Using genome data from highly recombinant bacteria of the genus Neisseria to examine bacterial population structure, antibiotic resistance, and geneti

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The availability of bacterial whole genome sequence (WGS) data, the complete DNA blueprint of a bacterium, has improved our understanding of these organisms and facilitated population-level approaches to delineate related strains and investigate antibiotic resistance. As DNA sequencing has become cheaper and easier, the volume of WGS data has increased exponentially. However, resources for examining these large genetic datasets are typically lacking or under-utilised. Analysis is further complicated by the diversity in genome complexity found across different bacterial species, often generated by high levels of genetic exchange through horizontal gene transfer (HGT) between individuals. This process also facilitates the rapid spread of genes that confer antibiotic resistance, while simultaneously making current approaches for tracking related groups of these organisms less effective, hindering their surveillance. Members of the Neisseria genus, particularly Neisseria gonorrhoeae, have highly complex genomes resulting from extensive genetic exchange. The proposed research will exploit Neisseria WGS from over 30,000 isolates to develop new computational approaches that enable the full utilisation of large genetic datasets including genomes affected by frequent HGT. This will include tools to analyse bacterial population structure and predict antibiotic resistance phenotypes, alongside an examination of the impact of HGT through 'mosaic' genes implicated in antibiotic resistance and an investigation into gene networks.