Novel informed modelling approaches to investigate the evolution and management of herbicide resistance in Alopecurus myosuroides

摘要麦冬(Alopecurus myosuroides，黑草)是一种竞争激烈的杂草，对除草剂的抗性发生进化。目前，英国和法国对除草剂的进化抗性程度最高，这两个国家对乙酰辅酶A羧基酶(ACCase)抑制和乙酰乳酸合成酶(ALS)抑制除草剂的抗药性案例很常见和普遍。最近，该物种正在迅速向北和向东扩展其活动范围，德国和其他国家的抗药性报告也在增加。与此同时，目前欧盟农药注册的变化正在减少可用于黑草防治的除草剂作用模式的数量，使得对剩余作用模式的抗药性在未来可能成为一个更大的问题。除草剂抗药性流行率和风险的增加必须促使农化公司、农民和顾问提供更可持续的除草剂使用战略，并更多地采用综合杂草管理。演示遗传模型可以将黑草种群和生活史的知识与我们目前对除草剂抗性遗传基础的理解结合起来，研究各种管理措施对除草剂抗性进化和传播的影响。这些模型以前是由学术主管为其他杂草物种开发的。该项目将通过使用拜耳作物科学公司自2008年以来一直在整理的黑草抗性病例的广泛数据库来加强和加强这些方法。对于被送往拜耳的每个疑似抗性的黑草种群，都会进行一套广泛的实验室和温室测试，以确定抗性的程度及其遗传和机制基础。这提供了一个独特的数据集，记录了不同耐药机制在整个欧洲的范围和分布，以及关于各种赋予抗药性的点突变的频率和表型后果(抗药性图谱)的信息。这个数据集甚至更令人信服，因为它包括从每个地点采集黑草的田间管理历史，从而提供了将抗性程度和机制与过去的管理联系起来的机会。以前的除草剂抗性进化模型的一个主要局限性是缺乏关于各种抗性机制和突变的相对频率的数据，预计通过将这些数据合并到一种建模格式中，可以在抗性管理方面取得重大进展。开发的模型将能够通过多种耐药机制同时模拟耐药性的进化，并将利用机制和突变的相对频率及其耐药性图谱的知识来探索降低耐药性进化风险的管理策略。首先，将在个别领域模拟抗性的演变。然而，该项目的较长期目标将是通过模拟田地网络中具有对比管理和田地之间基因流动的黑草种群来考虑景观规模的抗性进化。除了建模，这名学生还将对来自英国的黑草种群进行年度随机调查。这些人群的抗药性程度将在华威大学的温室试验中确定。将进行进一步的实验，以检查这些种群中抗性的适合性后果，这些信息将被用于帮助开发演示遗传模型。每年，学生将在位于法兰克福的拜耳作物科学综合杂草管理和除草剂抗性诊断实验室度过2-3个月的时间。在此期间，他们将进行一系列分子生理分析，以确定英国收集的种群中存在的抗性机制。

The highly competitive grass weed, Alopecurus myosuroides (black-grass) is prone to the evolution of resistance to herbicides. The extent of evolved herbicide resistance is currently greatest in the UK and France where documented cases of resistance to acetyl co-enzyme A carboxylase (ACCase) -inhibiting and acetolactate synthase (ALS) -inhibiting herbicides are common and widespread. More recently, the species is rapidly expanding its range northwards and eastwards and reports of resistance are increasing in Germany and other countries. At the same time, current changes to EU pesticide registration are reducing the number of herbicide modes of action available for black-grass control, making it probable that resistance to remaining modes of action will be an even greater issue in future. These increases in the prevalence and risk of herbicide resistance must drive agrichemical companies, farmers and advisers towards the provision of more sustainable herbicide use strategies and greater adoption of integrated weed management. Demo-genetic models can combine knowledge of the demography and life cycle of black-grass with our current understanding of the genetic basis of herbicide resistance to examine the influence of various management practices on the evolution and spread of herbicide resistance. These models have been developed previously by the academic supervisor for other weed species. This project will build upon and enhance these approaches by using the extensive database of black-grass resistance cases that Bayer CropScience has been collating since 2008. For each suspected resistant black-grass population that is sent to Bayer, an extensive suite of laboratory and glasshouse tests are performed to determine the extent of resistance, as well its genetic and mechanistic basis. This provides a unique dataset documenting the extent and distribution of different resistance mechanisms across Europe as well as information on the frequency and phenotypic consequences (resistance profile) of various resistance-endowing point mutations. This dataset is even more compelling as it includes a field management history for each location from which black-grass is sampled, providing the opportunity to relate the extent and mechanism of resistance to past management. A major limitation of previous models of herbicide resistance evolution has been the lack of data on the relative frequency of various resistance mechanisms and mutations and it is envisaged that major advances in resistance management can be achieved by combining this data into a modelling format. The developed model will be able to simultaneously simulate evolution of resistance via multiple resistance mechanisms and knowledge of the relative frequencies of mechanisms and mutations together with their resistance profile will be used to explore management strategies to reduce risks of resistance evolution. In the first instance, evolution of resistance will be simulated in individual fields. However, a longer term aim of the project will be to consider evolution of resistance on a landscape scale by simulating black-grass populations in a network of fields with contrasting management and with gene flow between the fields. In addition to modelling, the student will also conduct an annual random survey of black-grass populations from the UK. The extent of resistance in these populations will be determined in glasshouse assays at the University of Warwick. Further experiments will be conducted to examine the fitness consequences of resistance in these populations and this information will be used to help develop the demo-genetic model. During each year, the student will spend a 2-3 month period at the Bayer Crop Science Integrated Weed Management and Herbicide Resistance diagnostics laboratory in Frankfurt. During this time they will perform a suite of molecular physiological assays to determine the resistance mechanisms present in the UK collected populations.