Characterization of Vibrio parahaemolyticus clinical strains from Maryland (2012–2013) and comparisons to a locally and globally diverse V. parahaemolyticus strains by whole-genome sequence analysis

The introduction of multilocus sequence typing (MLST) for the precise characterization of isolates of bacterial pathogens has had a marked impact on both routine epidemiological surveillance and microbial population biology. In both fields, a key prerequisite for exploiting this resource is the ability to discern the relatedness and patterns of evolutionary descent among isolates with similar genotypes. Traditional clustering techniques, such as dendrograms, provide a very poor representation of recent evolutionary events, as they attempt to reconstruct relationships in the absence of a realistic model of the way in which bacterial clones emerge and diversify to form clonal complexes. An increasingly popular approach, called BURST, has been used as an alternative, but present implementations are unable to cope with very large data sets and offer crude graphical outputs. Here we present a new implementation of this algorithm, eBURST, which divides an MLST data set of any size into groups of related isolates and clonal complexes, predicts the founding (ancestral) genotype of each clonal complex, and computes the bootstrap support for the assignment. The most parsimonious patterns of descent of all isolates in each clonal complex from the predicted founder(s) are then displayed. The advantages of eBURST for exploring patterns of evolutionary descent are demonstrated with a number of examples, including the simple Spain(23F)-1 clonal complex of Streptococcus pneumoniae, "population snapshots" of the entire S. pneumoniae and Staphylococcus aureus MLST databases, and the more complicated clonal complexes observed for Campylobacter jejuni and Neisseria meningitidis.

Vibrio parahaemolyticus, a gram-negative marine bacterium, is a worldwide cause of food-borne gastroenteritis. V parahaemolyticus strains of a few specific serotypes, probably derived from a common clonal ancestor, have lately caused a pandemic of gastroenteritis. The organism is phylogenetically close to V cholerae, the causative agent of cholera. The whole genome sequence of a clinical V parahaemolyticus strain RIMD2210633 was established by shotgun sequencing. The coding sequences were identified by use of Gambler and Glimmer programs. Comparative analysis with the V cholerae genome was undertaken with MUMmer. The genome consisted of two circular chromosomes of 3288558 bp and 1877212 bp; it contained 4832 genes. Comparison of the V parahaemolyticus genome with that of V cholerae showed many rearrangements within and between the two chromosomes. Genes for the type III secretion system (TTSS) were identified in the genome of V parahaemolyticus; V cholerae does not have these genes. The TTSS is a central virulence factor of diarrhoea-causing bacteria such as shigella, salmonella, and enteropathogenic Escherichia coli, which cause gastroenteritis by invading or intimately interacting with intestinal epithelial cells. Our results suggest that V parahaemolyticus and V cholerae use distinct mechanisms to establish infection. This finding explains clinical features of V parahaemolyticus infections, which commonly include inflammatory diarrhoea and in some cases systemic manifestations such as septicaemia, distinct from those of V cholerae infections, which are generally associated with non-inflammatory diarrhoea.

Background Multilocus Sequence Typing (MLST) is a frequently used typing method for the analysis of the clonal relationships among strains of several clinically relevant microbial species. MLST is based on the sequence of housekeeping genes that result in each strain having a distinct numerical allelic profile, which is abbreviated to a unique identifier: the sequence type (ST). The relatedness between two strains can then be inferred by the differences between allelic profiles. For a more comprehensive analysis of the possible patterns of evolutionary descent, a set of rules were proposed and implemented in the eBURST algorithm. These rules allow the division of a data set into several clusters of related strains, dubbed clonal complexes, by implementing a simple model of clonal expansion and diversification. Within each clonal complex, the rules identify which links between STs correspond to the most probable pattern of descent. However, the eBURST algorithm is not globally optimized, which can result in links, within the clonal complexes, that violate the rules proposed. Results Here, we present a globally optimized implementation of the eBURST algorithm – goeBURST. The search for a global optimal solution led to the formalization of the problem as a graphic matroid, for which greedy algorithms that provide an optimal solution exist. Several public data sets of MLST data were tested and differences between the two implementations were found and are discussed for five bacterial species: Enterococcus faecium, Streptococcus pneumoniae, Burkholderia pseudomallei, Campylobacter jejuni and Neisseria spp.. A novel feature implemented in goeBURST is the representation of the level of tiebreak rule reached before deciding if a link should be drawn, which can used to visually evaluate the reliability of the represented hypothetical pattern of descent. Conclusion goeBURST is a globally optimized implementation of the eBURST algorithm, that identifies alternative patterns of descent for several bacterial species. Furthermore, the algorithm can be applied to any multilocus typing data based on the number of differences between numeric profiles. A software implementation is available at .