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. But unfortunately, there remain significant gaps in our knowledge about Klebsiella biology, which limit our ability to design effective controls. One important knowledge gap concerns our understanding of its metabolism, in particular how metabolic variation is related to drug-resistance, and the ability to colonise and infect humans. Recent genomic analyses have shown that gene content can differ substantially between K. pneumoniae strains. A single strain carries approximately 5500 genes but only 1700 of these are shared among all strains. The remaining ~3800 genes are drawn from a gene-pool that exceeds 30,000 unique proteins, with more than a third predicted to be involved in metabolism. These data indicate that metabolic ability varies substantially between strains, but we cannot fully understand the true extent of this variation from genome data alone. In this project the student will apply the latest genomic analysis and computational metabolic modelling techniques to; i) build high quality genome-scale metabolic models for Klebsiella that link gene content information to predicted metabolic protein functions; ii) predict metabolic growth capabilities of clinical strains; iii) investigate differences in the metabolic capabilities of strains that differ in their clinical properties, e.g. drug resistance or transmission frequency in hospitals. The outcomes will greatly enhance our understanding of Klebsiella metabolism and its relationship to clinical risk, and may inform the design of novel infection control strategies. 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.
infectious disease, Klebsiella, antibiotic resistance, genomics, metabolism, computational biology, microbiology, molecular biology, biochemistry
Central Clinical School » Infectious Diseases
Masters by research
Top-up scholarship funding available
The Burnet Institute