Host: John Innes Centre, Norwich
Wheat yellow rust disease, caused by the fungus Puccinia striiformis f. sp tritici (PST), is a substantial threat to wheat production worldwide and has recently re-emerged as a major constraint on UK agriculture.
We recently developed a novel approach called “field pathogenomics" for pathogen population surveillance, described in this video. This method uses high-resolution genotypic data to improve our understanding of the genetic sub-structure within a population, which provides essential information on the evolutionary forces that drive pathogen evolution within an agroecosystem.
However, for wheat yellow rust our understanding of the patterns of transmission and dispersal remain limited. Building effective agro-ecological models that address this lack of knowledge could contribute to a proactive early warning system for wheat yellow rust in the UK.
A mathematical model of the spatio-temporal population dynamics of yellow rust would have a number of applications, including optimizing surveillance, targeting chemical sprays and designing regional diversification schemes.Models are already used by the UK Government to inform policy surrounding tree diseases.
However, models are less well developed for crop pathogens. Through this studentship, we will build detailed in-field information on PST genetics, providing an opportunity to uniquely link epidemiological modelling and genomic data, improving the predictive power of models.
This project will provide a unique opportunity for training in general molecular biology, plant pathology, and computational biology.
The student will be embedded in a multi-disciplinary research group at the John Innes Centre and have full use of the extensive facilities available in the Crop Genetics department to ensure they have training in the broadest possible skill set. The student will also be supported day-to-day by a post-doctoral mentor who will closely guide and work with the student through all aspects of the project.
Host: Fera, York
Recent advances in genomic techniques have provided us with powerful tools for identifying new viruses in plants. We frequently identify viruses that are new to science but which are not associated with deleterious symptoms of disease in the host in which they are found.
These viruses may be part of the natural community of organisms that live in association with plants, but whilst the bacterial and fungal components of the phytobiome are widely studied the viral members are not.
One hypothesis is that these viruses are being retained by the plants because they provide benefits, potentially enabling plants to adapt to environmental stresses. However, they may also be hitch-hikers, moving cryptically across borders and may pose a significant threat to the environment or crops into which they move.
The major initial challenge with studying these viruses is transmitting them into experimental hosts. Whilst sometimes easier with disease causing viruses where the symptoms provide an obvious indicator of infection, asymptomatic viruses become significantly more challenging.
This project proposes to investigate the various methods we have for mechanical transmission of viruses to experimental and natural hosts, providing us with systematic protocols to apply to newly discovered viruses.
Host: Fera, York
Sensitive and specific methods for the detection of plant pathogens and the identification of plant pests are necessary to inform decisions relating to disease control and management. In some scenarios, methods which can be used away from conventional laboratory facilities are of particular value as they can increase the speed with which results are obtained and decisions can be made.
Examples of this include testing at disease outbreak locations, and testing for listed pests and diseases at ports of entry. In order to ensure the accuracy of results obtained using a particular method, tests are validated in accordance with international standards.
Detection methods based on the amplification of nucleic acid have advantages over other approaches in terms of sensitivity and specificity. Further to this, methods based on loop-mediated isothermal amplification (LAMP) have the potential to be extremely rapid and robust and therefore suitable for use in non-laboratory conditions. Tolerance of inhibitory compounds allows the use of simplified sample preparation methods in conjunction with LAMP.
In this project we propose to optimise the sampling / sample preparation protocol for detection of fungal plant pathogens in their relevant host matrices, and document the optimised methods for potential use by non-specialist operators.
We will then validate a test consisting of the optimised extraction method followed by pathogen-specific LAMP for detection of at least one fungal plant pathogen, with reference to the international standard. Selection of target pathogen(s) will be informed by current national and international plant health priorities.
Host: University of Salford
With increased global trade and travel more plants are being moved around the world and new diseases are showing up in unexpected places. Recent high profile examples include ash dieback and ramorum dieback, which currently threaten tree health in the UK.
When a new pest or disease is discovered it is crucial to understand how bad the problem might be so that an appropriate response to the threat can be made.
We have recently developed a novel statistical method to calculate the incidence of disease following the discovery of the first case. Crucial to this method is an ability to accurately quantify 'survey effort' - i.e. how hard we have been searching.
For regulatory surveys conducted by public agencies this is relatively straightforward. However increasingly diseases are being reported by members of the public and land owners. Such surveys are inherently more ad-hoc and variable yet no current study has investigated the consequence of this for our ability to predict initial disease incidence.
In this project the student will use epidemiological modelling to investigate how ad-hoc and variable search efforts, typical of 'citizen surveillance’, impact our ability to predict incidence following first detection of an emerging disease threat.