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Volume 9, Issue 1

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 Open Access

Abstract

The increase of Vancomycin-resistant (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 -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 % (=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 % (=3064/45 596) of pairs in clusters determined by standard cgMLST, 15 % (=1222/8500) of pairs in core SNP-clusters and 51 % (=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.

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  • Accepted:
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Keyword(s): cgMLST , outbreak , SNP , split k-mer and Vancomycin-resistant Enterococcus faecium
© 2023 The Authors
  • This is an open-access article distributed under the terms of the Creative Commons Attribution License.
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2023-01-30
2024-05-04
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References

  1. European Centre for Disease Prevention and Control Antimicrobial Resistance in the EU/EEA (EARS-Net) - Annual Epidemiological Report 2019. 2020 Stockholm: ECDC; 2019
     [Google Scholar]
  2. Remschmidt C, Schröder C, Behnke M, Gastmeier P, Geffers C et al. Continuous increase of vancomycin resistance in enterococci causing nosocomial infections in Germany - 10 years of surveillance. Antimicrob Resist Infect Control 2018; 7:54 [View Article]
     [Google Scholar]
  3. Werner G, Neumann B, Weber RE, Kresken M, Wendt C et al. Thirty years of VRE in Germany - “expect the unexpected”: the view from the national reference centre for Staphylococci and Enterococci. Drug Resist Updat 2020; 53:100732 [View Article]
     [Google Scholar]
  4. Xanthopoulou K, Peter S, Tobys D, Behnke M, Dinkelacker AG et al. Vancomycin-resistant Enterococcus faecium colonizing patients on hospital admission in Germany: prevalence and molecular epidemiology. J Antimicrob Chemother 2020; 75:2743–2751 [View Article] [PubMed]
     [Google Scholar]
  5. Weber A, Maechler F, Schwab F, Gastmeier P, Kola A. Increase of vancomycin-resistant Enterococcus faecium strain type ST117 CT71 at Charité - Universitätsmedizin Berlin, 2008 to 2018. Antimicrob Resist Infect Control 2020; 9:109 [View Article]
     [Google Scholar]
  6. Van Goethem N, Descamps T, Devleesschauwer B, Roosens NHC, Boon NAM et al. Status and potential of bacterial genomics for public health practice: a scoping review. Implement Sci 2019; 14:79 [View Article] [PubMed]
     [Google Scholar]
  7. Higgs C, Sherry NL, Seemann T, Horan K, Walpola H et al. Optimising genomic approaches for identifying vancomycin-resistant Enterococcus faecium transmission in healthcare settings. Nat Commun 2022; 13:509 [View Article]
     [Google Scholar]
  8. Deurenberg RH, Bathoorn E, Chlebowicz MA, Couto N, Ferdous M et al. Application of next generation sequencing in clinical microbiology and infection prevention. J Biotechnol 2017; 243:16–24 [View Article] [PubMed]
     [Google Scholar]
  9. Nadon C, Van Walle I, Gerner-Smidt P, Campos J, Chinen I et al. PulseNet International: vision for the implementation of whole genome sequencing (WGS) for global food-borne disease surveillance. Euro Surveill 2017; 22:23 [View Article]
     [Google Scholar]
  10. Harris SR. SKA: Split Kmer Analysis toolkit for bacterial genomic epidemiology. Genomics 2018453142 [View Article]
     [Google Scholar]
  11. Schröder C, Peña Diaz LA, Rohde AM, Piening B, Aghdassi SJS et al. Lean back and wait for the alarm? Testing an automated alarm system for nosocomial outbreaks to provide support for infection control professionals. PLoS One 2020; 15:e0227955 [View Article]
     [Google Scholar]
  12. KRINKO beim Robert Koch-Institut Hygienemaßnahmen zur prävention der infektion durch enterokokken mit speziellen antibiotikaresistenzen. Bundesgesundheitsblatt-Gesundheitsforschung-Gesundheitsschutz 2018; 10:1310
     [Google Scholar]
