Rapid and accurate typing of pathogens is crucial for effective surveillance and outbreak
investigation. Although classical typing methods are still well implemented in clinical
microbiology laboratories, whole genome sequencing (WGS) is emerging as a powerful molecular
typing tool with considerable power of discrimination between outbreak and non-outbreak
isolates. This technique has been used to study the epidemiology of important pathogens, such as
Pseudomonas aeruginosa and Staphylococcus aureus.
An increase in P. aeruginosa incidence was observed in the intensive care units (ICUs) of
the University Hospital of Lausanne. Double locus sequence typing (DLST) detected the presence
of three major genotypes during the study period with different epidemiological behaviours. One
of the projects developed during this doctoral thesis aimed to use WGS to further investigate these
three DLST types. A standard methodology was defined by incorporating open access
bioinformatic methods for SNPs analysis using P. aeruginosa PA14 as the reference. Results
showed an unexpected high number of SNP differences between isolates suspected to be part of
an outbreak. The original methodology was altered by adding additional steps of stricter quality
filtering which resulted in a more accurate number of SNP differences found. Using a closer
reference to each DLST type gave similar SNP differences to when the adapted methodology was
used. Changing specific mapping and site coverage thresholds resulted in minor changes in SNPs
between isolates. When a definitive methodology was finally chosen, WGS was able to
differentiate between outbreak (< 10 SNPs) and non-outbreak isolates, to confirm suspected
epidemiological links, and infer relatedness between isolates/environment that were not
epidemiologically linked. Combining DLST with the discriminatory power of WGS efficiently
elucidated on the P. aeruginosa epidemiology in our ICUs.
Genomic data is mainly exploited by SNP analysis or by gene-by-gene methods. The
objective of this doctoral thesis’ second project was to assess the performance of these
genomic methods by using a previously published ST228 Methicillin-resistant
Staphylococcus aureus (MRSA) dataset. Original published results were compared to the
ones obtained with the whole genome SNPs (wgSNPs) and whole genome MLST
(wgMLST) tools implemented in BioNumerics. Clustering of isolates was identical
between the three analysis and distances were similar between wgSNPs and wgMLST.
The advantages of using the BioNumerics wgMLST tool for real-time outbreak
investigation, i.e. no need for a close reference, high interlaboratory reproducibility, and
almost no bioinformatic skills needed, turn this method into a simple and easy alternative
to other analysis approaches.