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

项目概要 疟疾是撒哈拉以南非洲地区 (SSA) 死亡和发病的主要原因，也是最大的疾病之一 经济发展的障碍。控制这些传播疟疾的主要方法 蚊子是通过使用化学杀虫剂来消灭的，但抗药性已经出现，并且是对人类的主要威胁 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耐药性的程度 影响控制以及何时采取行动，例如切换到新的干预措施。在本次第一个周期中 我们利用群体基因组技术的出现来加深对 杀虫剂抗性机制的演变和分布。在本提案中，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型载体相结合 对照试验展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开展的两项大型病媒控制试验的工作成果。在乌干达，我们嵌入了一个集群- 使用和不使用增效剂 PBO 的长效杀虫蚊帐 (LLIN) 的随机对照试验 (RCT) 进入全国范围的分发活动。在肯尼亚，我们正在与 KEMRI 一起开展小说的随机对照试验 干预，有吸引力的目标糖诱饵。我们将使用三大基因组的全基因组测序 疟疾媒介冈比亚按蚊，An。 funestus 和 An.阿拉伯树从这些试验地点鉴定基因组 与杀虫剂抗性相关的区域。然后我们将开发两个对比模型 耐药性的遗传。第一个假设我们可以准确地描述蚊子的可能性 通过检查少量特征明确的标记来确定对杀虫剂的抗药性。该模型利用了 我们在按蚊 CRISPR/Cas9 转化方面的最新进展。第二个模型使用多基因 评分方法需要更多数量的标记，与耐药性显着相关，但与 无需了解因果机制。 通过从随机对照试验中的群体中筛选蚊子样本，并重新分析流行病学 包含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 是否有足够的预测能力来进行耐药性监测或更多的基因座是否提供了更好的效果 预测能力。前者将有助于广泛采用基因监测项目。最后 通过两种建模方法，我们将 1. 预测分子定义的耐药性 2. 将这些阻力数据整合到东非行政单位层面的传播模型中 恶性疟疾为有关部署最佳病媒控制类型的决策提供信息。

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.