METABOLITE PROFILING OF CROP SPECIES UNDER LOW WATER REGIMES. Alisdair Fernie

Transcription

1 METABOLITE PROFILING OF CROP SPECIES UNDER LOW WATER REGIMES Alisdair Fernie

2 TALK OUTLINE Overview of Metabolomics Why study metabolite levels? Brief overview of GC-MS Examples of profiling of drought stressed maize Evaluation of greenhouse grown drought stressed plants -tissue type studies -genotype and tissue type studies..ultimate aim is to take this toward marker assisted breeding

3 Why study metabolites? Metabolite concentrations are highly variable within cellular systems Reported metabolite concentrations vary between milli- and fento- molar. This dynamic range has evolved in cells largely due to space constraints, since if ALL metabolites were kept at high concentrations there is not enough room in the cell. Also at high concentrations metabolites react in the absence of enzymic catalysis Similarly turnover time of metabolites can be in the order of fractions of a second for example high energy compounds such as ATP, or over far longer timescales in the case of polymeric compounds

4 Why study metabolites? In a genomics context -As part of a systems-orientated characterisation of metabolism -Integration with transcript and protein profiling -Identification of regulated key sites in networks -Investigation of gene function and equally importantly due to its relative cheapness information coming from such studies can be used as a tool in experimental design

5 GC-MS Quadrupole-MS Time-of-flight (TOF)-MS

6 Why GC-MS GC-MS, argueably was the first technology used for metabolite profiling. The use of a mass spectrometer as the detector in gas chromatography was developed during the 1950s by Roland Gohlke and Fred McLafferty. In the 70s Sauter of BASF published a proof of concept paper wherein he showed that GC-MS evaluation of plants treated with herbicides of different mode of action. He showed they also displayed different chemotypes i.e. had a different pattern in the levels of the 50 or so metabolites he quantified. The term metabolomics was introduced in a review paper by Steven Oliver in The earliest reseach paper claiming to perform metabolomics was published by the Group of Fan in Australia who used 2D chromatography, however, a more realistic birth would probably be the publication of Fiehn in 1999 wherein upwards of 300 metabolites were reported

7 The challenge Complexity

8 Complexity

9 Metabolomics is a branch of Analytical Biochemistry Analytical Biochemistry is defined as the analysis of material samples In order to gain understanfing of their biochemical composition and Structure. Qualitative analysis seeks the presence of a given compound in a sample Quantative analysis seeks the amount of a given compound in a sample Separation types: (a) Physiochemical (electrical charge, lipophilicity, volatility, size..) (b) Biochemical properties (binding, metabolism, precipitation ) Detection types: (a) Physiochemical (mass, vibration, rotation, magnetic resonance, radiation absorption/fluorescence/scattering) (b) Biochemical properties (antibody, protein folding/binding/fret)

10 back to GC-MS Whilst this only allows detection of only metabolites (which is only a small percentage of the metabolome), this technique is incredibly robost and has certainly produced more metabolite data than any other technique in the last few years. GC-MS is a fantastic way to evaluate small low molecular weight metabolites ( Da). Therefore, it provides excellant coverage of primary metabolites, being highly suitable for the quantification of sugars, sugar alcohols, organic acids, amino acids and select secondary metabolites and vitamins. MANY OF WHICH HAVE RELEVANCE WITH REGARD TO DROUGHT STRESS It is highly robust, like all metabolomics platforms is genome independent, and furthermore is largely independent of ion-supression effects

11 THE BIOLOGICAL QUESTION Our Biological approach AIM: Influence of abiotic stresses like drought, heat and a combination of both on different physiological parameters and at the level of the metabolome APPROACH: Testing of different genotypes and comparison of stress adaptation and tolerance

12 GENOTYPES USED IN THE EXPERIMENT Genotypes evaluated Pedigree Origin Tolerance to drought NPE1 CML-486-B-B / CML-312 SR Mexico, subtropical Moderate CML311/MBR C2 Bc F41-2-BBBBB-B-B-B-B / CML-312 Mexico,insect NPE2 SR resistance Susceptible NPE3 [GQL5/[GQL5/[MSRXPOOL9]C1F (OSU23i)-5-3- X-X-1-BB]F2-4sx] B*5-B-B / CML-312 SR Zimbabwe, subtropical Tolerant NPE4 DTPWC9-F B-B-B-B-B / CML-312 SR Mexico, subtropical Tolerant CML311/MBR C3 Bc F B-B-B-B-B-B-B / CML- Mexico,insect NPE5 312 SR resistance Moderate NPE6 La Posta Seq C7-F B-B-B-B / CML-312 SR Mexico, tropical Tolerant

13 WORKING PROCEDURE Preliminary experiments Metabolic profiling of different maize organs under well watered condition and drought stress condition as a preexperiment to find the best contrasting profile Chosen organs: silks, ear, leaf sheet, leaf blade, husk Find the best organ for more detailed analyses...

