Genome-based diagnostics for mapping, monitoring and management of insecticide resistance in major African malaria vectors

基于基因组的诊断，用于绘制、监测和管理非洲主要疟疾病媒的杀虫剂抗药性

• 批准号: 10444139
• 负责人: Martin James Donnelly
• 金额: $ 49.68万
• 依托单位: TMLIVERPOOL SCHOOL OF TROPICAL MEDICINE
• 依托单位国家: 美国
• 财政年份: 2016
• 资助国家: 美国
• 起止时间: 2016-02-15 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10444139
• 关键词: Adoption; Africa; Africa South of the Sahara; African; Anopheles Genus; Anopheles gambiae; Award; Biological Assay; CRISPR/Cas technology; Cessation of life; Chemicals; Clustered Regularly Interspaced Short Palindromic Repeats; Collection; Country; Culicidae; Data; Development; Diagnostic; Economic Development; Entomology; Epidemiology; Evaluation; Evolution; Falciparum Malaria; Future; Gene Frequency; Genetic; Genetic Markers; Genetic Models; Genetic Transformation; Genome; Genomic Segment; Genomic approach; Genomics; Goals; Heterogeneity; Insecticide Resistance; Insecticides; Intervention; Kenya; Malaria; Maps; Marker Discovery; Methods; Modeling; Molecular; Monitor; Morbidity - disease rate; Outcome; Population; Predisposition; Randomized Controlled Trials; Recommendation; Research Project Grants; Resistance; Resistance development; Resources; Site; Speed; Surveillance Program; Technology; Testing; Treatment Efficacy; Uganda; Variant; Work; base; biomarker identification; biomarker panel; control trial; diagnostic biomarker; epidemiologic data; functional genomics; genetic resistance; genetic variant; genome sequencing; genome wide association study; genomic signature; improved; malaria infection; mortality; novel; predictive modeling; programs; public health relevance; pyrethroid; resistance mechanism; scale up; screening; screening panel; success; sugar; tool; transmission process; vector; vector control; whole genome

Project Summary Malaria is a major cause of mortality and morbidity in Sub-Saharan Africa (SSA) and one of the biggest impediments to the economic development. The major method for controlling these malaria-transmitting mosquitoes is through the use of chemical insecticides but resistance has emerged and is a major threat to the recent reductions in both deaths and malaria infections. A major challenge facing malaria control program managers is knowing to what extent insecticide resistance is impacting control and when to take action eg by switching to a new intervention. In the first cycle of this award we exploited the advent of population genomic technologies to develop an improved understanding of the evolution and distribution of insecticide resistance mechanisms. In this proposal we describe how we will integrate this resistance marker discovery work with new functional genomic approaches and large vector control trials to demonstrate how genomic surveillance can be used to guide vector control. We will leverage our work on two large vector control trials in East Africa. In Uganda we embedded a cluster- randomised control trial (RCT) of long-lasting insecticidal nets (LLINs) with, and without, the synergist PBO into a countrywide distribution campaign. In Kenya together with KEMRI we are conducting an RCT of novel intervention, Attractive Targeted Sugar Baits. We will use whole genome sequencing of the three major malaria vectors Anopheles gambiae, An. funestus and An. arabiensis from these trial sites to identify genomic regions that are associated with insecticide resistance. We will then develop two contrasting models of the genetics of resistance. The first that assumes that we can accurately describe the likelihood of mosquito being insecticide resistant to by examining a small number of well characterised markers. This model capitalises on our recent developments in CRISPR/Cas9 transformation of Anopheles. The second model uses a polygenic score approach that requires a far larger number of markers, significantly-associated with resistance, but with no need for an understanding of causal mechanisms. By screening mosquito collections from the clusters within the RCTs and by, re-analysing the epidemiological data with the inclusion of the two resistance models, we will quantify the impact of resistance on the intervention efficacy. We will test whether the model based on a small number of genetic variants has sufficient predictive power for resistance monitoring or whether a larger number of loci provides superior predictive power. The former would aid widespread adoption of genetic surveillance of programmes. Finally through two modelling approaches we will 1. deliver predictions of molecularly-defined resistance at the administrative unit level in East Africa and 2. integrate these resistance data into transmission models of falciparum malaria to inform decisions on what is the optimal type of vector control to deploy.

项目摘要 疟疾是撒哈拉以南非洲（SSA）死亡率和发病率的主要原因，也是最大的原因之一 障碍经济发展。控制这些疟疾传播的主要方法 蚊子是通过使用化学杀虫剂的，但已经出现了抗药性，对 死亡和疟疾感染的最新减少。 疟疾控制计划经理面临的一个重大挑战是知道杀虫剂的耐药性在多大程度上是 影响控制以及何时采取行动，例如切换到新的干预措施。在第一个周期中 奖项我们利用了人口基因组技术的出现，以提高对 杀虫剂耐药机制的进化和分布。在此提案中，我们描述了我们将如何 将这种阻力标记发现工作与新功能基因组方法和大型矢量相结合 控制试验以证明如何使用基因组监测来指导载体控制。 我们将在东非的两项大型矢量控制试验中利用我们的工作。在乌干达，我们嵌入了一个集群 - 持久的杀虫网（LLIN）的随机对照试验（RCT），并没有协同作用PBO 进入全国分销活动。在肯尼亚，我们与Kemri一起进行了新颖的RCT 干预，有吸引力的靶向糖诱饵。我们将使用三个主要的整个基因组测序 疟疾矢量冈比亚河流，an。 Funestus和An。来自这些试验地点的阿拉伯语以鉴定基因组 与杀虫剂耐药性相关的区域。然后，我们将开发两个对比的模型 抵抗的遗传学。第一个假设我们可以准确地描述蚊子的可能性 通过检查少数特征性良好的标记来抗杀虫剂。该模型大写 我们最近在CRISPR/CAS9转换atopheles方面的发展。第二个模型使用多基因 得分方法需要大得多的标记，与电阻相关，但与 无需了解因果机制。 通过从RCT内的簇中筛选蚊子收集，然后重新分析流行病学 包括两个电阻模型的数据，我们将量化电阻对 干预功效。我们将测试基于少数遗传变异的模型是否具有 抵抗监测的足够预测能力，或者是否有更多基因座提供优越性 预测能力。前者将有助于广泛采用计划的遗传监测。最后 通过两种建模方法，我们将1。 东非的行政单位级别和2。将这些阻力数据集成到传输模型中 恶性疟疾以告知有关部署矢量控制最佳类型的决定。