Navigating the future of bacterial molecular epidemiology

Abstract

Technological advances in high-throughput genome sequencing have led to an enhanced appreciation of the genetic diversity found within populations of pathogenic bacteria. Methods based on single nucleotide polymorphisms (SNPs) and insertions or deletions (indels) build upon the framework established by multi-locus sequence typing (MLST) and permit a detailed, targeted analysis of variation within related organisms. Robust phylogenetics, when combined with epidemiologically informative data, can be applied to study ongoing temporal and geographical fluctuations in bacterial pathogens. As genome sequencing, SNP detection and geospatial information become more accessible these methods will continue to transform the way molecular epidemiology is used to study populations of bacterial pathogens.

Figure 1

Google map and haplotype map outlining the circulation of multiple Salmonella Typhi haplotypes in a small urban area of Jakarta. The SNP typing of 54 S. Typhi strains from a single location in Jakarta identified several different haplotypes circulating within a two-year period [17]. (a) A minimum spanning tree showing relationships between the eight different S. Typhi haplotypes (e.g. H45) identified. The tree shows the overall population structure defined by the SNPs targeted in the assay, defined in ref. [14]. Coloured circles correspond to haplotypes found in the sample (colour corresponds to the colour scheme in part b below). Grey circles are haplotypes that were not identified among isolates from Jakarta. H45 is the ancestral group and the red circle denotes Salmonella Paratyphi A strains. (b) A Google maps image created by inputting data at http://www.spatialepidemiology.net/showing the local distribution of the various S. Typhi haplotypes and S. Paratyphi A in a suburb of Jakarta.

Figure 2

Combining and exploring MLST and geographical data for Staphylococcus aureus with MLST maps. MLST maps (http://maps.mlst.net/view_maps.php) allows the user to enter and integrate MLST data together with location as an additional variable. (a) Datasets can be downloaded (in this case S. aureus) and then opened and viewed in Google earth (http://earth.google.com/). The image shows the locations in Europe and the corresponding quantity of isolates of S. aureus with available MLST data. (b) By clicking on a country of origin the user can view the number of strains and the various sequences types (STs) in a text format. (c) MLST data for strains selected from within the MLST maps software can be sent directly to the clustering algorithm eBurst, allowing the user to identify groups of related genotypes in a single location or across multiple locations, in this case the Staphylococcus aureus ST30 clonal complex from the United Kingdom is shown.