Abstract
Background: Salmonellosis is one of the most common foodborne diseases worldwide. Although human infection by non-typhoidal Salmonella (NTS) enterica subspecies enterica is associated primarily with a self-limiting diarrhoeal illness, invasive bacterial infections (such as septicaemia, bacteraemia and meningitis) were also reported. Human outbreaks of NTS were reported in several countries all over the world including developing as well as high-income countries. Conventional laboratory methods such as pulsed field gel electrophoresis (PFGE) do not display adequate discrimination and have their limitations in epidemiological surveillance. It is therefore very crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological characterisation and outbreak investigation. Methods: Here, we used different whole genome sequence (WGS)-based subtyping methods for retrospective investigation of two different outbreaks of Salmonella Typhimurium and Salmonella Dublin that occurred in 2013 in UK and Ireland respectively. Results: The core genome multilocus sequence typing (cgMLST) was discriminatory and separated the outbreak strains of Salmonella Dublin from the non-outbreak strains that were concordant with the epidemiological data however cgMLST could not discriminate between outbreak and non-outbreak strains of Salmonella Typhimurium, On the other hand, other WGS-based subtyping methods including multilocus sequence typing (MLST), ribosomal MLST (rMLST), whole genome MLST (wgMLST), clustered regularly interspaced short palindromic repeats (CRISPRs), prophage sequence profiling, antibiotic resistance profile and plasmid typing methods were less discriminatory. Conclusions: Foodborne salmonellosis is an important concern for public health therefore, it is crucial to use accurate, reliable and highly discriminative subtyping methods for epidemiological surveillance and outbreak investigation. The rapid development of next-generation sequencing (NGS) technology and bioinformatics tools have enabled WGS of any bacterial strain feasible. Various typing tools have been proposed by using WGS data but currently, the adoption of WGS-based methods have proved to be difficult due to lack of standardization. There are many layers on obtaining WGS data and there is need of standardization from the type of sequencers used to the bioinformatics analysis. Therefore, the emerging genetic analysis techniques should be combined with conventional phenotypic and molecular methods for routine surveillance and outbreak investigation until the WGS-based methods can be fully exploited, improved and standardized.