Resolution of a meningococcal disease outbreak from whole-genome sequence data with rapid Web-based analysis methods

The increase in the capacity and reduction in cost of whole-genome sequencing methods present the imminent prospect of such data being used routinely in real time for investigations of bacterial disease outbreaks. For this to be realized, however, it is necessary that generic, portable, and robust analysis frameworks be available, which can be readily interpreted and used in real time by microbiologists, clinicians, and public health epidemiologists. We have achieved this with a set of analysis tools integrated into the PubMLST.org website, which can in principle be used for the analysis of any pathogen. The approach is demonstrated with genomic data from isolates obtained during a well-characterized meningococcal disease outbreak at the University of Southampton, United Kingdom, that occurred in 1997. Whole-genome sequence data were collected, de novo assembled, and deposited into the PubMLST Neisseria BIGSdb database, which automatically annotated the sequences. This enabled the immediate and backwards-compatible classification of the isolates with a number of schemes, including the following: conventional, extended, and ribosomal multilocus sequence typing (MLST, eMLST, and rMLST); antigen gene sequence typing (AGST); analysis based on genes conferring antibiotic susceptibility. The isolates were also compared to a reference isolate belonging to the same clonal complex (ST-11) at 1,975 loci. Visualization of the data with the NeighborNet algorithm, implemented in SplitsTree 4 within the PubMLST website, permitted complete resolution of the outbreak and related isolates, demonstrating that multiple closely related but distinct strains were simultaneously present in asymptomatic carriage and disease, with two causing disease and one responsible for the outbreak itself.