MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito population.
MGSurvE: A framework to optimize trap placement for genetic surveillance of mosquito population.
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MGSurvE：优化蚊子种群遗传监测陷阱放置的框架。
DOI：
10.1101/2023.06.26.546301
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Marshall,JohnM
中科院分区：
文献类型：
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作者：
SánchezC,HéctorM;Smith,DavidL;Marshall,JohnM
随着基于遗传学的蚊虫控制策略从实验室发展到现场试验，蚊子种群的遗传监测变得越来越重要。特别适用于蚊子基因驱动项目，其潜在规模导致监测成为一项重要的成本驱动因素。对于这些项目，将需要进行监测，以发现基因驱动蚊子在野外站点以外的意外传播，以及在干预站点内出现替代等位基因，例如驱动抗性等位基因或无功能效应基因。这就需要有效地分发捕蚊器，以便尽快发现感兴趣的等位基因——最好是在补救措施仍然可行的情况下。此外，蚊帐等以杀虫剂为基础的工具受到杀虫剂抗性等位基因的影响，因此也需要尽快发现这些等位基因。为此，我们提出了MGSurvE（蚊子基因监测）：这是一个计算框架，可以优化蚊子种群遗传监测的陷阱放置，从而最大限度地减少检测感兴趣等位基因的时间。MGSurvE的一个关键优势在于，它可以解释蚊子和它们栖息的景观的重要生物学特征，即：i)蚊子所需的资源（例如，食物来源和水生繁殖地）可以通过景观明确分布；ii)蚊子的运动可能取决于它们的性别、它们的淋养循环的当前状态（如果是雌性）和资源吸引力；iii)陷阱可能在吸引力方面有所不同。举例MGSurvE分析显示最佳陷阱放置：i)埃及伊蚊种群在澳大利亚昆士兰州郊区景观，ii)冈比亚按蚊种群在s<s:1> o tom<e:1>, s<s:1> o tom<e:1>和Príncipe岛。项目文档中提供了进一步的文档和使用示例。MGSurvE旨在为对蚊子基因监测感兴趣的现场和计算研究人员提供资源。
Genetic surveillance of mosquito populations is becoming increasingly relevant as genetics-based mosquito control strategies advance from laboratory to field testing. Especially applicable are mosquito gene drive projects, the potential scale of which leads monitoring to be a significant cost driver. For these projects, monitoring will be required to detect unintended spread of gene drive mosquitoes beyond field sites, and the emergence of alternative alleles, such as drive-resistant alleles or non-functional effector genes, within intervention sites. This entails the need to distribute mosquito traps efficiently such that an allele of interest is detected as quickly as possible—ideally when remediation is still viable. Additionally, insecticide-based tools such as bednets are compromised by insecticide-resistance alleles for which there is also a need to detect as quickly as possible. To this end, we present MGSurvE (Mosquito Gene SurveillancE): a computational framework that optimizes trap placement for genetic surveillance of mosquito populations such that the time to detection of an allele of interest is minimized. A key strength of MGSurvE is that it allows important biological features of mosquitoes and the landscapes they inhabit to be accounted for, namely: i) resources required by mosquitoes (eg, food sources and aquatic breeding sites) can be explicitly distributed through a landscape, ii) movement of mosquitoes may depend on their sex, the current state of their gonotrophic cycle (if female) and resource attractiveness, and iii) traps may differ in their attractiveness profile. Example MGSurvE analyses are presented to demonstrate optimal trap placement for: i) an Aedes aegypti population in a suburban landscape in Queensland, Australia, and ii) an Anopheles gambiae population on the island of São Tomé, São Tomé and Príncipe. Further documentation and use examples are provided in project’s documentation. MGSurvE is intended as a resource for both field and computational researchers interested in mosquito gene surveillance.