IB496-April 25

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Transcript IB496-April 25

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.
Metabolomics, spring 06
Metabolomics Essentiality
Hans Bohnert
ERML 196
[email protected]
265-5475
333-5574
http://www.life.uiuc.edu/bohnert/
Today’s discussion topic
(single cell profiles)
class April 20
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.
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
1,3-butanediol
chemical shift
imprinted by
neighboring nuclei
characteristic for each bond
two-dimensional
(change field by 90o
repeated scans at
different frequencies)
(1)
(2)
(3)
(4)
quartet
doublet
triplet
triplet
one-dimensional
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.
Not only 13C or 1H – other atoms as well can be seen
Isotope
Occurre
nce
in nature
(%)
H
99.984
1/2
2.79628
0.016
1
0.85739
2.8 x 10
B
18.8
3
1.8005
7.4 x 10
B
81.2
3/2
2.6880
2.6 x 10
98.9
0
1.1
1/2
0.70220
99.64
1
0.40358
0.37
1/2
−0.28304
99.76
0
0.0317
5/2
1
2
H
10
11
12
C
13
C
14
N
15
N
16
O
17
O
Spin
number l
Magnetic
moment
μ
(A·m²)
−1.8930
Electric
quadrupole
moment
-24
2
(e×10 cm )
7.1 x 10
-3
-2
-2
-2
−4.0 x 10
-3
Frequen
cy at 7 T
(MHz)
Relative
sensitivit
y
300.13
1
46.07
0.0964
32.25
0.0199
96.29
0.165
75.47
0.0159
21.68
0.00101
30.41
0.00104
40.69
0.0291
What NMR signals mean
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
apoptosis
Selenite effects proliferating cells;
Selenite-rich diets may have anti-cancer
applications.
dye: mito-tracker
Se leads to apoptosis/degradation of DNA
TUNEL assay?
terminal dUTP nick-end labeling
control
Se treated
High 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 12C/13C and 1H connectivity
boxes trace
1H connectivity
of 13C-labeled C
Se-cells (13C-glc)
spectral differences
control/Se
Confirmation of putative changes
Additional use of HPLC/UV spectroscopy for substances
in 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.
high
resolution
Control 1D
Control 2D
boxes trace
1H connectivity
of 13C-labeled C
down
up
*depletion 13C
Metabolomics, spring 06
Hans Bohnert
ERML 196
[email protected]
265-5475
333-5574
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 - reproducibility
and quantitative comparisons across platforms –
size and complexity of metabolite libraries
• plant volatiles – tri-trophic interactions
• static vs. dynamic metabolite profiling;
stable isotopes - flux determinations
sugars 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 transport
as a metabolomics topic
sink 1
source
symplastic
apoplastic
mixed
sink 2
Oliver Fiehn, 2003
Baichen Zhang, 2006
Pressure flow concept
hydraulic
connectivity
transporters
directionality
phloem metabolic
activity?
Too simple?
stem
major/minor veins
monocot – dicot
bacterial contributions
(e.g., Rhizobia)
Types of companion cells
We are far from understanding cell-specificity
Comparison phloem/xylem exudate – leaf
Phloem exudates are very different from leaf profiles
- no reducing sugars in phloem
Polymer trapping concept
What polymers?
An osmotically
“neutral”
transport
form of
sucrose
Galactinol
synthase
as the
rate-limiting
enzyme
Raffinose/ stachyose synthases catalyze reversible reactions
Enrichment and differences – leaf/phloem
high MW
O - linked
glycans
leaf discs
accumulate
RFO sugars
verbascose
Hydrophilic Interaction Liquid Chromatography (HILIC) – Ion Trap (IT) - MS
RFO –
Raffinose
Family
Oligosacch.
Labeling of O-glycans
(C52H1NO42) M0 (M+H)+ = 1402 m/z
contents of
phloem
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)
Identification &
relative amount of
metabolites in (b)
(GC-MS)
phloem exudate
collected
dissected central
phloem tissue
glycan
Distinguishing tissues by metabolites
pith
cortex
dissected xylem
acids/amino acids
Stem phloem
exudate
Central phloem
tissue
Selected metabolites
Relative amounts
Surprising complexity and differences
PCA
grouping of
samples
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
Metabolic correlation networks
Pair-wise computation of Pearson
correlations for individual leaves
P – plant
L - leaf
Nodes mark individual metabolites
connected with others in clusters
Details
Deviations from an assumed average
phloem metabolite composition
Fiehn, 2003
Zhang, 2006
take home message?
Take home