Laboratories. We used rMLST and KmerFinder for species identification, ResFinder for prediction of AMR, and PlasmidFinder for plasmid identification. We used Center for Genomic Epidemiology MLST, cgMLSTFinder and CSI Phylogeny to perform typing and clustering analysis.RESULTS: Genetic AMR determinants were detected in 56.2% of isolates. We identified 182 MLSTs, most frequently ST-69, ST-73, ST-95 and ST-131. Using a maximum 15-allele difference as the threshold for genetic relatedness, we identified 23 clusters. SNP-based phylogenetic analysis within clusters revealed from 0 to 13 SNPs, except two cases with 111 and 461 SNPs.CONCLUSIONS: WGS data are useful to characterize clinical E. coli isolates, including predicting AMR profiles and subtyping in concordance with surveillance data. We have shown that it is possible to adequately cluster isolates through a cgMLST approach, but it remains necessary to define proper interpretative criteria.
AB - BACKGROUND: WGS can potentially be routinely used in clinical microbiology settings, especially with the increase in sequencing accuracy and decrease in cost. Escherichia coli is the most common bacterial species analysed in those settings, thus fast and accurate diagnostics can lead to reductions in morbidity, mortality and healthcare costs.OBJECTIVES: To evaluate WGS for diagnostics and surveillance in a collection of clinical E. coli; to examine the pool of antimicrobial resistance (AMR) determinants circulating in Denmark and the most frequent STs; and to evaluate core-genome MLST (cgMLST) and SNP-based clustering approaches for detecting genetically related isolates.METHODS: We analysed the genomes of 699 E. coli isolates collected throughout all Danish Clinical Microbiology Laboratories. We used rMLST and KmerFinder for species identification, ResFinder for prediction of AMR, and PlasmidFinder for plasmid identification. We used Center for Genomic Epidemiology MLST, cgMLSTFinder and CSI Phylogeny to perform typing and clustering analysis.RESULTS: Genetic AMR determinants were detected in 56.2% of isolates. We identified 182 MLSTs, most frequently ST-69, ST-73, ST-95 and ST-131. Using a maximum 15-allele difference as the threshold for genetic relatedness, we identified 23 clusters. SNP-based phylogenetic analysis within clusters revealed from 0 to 13 SNPs, except two cases with 111 and 461 SNPs.CONCLUSIONS: WGS data are useful to characterize clinical E. coli isolates, including predicting AMR profiles and subtyping in concordance with surveillance data. We have shown that it is possible to adequately cluster isolates through a cgMLST approach, but it remains necessary to define proper interpretative criteria.
U2 - 10.1093/jac/dkaf028
DO - 10.1093/jac/dkaf028
M3 - Article
C2 - 39881516
SN - 0305-7453
JO - Journal of Antimicrobial Chemotherapy
JF - Journal of Antimicrobial Chemotherapy
M1 - dkaf028
ER -