Comparative Genome Analysis and Characterization of Salmonella enterica using Whole-Genome Sequencing Approach. (c2020)

Abstract

Salmonella spp. are facultative rod-shaped Gram-negative bacteria and are a leading cause of worldwide foodborne gastroenteritis. We used in this study whole-genome sequencing and conventional approaches to characterize Salmonella enterica spp. clinical isolates recovered from Lebanon. Pulse-field gel electrophoresis (PFGE) and PCR-based replicon typing (PBRT) were used to type the isolates and determine plasmid types and distribution. blaTEM1B and aac(6’)-Iaa genes mediating resistance to ampicillin and aminoglycosides were among the detected resistance determinants. IncFIB and IncFII were the two most common incompatibility groups, with the IncX1 plasmid harboring the blaTEM1B. We also found six multi-locus sequence types (MLST) with ST11 being the most prevalent. Salmonella pathogenicity islands (SPI) encode for virulence determinants linked to survival, invasion, and ability of the organism to spread outside the intestines. SPI-1 to 5 were commonly detected across the different serovars in this study. The SPI-13 is conserved in the main serovars and is replaced in the same genomic locus by SPI-8, and this was evident in Salmonella enterica serovar Typhimurium. The question of whether the isolates were part of an outbreak or not was raised. We used Outbreaker workflow to follow and determine if they were close enough to be classified as originating from a common source. The constructed Gengraph, showed that the number of observed mutations was in the range of 1 to 50, thus ruling out the possibility of the isolates being linked to an epidemiologically defined outbreak. Our study provided a detailed whole genome molecular characterization of S. enterica isolates collected from Lebanon. The approach we used helped in having a more accurate overview, and SNP-based analysis proved to give a better insight into the genetic diversity than the traditionally used approaches. Our findings, accordingly, supported the notion of shifting towards the implementation of WGS as the best approach for outbreak detection being it in the food industry or hospital settings.