Staphylococcal Enterotoxin Gene Cluster: Prediction of Enterotoxin (SEG and SEI) Production and of the Source of Food Poisoning on the Basis of Saβ Typing.

Currently, only 5 (SEA to SEE) out of 27 known staphylococcal enterotoxins can be analyzed using commercially available kits. Six genes (, , , , , and ), encoding putative and undetectable enterotoxins, are located on the enterotoxin gene cluster (), which is part of the Staphylococcus aureus genomic island Saβ. These enterotoxins have been described as likely being involved in staphylococcal food-poisoning outbreaks. The aim of the present study was to determine if whole-genome data can be used for the prediction of staphylococcal enterotoxin production, particularly enterotoxin G (SEG) and enterotoxin I (SEI). For this purpose, whole-genome sequences of 75 Staphylococcus aureus strains from different origins (food-poisoning outbreaks, human, and animal) were investigated by applying bioinformatics methods (phylogenetic analysis using the core genome and different alignments). SEG and SEI expression was tested using a sandwich enzyme-linked immunosorbent assay method. Strains could be allocated to 14 different Saβ types, each type being associated with a single clonal complex (CC). In addition, the Saβ type and CC were associated with the origin of the strain (human or cattle derived). The amount of SEG and SEI produced also correlated with the Saβ type and the CC of a strain. The present results show promising indications that the production of SEG and SEI can be predicted based on the Saβ type or CC of a strain. Besides having infectious properties in human and animals, S. aureus can produce different enterotoxins in food. The enterotoxins can cause vomiting and diarrhea, often involving many people. Most of these outbreaks remain undiscovered, as detection methods for enterotoxins are only available for a few enterotoxins but not for the more recently discovered enterotoxins G (SEG) and I (SEI). In this study, we show promising results that production of SEG and SEI can be predicted based on the whole-genome sequencing data of a strain. In addition, these data could be used to find the source (human or cattle derived) of an outbreak strain, which is the key for a better understanding of the role SEG and SEI play in foodborne outbreaks caused by S. aureus.