AUTHOR=Merlotti Alessandra , Manfreda Gerardo , Munck Nanna , Hald Tine , Litrup Eva , Nielsen Eva Møller , Remondini Daniel , Pasquali Frédérique 

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

JOURNAL=Frontiers in Microbiology

VOLUME=Volume 11 - 2020

YEAR=2020

URL=https://www.frontiersin.org/journals/microbiology/articles/10.3389/fmicb.2020.01205

DOI=10.3389/fmicb.2020.01205

ISSN=1664-302X

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 distances. Analyzing food and animal genomes, the coherence of source clustering was 90% for animal source, 85% for country, 82% for serotype and 65 % for year of isolation independently from the type of input data, suggesting animal source as the first driver of clustering formation. Adding human isolate genomes to the network, a percentage between 93.6% and 95% clustered with the existing component and only a percentage between 5% and 6.4% appeared as not attributed to any animal source. 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, weighted network approach based on pairwise SNPs, cgMLST and wgMLST matrices showed promising results for source attribution studies.