ENDORSE: ENhancing Diversity to Overcome ReSistance Evolution

赞同：增强多样性以克服抵抗进化

• 批准号: BB/S018956/1
• 负责人: Matthew Tinsley
• 金额: $ 66.55万
• 依托单位: University of Stirling
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2019
• 资助国家: 英国
• 起止时间: 2019 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=BB%2FS018956%2F1
• 关键词: ENDORSE; ENhancing; Diversity; Overcome; ReSistance

Despite its place as a global leader in agriculture, each year the Brazilian agricultural economy loses approximately $15 billion to insect crop pest outbreaks. Indeed, insects consume 10-20% of all global crops while growing or in storage. Current agricultural practices in Brazil rely heavily on widespread pesticide application, which has led to the evolution of pesticide resistance in several significant insect pests. Such practices undermine the sustainability of important crop pest control technologies, reduce associated economic returns, and exacerbate the risks to economic production and food security in Brazil. We propose a revolutionary approach to pest management that will enhance the sustainability and long-term resilience of crop production, providing the benefit of managing insect pests more predictably.Our solution comes from evolutionary science and the particular features of host-pathogen interactions. Insecticide resistance evolution occurs when a single control agent is applied over a broad area, then consistent evolutionary pressures drive rare resistance genes to spread rapidly through the pest population. To prevent these sweeps of resistant alleles, we are investigating how multiple fungal pathogen strains can be used in a spatial matrix across agricultural landscapes, so that selection for resistance varies in different locations, preventing a uniform evolutionary response. On their own, multiple pathogen strains may not be sufficient because of cross resistance: genes making pests resistant to one fungal strain could also confer resistance to others. However, in host-pathogen systems, the optimum genotype to defend against one pathogen is often highly sensitive to the organism's environment. Simultaneous manipulation of an environmental landscape variable (the type of crop grown by farmers) will substantially decrease the consistency of selection: we predict this will prevent resistance evolution.In order to achieve real-world effectiveness of this pest control system, an integrated team of Brazilian and UK researchers will work together to establish the long-term prospects of our new solution. The aims are to:1. Examine whether genetic variation for insect susceptibility to multiple fungal biopesticides under heterogeneous agricultural landscapes is stable, and assess how it responds to selection in the long-term. This will allow us to anticipate and avoid selection for resistance to multiple strains, and ensure the long-term sustainability of our pesticide resistance management system.2. Investigate the suitability of fungal biopesticides for industrial scale production and field application in Brazil, which will facilitate product development for future industrial investment. We will also provide farmers and the crop protection industry with solutions for crop protection technology deployment, including improved delivery systems, higher pest control consistency and enhanced performance under field conditions.3. Identify the barriers to uptake of our new pest control technologies and research methods to encourage farmer behavioural change. This research will provide economic and social science data to underpin advice for policy recommendations regarding incentive schemes, publicity campaigns and marketing strategies, thereby promoting uptake of these sustainable pest management practices.

尽管巴西是全球农业的领导者，但每年巴西农业经济因作物虫害爆发而损失约150亿美元。事实上，昆虫在生长或储存期间消耗了全球所有作物的10-20%。巴西目前的农业做法严重依赖于广泛使用农药，这导致了几种重要害虫对农药的抗药性。这种做法破坏了重要的作物虫害控制技术的可持续性，减少了相关的经济回报，并加剧了巴西经济生产和粮食安全的风险。我们提出了一种革命性的害虫管理方法，将提高作物生产的可持续性和长期弹性，提供更可预测地管理害虫的好处。我们的解决方案来自进化科学和宿主-病原体相互作用的特殊特征。当一种单一的控制剂被广泛应用于一个广阔的区域时，就会发生抗药性进化，然后持续的进化压力会驱使稀有的抗性基因在害虫种群中迅速传播。为了防止这些抗性等位基因的横扫，我们正在研究如何在农业景观的空间矩阵中使用多种真菌病原体菌株，以便在不同的位置选择不同的抗性，防止统一的进化反应。单靠多种病原体菌株可能不足以产生交叉抗性：使害虫对一种真菌菌株产生抗性的基因也可能赋予对其他菌株的抗性。然而，在宿主-病原体系统中，防御一种病原体的最佳基因型通常对生物体的环境高度敏感。同时操纵一个环境景观变量（农民种植的作物类型）将大大降低选择的一致性：我们预测这将阻止抗性进化。为了实现这种害虫控制系统的实际效果，巴西和英国研究人员的综合团队将共同努力，建立我们新解决方案的长期前景。目的是：1.检查在异质农业景观下昆虫对多种真菌生物农药敏感性的遗传变异是否稳定，并评估其如何对长期选择做出反应。这将使我们能够预测和避免选择对多种菌株的抗性，并确保我们的农药抗性管理系统的长期可持续性。2.调查真菌生物农药在巴西的工业规模生产和田间应用的适用性，这将有助于未来工业投资的产品开发。我们还将为农民和作物保护行业提供作物保护技术部署的解决方案，包括改进的交付系统，更高的虫害控制一致性和田间条件下的性能增强。确定采用我们新的虫害控制技术和研究方法的障碍，以鼓励农民改变行为。这项研究将提供经济和社会科学数据，以支持有关奖励计划，宣传活动和营销战略的政策建议，从而促进这些可持续虫害管理做法的采用。

项目成果

resevol: An R package for spatially explicit models of pesticide resistance given evolving pest genomes.

• DOI: 10.1371/journal.pcbi.1011691
• 发表时间: 2023-12
• 期刊: PLoS computational biology
• 影响因子: 4.3

resevol: An R package for spatially explicit models of pesticide resistance given evolving pest genomes

• DOI: 10.1101/2022.08.22.504740
• 发表时间: 2023-07
• 期刊: PLOS Computational Biology
• 影响因子: 4.3
• 作者: A. B. Duthie1;Rosie Mangan1;Chintamani Rose McKeon1;Matthew C. Tinsley1;Luc F. Bussière2