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In silico Identification of Serovar-Specific Genes for Salmonella Serotyping

Xiaomei Zhang, Michael Payne, Ruiting Lan

Frontiers in Microbiology, 2019

26 citations

Abstract

Salmonella enterica subspecies enterica is a highly diverse subspecies with more than 2600 serovars and the ability to distinguish serovars within this group is vital for surveillance. With the development of whole-genome sequencing technology, serovar prediction by traditional serotyping is being replaced by molecular serotyping. Existing in silico serovar prediction approaches utilise surface antigen encoding genes, core genome MLST and serovar-specific gene markers or DNA fragments for serotyping. However, these serovar-specific gene markers or DNA fragments only distinguished a smaller number of serovars. In this study, we compared 2258 Salmonella accessory genomes to identify 414 candidate serovar-specific or lineage specific gene markers for 106 serovars which includes 24 polyphyletic serovars and the paraphyletic serovar Enteritidis. A combination of several lineage-specific gene markers can be used for the clear identification of the polyphyletic serovars and paraphyletic serovars. We designed and evaluated an in silico serovar prediction approach by screening 1089 genomes representing 106 serovars against a set of 131 serovar-specific gene markers. The presence or absence of one or more serovar-specific gene markers was used to predict the serovar of an isolate from genomic data. We show that serovar-specific gene markers have comparable accuracy to other in silico serotyping methods with 84.8% of isolates assigned to the correct serovar with no false positives and…

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