1. Metabolomics, spring 06 Hans Bohnert ERML 196 bohnerth@life.uiuc.edu 265-5475 333-5574 http://www.life.uiuc.edu/bohnert/ class April 27 Metabolomics Essentiality Today’s discussion topic Schauer N, Zamir D, Fernie, AR (2005) Metabolic profiling of leaves and fruit of wild species tomato: a survey of the Solanum lycopersicum complex. J Exp Bot. 56: 297-307. Schauer N, Semel Y Roessner Um Gur A, Balbo I, Carrari F, Pleban T, Perez-Melis A, Bruedigam C, Kopka J, Willmitzer L, Zamir D, Fernie AR (2006) Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement. Nat Biotechnol. 24: 447-454.

2. From single genes to multi-gene traits 1.‚Systems biology‘ 2.Use of natural diversity Mark Stitt lecture

3. From ‚biased‘ inhibition of candidate gene function to multisite ‚unbiased‘ analysis of change-of-function alleles: Natural diversity is a central resource for the analysis of regulation and gene function Alleles are the key to breeding, fitness‘ and evolution

4. Leaf (A) and fruit (B) phenotypes of the S. lycopersicum complex. (I)S. chmielewskii, (II) S. habrochaites, (III) S. lycopersicum, (IV) S. pimpinellifolium, (V) S. neorickii, and (VI) S. pennellii.

5. Protein and starch levels of fruits of the S. lycopersicum complex. Six independent fruit samples were measured. Fruits were harvested 45 DAF 6 h into the light. Protein values (dark bars) are presented as mg protein/g FW. Starch values (grey bars) are presented as umol hexose/g FW.

6. Metabolite composition in leaves from species of the S. lycopersicum complex Single leaf samples of six plants were measured. Leaves were harvested 6 h into the light period from fully-expanded mature leaves of 6-week-old plants. Values are presented as the mean 6SE of six independent biological determinations. Those metabolites that are significantly different to S. lycopersicum are in bold type. Metabolites in italics represent relative differences with respect to S. lycopersicum, nd indicates metabolites were not detected. A part of a very long table Repeat at different ages of fruit development Crosses Mapping Grow under field conditions Repeat in several seasons

7. Cross between the modern tomato cultivar M82 and a related wild species ca. 100 Introgression Lines Each contains a small part of the genome from the ‚donor‘ (here, the wild species) in the background of the otherwise unaltered genome from the acceptor (elite cultivar) Dani Zamir, HU Jerusalem Lyco Lycopersicon pennellii x Lycopersicon esculentum Wild relative Elite cultivar

8. Dani Zamir HU Jerusalem Chromosome 9 GP39 TG254 TG18 TG9 CT143 TG223A CT32 CP44 CD32A,CT215A,CT284B TG568 TG79 TG207,CT17,TG486,TG589,TG640,Tm2a CT208.,CT235,TG79,TG415,CT17,CD3,TG591B TG390 TG551 TG404 TG186,CT236 TG429 TG348,TG347 TG248 GP94B CT74,CT177 GP129 CT198 CT218 TG421 TG424 GP101, CHS4 CT112A TG328,GP41,TG591A CT71 CT220 8.9 5.5 3.6 4.0 6.9 10.1 4.7 1.1 5.3 3.3 0.0 4.0 2.8 1.9 2.0 1.0 3.0 1.0 2.0 1.7 2.2 3.9 3.8 5.5 5.9 4.5 2.2 1.6 2.7 (CT225A) (CT283A) (TG225,TG10) (TG3A,CD8,CT215B,CT215C) (CT279,TG35,CT183,TG558,TG409) (TG144) (Est-2) (GP123A) (TG8, Nr) (CT96) (CT210) PC6 CT208 TG101,TG291 GP125A 9-A 9-B 9-C 9-D 9-E 9-F 9-G 9-H 9-I 9-J 9-K IL9-1 IL9-1-2 IL9-1-3 IL9-3 IL9-3-2 IL9-2 IL9-2-5 +CD32A +TG9 -CT143 -GP39 -TG591B +TG254 +TG186 -TG348 -CT32 -GP129 +CT198 +GP101 -CT112A IL9-2-6 -CT208 +TG390 -TG551 +TG404 +GP263 -CT143 +TG223A +GP39 +CT220 IL9-3-1 Metabolite profiles for each introgression line Field grown plants

9. Massively parallel identification of QTL’s for metabolite levels in tomato introgression lines Metabolites, 1.......n Introgression lines GC-MS Metabolite -profiling 351 QTL‘s Ally Fernie, MPI-MP Dani Zamir HU Jerusalem Old Hypothesis: Cell wall invertase hydrolyses sucrose and increases import into growing organs 500 mM sucrose INV GlcFru Example: high sugars. This is an important yield trait (BRIX)

