Genome-wide networks reveal emergence of epidemic strains of Salmonella Enteritidis. (April 2022)

• Record Type: Journal Article
• Authors: Svahn, Adam J.; Chang, Sheryl L.; Rockett, Rebecca J.; Cliff, Oliver M.; Wang, Qinning; Arnott, Alicia; Ramsperger, Marc; Sorrell, Tania C.; Sintchenko, Vitali; Prokopenko, Mikhail
• Is Part Of: International journal of infectious diseases. Volume 117(2022)
• Page Start: 65
• Page End: 73
• Publication Date: 2022-04
• Subjects: Salmonella Enteritidis -- Foodborne pathogen -- Genotype network; Communicable diseases -- Periodicals; Communicable Diseases -- Periodicals; Communicable diseases; Periodicals; Electronic journals; 616.9
• DOI: 10.1016/j.ijid.2022.01.056
• Languages: English
• ISSNs: 1201-9712

Abstract

Highlights: Investigated whole-genome network analysis to complement phylogenetic analysis Covered Salmonella enterica serovar enteritidis isolates over 2015-2019 Covered Salmonella enterica outbreak in a non-endemic context Network at whole-genome resolution gives better power to delineate sub-populations Whole-genome network analysis is a fast and easy tool for the detection of epidemics Abstract: Objectives: To enhance monitoring of high-burden foodborne pathogens, there is opportunity to combine pangenome data with network analysis. Methods: Salmonella enterica subspecies Enterica serovar Enteritidis isolates were referred to the New South Wales (NSW) Enteric Reference Laboratory between August 2015 and December 2019 (1033 isolates in total), inclusive of a confirmed outbreak. All isolates underwent whole genome sequencing. Distances between genomes were quantified by in silico multiple-locus variable-number tandem repeat analysis (MLVA) as well as core single nucleotide polymorphisms (SNPs), which informed the construction of undirected networks. Centrality-prevalence spaces were generated from the undirected networks. Components on the undirected SNP network were considered alongside a phylogenetic tree representation. Results: Outbreak isolates were identified as distinct components on the MLVA and SNP networks. The MLVA network-based centrality-prevalence space did not delineate the outbreak, whereas the outbreak was delineated in the SNP network-based centrality-prevalence space. Components on the undirected SNP network showed a high concordance to the SNP clusters based on phylogenetic analysis. Conclusions: Bacterial whole-genome data in network-based analysis can improve the resolution of population analysis. High concordance of network components and SNP clusters is promising for rapid population analyses of foodborne Salmonella spp. owing to the low overhead of network analysis.