Titel: NGS based molecular typing of Staphylococcus haemolyticus - replacing PFGE to increase resolution
ID: 22/MSV
Art: Abstractautor
Session: Workshop 04
Molecular Epidemiology of Infectious Diseases (StAG RK, FG MS)

Referent: Birgit Strommenger (Wernigerode)

Abstract - Text

Background: Staphylococcus haemolyticus (S. haemolyticus) is a colonizer of skin and mucous membranes, associated with infections in immunocompromised patients and premature infants. Epidemiological investigations of infection clusters are predominantly realized via macrorestriction analysis by pulsed-field gel electrophoresis (PFGE). Since macrorestriction analysis suffers from several disadvantages, this study aims at validating a whole genome sequencing (WGS) based approach.

Methods: In total 131 S. haemolyticus isolates from Germany (n=118) and from abroad (n=13), spanning a ten year period (2007-2017), were included. Besides unrelated isolates, isolates from suspected outbreaks and from a long-term-colonization study at a single hospital were comprised. All strains were subjected to resistance testing using broth microdilution, SmaI-macrorestriction analysis (BioNumerics) and WGS (Illumina technology). Genome data was subjected to in house pipelines for quality control and sequence reconstruction. SeqSphere+ was used to generate an ad hoc core genome multilocus sequence typing (cgMLST) scheme. Clustering results were compared to those obtained from macrorestriction analysis, focusing on discriminatory power, reproducibility and cluster concordance.

Results: Multidrug resistance was expressed by 88.6 % of all isolates. Macrorestriction analysis resulted in the identification of eleven clusters containing 82.4 % of all isolates. Remaining "singletons" were predominantly sensitive isolates. Reproducibility of PFGE was low and clusters were partly not supported by epidemiological metadata indicating the need for alternative typing strategies.

Outlook: Ongoing work is focusing on the comparison of typing results, the validation of the cgMLST scheme and a deeper analysis of genome data with respect to antibiotic resistance and virulence associated genes.