Abstract
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The isolates related to the two outbreaks were received in the framework of different contexts. For outbreak 1, the cases were related to food consumption at a specific restaurant, which was identified as the common exposure. The food strains related to outbreak 1 were collected in the context of the epidemiological investigation conducted to identify the potential sources of the human infections.
For outbreak 2, the isolates were obtained from a passive surveillance system aimed at collecting human isolates of Salmonella from regional hospitals. Looking at epidemiological data related to the isolates in a specific timeframe, it was evident that all isolates were from a unique geographical area, in a short time period, and from patients of a single rest home. When such a potential association was identified, several weeks were past from the original human isolation and it was not possible to identify the putative sources.
Therefore, the definition for the first outbreak was “all cases who ate in a specific restaurant and were infected with S. Stanleyville.” In the case of the second outbreak, the definition was “all the hosts of a rest home infected with S. Stanleyville.” The whole genome of 18 isolates from the outbreak of 2016 and 3 from the 2015 outbreak, 1 from human and 2 from food, exhibiting that the same pulsotype was analyzed.
Analysis of genomes was performed by means of tools provided by the Danish Technical University 3 to confirm the serovar, detect acquired resistance genes and chromosomal mutations that could determine antimicrobial resistance, and identify multilocus sequence typing (MLST) profile and the presence of Salmonella Pathogenicity Islands and plasmids. Finally, a Single Nucleotide Polymorphism (SNP)-based phylogenetic tree was designed by using Salmonella Stanleyville CFSAN000624 as reference genome.
Data allowed to confirm the Stanleyville serovar, the MLST profile (ST-97), the absence of plasmids and antimicrobial resistance genes or chromosomal mutations, and the presence of C63PI previously identified in Salmonella Typhimurium SL1344 in all the tested strains. Thus, none of the aforementioned data was useful to discriminate between the two outbreaks.
The SNP-based phylogenetic tree allowed to differentiate strains from the two outbreaks. However, the number of SNPs ranged between 31 and 43 within the outbreak of 2015 isolates and between 19 and 56 within the outbreak of 2016 isolates, with the two groups differentiating for a number of SNPs from 54 to 84. The difference in terms of number of SNPs among the isolates belonging to the same outbreak was comparable between the two groups even though it was quite high when compared with the data reported for Salmonella Typhimurium, 4 suggesting the possibility for a mutation rate per generation higher for Salmonella Stanleyville than for Salmonella Typhimurium. This highlights that the SNPs-based analysis for epidemiological purposes should not overlook the patterning of mutation rates across Salmonella serovars. Another plausible assumption to explain the large SNP differences is that the two outbreaks would be related and associated with the same continuous source, where the organism has evolved over time. However, no exhaustive epidemiological data were available to properly investigate this hypothesis.
Footnotes
Acknowledgments
The authors thank all the laboratory technicians who participated in the study.
Disclosure Statement
None of the authors have competing interests to declare.