Four Genotyping Schemes for Phylogenetic Analysis of Pseudomonas aeruginosa: Comparison of Their Congruence with Multi-Locus Sequence Typing

摘要

Several molecular typing schemes have been proposed to differentiate among isolates and clonal groups, and hence establish epidemiological or phylogenetic links. It has been widely accepted that multi-locus sequence typing (MLST) is the gold standard for phylogenetic typing/long-term epidemiological surveillance, but other recently described methods may be easier to carry out, especially in settings with limited access to DNA sequencing. Comparing the performance of such techniques to MLST is therefore of relevance. A study was therefore carried out with a collection of P. aeruginosa strains (n = 133) typed by four typing schemes: MLST, multiple-locus variable number tandem repeat analysis (MLVA), pulsed-field gel electrophoresis (PFGE) and the commercial DiversiLab microbial typing system (DL). The aim of this study was to compare the results of each typing method with MLST. The Simpson's indices of diversity were 0.989, 0.980, 0.961 and 0.906 respectively for PFGE, MLVA, DL and MLST. The congruence between techniques was measured by the adjusted Wallace index (W): this coefficient indicates the probability that a pair of isolates which is assigned to the same type by one typing method is also typed as identical by the other. In this context, the congruence between techniques was recorded as follow: MLVA-type to predict MLST-type (93%), PFGE to MLST (92%), DL to MLST (64.2%), PFGE to MLVA (63.5%) and PFGE to DL (61.7%). Conversely, for all above combinations, prediction was very poor. The congruence was increased at the clonal complex (CC) level. MLST is regarded the gold standard for phylogenetic classification of bacteria, but is rather laborious to carry out in many settings. Our data suggest that MLVA can predict the MLST-type with high accuracy, and even higher when studying the clonal complex level. Of the studied three techniques MLVA was therefore the best surrogate method to predict MLST.