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.
Highlights
Salmonella enterica subspecies enterica serovar Typhimurium and its monophasic variant (STm) are among the top three serovars in confirmed human cases of salmonellosis each year in Europe (EFSA and ECDC, 2018)
We remark that even if the dataset presents a large abundance of pig and broiler samples, we found human isolates with 100% links toward less abundant animal sources, such as layers and ducks, reflecting the fact that our analysis does not seem heavily affected by such source representation imbalance
Along with the model approach, the dataset itself might strongly influence this estimate especially in case the dataset does not fully represent the real temporal and spatial distribution of human and animal subtypes/isolates. This is the first report in which a method based on weighted networks is successfully applied to a source attribution research question
Summary
Salmonella enterica subspecies enterica serovar Typhimurium and its monophasic variant (STm) are among the top three serovars in confirmed human cases of salmonellosis each year in Europe (EFSA and ECDC, 2018). Many methods have been developed to estimate the relative contribution of different food sources to human foodborne diseases worldwide, including microbial subtyping, comparative exposure assessment, epidemiological analysis of sporadic cases, analysis of data from outbreak investigations, and expert elicitation (Pires et al, 2009, 2014). Each of these approaches has strengths and limitations, and the usefulness of each depends on the public health questions being addressed (Pires et al, 2014). Source attribution studies are conducted by using frequency-matching models like the Dutch and Danish models based on phenotyping data (serotyping, phage-typing, and antimicrobial resistance profiling) (Pires et al, 2009, 2014; Mughini-Gras et al, 2018)
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