| Sumario: | <p>The bacterial pathogen <em>Neisseria gonorrhoeae</em> undergoes large amounts of horizontal gene transfer, a process that adds complexity to its population structure, and confers a level of genetic flexibility that has enabled this species to become rapidly multi-drug resistant. This thesis addresses three key research areas in <em>N. gonorrhoeae</em> genetics. Firstly, the need for an accurate and consistent method of defining gonococcal lineages. Secondly, exploration of the diversity and commensal origins of mosaic regions in <em>penA</em>, an important antimicrobial resistance determinant. And thirdly, description of the <em>N. gonorrhoeae</em> lineages and <em>penA</em> alleles circulating in Africa.</p>
<p>These research gaps were investigated using a population genetics approach, analysing large datasets comprising thousands of bacterial isolates. This enabled insights that reflect the properties of the wider gonococcal population. The results were as follows: 1) A new, stable taxonomic nomenclature using LIN codes was developed, and demonstrated to provide improved resolution and reliability when defining gonococcal lineages. 2) The most common species contributing to mosaic <em>N. gonorrhoeae penA</em> alleles were shown to be <em>Neisseria subflava</em> and <em>Neisseria cinerea</em>, and resistance associated polymorphisms were found to be widespread across the genus. 3) African gonococci were shown to predominantly carry non-mosaic <em>penA</em> alleles and belong to a distinct range of lineages, with LIN code lineage 0_0_33 dominating, while common global lineages 0_2_0 and 0_2_1 were rare.</p>
<p>Together, these results provide an improved understanding of <em>N. gonorrhoeae</em> genetics with implications for public health: informing efforts to survey and suppress the evolution of resistance inducing mosaic <em>penA</em> alleles, and describing how gonococcal population structure relates to features such as geographical location and antibiotic resistance, while providing a publicly available lineage nomenclature for future analyses. These findings highlight the advantages of using population-level analysis of bacterial whole genome sequence data to reveal new facets of pathogen biology.</p>
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