NGS-Based S. aureus Typing and Outbreak Analysis in Clinical Microbiology Laboratories: Lessons Learned From a Swiss-Wide Proficiency Test

• Published in: Frontiers in microbiology Vol. 11; p. 591093
• Main Authors: Dylus, David, Pillonel, Trestan, Opota, Onya, Wüthrich, Daniel, Seth-Smith, Helena M B, Egli, Adrian, Leo, Stefano, Lazarevic, Vladimir, Schrenzel, Jacques, Laurent, Sacha, Bertelli, Claire, Blanc, Dominique S, Neuenschwander, Stefan, Ramette, Alban, Falquet, Laurent, Imkamp, Frank, Keller, Peter M, Kahles, Andre, Oberhaensli, Simone, Barbié, Valérie, Dessimoz, Christophe, Greub, Gilbert, Lebrand, Aitana
• Format: Journal Article
• Language: English
• Published: Switzerland Frontiers Media S.A 24.11.2020
• Subjects: bacterial typing; external quality assessment; Microbiology; next generation sequencing; NGS; ring trial; SNP; EQA; SNP; quality control; next generation sequencing; ring trial; NGS; external quality assessment; bacterial typing
• ISSN: 1664-302X; 1664-302X
• DOI: 10.3389/fmicb.2020.591093

Whole genome sequencing (WGS) enables high resolution typing of bacteria up to the single nucleotide polymorphism (SNP) level. WGS is used in clinical microbiology laboratories for infection control, molecular surveillance and outbreak analyses. Given the large palette of WGS reagents and bioinformatics tools, the Swiss clinical bacteriology community decided to conduct a ring trial (RT) to foster harmonization of NGS-based bacterial typing. The RT aimed at assessing methicillin-susceptible strain relatedness from WGS and epidemiological data. The RT was designed to disentangle the variability arising from differences in sample preparation, SNP calling and phylogenetic methods. Nine laboratories participated. The resulting phylogenetic tree and cluster identification were highly reproducible across the laboratories. Cluster interpretation was, however, more laboratory dependent, suggesting that an increased sharing of expertise across laboratories would contribute to further harmonization of practices. More detailed bioinformatic analyses unveiled that while similar clusters were found across laboratories, these were actually based on different sets of SNPs, differentially retained after sample preparation and SNP calling procedures. Despite this, the observed number of SNP differences between pairs of strains, an important criterion to determine strain relatedness given epidemiological information, was similar across pipelines for closely related strains when restricting SNP calls to a common core genome defined by cgMLST schema. The lessons learned from this pilot study will serve the implementation of larger-scale RT, as a mean to have regular external quality assessments for laboratories performing WGS analyses in a clinical setting.