Anti-infective therapeutics and predictive modelling to tackle Staphylococcus aureus disease

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Staphylococcus aureus is a major human pathogen that causes a broad range of infections resulting in significant morbidity andmortality globally. Due to the constant threat of antimicrobial resistance, the WHO has placed S aureus on the list of prioritypathogens for which the development of antibiotics and novel immunotherapeutics is urgently required. All successful pathogenshave evolved mechanisms to resist host immunity which are intimately aligned with their pathogenicity. Importantly, the primaryhost response to S aureus occurs via complement. Complement is an elegant evolutionarily conserved system, playing essential rolesin early defences by working in concert with immune cells to survey, label and destroy microbial intruders and coordinateinflammation. To tackle S aureus infection we have designed this project with two major goals: 1) Construct novel anti-infectiveimmunotherapeutic fusion proteins which will bind to the surface of S aureus and disrupt essential virulence mechanisms whilesimultaneously activate the complement system, facilitating enhanced complement fixation and subsequent clearance by immunecells. 2) Develop a machine learning framework to predict the severity of S aureus infection. By combining genotype and virulencephenotype generated in this proposal, this aim will first identify and functionally confirm virulence signatures associated withimmune evasion. Secondly, this data together with previously obtained clinical patient data, will be incorporated into mathematicaland statistical models designed to predict determinants associated with poor infection outcome. Combined, these goals will addresscentral issues regarding the treatment and disease management of multi-drug resistant S aureus infections.