Unfinished business from April 6 !

1. Hans Bohnert ERML 196 bohnerth@life.uiuc.edu 265-5475 333-5574 http://www.life.uiuc.edu/bohnert/ Metabolomics, spring 06 Unfinished business from April 6! Metabolite profiling = a static picture, a snapshot! Does it matter?* class April 11 *Fernie AR et al. (2005) Flux an important, but neglected, component of functional genomics. Curr. Opin. Plant Biology 8, 174. *Fell DA (2005) Enzymes, metabolites and fluxes. J. Exptl Botany 56, 267. Crossed out below what has been covered already! … one leftover case study and new material.

2. Metabolite profiling = a static picture, a snapshot! Does it matter? Static (steady-state) “knowledge units” - genome sequence, microarray profile, proteome composition How to understand cellular dynamics? Flux – where to measure, how and what is the most important “link”? Metabolites – intermediates in pathways to end-products (starch, cellulose, proteins, fats, lipids, second. products) Enzyme activity changes: steady-state of intermediates or flux? What is affected? yeast metabolomics (mutants) metabolites do change. Plants – metabolites +/- constant, flux altered photosynthesis – Calvin cycle – [NAD(P)H] – [ATP] – sucrose to starch [ADP-glucose pyrophosporylase] Steady state alone can be misleading pool size constant but coordinated increase in flux (activities altered)

3. Monitoring flux Rate of depletion of an initial substrate Rate of accumulation of an end product Isotope labeling of (a) metabolite(s) (complete or in certain atoms) radioactive or stable isotopes (2H, 3H, 13C, 14C, 15N, 18O, 32P, 35S) Can we infer flux from changes in intermediates? think allosteric effects of metabolites measuring regulated steps in a pathway is intermediates [conc] (consider the Mark Stitt lecture) Pathways branch (label lost) Different pathway(s) provide(s) intermediate (label diluted by unknown) Tracer addition may change the equilibrium of the system Plants: where, and how, to introduce the tracer Pool size – dilution of label Is end-product transported – loss of label Do we know the pathway, or assume we know, and are we right Need certainty about pathway structures – (MapMan, TAIR, KEGG) – do we?

4. More pitfalls and traps! Measuring (labeled) substrate consumption – insensitive, inaccurate Measuring end-product – stable, transported or metabolized (e.g., disappear in cell wall; does CO2 production and glycolysis) Branched pathways – do we know Linear relationship between product level and time (growth!) Experimental material – entire plant, organ (or part of organ), tissue slice, cells, organelles How “big” is the flux, the pathway – can we actually measure it? NMR (stable isot.), GC-MS, LC-MS - sensitivity and accuracy Positional information of tracer substrate modification may be important Long-term feeding expt, or pulse labeling, or pulse/chase expts

5. C3 C2 non-oxidative PPP Figure 1a Fixation Glycolysis Some green seeds (mostly oil seeds) have Rubisco – why? C1 lost Schwender et al. (2004) Rubisco without the Calvin cycle improves the carbon efficiency of developing green seeds. Nature 432, 779. (on web as: Shachar-Hill-Nature-2004) what a waste!

6. Figure 1b Expanded part 1a fluxes Vgapdh- TCA Vpdh - PDH Vrub - refix Vx - OPPP + other Label from Rubisco - always C1 in PGA

