Metabolomic analysis of the effect of priming agents on plant cells Msizi Mhlongo, Paul Steenkamp, Lizelle Piater, Edwin Madala and Ian Dubery Department of Biochemistry Auckland Park Kingsway Campus UJ Biochem. Dept. Research day – November 2014
Outline: Problem statement Background information: Plant immunity, Plant priming and Metabolomics Present study and objectives Hypothesis Materials and methods Results and interpretation Conclusion Acknowledgements
Problem statement: Food and nutrition library 2.2 2020 Global population ≤ 8 billion 90% in developing countries (Asia 1.5 billion and Africa 1.2 billion) “ enough food be produced sustainably to meet the food needs for every person in the world and everyone must have economic and physical access to sufficient food” intensive agriculture and horticulture for food production International food policy and institute; Liu et al., 2013
Background Information: Plant immunity Plant defence: Physical barrier and immune response Immune response is specific, self-tolerance and memorized Plant immune vs vertebrate immune
Background information: Plant Priming
Background Information: Metabolomics Newly emerging field of 'omics' research Comprehensive and simultaneous systematic determination of metabolite levels in the metabolome and their changes over time as a consequence of stimuli Metabolome Refers to the complete set of small-molecule metabolites in a living biological system Reflects dynamic changes to environmental stimulus
Background information: Metabolomics
Present Study and Objectives FLG = Bacterial flagellin LPS = Bacterial lipopolysaccharides CHIT = Fungal chitosan SA = Salicylic acid SA FLG LPS CHIT
Hypothesis: Metabolomics can be used to decipher the underlying secondary metabolic changes due to different chemical and biological priming agents.
Materials and methods: Sample preparation Plant cells (Tobacco) Treated with various elicitors / priming agents Extraction with MeOH Data acquisition and mining Chromatographic separation UHPLC-qTOF-MS/MS ( HSS T3 column (1.7 µm, 2.2 mm X 150 mm) Using chemometric methods i.e. Multivariate Data analysis (SIMCA-P13) : PCA and OPLS-DA and XCMS online Compound identification MS-based identification (accurate mass) From metabolomics databases (e.g. PantCyc, AraCyc, Dictionary of Natural Products, etc.)
Materials and methods: UPLC parameters Waters Acquity PDA: Sampling rate: 20 points/sec Range: 200 nm – 500 nm (resolution : 1.2 nm)
Materials and methods: MS parameters
Results and interpretation: Mhlongo et al., 2014, Mhlongo et al., 2014
Results and interpretation:
Results and interpretation: PHENYLPROPANOID DEFENCES Mhlongo et al., 2014
Conclusion Metabolomic approach was successful to study metabolites associated with primed state High definition MS in combination with multivariate statistics helped to identify biomarkers associated with priming Simca and XCMS adds to the data complementarity First study of CGAs as biomarkers for plant priming.
Acknowledgements: UJ and the NRF for financial support Dr Madala and Mr Tugizimana for sharing their knowledge Prof Dubery and Dr Piater for their guidance and manuscript preparation Prof Steenkamp for sample analysis and method development
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