Split k-mer analysis compared to cgMLST and SNP-based core genome analysis for detecting transmission of vancomycin-resistant enterococci: results from routine outbreak analyses across different hospitals and hospitals networks in Berlin, Germany

Abstract

The increase of Vancomycin-resistant Enterococcus faecium (VREfm) in recent years has been partially attributed to the rise of specific clonal lineages, which have been identified throughout Germany. To date, there is no gold standard for the interpretation of genomic data for outbreak analyses. New genomic approaches such as split k-mer analysis (SKA) could support cluster attribution for routine outbreak investigation. The aim of this project was to investigate frequent clonal lineages of VREfm identified during suspected outbreaks across different hospitals, and to compare genomic approaches including SKA in routine outbreak investigation. We used routine outbreak laboratory data from seven hospitals and three different hospital networks in Berlin, Germany. Short-read libraries were sequenced on the Illumina MiSeq system. We determined clusters using the published Enterococcus faecium -cgMLST scheme (threshold ≤20 alleles), and assigned sequence and complex types (ST, CT), using the Ridom SeqSphere+ software. For each cluster as determined by cgMLST, we used pairwise core-genome SNP-analysis and SKA at thresholds of ten and seven SNPs, respectively, to further distinguish cgMLST clusters. In order to investigate clinical relevance, we analysed to what extent epidemiological linkage backed the clusters determined with different genomic approaches. Between 2014 and 2021, we sequenced 693 VREfm strains, and 644 (93 %) were associated within cgMLST clusters. More than 74 % (n=475) of the strains belonged to the six largest cgMLST clusters, comprising ST117, ST78 and ST80. All six clusters were detected across several years and hospitals without apparent epidemiological links. Core SNP analysis identified 44 clusters with a median cluster size of three isolates (IQR 2–7, min-max 2–63), as well as 197 singletons (41.4 % of 475 isolates). SKA identified 67 clusters with a median cluster size of two isolates (IQR 2–4, min-max 2–19), and 261 singletons (54.9 % of 475 isolates). Of the isolate pairs attributed to clusters, 7 % (n=3064/45 596) of pairs in clusters determined by standard cgMLST, 15 % (n=1222/8500) of pairs in core SNP-clusters and 51 % (n=942/1880) of pairs in SKA-clusters showed epidemiological linkage. The proportion of epidemiological linkage differed between sequence types. For VREfm, the discriminative ability of the widely used cgMLST based approach at ≤20 alleles difference was insufficient to rule out hospital outbreaks without further analytical methods. Cluster assignment guided by core genome SNP analysis and the reference free SKA was more discriminative and correlated better with obvious epidemiological linkage, at least recently published thresholds (ten and seven SNPs, respectively) and for frequent STs. Besides higher overall discriminative power, the whole-genome approach implemented in SKA is also easier and faster to conduct and requires less computational resources.

Outbreak investigation of Serratia marcescens bloodstream infection in an obstetric ward for high-risk pregnant women

“…Two samples from patients C and D were more closely related, whereas the sample from patient B was relatively less correlated with the other two samples in the sequencing of antibiotic resistance proteins. However, all three were close enough to be considered causative of the same outbreak in the WGS analysis (cutoff ≤ 7 to 10 core SNP differences) [ 21 ]. The cgSNP distanace between the 3 strains was: (SNP sites per total core genome sites) and the maximum number of cgSNPs was no more than 2.…”

“…If the three identified isolates belonged differently to several clusters or if the SNP difference was too significant compared to the time scale in which transmission could occur, it was regarded as individually acquired rather than transmission between patients. To screen for potential transmission caused by pathogen, we defined isolate pairs with ≤ 7 to 10 core SNP differences [ 21 ].…”

Rifaximin prophylaxis causes resistance to the last-resort antibiotic daptomycin

“…Patients with VREfm isolates that were assessed as genetically clustered with other VREfm isolates in the cohort and likely represented direct transmission were excluded. Genetic clustering was defined using an international standard SNP cut-off (7 SNPs) 69,70 using a split Kmer (k=15) analysis (SKA) (https://github.com/simonrharris/SKA) (v.1.0), a reference-free pairwise method that compares the entire genome (unlike traditional core-genome based comparisons). Medical records from patients were reviewed for comorbidity and antibiotic prescribing data.…”

Two artificial tears outbreak-associated cases of XDRPseudomonas aeruginosadetected through whole genome sequencing-based surveillance

“…11 P. aeruginosa isolates were considered part of a genetically related cluster if ≥2 isolates differed from one another by no more than 15 single nucleotide polymorphisms (SNPs) using split kmer analysis (ska). 4,12 Additional P. aeruginosa ST1203 genomes, including those of three representative outbreak isolates, were downloaded from the NCBI database on April 6, 2023 (Table S1). A phylogenetic tree was made using RAxML from a core genome alignment generated with snippy (https://github.com/tseemann/snippy).…”