Metabolomics, spring 06

Hans BohnertERML 196

bohnerth@life.uiuc.edu

265-5475333-5574

http://www.life.uiuc.edu/bohnert/

Today’s discussion topic (single cell profiles):

Fan TWM, Bandura LL, Higashi RM, Lane AN (2005) Metabolomics-edited transcriptomics of Se anticancer action in human lung cancer cells. Metabolomics 1, 325.

class April 18

One important aspect of plant genomics,probably the most important one,

will be to define cell-specific transcript profiles under a variety of conditions

Chris Somerville, April 2006

This will be equally important for metabolite concentrations, the flux through pathways

and changes in photosynthesis during the day, under stresses and in sinks.

Metabolomics Essentiality

Metabolites Col 21 CO2/ P-ambient valuefold change

glutamic acid 0.39 0.00threonine 0.61 0.11

Total 0.56 0.02*0.02*

citric acid 0.73 0.05malic acid 0.77 0.10p-hydroxybenzoic

acid 0.55 0.09

Total 0.86 0.07*0.07*

maltose 1.55 0.09trehalose 0.80 0.31Total 1.08 0.53

mannitol 1.33 0.11glycerol 0.87 0.23inositol 0.96 0.84Total 0.97 0.74

glucose-6-P 0.55 0.10

phosphate 0.71 0.24

Alc

oh

ols

Su

gar

sO

rgan

ic

acid

sA

min

o

acid

s

Absolute change and Confidence

In total ~60 metabolitescould be scored

Thimm et al. (2004) Mapman: a user-driven tool to display genomics datasets onto diagrams of metabolic pathways and other biological Processes. Plant Journal 37, 914.

Usadel et al. (2005) Extension of the visualization tool MapMan to allow statistical analysis of arrays, display of corresponding genes, and comparison with known responses. Plant Physiol. 138, 1195.

Cvi June 21 A

Cvi June 27 C

Cvi June 27 A

Cvi June 21 C

Col June 21 A

Col June 27 ACol June 21 C

Col June 27 C

-60

-40

-20

0

20

40

60

-60 -40 -20 0 20 40 60

F1 77.71 %

F2

16.9

2 %

Supplemental Figure 5.

Linear discriminant analysis, LDA(Catchpole et al., 2005)

• all metabolites

• biological repeats

• scale according to factors analyzing similarity in the response

The chosen exampleexplains >90% of the variability

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

-1.5 -1 -0.5 0 0.5 1 1.5

Jun

e 21

June 27

SugarsOrganic acid

Amino acid Polyols

6 carbon sugars

12 carbon sugars

18 carbon sugars

Col-0

Cvi-0

Figure 6.

Metabolite changes(major categories)over time points

down early,up late

down

up

up earlydown late

Cook et al. (2004) A prominent role for the CBF cold response pathway in configuring the low-temperature metabolome of Arabidopsis. PNAS 101, 15243.

Effect of cold treatment on theArabidopsis metabolome

CBF3 confers metabolicsignatures in non-acclimated

plants similar to that inacclimated plants

Low temperature and Cvi

Cvi – cold treatment and the raffinose pathway

Cvi – deficiency in metabolites that increase in WS

Catchpole et al. (2005) Hierarchical metabolomics demonstrates substantial compositional similarity between genetically modified and conventional potato crops. PNAS 102, 14458. We show that, apart from targeted changes, these GM potatoes in

this study appear substantially equivalent to traditional cultivars.

• metabolite fingerprints GC-TOF-MS

• potato cultivars

• GMOs (SST & SST/FFT) fructan 1-fructosyl- transferase

• matrix presentation of sample frequency in predicted/observed diagram fashion

• most differences in fructan polymerization

• “substantial equivalence”

PCA LDA (Df1 vs. Df2 Df2 vs. Df3)

Overlaid single-ion chromatograms

potato diagrams vs. sampleof different degree of polymerization

of fructans

GC-TOF-MS of m/z 217 in cultivars

Desiree (= precursor for GMO)lacks kestose –

which is present in GMOSST & SST/FFT

(fructose polymers,

n >10)

• Absorption by nuclei [not electrons] of electromagnetic radiation (up to ~900 MHz)• Certain nuclei with spin and magnetic moment split energy levels in a field• The split is characteristic of the nucleus and the bonds in which it is involved• Continuous wave (CW) and pulsed (Fourier-transformed, FT) spectrometers• http://en.wikipedia.org/wiki/Nuclear_magnetic_resonance

NMR

What NMR signals mean

one-dimensional

two-dimensional

1,3-butanediol

(change field by 90o

repeated scans at different frequencies)

(1) quartet(2) doublet(3) triplet(4) triplet

chemical shiftimprinted by neighboring nuclei

characteristic for each bond

compare signals with a library of known signals

Chemical shift is usually expressed in parts per million (ppm) by frequency, because it is calculated from:

                                                                         

Since the numerator is usually in hertz, and the denominator in megahertz, delta is expressed in ppm.

The detected frequencies (in Hz) for 1H, 13C, and 29Si nuclei are usually referenced against TMS (tetramethylsilane), which is assigned the chemical shift of zero.

Other standard materials are used for setting the chemical shift for other nuclei.The operating frequency of a magnet is calculate from the Larmor equation:

Flarmor = γ * B0, where B0 is the actual strength of the magnet

in units like teslas or gauss, and

γ is the gyromagnetic ratio of the nucleus being tested.

Isotope Occurrence

in nature(%)

spin number l

Magnetic moment

μ(A·m²)

Electric quadrupole

moment(e×10-24 cm2)

Frequency at 7 T

(MHz)

Relative sensitivit

y

1H 99.984 1/2 2.79628 300.13 1

2H 0.016 1 0.85739 2.8 x 10-3 46.07 0.0964

10B 18.8 3 1.8005 7.4 x 10-2 32.25 0.0199

11B 81.2 3/2 2.6880 2.6 x 10-2 96.29 0.165

12C 98.9 0

13C 1.1 1/2 0.70220 75.47 0.0159

14N 99.64 1 0.40358 7.1 x 10-2 21.68 0.00101

15N 0.37 1/2 −0.28304 30.41 0.00104

16O 99.76 0

17O 0.0317 5/2 −1.8930 −4.0 x 10-3 40.69 0.0291

Not only 13C or 1H – other atoms as well can be seen

What NMR signals mean

Metabolomics-edited transcriptomics analysis ofMetabolomics-edited transcriptomics analysis ofSe anticancer action in human lung cancer cellsSe anticancer action in human lung cancer cells

Fan, Bandura, Higashi & Lane (2005) Metabolomics 1, 325-339

(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 transcriptomicanalysis (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 dysfunctionsto 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 inproteins 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-cancerapplications.

Se leads to degradation of DNA

TUNEL assay?

control

Se treated

High resolution 2D NMR spectra of control and Se-treated cellsHigh resolution 2D NMR spectra of control and Se-treated cells

*

*

“1H chemical shift”

Metabolites with chemical shift indicative of changes Metabolites with chemical shift indicative of changes 1212C/C/1313C and C and 11H connectivityH connectivity

Se-cells (13C-glc)spectral differences

control/Se

GC-MS + NMR

absolute amount

labeled positions (12C-13C)

Control 1D

Control 2D

highresolution

*depletion 13C

down

up