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Background   Cronobacter , formerly known as  Enterobacter sakazakii , is a food-borne pathogen known to cause neonatal meningitis, septicaemia and death. Current diagnostic tests for identification of  Cronobacter  do not differentiate between species, necessitating time consuming 16S rDNA gene sequencing or multilocus sequence typing (MLST). The organism is ubiquitous, being found in the environment and in a wide range of foods, although there is variation in pathogenicity between  Cronobacter  isolates and between species. Therefore to be able to differentiate between the pathogenic and non-pathogenic strains is of interest to the food industry and regulators.    Results  Here we report the use of Expectation Maximization clustering to categorise 98 strains of  Cronobacter  as pathogenic or non-pathogenic based on biochemical test results from standard diagnostic test kits. Pathogenicity of a strain was postulated on the basis of either pathogenic symptoms associated with strain source or corresponding MLST sequence types, allowing the clusters to be labelled as containing either pathogenic or non-pathogenic strains. The resulting clusters gave good differentiation of strains into pathogenic and non-pathogenic groups, corresponding well to isolate source and MLST sequence type. The results also revealed a potential association between pathogenicity and inositol fermentation. An investigation of the genomes of  Cronobacter sakazakii  and  C. turicensis  revealed the gene for inositol monophosphatase is associated with putative virulence factors in pathogenic strains of  Cronobacter .    Conclusions  We demonstrated a computational approach allowing existing diagnostic kits to be used to identify pathogenic strains of  Cronobacter . The resulting clusters correlated well with MLST sequence types and revealed new information about the pathogenicity of  Cronobacter  species.

In Silico identification of pathogenic strains of Cronobacter from Biochemical data reveals association of inositol fermentation with pathogenicity