Development of a new tool for malaria mosquito surveillance to improve vector control

自2000年以来，通过基于杀虫剂的干预措施控制蚊子，全球疟疾死亡率降低了37%。尽管如此，2015年疟疾仍造成43.8万人死亡，蚊子媒介对杀虫剂的抗药性水平不断提高，威胁着进一步的进展。全球疟疾社区迫切需要新的工具来监测蚊媒种群。蚊子的人口统计学和生理学的几个方面对于规划和评估病媒控制战略特别重要。其中最重要的是确定蚊子媒介物种和年龄结构。需要进行准确的物种鉴定，以确认哪些病媒是传播的原因。气候变化是其传播潜力的一个关键决定因素。这是因为疟疾寄生虫在传播之前要在病媒中发育一段时间。此外，对杀虫剂抗药性状况的测量对于评估目前的一线控制方法（如驱虫蚊帐和喷洒）如何有效地将病媒作为目标至关重要。不幸的是，目前没有方法可用于快速，大规模和同时测量这些关键的蚊子病媒的人口统计和生理特征。该提案旨在通过开发和验证一种高通量、高精度监测中低收入国家疟疾病媒种群的新技术来填补这一空白。该项目旨在与非洲主要疟疾研究人员以及英国世界级物理化学家和病媒生物学家建立强有力的现有伙伴关系，为疟疾病媒开发这样一种工具。具体而言，该技术基于测量蚊子表皮（昆虫的外部）的分子特征，以预测关键特征（物种，年龄和杀虫剂抗性）。事实上，蚊子角质层的组成和结构，类似于哺乳动物的皮肤，随着生物体的衰老而变化，在不同的物种中是不同的，并且在对杀虫剂有抗性的蚊子中也会改变。在这里，我们建议描述与上述特征相关的表皮变化，然后对未知条件下的个体蚊子进行预测。所提出的技术是快速和具有成本效益的，因为它是基于蚊子吸收的光的测量，这是一个不需要任何样品制备或化学试剂的过程，并且它很容易在几秒钟内通过分光光度计进行。光谱-对应于单个蚊子吸收的光-将通过强大的计算分析进行分析，这将能够估计蚊子的特征。我们将遵循一个两阶段的过程，首先在格拉斯哥大学进行蚊子的实验室评估，以建立在我们使用中红外光谱（MIRS）与人工神经网络（ANN）相结合的引人注目的试点工作。在进一步优化和开发方法后，我们将把这项技术转让给非洲两个领先的疟疾病媒研究和控制机构，在那里它将在各种蚊子病媒种群中进行进一步的评估和应用。我们设想这将成为一条途径的第一步，沿着这条途径，这项技术可以转移到LMIC，以整合到一系列蚊子监测应用中，包括消除疟疾的计划，以及控制其他蚊子传播的病原体，如寨卡病毒和登革热，迫切需要有效的解决方案。

Since the year 2000, controlling mosquitoes with insecticide-based interventions has led to a 37% reduction in malaria mortality globally. Nevertheless, malaria still caused 438 000 deaths in 2015, and further progress is being threatened by increasing levels of insecticide resistance in mosquito vectors. The global malaria community urgently needs new tools to monitor mosquito vector populations. Several aspects of mosquito demography and physiology are particularly crucial for planning and assessing vector control strategies. Prime amongst these are determination of mosquito vector species and age structure. Accurate species identification is required to confirm what vectors are responsible for transmission. Mosquito age is a critical determinant of their transmission potential. This is because the malaria parasite undergo a period of development within the vector before they become transmissible. Additionally, measurement of insecticide resistance status is essential to assess how effectively vectors could be targeted by current frontline control methods such as Insecticide Treated Nets (ITNs) and spraying. Unfortunately, no methods are currently available for rapid, large-scale and simultaneous measurement of these crucial mosquito vector demographic and physiological traits. This proposal aims to fill that gap by developing and validating a novel technology for high throughput, high precision surveillance of malaria vector populations in LMICs. This project aims to develop such a tool for malaria vectors on the basis on strong existing partnerships with leading African malaria researchers, and world-class physical chemists and vector biologists in the UK. Specifically, the technology is based on the measurement of molecular signature of the mosquito cuticle, which is the outer part of the insects, to predict key traits (species, age and insecticide resistance). Indeed, the composition and structure of the mosquito cuticle, similar to the mammalian skin, changes when the organism ages, it is different in different species, and it is altered in mosquitoes that are resistant to insecticides. Here, we propose to characterize the cuticular changes associated with the traits mentioned above to then make predictions on individual mosquitoes of unknown conditions. The proposed technology is rapid and cost effective as it is based on the measurement of the light absorbed by mosquitoes, a procedure that do not require any sample preparation nor chemical reagents, and it is readily performed in few seconds by a spectrophotometer. The spectra - corresponding to the light absorbed by individual mosquitoes - will be analysed by powerful computational analysis which will enable to estimate the mosquito traits. We will follow a two-stage process starting with laboratory evaluation of mosquitoes at the University of Glasgow to build on our compelling pilot work on the use of Mid Infrared Spectroscopy (MIRS) coupled with artificial neural network (ANN). After further optimization and methods development, we will transfer this technology to two of Africa's leading malaria vector research and control institutes where it will undergo further evaluation and application within a diverse range of mosquito vector populations. We envision this will form the first steps of a pathway along which this technology can be transferred to LMICs for integration into a range of mosquito surveillance applications including programmes to eliminate malaria, and control of other mosquito-borne pathogens such as Zika and Dengue where effective solutions are desperately needed.