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

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.

Effective crop protection is vital for resilient and sustainable agricultural production, but any effective control measure exerts selective pressure in favour of resistance. As more chemical pesticides are lost to resistance, there is a need not only for new crop protection measures, but for improved understanding of resistance evolution and how it can be managed. This project will use experimental evolution, combined with high throughput sequencing, to quantify the differing levels of evolutionary repeatability seen for resistance against different plant protection products in plant pathogens, and to improve the predictive power of resistance risk assessments for future crop protection measures.

Fungicide selection will be used as a model system for resistance evolution, as it can be readily applied in vitro, the target sites are well characterised, and experimental predictions can be compared against field resistance. Populations of the fungal plant pathogens Zymoseptoria tritici and Botrytis cinerea will be selected in vitro with fungicides; resistant mutants will be characterised phenotypically and genotypically, with whole-genome sequencing and RNA sequencing to determine resistance mechanisms, and PCR amplicon sequencing to quantify mutations over the time course of selection. Mutational repeatability will be compared across replicate populations and between fungicides. Resistance-associated mutations will be recreated in isogenic transformants and competition assays performed under a range of conditions, to quantify the magnitude and variability of selection coefficients, since smaller or less consistent fitness differences may make evolutionary outcomes less predictable.

The same experimental evolution approach will be applied to biocontrol by allelopathic bacteria, and RNAi, to predict the overall risk, potential mechanisms and expected level of genotypic repeatability of resistance evolution for these non-chemical crop protection methods.