7. Figure 1 Metabolic transformation of sugars into fatty acids. a, Conversion of hexose phosphate to pentose phosphate through the non-oxidative steps of the pentose phosphate pathway and the subsequent formation of PGA by Rubisco bypasses the glycolytic enzymes glyceraldehyde-3-phosphate dehydrogenase and phosphoglycerate kinase while recycling half of the CO2 released by PDH. PGA is then further processed to pyruvate, acetyl-CoA and fatty acids. b, Part of a expanded to indicate carbon skeletons and to define relationships between V PDH (flux through PDH complex); V X (additional CO2 production by the OPPP, the TCA, and so on); V Rub (refixation by Rubisco). Metabolites: Ac-CoA, acetyl coenzyme-A; DHAP, dihydroxyacetone-3-phosphate; E4P, erythrose-4-phosphate; Fru-6P, fructose-6-phosphate; GAP, glyceraldehydes-3-phosphate; Glc-6P, glucose-6-phosphate; PGA, 3-phosphoglyceric acid; Pyr, pyruvate; R-5P, ribose-5-phosphate; Ru-1,5-P2, ribulose-1,5-bisphosphate; Ru-5P, ribulose-5-phosphate; S-7P, sedoheptulose-7-phosphate; Xu-5P, xylulose-5-phosphate. Enzymes: Aldo, fructose bisphosphate aldolase; Eno, 2-phosphoglycerate enolase; Xepi, xylulose-5-phosphate epimerase; FAS, fatty-acid synthase, PGM, phosphoglyceromutase; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GPI, phosphoglucose isomerase; Riso, ribose-5-phosphate isomerase; PDH, pyruvate dehydrogenase; PFK, phosphofructokinase; PK, pyruvate kinase, PGK, phosphoglycerate kinase; PRK, phosphoribulokinase; TA, transaldolase; TK, transketolase; TPI, triose phosphate isomerase.

8. Results of NMR and GC-MS analyses Remember – MS can “see” isotopomers! i.e., can observe which carbon is 13C.

9. Conclusions • Rubisco operates as part of a previously un-described metabolic route • between carbohydrate and oil (Fig. 1a). • Three stages: • conversion of hexose phosphates to ribulose-1,5-bisphosphate by • the non-oxidative reactions of the OPPP together with • phosphoribulokinase. • (2) conversion of ribulose-1,5-bisphosphate and CO2 (most produced • by PDH3) to PGA by Rubisco • (3) metabolism of PGA to pyruvate and then to fatty acids (Fig. 1a). • The net carbon stoichiometry of this conversion: • 5 hexose phosphate > 6 pentose phosphate > 12 acetyl-CoA + 6 CO2 • The conversion of the same amount of hexose phosphates by glycolysis: • 5 hexose phosphate >10 Acetyl-CoA + 10 CO2

10. Fate of labeled CO2 in fatty acid biosynthesis Scenarios and calculations calculate ratios from MS external feed feed by 13C1-ala feed by U-13C1-ala

12. A summary How about non-green seeds? (sunflower) 3 pathways glycolysis (not TCA) OPPP Calvin cycle C5 needs NAPH needs light + chloroplasts loss 1C Rubisco 15% of NADPH/ATP used in FA biosynthesis TPs C2 Only flux through Rubisco leads to increased efficiency by providing 40-50% of the PGA for FA-biosynthesis fatty acids

13. Summarizing statements • To arrive at the total carbon balance, all reactions leading to amino acids • had to be quantified (count isotopomers). • The contribution of carbon from OAA (which would be unlabeled in short- • time experiments) had to be tested. • The lack of re-utilization of pyruvate back to PEP had to be ruled out • 5 Glucose molecules (30 C) are transformed to 24 C-atoms in • acetyl-CoA with 6 CO2 being released. Therefore 80 % of the • carbon provided as carbohydrate is incorporated into fatty acid • by this novel route, compared to 66.7% by the conventional • glycolytic route. Bypass of GAPDH and PGA kinase requires • that ATP and reductant must be provided by light. Light does • indeed affect the ratio of carbon to oil. • Non-green plastids showed a ratio of carbon to oil of 1.2-16 (instead of ~3), • i.e., they operated through glycolysis and TCA cycle to generate • seed-deposited oil.

14. Flux mode analysis of seed metabolism. C-use efficiency and 4 characteristic fluxes shown relative to one mol C18:0

15. Where does the label go? • primary metabolism • potato tubers • wild type and transgenics • EI GC-MS (not CI) • U-13C/14C glucose feeding • pathway verification As much a science project as a test of the sensitivity of GC-MS Roessner-Tunali et al. (2004) Kinetics of labeling of organic and amino acids in potato tubers by gas chromatography-mass spectrometry following incubation in (13)C labeled isotopes. Plant J. 39, 668.

