Despite the recommendations of many expert groups, One Health surveillance systems have not yet improved to the point where emerging infectious threats can be better anticipated. The COVID epidemic is the latest to demonstrate that new pathogens often spread undetected for some time before being able to be diagnosed in a population. This is even more evident for pathogens in other areas such as crop production and diseases have a significant cost to agriculture.
Next-generation sequencing, particularly the use of portable genomic sequencers, offers an intriguing solution to the diagnosis and surveillance problems — it enables rapid in situ diagnostics through amplicon-based or metagenomics approaches. Findings from analyses of whole-genome sequences show great promise for informing strategies to mitigate risks from diseases caused by phytopathogens. It also creates a stream of genomic data that can reveal critical epidemiological aspects of an outbreak or epidemic's dynamics.
Genomic epidemiology for outbreak response approaches can be used to dramatically shorten response times to outbreaks and inform disease management in novel ways. However, the use of these approaches requires expertise in working with big, complex data sets and an understanding of their pitfalls and limitations to infer well-supported communications. This has demonstrated some early successes, but there are a number of challenges to overcome — some technical and some cultural. Data sharing is one of these, but other ethical and legal issues must be considered.
The power of a genomic epidemiology approach for plant pathogens could be extended by incorporating concepts from digital disease detection and One Health. By coupling sequencing to an enhanced surveillance, response platform and even for future biocontrols, we could take a more anticipatory approach to outbreak prevention and control.
The aim of this proposal is to create a framework approach to guide the use of genomic approaches in epidemiology and diagnostics of plant pathogens. The concept is to map pathogens (especially fungi) of concern in crops to have a substantive database and be able to use their genome and geonomics databases to devise rapid point of site testing methods and design future biocontrols as a model system to create an infrastructure for an immediate response mechanism for future new pathogens.
There are >20 databases relevant to this project, the research student will receive training in working with big complex data sets from these datasets to address the key questions using BLAST, Genebank etc. The student will also receive training in sequencing techniques and data analysis and understanding of the state-of-the-art NGS methods and point of site detection methods that can be applied such as Nanopore, LAMP and LFD for pathogen detection but also for the application of biocontrol methods. The student will also receive training in literature reviews, publication and scientific writing skills and will be linked in with the QUB Graduate School for transferable skills training in Leadership. At UCD the student will receive training in metagenomic analysis from DNA extracted from spore trap samples taken across field sites to identify the wheat pathogens present.
This project will be supervised by Professor Katrina Campbell and Dr Caroline Meharg (Queen's University Belfast) and Dr Angela Feehan (University College Dublin).
Start Date: 1 September 2022
Duration: 3 years
Skills/experience required: Besides any experience relevant to the particular project, students would benefit from having some experience with bioinformatics / computational biology tools and analyses, although training in these areas will be provided in the residential training programme at the start of the project.
How to apply: Applications must be submitted via: https://dap.qub.ac.uk/portal/user/u_login.php
Queen’s University Belfast is a partner of the SFI CRT in Genomics Data Science, which was formed in 2019. Over seven years of its operation, the Centre will train 100 PhD students in data analytical and computational skills that will help power the implementation of genomics on the island of Ireland. The Centre is built on a national cohort-based model of advanced training. At the outset of their training programme, each student cohort undertakes an intensive, semester-long series of training courses. Participation in the SFI CRT in Genomics Data Science includes a mandatory 3-month residential training programme for the students at NUI Galway, held in Semester 1 (start of September to mid-December) of Year 1 of the PhD programme. Through their experience of working and living together on this phase of the training programme, student cohorts form tight knit groups, enabling them to work collectively on the data science challenges they will encounter during their research. For more information on the SFI CRT in Genomics Data Science and the residential training programme see: https://genomicsdatascience.ie/