Different Resolution Power of Multilocus Variable-Number Tandem Repeat Analysis and Whole-Genome Sequencing in the Characterization of S. 1,4,[5],12:i:- Isolates

Abstract

Salmonella enterica serovar 1,4,[5],12:i:- has emerged over the last two decades as one of the most common serovars causing human salmonellosis in Europe. It is supposed to originate from Salmonella enterica serovar Typhimurium due to antigenic and genotypic similarities between the two serovars. Due to the high level of similarity, the multilocus variable-number tandem repeat analysis (MLVA) protocol designed for Salmonella Typhimurium routine typing is commonly used also for the characterization of S. 1,4,[5],12:i. Nevertheless, the Salmonella Typhimurium-based MLVA protocol often shows poor discriminatory power for S. 1,4,[5],12:i. Indeed, only a limited number of MLVA profiles have been described for S. 1,4,[5],12:i:-. Moreover, based on the MLVA clustering, S. 1,4,[5],12:i:- is supposed to display high clonality. The aim of the present work was to assess whether the five loci of Salmonella Typhimurium investigated by MLVA are sufficiently accurate to correctly assign S. 1,4,[5],12:i:- isolates. For this purpose, 38 epidemiologically unrelated S. 1,4,[5],12:i:- were subjected to whole-genome sequencing. Isolates were selected among a collection of monophasic strains isolated in Italy from different sources over the period 2014-2016 and belonging to the five most commonly detected MLVA profiles. Results confirmed the possible clonality for S. 1,4,[5],12:i:- serovar in the light of the scarce difference observed in terms of single-nucleotide polymorphisms (SNPs) among investigated isolates. Nevertheless, unrelated isolates on the basis of the difference of SNP number were characterized as indistinguishable by MLVA profile, thus suggesting an insufficient resolution of MLVA. Hence, we can conclude that MLVA-based approach does not seem a valuable proxy to deepen into the epidemiological relationship among S. 1,4,[5],12:i:- isolates. These evidences can be useful to avoid incorrect assignment especially when surveillance data are used for outbreak investigations.