Novel Salmonella enterica Serovar Typhimurium Genotype Levels as Herald of Seasonal Salmonellosis Epidemics

Inferring evolutionary pathways and directed genotype networks of foodborne pathogens

Modelling the emergence of foodborne pathogens is a crucial step in the prediction and prevention of disease outbreaks. Unfortunately, the mechanisms that drive the evolution of such continuously adapting pathogens remain poorly understood. Here, we combine molecular genotyping with network science and Bayesian inference to infer directed genotype networks-and trace the emergence and evolutionary paths-of Salmonella Typhimurium (STM) from nine years of Australian disease surveillance data. We construct networks where nodes represent STM strains and directed edges represent evolutionary steps, presenting evidence that the structural (i.e., network-based) features are relevant to understanding the functional (i.e., fitness-based) progression of co-evolving STM strains. This is argued by showing that outbreak severity, i.e., prevalence, correlates to: (i) the network path length to the most prevalent node (r = -0.613, N = 690); and (ii) the network connected-component size (r = 0.739). Moreover, we uncover distinct exploration-exploitation pathways in the genetic space of STM, including a strong evolutionary drive through a transition region. This is examined via the 6,897 distinct evolutionary paths in the directed network, where we observe a dominant 66% of these paths decrease in network centrality, whilst increasing in prevalence. Furthermore, 72.4% of all paths originate in the transition region, with 64% of those following the dominant direction. Further, we find that the length of an evolutionary path strongly correlates to its increase in prevalence (r = 0.497). Combined, these findings indicate that longer evolutionary paths result in genetically rare and virulent strains, which mostly evolve from a single transition point. Our results not only validate our widely-applicable approach for inferring directed genotype networks from data, but also provide a unique insight into the elusive functional and structural drivers of STM bacteria.

SoxS is a positive regulator of key pathogenesis genes and promotes intracellular replication and virulence of Salmonella Typhimurium

Salmonella enterica serovar Typhimurium (S. Typhimurium) is an important intracellular pathogen, causing gastroenteritis or severe systemic infection in a variety of hosts. During infection, S. Typhimurium must survive and replicate in host macrophages, which produce abundant oxidative compounds. SoxRS regulon is a well-known regulator that is activated in response to oxidative stress and promotes bacterial tolerance to oxidants in E. coli. However, the global regulatory function of SoxS in S. Typhimurium remains poorly characterized. Here, we used an RNA sequencing-based approach to investigate the role of SoxS in the expression of S. Typhimurium virulence genes. Besides the downregulation of genes related to resistance to oxidative stress, we found that in a soxS deletion mutant the expression of Salmonella pathogenicity island (SPI)-2 genes, which are crucial for replication within macrophages, was significantly repressed. Moreover, immunofluorescence and mice infection experiments showed that soxS deletion inhibited replication in macrophages and decreased virulence upon intraperitoneal inoculation in mice, respectively. Collectively, our findings demonstrate that SoxS is a positive regulator of SPI-2 genes and, therefore, plays a crucial role in S. Typhimurium intracellular replication and virulence.

In vivo passage of Salmonella Typhimurium results in minor mutations in the bacterial genome and increases in vitro invasiveness

Eggs and raw or undercooked egg-containing food items are frequently identified as the bacterial source during epidemiolocal investigation of Salmonella outbreaks. Multi-locus variable number of tandem repeats analysis (MLVA) is a widely used Salmonella typing method enabling the study of diversity within populations of the same serotype. In vivo passage, however, has been linked with changes in MLVA type and more broadly the Salmonella genome. We sought to investigate whether in vivo passage through layer hens had an effect on MLVA type as well as the bacterial genome and whether any mutations affected bacterial virulence. Layer hens were infected with either Salmonella Typhimurium DT9 (03-24-11-11-523) as part of a single infection or were co-infected with an equal amount of Salmonella Mbandaka. Salmonella shedding in both single and co-infected birds was variable over the course of the 16-week experiment. Salmonella Typhimurium and Salmonella Mbandaka were identified in feces of co-infected birds. Salmonella colonies isolated from fecal samples were subtyped using MLVA. A single change in SSTR-6 was observed in Salmonella Typhimurium strains isolated from co-infected birds. Isolates of Salmonella Typhimurium of both the parent (03-24-11-11-523) and modified (03-24-12-11-523) MLVA type were sequenced and compared with the genome of the parent strain. Sequence analysis revealed that in vivo passaging resulted in minor mutation events. Passaged isolates exhibited significantly higher invasiveness in cultured human intestinal epithelial cells than the parent strain. The microevolution observed in this study suggests that changes in MLVA may arise more commonly and may have clinical significance.

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 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.