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

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 specific
for 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 antibiotic
resistance, 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.