Development and Validation of a Burkholderia pseudomallei Core Genome Multilocus Sequence Typing Scheme To Facilitate Molecular Surveillance

Burkholderia pseudomallei causes the severe disease melioidosis. Whole-genome sequencing (WGS)-based typing methods currently offer the highest resolution for molecular investigations of this genetically diverse pathogen. Still, its routine application in diagnostic laboratories is limited by the need for high computing power, bioinformatic skills, and variable bioinformatic approaches, with the latter affecting the results. We therefore aimed to establish and validate a WGS-based core genome multilocus sequence typing (cgMLST) scheme, applicable in routine diagnostic settings. A soft defined core genome was obtained by challenging the B. pseudomallei reference genome K96243 with 469 environmental and clinical genomes, resulting in 4,221 core and 1,351 accessory targets. The scheme was validated with 320 WGS data sets. We compared our novel typing scheme with single nucleotide polymorphism-based approaches investigating closely and distantly related strains. Finally, we applied our scheme for tracking the environmental source of a recent infection. The validation of the scheme detected >95% good cgMLST target genes in 98.4% of the genomes. Comparison with existing typing methods revealed very good concordance. Our scheme proved to be applicable to investigating not only closely related strains but also the global B. pseudomallei population structure. We successfully utilized our scheme to identify a sugarcane field as the presumable source of a recent melioidosis case. In summary, we developed a robust cgMLST scheme that integrates high resolution, maximized standardization, and fast analysis for the nonbioinformatician. Our typing scheme has the potential to serve as a routinely applicable classification system in B. pseudomallei molecular epidemiology.

Automated summary

In this study, a WGS-based core genome multilocus sequence typing (cgMLST) scheme applicable in routine diagnostic settings was established and validated. The scheme was validated with 320 WGS data sets, showing >95% good cgMLST target genes in 98.4% of the genomes. Comparison with existing typing methods revealed very good concordance.