Use this URL to cite or link to this record in EThOS: https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.752831
Title: Integrated omics analyses of the interaction between Brachypodium distachyon and Magnaprthe oryzae
Author: Finch, Jasen
ISNI:       0000 0004 7425 9429
Awarding Body: Aberystwyth University
Current Institution: Aberystwyth University
Date of Award: 2016
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Abstract:
Fungal biotrophic phytopathogens such as Magnaporthe oryzae, the causal agent of rice blast disease, are becoming increasingly important in crop losses worldwide with the onset of anthropogenic climate change and monoculture farming practices. The use of model organisms such as the grass species Brachypodium distachyon provides the opportunity undertake large-scale integrated omics analyses that would otherwise be infeasible. These can provide insights into the system-wide responses of plants to pathogen infection as well as identify areas of the host plant system that could be targets for pathogen manipulation. A spectral binning method for high resolution metabolome ngerprinting was developed along with the R package binneR as a software implementation for routine application of the method. This was applied to investigate experimental control and robustness in plant-pathogen interactions, with the development of a new inoculation strategy for appropriately controlling inoculum derived responses unrelated to M. oryzae pathogenesis. Large-scale, high resolution metabolomic ngerprinting and proling, along with RNA-Seq transcriptomic analyses, were conducted for the interaction between B. distachyon and M. oryzae and identifed dynamic changes during the pre-symptomatic biotrophic phases. Both data and knowledge driven omics integration strategies identied associations between the transcriptomic and metabolomic changes during the interaction, with chloroplasts and nitrogen metabolism found as key response areas. A disease resistance locus Rbr1 was identied using QTL analyses on chromosome 4 of B. distachyon for resistance to M. oryzae utilising computer vision based phenotyping. Eight candidate NB-LRR resistance genes were found within this locus.
Supervisor: Draper, John Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.752831  DOI: Not available
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