16. Objectives (Roessner-Tunali) • What tissue to use • Type of MS to use • Type of ionization to use • Do we need fractionation? • Can global analysis provide data that are • equivalent to single metabolite analysis in accuracy? • Can global analysis provide an accurate picture to • evaluate the exchange of carbon between pools? • Can we use GC-MS for phenotyping/fingerprinting and/or • GMO-typing?

17. Possible reaction rates to measure Three lines: Wt INV-2-30 ?? SP-29 ?? What is U-13C or U-14C glucose?

18. Sucrose Phosphorylase Sucrose phosphorylase is the enzyme responsible for the conversion of sucrose to fructose and glucose-1-phosphate.  This reaction is reversible.  The enzyme is reported to have broad specificity, and so it may be possible for many other substrates to replace fructose as the glucosyl acceptor.  Sucrose phosphorylase has the potential, therefore, to covert sucrose to a number of industrially useful glucoslyated derivatives, with the commercially important sugar fructose as the by-product                            

19. 2nd take home, and final essay, for the remaining and dedicated undergraduate students. • The paper uses an INV line (INV-2-30). Please collect information on the • function of this enzyme, its position and function in metabolism, and its • effect(s) on carbon distribution in plants. • (2) What is the reaction catalyzed by INV, which comes in several forms and • may be found in different compartments. Please explain. • (3) Describe reactions and enzymes that counteract the presence of INV. • (4) You should consult one reference each that report on the over- and • under-expression of INV in transgenic plants (mainly tobacco and • potato) and discuss the results. Please identify the references. • Please return your essay, preferable typed and short and concise, by the • first week of May (i.e., before study day).

20. Amounts over time (up to 12h)

21. tuber slices 5h incubation wash – GC-MS Hex-P determined: P-ester pool/ hex-P Mean +/- SE (n=3) bold - transgenic difference to wild type (P < 0.05)

22. Sucrose reduction enzymes - may increase amino acids increase sucrose re-synthesis reduce starch amounts (not synthesis) reduce hex-P pool reduce cell wall material (not synthesis)

23. nmol x g FW-1

24. predominant fluxes important – watch differences in rates of synthesis (Δf = >100)

27. [CH3-O-NH3]+ Cl- isotopomer counting absolute amounts by standard dilutions

28. results comparable to • conventional methods • measuring individual • metabolites; faster & at • least as accurate • starch turnover in tubes • sucrose low in transgenics • leads to increased carbon • partitioning • identify uni-directional flux • in patterns • distribution of a single • isotopically labeled • precursor • Problems? • constant rate assumed • assume one cell type • no further use of products • accuracy of side reactions • maybe stable isotopes

29. A different experiment Arabidopsis ecotypes in high CO2 in FACE rings Attempts at correlating gene expression and metabolite concentrations

30. Transcripts -0.6 0 0.6 (log2 - fold change) Metabolites Galactinol 2.4.1.123 Galactose Raffinose 2.4.1.82 Starch Sucrose 3.2.1.1 Neutral Invertase 3.2.1.26 Invertase, cell wall 3.2.1.2 Invertase, vacuole 2.4.1.25 MEX1 Cysteine Fructose Maltose Glucose Melibiose Cvi 27 Col 21 Col 27 Cvi 21 DEP2 4.2.99.8 5.3.1.9 Tryptophan isoforms 2.7.1.1 2.3.1.30 4.1.1.48 1.2.1.12 5.3.1.24 2.7.2.3 2.1.2.1 3.1.3.3 2.6.1.52 1.1.1.95 2.4.2.18 Serine Glycine 3-Phosphoglycerate 5.4.2.1 4.1.3.27 2.6.1.42 1.1.1.85 4.2.1.33 4.1.3.12 4.2.1.11 Leucine Phenylalanine 4.1.1.49 PEP 4.2.3.4 2.7.1.40 4.2.1.10 4.1.1.31 2.6.1.5 2.6.1.42 Valine 1.1.1.86 2.2.1.6 Pyruvate 2.7.1.71 Oxaloacetate Asparagine 4.2.1.51 2.5.1.19 Acetyl-CoA 4.2.3.5 6.3.5.4 5.4.99.5 Chorismate Prephenate 4. 1.3.8 Aspartate 2.6.1.1 Oxaloacetate 2.3.3.1 1.3.1.12 Citrate 2.7.2.4 Tyrosine 1.1.99.16 1.2.1.11 4.2.1.3 Aspartate-4-semialdehyde Malate Isocitrate 1.1.1.3 Proline 4.2.1.52 2.7.1.39 1.3.1.26 1.1.1.42 Homoserine-4-phosphate 4.2.1.2 At5g14800 At5g62530 2.6.1.17 1.4.7.1 4.2.99 alpha-Ketoglutarate Glutamate Fumarate 3.5.1.18 4.4.1.8 4.2.3.1 AT5G65750 5.1.1.7 6.3.1.2 Threonine 1.3.5.1 6.2.1.4 2.1.1.14 Succinate Glutamine Lysine 2.2.1.6 2.1.1.10 Methionine Figure 7. 1.1.1.86 2.6.1.42 Isoleucine

