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

应对金黄色葡萄球菌疾病的抗感染疗法和预测模型

• 批准号: EP/X022935/2
• 负责人: Maisem Laabei
• 金额: --
• 依托单位: University of Bristol
• 依托单位国家: 英国
• 项目类别: Fellowship
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FX022935%2F2
• 关键词: Anti; infective; therapeutics; predictive; modelling

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.

金黄色葡萄球菌是一种主要的人类病原体，可引起广泛的感染，导致全球显着的发病率和死亡率。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治疗药物的优先病原体名单中。所有成功的病原体都已进化出抵抗宿主免疫的机制，这与其致病性密切相关。重要的是，对金黄色葡萄球菌的主要宿主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来调查、标记和消灭微生物入侵者并协调炎症，在早期防御中发挥着重要作用。为了解决金黄色葡萄球菌感染，我们设计了这个项目，有两个主要目标：1）构建新型抗感染免疫治疗融合蛋白，该融合蛋白将与金黄色葡萄球菌表面结合并破坏基本的毒力机制，同时激活补体系统，促进增强的补体固定和后续的治疗。被免疫细胞清除。 2）开发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，这些数据与之前获得的临床患者数据一起，将被纳入数学和统计模型中，旨在预测与不良感染结果相关的决定因素。这些目标相结合，将解决有关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。