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

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