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

金黄色葡萄球菌（Staphylococcus aureus）是一种主要的人类病原体，可引起广泛的感染，在全球范围内造成严重的发病率和死亡率.由于抗生素耐药性的持续威胁，WHO已将金黄色葡萄球菌列入优先病原体名单，迫切需要开发抗生素和新型免疫治疗药物。所有成功的病原体都进化出了抵抗宿主免疫的机制，这些机制与它们的致病性密切相关。重要的是，主要宿主对金黄色葡萄球菌的反应通过补体发生。补体是一个进化上保守的系统，在早期防御中发挥着重要作用，它与免疫细胞协同工作，调查、标记和破坏微生物入侵者，并协调炎症。为了解决金黄色葡萄球菌感染，我们设计了两个主要目标：1）构建新型抗感染免疫融合蛋白，其将结合到金黄色葡萄球菌的表面并破坏必要的毒力机制，同时激活补体系统，促进增强补体结合和随后的免疫细胞清除。2)开发一个机器学习框架来预测金黄色葡萄球菌感染的严重程度。通过结合基因型和毒力表型的产生，这一目标将首先确定和功能确认毒力签名与免疫逃避。其次，这些数据与先前获得的临床患者数据一起，将被纳入旨在预测与不良感染结局相关的决定因素的统计模型。结合起来，这些目标将解决关于多重耐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.