Using genetic epidemiology to inform vaccination policy and reduce the global burden of meningitis

利用遗传流行病学为疫苗接种政策提供信息并减轻全球脑膜炎负担

• 批准号: 2441147
• 金额: --
• 依托单位: University of Oxford
• 依托单位国家: 英国
• 项目类别: Studentship
• 财政年份: 2020
• 资助国家: 英国
• 起止时间: 2020 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=studentship-2441147
• 关键词: Using; genetic; epidemiology; inform; vaccination

By collaborating with medical centres and research institutions in Africa, this project will first help to organise and complete the Molecular Epidemiology for Vaccination Policy (MEVacP) and Global Meningitis Genome Laboratory (GMBL) databases of bacterial meningitis genomes in Africa. It will then analyse these data to provide an epidemiological map of meningitis in Africa, and identify new and emerging strains of bacteria. Using genomic analysis, the project will delineate the capsular types of the bacteria responsible for the greatest disease burden. Accurate diagnostic tests are crucial for clinicians to correctly diagnose disease, prescribe appropriate antibiotics, and correctly report disease to inform outbreak procedures. The GMBL database will contain patient demographic data, results of clinical diagnostic tests, bacterial isolate metadata and whole genome sequences of the causative meningitis bacteria. Interrogation of the genome sequences will allow for the most commonly used molecular (e.g. PCR-based) diagnostic tests to be assessed for sensitivity and specificity. If any of the current assays are suboptimal, then the project will aim to design new molecular diagnostic assays to identify the aetiological agents of meningitis. Genomic libraries can aid this process by allowing identification of genes that are specificfor a particular bacterial species. Microbiological assays for these gene targets can then be developed, which would allow African clinicians and scientists to diagnose the causative bacteria more accurately, identify outbreaks more quickly, and trigger faster responses from national and international public health authorities. Genomic analysis of the bacterial strains isolated in clinical samples, linked with knowledge of medication used for treatment and the outcome of the patient illness (death or survival) will be used to monitor the efficacy of drugs and analyse the genetic underpinning of antibiotic resistance. Using knowledge of antibiotic resistance genes, the project will aim to monitor their presence over time in the genomic libraries, thus tracking their transmission. The interpreted genomic data, mapped to the time and place of sample collection, will allow the identification of strains with acquired antibiotic resistance. This information can in turn be used by local healthcare workers to choose appropriate antibiotics. Further, this analysis can be used to identify the protein products of genes that cause antibioticresistance, which may aid the design of novel antibiotics via targeted drug development. Whole genome sequences can be used to predict the antigens a certain bacterial strain will display on its capsule surface. As vaccines immunise against these capsule epitopes, the genomic libraries can be used to estimate the potential effectiveness of different vaccines in specific regions of Africa. In addition, bacteria causing meningitis can have many different capsular types and current vaccines only protect against a minority subset. In this project, analysis of genomic libraries will beused to inform which capsules are most common among the bacterial strains causing the greatest health burdens in Africa, which will in turn guide prioritisation of which capsular types should be targeted by vaccine developers. Finally, genomic libraries will be used to investigate how bacterial populations respond to the introduction of a novel vaccine. This information can be used to inform vaccination implementation strategies and to advise immunisation programmes.

通过与非洲医疗中心和研究机构合作，该项目将首先帮助组织和完成非洲细菌性脑膜炎基因组疫苗政策分子流行病学（MEVacP）和全球脑膜炎基因组实验室（GMBL）数据库。然后，它将分析这些数据，提供非洲脑膜炎的流行病学图，并识别新出现的细菌菌株。该项目将利用基因组分析来描述造成最大疾病负担的细菌的荚膜类型。准确的诊断测试对于临床医生正确诊断疾病、开出适当的抗生素以及正确报告疾病以告知爆发程序至关重要。 GMBL 数据库将包含患者人口统计数据、临床诊断测试结果、细菌分离株元数据和致病脑膜炎细菌的全基因组序列。基因组序列的询问将允许评估最常用的分子（例如基于 PCR 的）诊断测试的敏感性和特异性。如果当前的任何检测方法都不理想，那么该项目将致力于设计新的分子诊断检测方法来识别脑膜炎的病因。基因组文库可以通过识别特定细菌物种特有的基因来帮助这一过程。然后可以开发针对这些基因靶标的微生物检测方法，这将使非洲临床医生和科学家能够更准确地诊断致病细菌，更快地识别疫情，并引发国家和国际公共卫生当局更快的反应。对临床样本中分离出的细菌菌株进行基因组分析，并结合用于治疗的药物知识和患者疾病结果（死亡或生存），将用于监测药物疗效并分析抗生素耐药性的遗传基础。该项目将利用抗生素抗性基因的知识，监测它们在基因组文库中随时间的存在，从而追踪它们的传播。解释后的基因组数据映射到样本采集的时间和地点，将能够识别具有获得性抗生素耐药性的菌株。当地医护人员可以利用这些信息来选择合适的抗生素。此外，这种分析可用于识别导致抗生素耐药性的基因的蛋白质产物，这可能有助于通过靶向药物开发来设计新型抗生素。全基因组序列可用于预测某种细菌菌株将在其荚膜表面展示的抗原。当疫苗针对这些胶囊表位进行免疫时，基因组文库可用于估计不同疫苗在非洲特定地区的潜在有效性。此外，引起脑膜炎的细菌可能有许多不同的荚膜类型，而目前的疫苗只能预防少数细菌。在该项目中，对基因组文库的分析将用于了解哪些胶囊是在非洲造成最大健康负担的细菌菌株中最常见的，这反过来将指导疫苗开发商应优先考虑哪些胶囊类型。最后，基因组文库将用于研究细菌群体对新型疫苗的引入有何反应。该信息可用于为疫苗接种实施策略提供信息并为免疫规划提供建议。