  13. Andrews S. FastQC: A Quality Control Tool for High Throughput Sequence Data Cambridge, United Kingdom: Babraham Bioinformatics, Babraham Institute; 2010
     [Google Scholar]
  14. de Been M, Pinholt M, Top J, Bletz S, Mellmann A et al. Core genome multilocus sequence typing scheme for high- resolution typing of Enterococcus faecium. J Clin Microbiol 2015; 53:3788–3797 [View Article]
     [Google Scholar]
  15. Jünemann S, Sedlazeck FJ, Prior K, Albersmeier A, John U et al. Updating benchtop sequencing performance comparison. Nat Biotechnol 2013; 31:294–296 [View Article] [PubMed]
     [Google Scholar]
  16. Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 2014; 30:2114–2120 [View Article]
     [Google Scholar]
  17. Zerbino DR, Birney E. Velvet: algorithms for de novo short read assembly using de Bruijn graphs. Genome Res 2008; 18:821–829 [View Article] [PubMed]
     [Google Scholar]
  18. Seemann T. Prokka: rapid prokaryotic genome annotation. Bioinformatics 2014; 30:2068–2069 [View Article]
     [Google Scholar]
  19. Schwengers O, Hoek A, Fritzenwanker M, Falgenhauer L, Hain T et al. ASA3P: an automatic and scalable pipeline for the assembly, annotation and higher-level analysis of closely related bacterial isolates. PLoS Comput Biol 2020; 16:e1007134 [View Article]
     [Google Scholar]
  20. Bankevich A, Nurk S, Antipov D, Gurevich AA, Dvorkin M et al. SPAdes: a new genome assembly algorithm and its applications to single-cell sequencing. J Comput Biol 2012; 19:455–477 [View Article] [PubMed]
     [Google Scholar]
  21. Bosi E, Donati B, Galardini M, Brunetti S, Sagot M-F et al. MeDuSa: a multi-draft based scaffolder. Bioinformatics 2015; 31:2443–2451 [View Article]
     [Google Scholar]
  22. Page AJ, Cummins CA, Hunt M, Wong VK, Reuter S et al. Roary: rapid large-scale prokaryote pan genome analysis. Bioinformatics 2015; 31:3691–3693 [View Article]
     [Google Scholar]
  23. Li H, Handsaker B, Wysoker A, Fennell T, Ruan J et al. The sequence alignment/map format and SAMtools. Bioinformatics 2009; 25:2078–2079 [View Article]
     [Google Scholar]
  24. Price MN, Dehal PS, Arkin AP. FastTree 2--approximately maximum-likelihood trees for large alignments. PLoS One 2010; 5:e9490 [View Article]
     [Google Scholar]
  25. Letunic I, Bork P. Interactive Tree Of Life (iTOL) v5: an online tool for phylogenetic tree display and annotation. Nucleic Acids Res 2021; 49:W293–W296 [View Article] [PubMed]
     [Google Scholar]
  26. Eisenberger D, Tuschak C, Werner M, Bogdan C, Bollinger T et al. Whole-genome analysis of vancomycin-resistant Enterococcus faecium causing nosocomial outbreaks suggests the occurrence of few endemic clonal lineages in Bavaria, Germany. J Antimicrob Chemother 2020; 75:1398–1404 [View Article] [PubMed]
     [Google Scholar]
  27. Falgenhauer L, Fritzenwanker M, Imirzalioglu C, Steul K, Scherer M et al. Near-ubiquitous presence of a vancomycin-resistant Enterococcus faecium ST117/CT71/vanB -clone in the Rhine-Main metropolitan area of Germany. Antimicrob Resist Infect Control 2019; 8:128 [View Article]
     [Google Scholar]
  28. Eichel V, Klein S, Bootsveld C, Frank U, Heeg K et al. Challenges in interpretation of WGS and epidemiological data to investigate nosocomial transmission of vancomycin-resistant Enterococcus faecium in an endemic region: incorporation of patient movement network and admission screening. J Antimicrob Chemother 2020; 75:1716–1721 [View Article] [PubMed]
     [Google Scholar]
  29. Neumann B, Bender JK, Maier BF, Wittig A, Fuchs S et al. Comprehensive integrated NGS-based surveillance and contact-network modeling unravels transmission dynamics of vancomycin-resistant enterococci in a high-risk population within a tertiary care hospital. PLoS One 2020; 15:e0235160 [View Article]
     [Google Scholar]
  30. Gona F, Comandatore F, Battaglia S, Piazza A, Trovato A et al. Comparison of core-genome MLST, coreSNP and PFGE methods for Klebsiella pneumoniae cluster analysis. Microb Genom 2020; 6:e000347 [View Article]