14 RESULTS PLANT HEIGHT Plant height 180 A 350 B plant height in cm plant height in cm NPE1 NPE2 NPE3 NPE4 NPE5 NPE6 0 NPE1 NPE2 NPE3 NPE4 NPE5 NPE6 C D

15 RESULTS LEAF TEMPERATURE Leaf temperature 35 A 50 B leaf temperature in C leaf temperature in C NPE1 NPE2 NPE3 NPE4 NPE5 NPE6 0 NPE1 NPE2 NPE3 NPE4 NPE5 NPE6 C D

16 Transpiration RESULTS TRANSPIRATION 180 A 140 B Transpiration in mmol(m 2 s) Transpiration in mmol/(m 2 s) NPE1 NPE2 NPE3 NPE4 NPE5 NPE6 NPE1 NPE2 NPE3 NPE4 NPE5 NPE6 C D

17 RESULTS EAR HEIGHT AND CHLOROPHYLL CONTENT 200 Ear height A 70 B Chlorophyll content ear height in cm % chlorophyll NPE1 NPE2 NPE3 NPE4 NPE5 NPE6 0 NPE1 NPE2 NPE3 NPE4 NPE5 NPE6 C D D

18 Metabolite levels RESULTS HEAT MAP OF METABOLITES ABUNDANCE Blade Ear Sheet Husk Silks Erythritol Serine, O-acetyl- Itaconic acid Urea Homoserine Alanine, beta- Pyrrole-2-carboxylic acid Proline Ornithine Pyroglutamic acid Lactulose Benzoic acid, 4-hydroxy- Caffeic acid, cis- Sucrose Ribose Benzoic acid, Malonic acid Glucopyranose Androst-4-en-3,17-dione, 19-hydroxy- Glutaric acid, 2-oxo- Idose Sorbose Glucose Aconitic acid, trans- Fructose-6-phosphate Jasmonic acid methyl ester, 2-trans- Fucose 1,3-Dihydroxyaceton Pyridine, 3-hydroxy- Androst-5-en-17-one, 3beta-hydroxy- Fumaric acid, 2-methyl- Hydroquinone Fumaric acid Guanosine myo-inositol-1-phosphate Octadecenoic acid, 6-(Z)- Inosine, 2'-deoxy- Cellobiose, D- Palatinose Cytidine-2',3'-cyclic-monophosphate Lactobionic acid Quinic acid Inositol, myo- Glycine Leucrose Tyrosine Serine Ferulic acid, cis- Glycerol Isoleucine Glyceric acid-2-phosphate Gulose Gluconic acid, 2-oxo- Talose Lumichrome Benzoic acid, 3-hydroxy- Glutamic acid Succinic acid Tyramine Tartronic acid Threonic acid Phenylalanine Phosphoric acid Putrescine Spermidine Butanoic acid, 2-amino- Hexadecanoic acid, 3-hydroxy- Glyceric acid Glucosamine, N-acetyl- Glycerol-3-phosphate Quinic acid, 5-caffeoyl-, trans- Rhamnose Ascorbic acid Dehydroascorbic acid dimer Squalene, all-trans- Octadecanoic acid beta-d-fructofuranosyl-(2,1)-beta-d-fructofuranose Butanoic acid, 4-amino- Turanose Alanine Glutamine Adenine Vanillic acid Serine, O-phospho- Quinic acid, 3-caffeoyl-, cis- Erythrose-4-phosphate Galactopyranoside, 1-O-methyl-, alpha- Quinic acid, 3-caffeoyl-, trans- Histidine Methionine Glucose-6-phosphate Propane, 1,3-diamino- Aspartic acid Valine Asparagine Xylose Nicotinamide Caffeic acid, trans- 2-Piperidinecarboxylic acid Kestose, 1- Adenosine, 5-methylthio- Nicotinic acid Tryptophan Cinnamic acid, 4-hydroxy-, trans- Aconitic acid, cis- Cholesterol-5beta,6beta-epoxide Lactic acid Benzylalcohol Isobutanoic acid, 3-amino- Propylamine-2,3-diol Hexadecanoic acid Quinic acid, 4-caffeoyl-, trans- Pyruvic acid Galactinol Dihydrosphingosine Ferulic acid, trans heat map represents the mean abundance of metabolites in different tissues blade shows the most contrasting metabolic profile compared to other tissues in ear tissue increased levels of mostly some amino acids and especially nicotinamide

19 PCA separation RESULTS PRINCIPAL COMPONENT ANALYSIS PC2 (13.82%) 0 PC2 (12.36%) Blade Ear Husk Sheet Silks -6 Blade Ear Husk Sheet Silks PC1 (47.37%) PC1 (24.75%) Principal components analysis (PCA) of metabolite profiles in different tissues. PCA shows clearly separation of metabolite composition within different tissues. Blade tissue can be pointed out here as the tissue what is separating the most from the other tissues. PCA of blade tissue metabolic profile is shown. PC1 clearly separates well watered and drought stress condition. PC2 separates different hybrids.

