Network properties of salmonella epidemics

We examine non-typhoidal Salmonella (S. Typhimurium or STM) epidemics as complex systems, driven by evolution and interactions of diverse microbial strains, and focus on emergence of successful strains. Our findings challenge the established view that seasonal epidemics are associated with random se...

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Bibliographic Details
Published in:Scientific reports 2019-04, Vol.9 (1), p.6159-6159, Article 6159
Main Authors: Cliff, Oliver M., Sintchenko, Vitali, Sorrell, Tania C., Vadlamudi, Kiranmayi, McLean, Natalia, Prokopenko, Mikhail
Format: Article
Language:English
Subjects:
639/705/1042
692/699/255/1318
Australia - epidemiology
Disease Outbreaks
Epidemics
Genotypes
Genotyping
Humanities and Social Sciences
Humans
Minisatellite Repeats
Molecular Epidemiology
Molecular Typing
multidisciplinary
Salmonella
Salmonella Infections - epidemiology
Salmonella Infections - microbiology
Salmonella typhimurium - genetics
Salmonella typhimurium - isolation & purification
Science
Science (multidisciplinary)
Seasons
Strains (organisms)
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Summary:We examine non-typhoidal Salmonella (S. Typhimurium or STM) epidemics as complex systems, driven by evolution and interactions of diverse microbial strains, and focus on emergence of successful strains. Our findings challenge the established view that seasonal epidemics are associated with random sets of co-circulating STM genotypes. We use high-resolution molecular genotyping data comprising 17,107 STM isolates representing nine consecutive seasonal epidemics in Australia, genotyped by multiple-locus variable-number tandem-repeats analysis (MLVA). From these data, we infer weighted undirected networks based on distances between the MLVA profiles, depicting epidemics as networks of individual bacterial strains. The network analysis demonstrated dichotomy in STM populations which split into two distinct genetic branches, with markedly different prevalences. This distinction revealed the emergence of dominant STM strains defined by their local network topological properties, such as centrality, while correlating the development of new epidemics with global network features, such as small-world propensity.
ISSN:2045-2322
2045-2322
DOI:10.1038/s41598-019-42582-3