Predicting fungicide resistance evolution: combining theoretical and experimental approaches (Ref: CTP-SAI-085)

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.

OBJECTIVES AND APPROACHES

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.

SUPERVISORS

Dr Nichola Hawkins (NIAB), Prof Nik Cunniffe (University of Cambridge), Dr Phil Madgwick and Dr Ariane Le Gros (Syngenta)

PRIMARY LOCATION OF THIS PHD

The student will be registered with the University of Cambridge and based at NIAB, Cambridge.

TRAINING

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.

The CTP – SAI (https://www.ctp-sai.org) is a groundbreaking partnership between leading businesses, charities and research providers offering outstanding training for the agri-food sector. Students will have access to training opportunities through their University to complement their scientific development. This will be augmented by training in key bioscience skills to enhance employability and research capability through the CTP-SAI.

There will be additional training to enhance employability and research capability. All CTP-SAI students will receive Graduate Training in Leadership and Management as well as personal development skills training from MDS (www.mds-ltd.co.uk).

INDUSTRIAL PLACEMENT

Placements are a key feature of CTP and UKRI-BBSRC expects all doctoral candidates on a CTP programme to undertake a placement. Placements can be in the form of research placements (3-18 months duration) or used more flexibly for experiential learning of professional skills for business and/or entrepreneurship. All placements are developed in collaboration between the partners with input from the doctoral candidate.

APPLICATION AND ELIGIBILITY

Please contact Dr Hawkins for more details of this project (Nichola.Hawkins@niab.com). 

This studentship will begin in October 2025. The successful candidate should have (or expect to have) an Honours Degree (or equivalent) with a minimum of 2.1 in Plant Science, Applied Statistics, or other related science subjects.

We welcome UK, EU, and international applicants. Candidates whose first language is not English must provide evidence that their English language is sufficient to meet the specific demands of their study. Candidates should check the requirements for each host organization they are applying to.

This studentship is for four years and is fully funded in line with UKRI-BBSRC standard rates. These were for 2024/25, an annual maintenance stipend of £19,237, fee support of £4,786, a research training support grant of £5,000 and conference and UK fieldwork expenses of £300.

To be classed as a home student, candidates must meet the following criteria and the associated residency requirements:

• Be a UK National or,
• Have settled status or,
• Have pre-settled status or,
• Have indefinite leave to remain or enter
• Be an Irish National

If a candidate does not meet the criteria above, they would be classed as an international student and must demonstrate the ability to meet the supplement in fees required for an international student.

Anyone interested should complete the online application form before the deadline of 5th January 2025. Interviews will be held during January and February 2025.

Please contact recruitment-ctp-sai@niab.com for further application details.

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