20 this finding is mirrored in individual metabolites RESULTS BOX PLOTS OF SELECTED METABOLITE LEVELS

21 Full data set RESULTS HEAT MAP NORMALIZED TO WELL WATERED CONDITION Silks NPE6 Silks NPE4 Silks NPE3 Silks NPE5 Silks NPE1 Silks NPE2 Sheet NPE6 Sheet NPE4 Sheet NPE3 Sheet NPE5 Sheet NPE1 Sheet NPE2 Husk NPE6 Husk NPE4 Husk NPE3 Husk NPE5 Husk NPE1 Husk NPE2 Ear NPE6 Ear NPE4 Ear NPE3 Ear NPE5 Ear NPE1 Ear NPE2 Blade NPE6 Blade NPE4 Blade NPE3 Blade NPE5 Blade NPE1 Blade NPE2 Adenosine, 5-methylthioOrnithine Lactulose beta-d-fructofuranosyl-(2,1)-beta-d-fructofuranose Ferulic acid, trans2-piperidinecarboxylic acid Quinic acid, 3-caffeoyl-, transquinic acid, 3-caffeoyl-, cispyruvic acid Aconitic acid, transidose Aspartic acid Succinic acid Glucose Glutaric acid, 2-oxoKestose, 1Ascorbic acid Glyceric acid Cholesterol-5beta,6beta-epoxide Hexadecanoic acid, 3-hydroxyThreonic acid Pyridine, 3-hydroxyMalonic acid Vanillic acid Octadecanoic acid Glucosamine, N-acetylGlutamic acid Nicotinic acid Spermidine Cellobiose, DAlanine Gluconic acid, 2-oxoGulose Glyceric acid-2-phosphate Inosine, 2'-deoxyHydroquinone Androst-4-en-3,17-dione, 19-hydroxyTuranose Quinic acid, 5-caffeoyl-, transquinic acid, 4-caffeoyl-, transcaffeic acid, transcaffeic acid, ciscytidine-2',3'-cyclic-monophosphate Palatinose Leucrose Lactobionic acid Glucopyranose Androst-5-en-17-one, 3beta-hydroxyAconitic acid, cissqualene, all-transtalose Pyrrole-2-carboxylic acid Urea Galactinol Proline Tryptophan Histidine Phenylalanine Adenine Butanoic acid, 2-aminoMethionine Alanine, betaputrescine Glycine Tyrosine Valine Isoleucine Benzoic acid, 3-hydroxyHomoserine Tartronic acid Guanosine Asparagine Glutamine Fumaric acid, 2-methylItaconic acid 1,3-Dihydroxyaceton Benzoic acid, Benzoic acid, 4-hydroxyHexadecanoic acid Fumaric acid Lumichrome Sucrose Isobutanoic acid, 3-aminoSorbose Galactopyranoside, 1-O-methyl-, alpharibose Fucose Propylamine-2,3-diol Benzylalcohol Cinnamic acid, 4-hydroxy-, translactic acid myo-inositol-1-phosphate Xylose Erythritol Serine, O-acetylRhamnose Fructose-6-phosphate Octadecenoic acid, 6-(Z)Ferulic acid, cisdehydroascorbic acid dimer Pyroglutamic acid Serine Jasmonic acid methyl ester, 2-transDihydrosphingosine Butanoic acid, 4-aminoTyramine Phosphoric acid Glycerol Erythrose-4-phosphate Serine, O-phosphoGlucose-6-phosphate general increase in metabolite levels effect is very dominant in leaf blade in opposite to this, lower levels of talose, pyrivic acid and quinic acid in blade

22 Correlation analysis of metabolites with RESULTS CORRELATION physiological ANALYSIS traits A Leaf temperature_d7 B Transpiration_D1 C Transpiration_D7

23 SUMMARY Summary drought stress has a large impact on the metabolome in plants most differences in metabolite levels could be observed in leaf sheet and leaf blade metabolic pattern of all tested traits showed a large number of metabolites which were significantly changed due to the applied treatment both susceptible and resistant genotypes display similar metabolic changes including increases in known stress response metabolites and more poorly characterized metabolites such as the branched chain amino acids we could verify a strong correlation between phenotypical changes in maize during drought stress response or/and adaptation and changes in plant metabolism Future work needs to establish mechanistic understanding of these interactions

24 Acknowledgements This work was supported in part by the Precision phenotyping for improving drought-stress tolerant maize in southern Asia and eastern Africa project, funded by the Bundesministerium für Wirtschaftliche Zusammenarbeit und Entwicklung (BMZ), Germany, and in part by the OPTIMAS project of the Bundesministerium für Bildung und Forschung (BMBF) Germany.