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)死亡和发病的主要原因，也是最大的 阻碍经济发展的因素。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，但抗药性已经出现，并是对 最近死亡人数和疟疾感染人数均有所减少。 疟疾控制项目管理者面临的一个主要挑战是了解杀虫剂抗药性的程度 影响控制和何时采取行动(如通过切换到新的干预措施)。在这个过程的第一个周期中 奖，我们利用种群基因组技术的出现来发展对 杀虫剂抗性机制的演变和分布。在这份提案中，我们描述了我们将如何 将这种抗性标记发现工作与新的功能基因组方法和大载体相结合 对照试验，以演示如何利用基因组监测来指导病媒控制。 我们将利用我们在东非两个大型病媒控制试验中的工作。在乌干达，我们嵌入了一个集群- 使用和不使用增效剂PBO的长效杀虫网的随机对照试验(RCT) 在全国范围内开展分销活动。在肯尼亚，我们与KEMRI一起进行了一项新的RCT 干预，诱人的针对性糖饵。我们将使用三个主要基因的全基因组测序 疟疾媒介冈比亚按蚊，An.Funestus和An.来自这些试验点的阿拉伯人来鉴定基因组 与杀虫剂抗药性相关的区域。然后我们将开发出两个截然不同的模型 抗性的遗传学。第一个假设是我们可以准确地描述蚊子 通过检查少数特征良好的标记来抗药性。这种模式充分利用了 我们在按蚊CRISPR/Cas9转化方面的最新进展。第二种模型使用多基因 需要更多标记物的计分方法，与耐药性显著相关，但 不需要理解因果机制。 通过从RCT内的集群中筛选蚊子收集，通过重新分析流行病学 包括两个阻力模型的数据，我们将量化阻力对 干预效果。我们将测试基于少量遗传变异的模型是否具有 足够的预测能力用于耐药性监测，或者更多的基因座是否提供更好的 预测能力。前者将有助于广泛采用基因监测方案。终于 通过两种建模方法，我们将1.提供分子定义的阻力的预测 东非行政单位一级和2.将这些抗药性数据整合到 以提供关于部署何种类型的最佳病媒控制的决策。