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Quantifying the evolution of multi-fungicide resistance and its effects on pathogen fitness and virulence.

University of Exeter - College of Life and Environmental Sciences.

Qualification Type: PhD
Location: Exeter
Funding for: UK Students, EU Students
Funding amount: £15,609 per annum
Hours: Full Time
Placed On: 25th October 2021
Closes: 6th December 2021
Reference: 4290

To feed the growing human population, we must double food production by 2050. Plant pathogens pose a major threat to food security and are responsible for significant crop losses. Therefore, farmers rely on antimicrobials to control pathogen outbreaks.  

However, pathogens can rapidly evolve resistance and threaten the effectiveness of these treatments. Improving our understanding of the evolutionary processes that drive resistance selection and its influence on disease virulence is critical for the development of resistance management strategies for sustainable crop protection.     

The proposed PhD CASE studentship in collaboration with the industrial partner Syngenta will tackle the challenge of understanding resistance evolution through an experimental, laboratory-based project that will be carried out in close collaboration with mathematical modellers.     

Studentship goals: (1) Empirically assess the relevance of single- and multi-fungicide resistance for pathogen fitness and virulence; (2) Generate data to support overarching mathematical models of antifungal resistance evolution    Outcome and impact: The student will generate new and precise knowledge on the interactions between fungicide resistance, pathogen fitness and virulence. This is essential to accurately predict fungicide resistance evolution. This project can therefore contribute to building science-based resistance management strategies and to developing more sustainable agriculture.    

Training: This PhD studentship will provide a unique opportunity to gain a range of experimental and quantitative skills essential to thrive in the modern fields of biology. To achieve this, we have put together a truly interdisciplinary team with expertise in modelling (Le Gros, Syngenta; Gudelj, Exeter), fungal genetics & antifungal resistance (Scalliet, Syngenta); quantitative evolutionary biology (Beardmore, Exeter) and plant pathology & synthetic fungal biology (Lindsay, Exeter). 

We will deploy our extensive expertise in a tractable laboratory experimental system with the fungus Magnaporthe oryzae, responsible for rice blast disease, the most severe and widespread disease of cultivated rice. It is also an ideal system for investigating the fundamental principles of antimicrobial resistance penalties as its genome, lifecycle and physiology are well understood and it is amenable to genetic engineering.    

Eligibility 

To be eligible for a fully-funded studentship, you must meet both the academic and residence criteria. 

Please refer to the regulations or Annex 1 of the Research Council Training Grant Guide to confirm that you meet the residence criteria for a fully-funded studentship.  Any further queries in relation to residency must be directed to the institution that you are applying to. 

* An enhanced stipend is available for students with a recognised veterinary degree qualification (£24,090 per annum for 2022-2023. There may also be enhanced stipends associated with projects that have a CASE partner (CASE projects are highlighted as *CASE in the project lists). 

Entry Requirements 

Applicants should have obtained, or be about to obtain, a 1st or 2:1 UK Honours degree, or equivalent qualifications gained outside the UK, in an appropriate area of science or technology.  Applicants with a Lower Second Class degree will be considered if they also have Masters degree or significant relevant non-academic experience.   

In addition, a minimum of a grade B in A-level Maths or equivalent qualification or experience is required.   

Equivalent qualifications/experience   

  • Physics A-level (grade B and above)  
  • Undertaking units as part of your degree that have a significant mathematical component*  

* eg maths, statistics, bioinformatics.   

Applicants must highlight their Maths background within their application and upload any supporting evidence.   

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