     [Google Scholar]
  31. Miro E, Rossen JWA, Chlebowicz MA, Harmsen D, Brisse S et al. Core/Whole genome multilocus sequence typing and core genome SNP-based typing of OXA-48-producing Klebsiella pneumoniae clinical isolates from Spain. Front Microbiol 2019; 10:2961 [View Article]
     [Google Scholar]
  32. Gorrie CL, Da Silva AG, Ingle DJ, Higgs C, Seemann T et al. Key parameters for genomics-based real-time detection and tracking of multidrug-resistant bacteria: a systematic analysis. Lancet Microbe 2021; 2:e575–e583 [View Article]
     [Google Scholar]
  33. van Hal SJ, Willems RJL, Gouliouris T, Ballard SA, Coque TM et al. The global dissemination of hospital clones of Enterococcus faecium. Genome Med 2021; 13:52 [View Article] [PubMed]
     [Google Scholar]
  34. Raven KE, Gouliouris T, Brodrick H, Coll F, Brown NM et al. Complex routes of nosocomial vancomycin-resistant Enterococcus faecium transmission revealed by genome sequencing. Clin Infect Dis 2017; 64:886–893 [View Article]
     [Google Scholar]
  35. Gorrie C, Higgs C, Carter G, Stinear TP, Howden B. Genomics of vancomycin-resistant Enterococcus faecium. Microb Genom 2019; 5:e000283 [View Article]
     [Google Scholar]
  36. de Been M, van Schaik W, Cheng L, Corander J, Willems RJ. Recent recombination events in the core genome are associated with adaptive evolution in Enterococcus faecium. Genome Biol Evol 2013; 5:1524–1535 [View Article] [PubMed]
     [Google Scholar]
  37. Palmer KL, Gilmore MS. Multidrug-resistant enterococci lack CRISPR-cas. mBio 2010; 1:e00227-10 [View Article]
     [Google Scholar]
  38. Freitas AR, Tedim AP, Novais C, Coque TM, Peixe L. Distribution of putative virulence markers in Enterococcus faecium: towards a safety profile review. J Antimicrob Chemother 2018; 73:306–319 [View Article] [PubMed]
     [Google Scholar]
  39. Kaier K, Heister T, Wolff J, Wolkewitz M. Mechanical ventilation and the daily cost of ICU care. BMC Health Serv Res 2020; 20:267 [View Article] [PubMed]
     [Google Scholar]
  40. Gouliouris T, Coll F, Ludden C, Blane B, Raven KE et al. Quantifying acquisition and transmission of Enterococcus faecium using genomic surveillance. Nat Microbiol 2021; 6:103–111 [View Article] [PubMed]
     [Google Scholar]
  41. Correa-Martinez CL, Tönnies H, Froböse NJ, Mellmann A, Kampmeier S. Transmission of vancomycin-resistant Enterococci in the hospital setting: uncovering the patient-environment interplay. Microorganisms 2020; 8:203 [View Article]
     [Google Scholar]
  42. Neumann B, Bender JK, Maier BF, Wittig A, Fuchs S et al. Comprehensive integrated NGS-based surveillance and contact-network modeling unravels transmission dynamics of vancomycin-resistant enterococci in a high-risk population within a tertiary care hospital. PLoS One 2020; 15:e0235160 [View Article]
     [Google Scholar]
  43. Arredondo-Alonso S, Top J, McNally A, Puranen S, Pesonen M et al. Plasmids shaped the recent emergence of the major nosocomial pathogen Enterococcus faecium. mBio 2020; 11:e03284-19 [View Article]
     [Google Scholar]
  44. Bender JK, Kalmbach A, Fleige C, Klare I, Fuchs S et al. Population structure and acquisition of the vanB resistance determinant in German clinical isolates of Enterococcus faecium ST192. Sci Rep 2016; 6:21847 [View Article]
     [Google Scholar]
  45. Zhou X, Chlebowicz MA, Bathoorn E, Rosema S, Couto N et al. Elucidating vancomycin-resistant Enterococcus faecium outbreaks: the role of clonal spread and movement of mobile genetic elements. J Antimicrob Chemother 2018; 73:3259–3267 [View Article] [PubMed]
     [Google Scholar]
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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
M Gen 9, 000937 (2023); https://doi.org/10.1099/mgen.0.000937
/content/journal/mgen/10.1099/mgen.0.000937
/content/journal/mgen/10.1099/mgen.0.000937
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