Metabolomics, spring 06
Unfinished business from April 6!
Hans Bohnert
ERML 196
bohnerth@life.uiuc.edu
265-5475
333-5574
http://www.life.uiuc.edu/bohnert/
class April 11
Metabolite profiling =
a static picture, a snapshot!
Does it matter?*
*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.
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)
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?
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
Glycolysis
non-oxidative PPP
Figure 1a
Fixation
C3
C1 lost
Some green seeds
(mostly oil seeds)
have Rubisco – why?
C2
what a waste!
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)
Figure 1b
Expanded part 1a
fluxes
Vgapdh - TCA
Vpdh
- PDH
Vrub
- refix
Vx
- OPPP
+ other
Label from Rubisco always C1 in PGA
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 coenzymeA; DHAP, dihydroxyacetone-3-phosphate; E4P, erythrose-4-phosphate; Fru-6P,
fructose-6-phosphate; GAP, glyceraldehydes-3-phosphate; Glc-6P, glucose-6phosphate; PGA, 3-phosphoglyceric acid; Pyr, pyruvate; R-5P, ribose-5-phosphate; Ru1,5-P2, ribulose-1,5-bisphosphate; Ru-5P, ribulose-5-phosphate; S-7P, sedoheptulose7-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-3phosphate 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.
Results of NMR and GC-MS analyses
Remember – MS can “see” isotopomers!
i.e., can observe which carbon is 13C.
Conclusions
Rubisco operates as part of a previously un-described metabolic route
between carbohydrate and oil (Fig. 1a).
Three stages:
(1) 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
Fate of labeled CO2 in fatty acid biosynthesis
Scenarios and calculations
A
O
Triose-P
Ru
C
bis
Ru1,5-P2
B
O
Triose-P
Triose-P
1
1
1
1
1
Pyruvate
[1-13C]Alanine
Val
1
Pyruvate
Val
PDH
PDH
CO2
Ac-CoA
external
feed
Val(1-5) Val
(2-5)
1
Val
Phe
PK
1
1
1
[U-13C3]Alanine
Pyruvate
Val
PDH
CO2
External 13CO2
PHE(2-9)
PHE(1-9)
PEP
Phe
1
PHE(1-2)
1
1
OAA
PK
Phe
1
Phe
PGA
PEP
PK
Ru1,5-P2
PGM, ENO
1
PEP
Ru
C
bis
1
PGM, ENO
1
D
O
PGA
PGA
PGM, ENO
Alanine
Ru1,5-P2
C
1
1
1
Ru
C
bis
Ac-CoA
feed by
13C1-ala
CO2
Ac-CoA
feed by
U-13C1-ala
calculate
ratios
from MS
A summary
How about non-green seeds? (sunflower)
3 pathways
glycolysis (not TCA)
OPPP
C5
needs NAPH
loss 1C
Rubisco
TPs
C2
fatty acids
Calvin cycle
needs light
+
chloroplasts
15% of
NADPH/ATP
used in FA
biosynthesis
Only flux through Rubisco
leads to increased efficiency
by providing 40-50% of
the PGA for FA-biosynthesis
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 shorttime 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.
Flux mode analysis of seed metabolism.
3
PP
2
HP
PP
HP
PPP
TP
5
Pyruvate
CO2
TP
PGA
CO2
PP
CO2
TP
CO2
CO2
PPP
TP
C
Bis
Ru
PGA
PP
HP
PPP
C
Bis
Ru
PGA
PP
HP
PPP
TP
CO2
6
Hexose
CO2
HP
PPP
4
PGA
Hexose
D. Autotrophy
CO2
CO2
O
Hexose
CO2
1
C. Non-oxidative
bypass
O
Hexose
B. Oxidative
bypass
O
A. Glycolysis
C
Bis
Ru
PGA
CO2
Pyruvate
Pyruvate
Pyruvate
Pyruvate
Ac-CoA
Ac-CoA
Ac-CoA
Ac-CoA
C18:0
C18:0
C18:0
C18:0
7
Ac-CoA
8
C18:0
Carbon in C18:0 /
Carbon uptake
(glucose)
66.7 %
66.7 %
80 %
∞
ATP balance
+1
-8
-8
-71
NADPH balance
+2
+2
-7
-52
1
9
7
3
Number of modes
of this type
C-use efficiency and 4 characteristic fluxes shown relative to one mol C18:0
CO2
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.
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?
Possible reaction rates
to measure
Three lines:
Wt
INV-2-30 ??
SP-29 ??
What is U-13C or U-14C glucose?
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
2nd take home, and final essay, for the remaining
and dedicated undergraduate students.
(1) 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).
Amounts over time (up to 12h)
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)
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)
nmol x g FW-1
predominant
fluxes
important – watch differences in rates of synthesis (Δf = >100)
[CH3-O-NH3]+ Cl-
isotopomer counting
absolute amounts by
standard dilutions
• 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
A different experiment
Arabidopsis ecotypes in high CO2 in FACE rings
Attempts at correlating
gene expression and
metabolite concentrations
Transcripts
-0.6
0
2.4.1.123
Galactose
0.6
Galactinol
Raffinose
2.4.1.82
Starch
(log2 - fold change)
Sucrose
3.2.1.1
Metabolites
Neutral
Invertase
Cvi 27
Cvi 21
Col 27
Col 21
3.2.1.2
2.4.1.25
MEX1
Cysteine
Maltose
3.2.1.26
Invertase, cell wall
Invertase, vacuole
Fructose
Glucose
DEP2
4.2.99.8
Melibiose
5.3.1.9
Tryptophan
isoforms
2.7.1.1
At4g02610
At4g27070
4.1.1.48
2.3.1.30
1.2.1.12
5.3.1.24
2.7.2.3
2.1.2.1
3.1.3.3
Serine
Glycine
2.6.1.52
1.1.1.95
2.4.2.18
3-Phosphoglycerate
5.4.2.1
Leucine
2.6.1.42
1.1.1.85
4.2.1.33
4.1.3.12
4.1.3.27
4.2.1.11
Phenylalanine
4.1.1.49
PEP
4.2.3.4
2.7.1.40
4.1.1.31
2.6.1.42
Valine
4.2.1.10
1.1.1.86
2.2.1.6
2.6.1.5
Pyruvate
2.7.1.71
Oxaloacetate
Asparagine
4.2.1.51
2.5.1.19
4.2.3.5
Acetyl-CoA
6.3.5.4
Chorismate
2.6.1.1
Aspartate
Oxaloacetate
1.3.1.12
Citrate
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
Homoserine-4-phosphate
1.1.1.42
4.2.1.2
At5g14800
At5g62530
2.6.1.17
4.2.99
3.5.1.18
alpha-Ketoglutarate
Fumarate
4.4.1.8
1.4.7.1
Glutamate
AT5G65750
5.1.1.7
6.3.1.2
Threonine
1.3.5.1
2.2.1.6
1.1.1.86
2.6.1.42
Isoleucine
2.1.1.14
2.1.1.10
Methionine
Prephenate
2.3.3.1
2.7.2.4
4.2.3.1
5.4.99.5
4. 1.3.8
Lysine
6.2.1.4
Succinate
Glutamine
Figure 7.
Adding an introduction to the next topic –
single cell analysis, using NMR a a major tool.
Hoping to get here!
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.
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
Se interferes with the cytoskeleton and mitochondrial activity
Selenite effects proliferating cells;
Selenite-rich diets may have anti-cancer
applications.
Se leads to degradation of DNA
TUNEL assay?