31. Adding an introduction to the next topic – single cell analysis, using NMR a a major tool. Hoping to get here!

32. Fan, Bandura, Higashi & Lane (2005) Metabolomics 1, 325-339 Metabolomics-edited transcriptomics analysis of Se anticancer action in human lung cancer cells (META) Transcriptomic analysis is an essential tool for systems biology but it has been stymied by a lack of global understanding of genomic functions, resulting in the inability to link functionally disparate gene expression events. Using the anticancer agent selenite and human lung cancer A549 cells as a model system, we demonstrate that these difficulties can be overcome by a progressive approach which harnesses the emerging power of metabolomics for transcriptomic analysis. We have named the approach Metabolomics-edited transcriptomic analysis (META). The main analytical engine was 13C isotopomer profiling using a combination of multi-nuclear 2-D NMR and GC-MS techniques. Using 13C-glucose as a tracer, multiple disruptions to the central metabolic network in A549 cells induced by selenite were defined. META was then achieved by coupling the metabolic dysfunctions to altered gene expression profiles to: (1) provide new insights into the regulatory network underlying the metabolic dysfunctions; (2) enable the assembly of disparate gene expression events into functional pathways that was not feasible by transcriptomic analysis alone. This was illustrated in particular by the connection of mitochondrial dysfunctions to perturbed lipid metabolism via the AMP-AMPK pathway. Thus, META generated both extensive and highly specific working hypotheses for further validation, thereby accelerating the resolution of complex biological problems such as the anticancer mechanism of selenite. Key words (3-6) two-dimensional NMR; GC-tandem MS; 13C isotopomer profiling; selenite; lung adenocarcinoma A549 cells. Abbreviations 1H–13C HMBC: 1H–13C heteronuclear multiple bond correlation spectroscopy; 1H–13C HSQC: 1H–13C heteronuclear single quantum coherence spectroscopy; 2-D 1H TOCSY: two dimensional 1H total correlation spectroscopy; [U)13C]-glucose: uniformly 13C-labeled glucose; MSn: mass spectrometry to the nth dimension; MTBSTFA: N-methyl-N-[tert-butyldimethylsilyl]trifluoroacetamide; P-choline or PC: phosphorylcholine; PDA: photodiode array; TCA: trichloroacetic acid.

33. Knowledge: Se is an essential atom, high amounts affect (cancer) growth, Se in proteins is related to ROS homeostasis (somehow) Experiment: The addition of Se to lung cells affects growth – what is the basis? Use genomics platforms (transcript analysis), GC-MS & esp. NMR Hypothesis: gene expression is altered, and metabolite analysis can be correlated with transcript changes – can it, is the question! Approaches Microscopy, NMR, GC-MS, transcripts

34. Se interferes with the cytoskeleton and mitochondrial activity Selenite effects proliferating cells; Selenite-rich diets may have anti-cancer applications.

35. Se leads to degradation of DNA TUNEL assay?