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

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，也是最大的疟疾流行病之一。 阻碍经济发展。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，但抗药性已经出现，是一个主要威胁， 最近死亡人数和疟疾感染人数都有所减少。 疟疾控制项目管理人员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。在第一个周期中， 我们利用人口基因组技术的出现， 杀虫剂抗性机制的演变和分布。在本提案中，我们将描述我们将如何 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，以证明基因组监测如何用于指导病媒控制。 我们将利用我们在东非进行的两项大型病媒控制试验。在乌干达，我们嵌入了一个集群- 长效驱虫蚊帐（LLINs）的随机对照试验（RCT），使用和不使用长效驱虫蚊帐 变成了一个全国性的发行活动在肯尼亚，我们与KEMRI一起进行了一项新的随机对照试验， 干预，有吸引力的目标糖诱饵。我们将使用全基因组测序的三个主要 疟疾媒介冈比亚按蚊（Anopheles gambiae）、冈比亚按蚊（An. funestus和An.从这些试验地点，以确定基因组 与杀虫剂抗药性有关的地区。然后，我们将开发两个对比模型， 抗性基因第一个假设是我们可以准确地描述蚊子 通过检查少量的良好表征的标记物来确定杀虫剂的抗性。这种模式利用了 我们最近在按蚊CRISPR/Cas9转化方面的进展。第二种模型使用多基因 评分方法需要大量的标记，与抗性显著相关，但与 不需要了解因果机制。 通过在随机对照试验中筛选蚊子样本， 数据与包括两个阻力模型，我们将量化阻力的影响， 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进行耐药性监测，或是否有更多的基因座提供了上级 预测能力。前者将有助于广泛采用基因监测方案。最后 通过两种建模方法，我们将1.提供分子定义的耐药性预测， 东非行政单位一级和2.将这些抗性数据整合到 疟疾的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.