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Investigation of the protective role of anti-PfGARP antibodies against severe malaria

抗 PfGARP 抗体对重症疟疾的保护作用研究

基本信息

批准号：
2723073
负责人：
金额：
--
依托单位：
London School of Economics and Political Science
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2022
资助国家：
英国
起止时间：
2022 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2723073
关键词：
Investigation protective role anti PfGARP

项目摘要

The aim of the project is to decipher the mechanisms leading to severe malariaprotection conferred by anti-PfGARP antibodies in different endemic settings, using acombination of serological and machine learning analyses. The objectives of theproject are: 1. Set up and optimise a serological screening platform for PfGARP usingtruncated protein constructs and screened using our MagPix (Luminex) quantitativesuspension bead array platform 2. Leverage the screening platform to measure levelsof anti-PfGARP in the plasma of individuals with asymptomatic, uncomplicated andsevere malaria infection from different cohorts (high and low-endemic areas, India andUganda) 3. Assess the different protective effects of anti-PfGARP antibodies in thesecohorts by performing machine learning analyses including clinical (parasitaemia,parasite biomass, platelet levels, brain swelling assessed by MRI, complications,outcome, etc.) and biological parameters (selection of less virulent parasites, etc.) 4.Develop an anti-PfGARP antibody detection test and evaluate its potential as aprognostic tool in a clinical setting in India. Dr. Tetteh will oversee research related toserology platform development and optimization, as well as the sample screening. Dr.Wassmer will supervise the machine learning analysis using the extensive clinical andbiological database of the cohorts, as well as the development and assessment of anti-PfGARP antibody detection test on-site in India. The proposed research project is highlyinterdisciplinary and both supervisors will be actively engaged in all aspects of theproject.

该项目的目的是使用血清学和机器学习分析的组合，破译导致抗PfGARP抗体在不同地方性环境中赋予的严重疟疾保护的机制。本项目的目标是：1.建立并优化PfGARP的血清学筛选平台，使用截短的蛋白构建体，并使用我们的MagPix（Luminex）定量悬浮珠阵列平台进行筛选2。利用筛查平台测量来自不同队列（高流行区和低流行区，印度和乌干达）的无症状、无并发症和重度疟疾感染个体血浆中的抗PfGARP水平。通过进行包括临床（寄生虫血症、寄生虫生物量、血小板水平、通过MRI评估的脑肿胀、并发症、结局等）在内的机器学习分析，评估抗PfGARP抗体在这些队列中的不同保护作用。和生物学参数（选择毒性较小的寄生虫等）4.开发一种抗PfGARP抗体检测试验，并评估其在印度临床环境中作为预后工具的潜力。Tetteh博士将监督与血清学平台开发和优化相关的研究，以及样本筛选。Wassmer博士将使用队列的广泛临床和生物学数据库监督机器学习分析，以及在印度现场开发和评估抗PfGARP抗体检测测试。拟议的研究项目是高度跨学科的，双方主管将积极参与该项目的各个方面。