Included is the set of slides as discussed on April 20, which had not been distributed completely....

Included is the set of slides as discussed on April 20,which had not been distributed completely.

This is followed by slides (from #21) discussing the (surprising) phloem metabolite analysis.

Our discussion went further, to slide 29/30.

Fan TWM, Bandura LL, Higashi RM, Lane AN (2005) Metabolomics-Edited

Transcriptomics Analysis of Se anticancer action in human lung cancer

cells. Metabolomics 1, 325.

Metabolomics, spring 06

Hans BohnertERML 196

bohnerth@life.uiuc.edu

265-5475333-5574

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

class April 20

Metabolomics Essentiality

Today’s discussion topic

(single cell profiles)

Fiehn O (2003) Metabolic Networks of Cucurbita maxima phloem. Phytochemistry 62, 875-886.

Zhang B (2006) Dissection of Phloem Transport in Cucurbitaceae by Metabolomic Analysis. PhD thesis, MPI-Golm

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 Tesla or Gauss, and

γ is the gyromagnetic ratio* of the nucleus being tested.

*the ratio of the magnetic dipole moment to the angular momentum of an elementary particle.

Isotope Occurrence

in nature(%)

Spinnumber 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.

dye: mito-tracker

apoptosis

Se leads to apoptosis/degradation of DNA

TUNEL assay?

control

Se treated

terminal dUTP nick-end labeling

High resolution 1D NMR spectra of control and Se-treated cellsHigh resolution 1D NMR spectra of control and Se-treated cells

*

*

“1H chemical shift”

Next slide – convert to 2D representation of differences in 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

boxes trace1H connectivityof 13C-labeled C

Confirmation of putative changes

Additional use of HPLC/UV spectroscopy for substancesin crowded regions of the NMR spectra (adenine/uracil nucleotides, NAD/NADP,aromatic amino acids)

GC-MS + NMR absolute amount

labeled positions (12C-13C)

From enrichment / incorporation deduce pathways – e.g., P-choline,cysteine in GSH, or methionine can be recognized whether from medium (13C) or from internal turnover/stores.

Control 1D

Control 2D

highresolution

boxes trace1H connectivityof 13C-labeled C

*depletion 13C

down

up

Metabolomics, spring 06

Hans BohnertERML 196

bohnerth@life.uiuc.edu

265-5475333-5574

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

class April 25

Metabolomics Essentiality

Today’s discussion topic

Fiehn O (2003) Metabolic Networks of Cucurbita maxima phloem. Phytochemistry 62, 875-886.

Zhang B (2006) Dissection of Phloem Transport in Cucurbitaceae by Metabolomic Analysis. PhD thesis, MPI-Golm

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.

What we discussed so far

• metabolomics technologies• GC-MS profiling – six steps:

extraction – derivatization – separation – ionization – detection – acquisition/evaluation

• relative advantages of different technologies (LC, GC, TOF, MS-MS, NMR)• challenges:

automation – analytic scope – trace compound calling - reproducibilityand quantitative comparisons across platforms – size and complexity of metabolite libraries

• plant volatiles – tri-trophic interactions• static vs. dynamic metabolite profiling;

stable isotopes - flux determinationssugars to fatty acids (Rubisco in green seeds), TPs to amino acids

• integration of transcriptomics and metabolomics • the cold-metabolome – certainty from highly variable datasets (ecotypes/lines)• cell-specific reactions [animal] (how can we use plant cell cultures?)

• long-distance transport metabolomics • metabolomics – transcriptomics – QTLs (tomato – wild tomato crosses)• towards systems understanding• discussion

still to

come

Phloem transportas a metabolomics topic

Pressure flow concept

Oliver Fiehn, 2003Baichen Zhang, 2006

source

sink 1

sink 2

symplastic

apoplastic

mixed

hydraulic connectivity

transporters

directionality

phloem metabolicactivity?

Too simple?

stem

major/minor veins

monocot – dicot

bacterial contributions(e.g., Rhizobia)

Types of companion cells

We are far from understanding cell-specificity

Phloem exudates are very different from leaf profiles- no reducing sugars in phloem

Comparison phloem/xylem exudate – leaf

Polymer trapping concept

What polymers?

Raffinose/ stachyose synthases catalyze reversible reactions

An osmotically“neutral”transportform of sucrose

Galactinolsynthase

as the rate-limiting

enzyme

Enrichment and differences – leaf/phloem

Hydrophilic Interaction Liquid Chromatography (HILIC) – Ion Trap (IT) - MS

verbascose

high MWO - linkedglycans

leaf discsaccumulateRFO sugars

RFO – Raffinose Family Oligosacch.

Labeling of O-glycans

(C52H1NO42) M0 (M+H)+ = 1402 m/z

contents ofphloem

exudates are

species-specific

directionality

How viable are “older” studies? Or

Do textbooks tell the real story?

Morphology of cucurbit

vasculature

Fluorescein (CF5) labeling of“active” phloem

short-/long-term

stem phloem exudate

Central phloem tissue(TIC)

(GC-MS)

Identification & relative amount of metabolites in (b)

phloem exudatecollected

dissected centralphloem tissue

glycan

Distinguishing tissues by metabolites

dissected xylem

pith

cortex

acids/amino acids

Stem phloemexudate

Central phloem tissue

Selected metabolitesRelative amounts

Surprising complexity and differences

PCA

grouping ofsamples

A new model for phloem transport – vascular complexity

Phloem is not just transport -is metabolically active

Labeling of amino acids in phloem exudates (petiole collection) and leaf discs

Phloem snapshots

PCA

Data from phloem snapshots

A clearly individualistic streak! some correlations

P – plantL - leaf

Metabolic correlation networks

Pair-wise computation of Pearson correlations for individual leaves

Nodes mark individual metabolitesconnected with others in clusters

Details

Deviations from an assumed averagephloem metabolite composition

take home message? Fiehn, 2003Zhang, 2006

Take home