Predicting the durability and resistance risk of crop protection measures through experimental evolution of plant pathogens

进化的可预测性有多大？如果进化史被重复，结果是每次都一样，还是截然不同？这个问题几十年来一直让进化生物学家着迷，但当进化出的特征是对药物、杀虫剂或其他治疗方法产生抗药性时，这个问题就具有迫切的现实意义。粮食安全不断受到植物病虫害的威胁，因此需要保护作物，以保障收成，避免将土地和其他投入浪费在因病虫害而损失的作物上。农药目前是植物病害防治的主要组成部分，但正如抗生素的广泛使用导致了抗药性细菌的进化一样，农业杀菌剂、杀虫剂和除草剂的广泛使用也导致了抗药性疾病、害虫和杂草的进化。任何有效的控制措施都将选择克服该控制措施的能力，无论该控制措施是治疗感染的药物还是作物的抗病基因。在农业方面，正在逐步转向杀虫剂的替代品，但这些替代品仍然存在抗药性的风险，而且对任何控制措施的抗药性都涉及相同的基本进化原则。50多年前，在一种植物病原菌中报告了第一例杀菌剂抗药性。这个项目将着眼于植物病原体抗药性进化所涉及的一般进化原理，因此数十年来对化学杀菌剂的抗药性进化的经验教训可以应用于作物保护的新方法，并且可以从一开始就以进化智能的方式进行管理，在抗药性成为问题之前减缓其发展。为了主动管理耐药性，我们需要能够预测它将如何演变。某一特定控制措施的抗药性风险是高还是低？这些突变会导致低水平还是高水平的耐药性？它们会对一种特定的产品还是对广泛的产品产生抗药性？我们能否预测准确的突变，并开发DNA测试，在这些突变第一次出现时就将其检测出来？本项目将采用实验进化、筛选抗杀菌剂真菌植物病原菌等防治措施。我将使用杀菌剂选择，这样结果就可以与已经发生的现实世界抗药性进化进行比较。我将测试不同类别杀菌剂的抗药性进化的重复性：抗药性是否每次都是由相同的突变引起的，或者相同的实验是否每次都有不同的结果。我还将设立竞争实验，看看某些突变是否比其他突变具有更大的优势，以及这是否取决于温度和营养水平等环境条件。这将告诉我们，当几种不同的突变都产生类似水平的耐药性时，或者当耐药性和竞争力之间存在权衡时，或者当不同的突变在不同的条件下受到青睐时，进化是更难预测的。然后，这些方法将被应用于两种可能的替代控制措施：生物防治和RNAi。我将测试引起植物病害的真菌是否能够进化出对抑制其生长的细菌菌株的抵抗力，以及这种抵抗力是否通过相同的突变重复进化，或者是否出现了各种不同的机制。我还将测试导致植物病害的真菌是否可以进化出对RNAi的抵抗力，这是一种通过沉默特定基因的表达来发挥作用的控制方法，以及这对设计RNAi以降低抗药性风险意味着什么。所开发的方法也将适用于未来进一步的新的作物保护方法。

How predictable is evolution? If evolutionary history were repeated, would the result be the same every time, or dramatically different? This question has fascinated evolutionary biologists for decades, but when the trait evolving is resistance against a drug, pesticide or other treatment, the question takes on an urgent practical relevance. Food security is under constant threat from plant diseases and pests, so crop protection is needed to safeguard harvests and to avoid wasting land and other inputs on crops lost to pests and diseases. Pesticides are currently a major component of plant disease control, but just as the widespread use of antibiotics has led to the evolution of drug-resistant bacteria, the widespread use of agriculture fungicides, insecticides and herbicides has resulted in the evolution of pesticide-resistant diseases, pests and weeds. Any effective control measure will select for the ability to overcome that control measure, whether that control measure is a drug to treat an infection, or a disease-resistance gene in a crop plant. In agriculture, a gradual shift is occurring towards alternatives to pesticides, but these alternatives still have a risk of resistance, and the same fundamental evolutionary principles are involved for resistance to any control measure. The first case of fungicide resistance was reported in a plant pathogen over 50 years ago. This project will look at the general evolutionary principles involved in the evolution of resistance in plant pathogens, so lessons from decades of resistance evolution against chemical fungicides can be applied to new methods of crop protection and they can be managed in an evolution-smart way from the start, slowing the development of resistance before it becomes a problem. In order to manage resistance proactively, we need to be able to predict how it will evolve. Is the resistance risk for a particular control measure high or low? Will the mutations cause low or high levels of resistance? Will they cause resistance to one specific product or a wide range? Can we predict the exact mutations and develop DNA tests to detect those mutations as soon as they first emerge? This project will use experimental evolution, selecting a fungal plant pathogen for resistance against fungicides and other control measures. I will use fungicide selection so results can be compared to real-world resistance evolution that has already occurred. I will test how repeatable the evolution of resistance is for different classes of fungicides: whether resistance is caused by the same mutation every time, or whether the same experiment has different results each time. I will also set up competition experiments, to see whether some mutations have a bigger advantage than others, and whether this depends on environmental conditions such as temperature and nutrient levels. This will tell us whether evolution is less predictable when several different mutations all give a similar level of resistance, or when there are trade-offs between resistance and competitiveness or when different mutations are favoured under different conditions. These methods will then be applied to two potential alternative control measures: biological control, and RNAi. I will test whether a plant-disease-causing fungus is able to evolve resistance against a bacterial strain that inhibits its growth, and whether that resistance repeatedly evolved through the same mutation or whether various different mechanisms emerge. I will also test whether a plant-disease-causing fungus can evolve resistance against RNAi, a control method that works by silencing the expression of a specific gene, and what this means for designing RNAi to reduce the resistance risk. The methods developed here will also be applicable to further new crop protection methods in future.