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

金黄色葡萄球菌是一种主要的人类病原体，在全球范围内引起广泛的感染，导致显著的发病率和死亡率。由于抗菌素耐药性的持续威胁，世界卫生组织已将金黄色葡萄球菌列入迫切需要开发抗生素和新型免疫疗法的重点病原体名单。所有成功的病原体都进化出了抵抗宿主免疫的机制，这与它们的致病性密切相关。重要的是，主宿主对金黄色葡萄球菌的反应是通过补体发生的。补体是一个优雅的进化保守系统，通过与免疫细胞协同工作，在早期防御中发挥重要作用，以调查，标记和摧毁微生物入侵者并协调炎症。为了解决金黄色葡萄球菌感染，我们设计了这个项目，主要有两个目标：1)构建新的抗感染免疫治疗融合蛋白，该融合蛋白将结合到金黄色葡萄球菌表面并破坏基本的毒力机制，同时激活补体系统，促进补体固定和随后的免疫细胞清除。2)开发预测金黄色葡萄球菌感染严重程度的机器学习框架。通过结合本提案中产生的基因型和毒力表型，该目标将首先识别并在功能上确认与免疫逃避相关的毒力特征。其次，该数据与先前获得的临床患者数据将被纳入数学和统计模型，旨在预测与不良感染结果相关的决定因素。综合起来，这些目标将解决有关耐多药金黄色葡萄球菌感染的治疗和疾病管理的核心问题。

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.