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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.
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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
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
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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
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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.
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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
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What NMR signals mean
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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.
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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
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Se interferes with the cytoskeleton and mitochondrial activity
Selenite effects proliferating cells;
Selenite-rich diets may have anti-cancerapplications.
dye: mito-tracker
apoptosis
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Se leads to apoptosis/degradation of DNA
TUNEL assay?
control
Se treated
terminal dUTP nick-end labeling
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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
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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
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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)
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GC-MS + NMR absolute amount
labeled positions (12C-13C)
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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.
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Control 1D
Control 2D
highresolution
boxes trace1H connectivityof 13C-labeled C
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*depletion 13C
down
up
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Metabolomics, spring 06
Hans BohnertERML 196
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.
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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
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Phloem transportas a metabolomics topic
Pressure flow concept
Oliver Fiehn, 2003Baichen Zhang, 2006
source
sink 1
sink 2
symplastic
apoplastic
mixed
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hydraulic connectivity
transporters
directionality
phloem metabolicactivity?
Too simple?
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stem
major/minor veins
monocot – dicot
bacterial contributions(e.g., Rhizobia)
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Types of companion cells
We are far from understanding cell-specificity
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Phloem exudates are very different from leaf profiles- no reducing sugars in phloem
Comparison phloem/xylem exudate – leaf
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Polymer trapping concept
What polymers?
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Raffinose/ stachyose synthases catalyze reversible reactions
An osmotically“neutral”transportform of sucrose
Galactinolsynthase
as the rate-limiting
enzyme
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Enrichment and differences – leaf/phloem
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Hydrophilic Interaction Liquid Chromatography (HILIC) – Ion Trap (IT) - MS
verbascose
high MWO - linkedglycans
leaf discsaccumulateRFO sugars
RFO – Raffinose Family Oligosacch.
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Labeling of O-glycans
(C52H1NO42) M0 (M+H)+ = 1402 m/z
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contents ofphloem
exudates are
species-specific
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directionality
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How viable are “older” studies? Or
Do textbooks tell the real story?
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Morphology of cucurbit
vasculature
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Fluorescein (CF5) labeling of“active” phloem
short-/long-term
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stem phloem exudate
Central phloem tissue(TIC)
(GC-MS)
Identification & relative amount of metabolites in (b)
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phloem exudatecollected
dissected centralphloem tissue
glycan
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Distinguishing tissues by metabolites
dissected xylem
pith
cortex
acids/amino acids
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Stem phloemexudate
Central phloem tissue
Selected metabolitesRelative amounts
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Surprising complexity and differences
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PCA
grouping ofsamples
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A new model for phloem transport – vascular complexity
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Phloem is not just transport -is metabolically active
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Labeling of amino acids in phloem exudates (petiole collection) and leaf discs
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Phloem snapshots
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PCA
Data from phloem snapshots
A clearly individualistic streak! some correlations
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P – plantL - leaf
Metabolic correlation networks
Pair-wise computation of Pearson correlations for individual leaves
Nodes mark individual metabolitesconnected with others in clusters
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Details
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Deviations from an assumed averagephloem metabolite composition
take home message? Fiehn, 2003Zhang, 2006
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Take home