Predicting fungicide resistance evolution: combining theoretical and experimental approaches Closing date January 5, start date October 2025. Further details and link to application form at https://www.ctp-sai.org/ We are recruiting a PhD student supervised by Dr Nichola Hawkins (Niab), Prof Nik Cunniffe (University of Cambridge), Dr Phil Madgwick and Dr Ariane Le Gros (Syngenta). The student will be based in Cambridge UK, as part of the Collaborative Training Programme in Sustainable Agricultural Innovation. Please note that the CTP funding covers tuition fees at the "home student" level only (in addition to stipend and research training funding), therefore applicants liable for the overseas student rate of tuition fees will need to be able to cover the difference by other means. This is a 4-year studentship including an industry placement. The predictability of evolution by natural selection is one of the big questions in evolutionary biology. It also has enormous practical importance in crop protection. Plant pathogens have proved highly adaptable to crop protection methods, including fungicides, leading to the rapid evolution of fungicide resistance. If the evolution of resistance and the characteristics of resistant strains can be predicted in advance, more effective resistance management strategies can be developed to use fungicides more sustainably. The fungal pathogen Zymoseptoria tritici causes Septoria leaf blotch, a major yield-limiting disease in wheat, and it has evolved resistance against multiple classes of fungicides. For some fungicides, a small number of major mutations have led to high levels of resistance, and resistance management is well understood. However, for the commonly- and currently-used azole and SDHI fungicides, the situation is more complex, with multiple different mutations leading to gradual shifts in sensitivity and affecting different fungicides within the affected class to different degrees. For the azole fungicide target site, CYP51, single isolates of Z. tritici can have up to ten mutations. Epistatic interactions between different mutations affect the overall phenotype of mutants, both in terms of resistance to different azole fungicides, and with fitness costs or compensatory effects on enzyme function. This can produce a rugged fitness landscape, making evolutionary outcomes more contingent upon selection history. Z. tritici can be readily cultured and fungicide resistant mutants can be generated under lab conditions, making it a useful model system for experimental studies of resistance evolution. This project will combine theoretical and experimental approaches to investigate the evolution of fungicide resistance, working with the fungal pathogen Zymoseptoria tritici. Supported by a supervisory team comprising mathematical modellers and an experimental biologist, the student will develop mathematical models to generate theoretical predictions of resistance evolution, which they will test using experimental evolution and competition assays. The student will generate near-isogenic transformants with combinatorial sets of CYP51 mutations, to quantify the effects of mutations and their epistatic interactions on fungicide resistance and other fitness parameters. The student will then use these fitness parameters in population genetic and epidemiological models to discover viable evolutionary trajectories through the rugged fitness landscape as shaped by selection and stochasticity. Potential resistance management strategies can be compared in terms of how they manipulate the fitness landscape to delay resistance evolution. To test these predictions, competition assays will be run under the different selective conditions of alternative resistance management strategies, and DNA tests such as qPCR will be developed to quantify the mutations after selection. The student will be able to address fundamental questions in evolutionary biology, while also contributing towards finding solutions to a practical problem in plant protection. In testing management strategies such as mixtures or alternations of fungicides or different dose rates, their findings will have direct impact on resistance management guidelines for Z. tritici, as well as having broader application to resistance evolution in other pathogens and pests. The student will have the opportunity to develop skills in experimental biology and mathematical modelling. Lab skills will include microbiological culturing and phenotyping; cloning and transformation; mutagenesis and experimental evolution; and molecular diagnostics to detect and quantify resistant genotypes. Modelling skills will include mapping a complex biological system to a parsimonious model; and methods for simulating and fitting stochastic population dynamic/genetics models. This project would suit a student with a background in molecular, evolutionary or computational biology who is enthusiastic to expand their skill set across the full range of these areas. Further details: https://www.ctp-sai.org/projects-for-2024-1/predicting-fungicide-resistance-evolution%3A-combining-theoretical-and-experimental-approaches- Application form: https://www.niab.com/niab_job_apply/239 Disclaimer The information contained in this communication from the sender is confidential. It is intended solely for use by the recipient and others authorized to receive it. If you are not the recipient, you are hereby notified that any disclosure, copying, distribution or taking action in relation of the contents of this information is strictly prohibited and may be unlawful. This email has been scanned for viruses and malware, and may have been automatically archived by Mimecast Ltd. Nichola Hawkins <Nichola.Hawkins@niab.com> (to subscribe/unsubscribe the EvolDir send mail to golding@mcmaster.ca)