Network Approach to Source Attribution of Salmonella enterica Serovar Typhimurium and Its Monophasic Variant

Alessandra Merlotti, Gerardo Manfreda, Nanna Sophia Mucha Munck, Tine Hald, Eva Litrup, Eva Møller Nielsen, Daniel Remondini*, Frédérique Pasquali

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

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Abstract

Salmonella enterica subspecies enterica serovar Typhimurium and its monophasic variant are among the most common Salmonella serovars associated with human salmonellosis each year. Related infections are often due to consumption of contaminated meat of pig, cattle, and poultry origin. In order to evaluate novel microbial subtyping methods for source attribution, an approach based on weighted networks was applied on 141 human and 210 food and animal isolates of pigs, broilers, layers, ducks, and cattle collected in Denmark from 2013 to 2014. A whole-genome SNP calling was performed along with cgMLST and wgMLST. Based on these genomic input data, pairwise distance matrices were built and used as input for construction of a weighted network where nodes represent genomes and links to distances. Analyzing food and animal Typhimurium genomes, the coherence of source clustering ranged from 89 to 90% for animal source, from 84 to 85% for country, and from 63 to 65% for year of isolation and was equal to 82% for serotype, suggesting animal source as the first driver of clustering formation. Adding human isolate genomes to the network, a percentage between 93.6 and 97.2% clustered with the existing component and only a percentage between 2.8 and 6.4% appeared as not attributed to any animal sources. The majority of human genomes were attributed to pigs with probabilities ranging from 83.9 to 84.5%, followed by broilers, ducks, cattle, and layers in descending order. In conclusion, a weighted network approach based on pairwise SNPs, cgMLST, and wgMLST matrices showed promising results for source attribution studies.

Original languageEnglish
Article number1205
JournalFrontiers in Microbiology
Volume11
Number of pages10
ISSN1664-302X
DOIs
Publication statusPublished - 2020

Keywords

  • multi locus sequence typing
  • network analysis
  • Salmonella enterica
  • SNP distance
  • source attribution
  • whole genome multi locus sequence typing

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