Genetic analyses to inform the design of vaccines targeting the hospital superbug, Klebsiella pneumoniae

Klebsiella pneumoniae is a major cause of antibiotic resistant hospital-associated infections that can be extremely difficult to treat. The World Health Organization has identified this bacterium as a high priority pathogen for which novel control strategies are urgently required, such as vaccines targeting the polysaccharide (sugar) capsules that surround Klebsiella cells. Each K. pneumoniae produces a single capsule type, but more than 70 have been defined through serological typing techniques, and a further 70+ are predicted on the basis of gene content variation within the so-called ‘capsule synthesis locus’. Our team develops and applies genomic analysis tools to understand the diversity of Klebsiella capsules among strains that cause human infections, and prioritise capsule types for inclusion in novel vaccines. So far, our analyses have primarily focussed on gene content variations, but we have anecdotally noted hundreds of instances of fine-scale variations at the DNA sequence level that may have an important impact on capsule types. In this project the student will use high-throughput DNA sequence analyses to characterise and quantify fine-scale sequence variations among Klebsiella capsule synthesis loci, and link these to their predicted impact on capsule type. The results will be used to identify common and/or otherwise important capsule locus sequence variants that should be considered for inclusion in vaccines and/or for testing during the vaccine development process. The scope of the work can be refined to accommodate projects of varying length and is best suited for students interested in the application of computational biology approaches (including command-line programs) to analyse and interpret large datasets. Prior experience using the Unix operating system and the Python programming language is preferred but not essential.