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

自2000年以来，通过以杀虫剂为基础的干预措施控制蚊子，使全球疟疾死亡率下降了37%。尽管如此，2015年疟疾仍造成43.8万人死亡，而且蚊媒对杀虫剂的抗药性水平不断提高，正威胁着进一步的进展。全球疟疾界迫切需要新的工具来监测蚊媒种群。蚊子人口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。其中最主要的是确定蚊子媒介种类和年龄结构。需要准确的物种识别来确定哪些媒介负责传播。蚊子的年龄是其传播潜力的关键决定因素。这是因为疟疾寄生虫在变得可传播之前，在媒介内经历了一段时间的发展。此外，对杀虫剂抗药性状况的测量对于评估目前的前线控制方法，如杀虫剂处理蚊帐和喷洒等如何有效地针对媒介至关重要。不幸的是，目前还没有方法可以快速、大规模和同时测量这些关键的蚊子媒介人口和生理特征。这项提议旨在通过开发和验证一种新技术来填补这一空白，该新技术用于在LMICs中对疟疾媒介种群进行高通量、高精度监测。该项目旨在与领先的非洲疟疾研究人员以及英国的世界级物理化学家和媒介生物学家建立强大的现有合作伙伴关系，开发这样一个疟疾媒介工具。具体地说，这项技术是基于对蚊子表层分子签名的测量，以预测关键特征(物种、年龄和抗药性)。事实上，蚊子皮肤的成分和结构类似于哺乳动物的皮肤，随着有机体的老化而变化，在不同物种中是不同的，在对杀虫剂有抵抗力的蚊子身上也会发生变化。在这里，我们建议表征与上述特征相关的表皮变化，然后对未知条件下的个别蚊子进行预测。这项拟议的技术快速且具有成本效益，因为它是基于对蚊子吸收的光的测量，这是一种不需要任何样品制备和化学试剂的程序，而且通过分光光度计很容易在几秒钟内完成。这些光谱--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，从而能够估计蚊子的特征。我们将遵循从格拉斯哥大学蚊子实验室评估开始的两个阶段的过程，以我们令人信服的使用中红外光谱(MIRS)和人工神经网络(ANN)的试点工作为基础。经过进一步的优化和方法开发，我们将把这项技术转移到非洲两家领先的疟疾媒介研究和控制机构，在那里它将在不同的蚊子媒介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，沿着这条道路，这项技术可以转移到LMIC，用于整合到一系列蚊子监测应用中，包括消除疟疾的计划，以及控制其他蚊媒病原体，如寨卡和登革热，这些领域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.