Using metabolic profiling to assess plant-pathogen interactions: an example using rice (<Emphasis Type="Italic">Oryza sativa)</Emphasis> and the blast pathogen <Emphasis Type="Italic">Magnaporthe grisea</Emphasis> |
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| Authors: | Oliver A H Jones Mahon L Maguire Julian L Griffin Young-Ho Jung Junko Shibato Randeep Rakwal Ganesh K Agrawal Nam-Soo Jwa |
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| Institution: | (1) The Hopkins Building, Department of Biochemistry, University of Cambridge, Tennis Court Road, Cambridge, CB2 1QW, UK;(2) Present address: The Christopherson Building, School of Engineering & Computing Sciences, University of Durham, South Road, Durham, DH1 3LE, UK;(3) BHF Magnetic Resonance Unit, Wellcome Trust Centre for Human Genetics, University of Oxford, Roosevelt Drive, Oxford, OX3 7BN, UK;(4) Department of Molecular Biology, College of Life Sciences, Sejong University, Seoul, 143-747, South Korea;(5) Analysis Department, General Analysis Division, Biotoxtech Co., Ltd., Ochang Scientific Industrial Complex, 686-2 Yangcheong-ri, Ochang-eup, Chungcheongbuk-do, 363-883, South Korea;(6) Health Technology Research Centre (HTRC), National Institute of Advanced Industrial Science and Technology (AIST), 16-1 Onogawa, Tsukuba 305-8569, Ibaraki, Japan;(7) Research Laboratory for Biotechnology and Biochemistry (RLABB), GPO Box 8207, Kathmandu, Nepal |
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| Abstract: | A metabolomics based approach has been used to study the infection of the Hwacheong rice cultivar (Oryza sativa L. cv. Hwacheong) with compatible (KJ201) and incompatible (KJ401) strains of the rice blast fungal pathogen Magnaporthe grisea. The metabolic response of the rice plants to each strain was assessed 0, 6, 12, 24, 36, and 48 h post inoculation. Nuclear
Magnetic Resonance (NMR) spectroscopy and Gas and Liquid Chromatography Tandem Mass spectrometry (GC/LC-MS/MS) were used to
study both aqueous and organic phase metabolites, collectively resulting in the identification of 93 compounds. Clear metabolic
profiles were observed at each time point but there were no significant differences in the metabolic response elicited by
each pathogen strain until 24 h post inoculation. The largest change was found to be in alanine, which was ~30% (±9%) higher
in the leaves from the compatible, compared to the resistant, plants. Together with several other metabolites (malate, glutamine,
proline, cinnamate and an unknown sugar) alanine exhibited a good correlation between time of fungal penetration into the
leaf and the divergence of metabolite profiles in each interaction. The results indicate both that a wide range of metabolites
can be identified in rice leaves and that metabolomics has potential for the study of biochemical changes in plant-pathogen
interactions. |
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