Abstract
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.
Highlights
Non-typhoidal Salmonella causes an estimated 93.8 million human cases of salmonellosis and over 155,000 deaths globally each year[1,2,3]
We used a collection of 17,107 STM isolates identified in the New South Wales (NSW) State Salmonella Reference Laboratory in Sydney, Australia between 1 January 2008 and 31 December 2016
All isolates were genotyped by multiple-locus variable-number tandem-repeats analysis (MLVA)
Summary
We inferred undirected STM networks from surveillance and molecular genotyping data representing nine consecutive seasonal epidemics of salmonellosis in Australia, quantified the diversity and variability of these evolving genetic networks, correlated their small-world network properties with the severity of STM epidemics in Australia; and identified distinct evolutionary branches in terms of the network nodes’ centrality. These findings enhance and broaden our view of epidemics of salmonellosis and support the feasibility and added value of network analysis of relationships between diverse bacterial strains within the same species. They quantify the fitness of invading populations and pave the way for a more systematic assessment of the structural and dynamic properties of epidemics and anticipation of critical transitions in disease incidence[23,24,25,26], providing early warning signs through disease surveillance and enabling improvements in emergency preparedness and response[27,28]
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