10. Massively parallel identification of QTL’s for metabolite levels in tomato introgression lines …. showed that a mutation leading to changed kinetic parameters of LIN5 - encoding Invertase Fridman et al., Science 2004 Metabolites, 1.......n Introgression lines GC-MS Metabolite- profiling 0,1 0,2 0,3 -0,2-0,100,10,2 1/mM sucrose 1/activity M82 9-2-5.….... Lee _ _ _ Invertase kinetics 351 QTL‘s GP39 TG254 TG18 TG9 CT143 TG223A CT32 CP44 TG568 TG79 TG390 TG551 TG404 TG186,CT236 TG429 TG348,TG347 TG248 GP94B CT74,CT177 GP129 CT198 CT218 TG421 TG424 GP101, CHS4 CT112A CT71 CT220 8.9 5.5 3.6 4.0 6.9 10.1 4.7 1.1 5.3 3.3 0.0 4.0 2.8 1.9 2.0 1.0 3.0 1.0 2.0 1.7 2.2 3.9 3.8 5.5 5.9 4.5 2.2 1.6 2.7 (CT225A) (CT283A) (TG225,TG10) (TG144) (Est-2) (GP123A) (TG8, Nr) (CT96) (CT210) PC6 CT208 TG101,TG291 GP125A 9-A 9-B 9-C 9-D 9-E 9-F 9-G 9-H 9-I 9-J 9-K IL9-1 IL9-1-2 IL9-1-3 IL9-3 IL9-3-2 IL9-2 IL9-2-5 +CD32A +TG9 -CT143 -GP39 -TG591B +TG254 +TG186 -TG348 -CT32 -GP129 +CT198 +GP101 -CT112A IL9-2-6 -CT208 +TG390 -TG551 +TG404 +GP263 -CT143 +TG223A +GP39 +CT220 IL9-3-1 Fine mapping LIN5 is the molecular basis of a sugar content ( BRIX ) QTL Ally Fernie, MPI-MP Dani Zamir HU Jerusalem

11. Use of diversity in crop plant populations and wild populations for analysis of gene function: A ‘strategic advantage’ for plant science Long experience of breeders in creating and phenotyping populations Large resources already available in the reference species Arabidopsis A wide range of resources are becoming available in several crops Their production will be aided by developments in genotyping Their production will be aided by information about genome sequences Analysis benefit from the analytics technologies developed in plant genomics will depend on and be driven by development of bioinformatics capabilities A tight link from basic science into the application realm

12. Overlay heat map of the metabolite profiles and other traits of the ILs in comparison to the parental control (S. lycopersicum). Large sections of each map are white or pale in color, reflecting the fact that many of the chromosomal segment substitutions do not have a great effect on the amount of every metabolite. Regions of red or blue indicate that the metabolite content is increased or decreased, respectively, after the introgression of S. pennellii segments. Very dark coloring indicates that a large change in metabolite content was conserved across harvests; purple indicates that, relative to S. lycopersicum, the metabolite was increased in one harvest but decreased in the other. For each harvest, gas chromatography/mass spectrometry was used to quantify 74 metabolites, including amino acids, organic acids, fatty acids, sugars, sugar alcohols and vitamins.

13. metabolite analysis What are we looking at? Overlay heat map of the metabolite (total 74) profiles and other traits of the ILs white – no effect red (up) & blue (down) in presence of L. pen. segment & purple (up in one, down in other harvest) dark – two seasons light – one season

14. Cartography of a network

15. Morphology associated & independent metabolites

16. Fine evaluation of genomic regions containing morphologically associated and independent metabolite QTLs.

17. Robustness of cartography algorithm each pair of nodes (20 partitions in network) how often in ILs classified the same always together – red never together – dark blue

21. GC–MS libraries for the rapid identification of metabolites in complex biological samples BRENDA – relational database for enzymes and their metabolites Schomburg et al. (2004) Nuc. Acids Res. 32, database issue D431-433. MIPS – protein db Mewes et al. (2004) Nuc Acids Res. 32 database issue D41-44. KEGG TAIR Golm Metabolome Database Oliver Fiehn lab, UC Davis NIST02/AMDIS www.chemdata.nist.gov/mass-spc/amdis www.chemdata.nist.gov/mass-spc/index.html Downloadable files from MPI Golm MSRI library downloadble files (for different Technologies GC/LC/TOF: www.csbdb.mpimp-golm.mpg.de/gmd.html merge with NIST02 or own libraries. Corynebacterium polar extract – >600 peaks, >50% unknown Spike extracts with known set of metabolites (that are NOT in the organism you study). Calibrate reference data sets Needed are dbs that allow data transfer between instruments and labs

22. Test cases - Spike animal/bacterial samples with plant-specific metabolites (kaempferol, phytosterol, a-tocopherol) Golm libraries are (a) manually analyzed, assigned to ID libraries (b) automated deconvolution libraries, assigned NS libraries Identify deconvolution errors: multiple mass spectra for single components accidental deconvolutions based on random fluctuations background noise (spill-over) chimeric mass spectra (mixed mass positions) Provide data on experiment, samples, sources of reference, investigator Provide both mass spectrum and retention time Q_MSRI_ID library with 1166 annotated, identified MSTs for 574 non-redundant compounds but only 306 are unambiguously identified Also a non-supervised collection of >30,000 MSTs from a range of plant species, models and crops, and different plant organs Schauer et al. (2005) FEBS Lett 579, 1332-1337.

24. How accurate are unsupervised analyses? Chlorogenic acid – typical 2 nd metabolite in Solanaceae Caffeic acid – precursor for chlorogenic acid Quinic acid – ubiquitous in plants

25. supplemental slides documenting variability of field data.

26. Supplem. Figure 1 (next slide) Heat maps of the metabolite profiles of the introgression lines in comparison to that of the parental control (S. lycopersicum) from the individual data sets of A) 2001 and B) 2003. Large sections of each map are white or pale in coloor reflecting the fact that many of the chromosomal segment substitutions do not have a large affect on the level of every metabolite. Regions of red or blue indicate that the metabolite content is either increased or decreased respectively following the introgression of S. pennellii segments. A total of 74 metabolites were quantified by gas chromatography-mass spectrometry for each harvest, including amino acids, organic acids, fatty acids, sugars, sugar alcohols and vitamins.

27. (a) Data set 2001 Variability

28. (b) Data set 2003 Variability