The first part presents slides that had been
on the handout for March 28;
We will go through these fast!
I will deposit the modified version on the web later.
Ionization techniques for GC
• Electron Impact (EI) (-/+)
library searchable spectra, fragmentation, most versatile
• Chemical Ionisation (CI+/-)
molecular weight information
• Desorption Chemical Ionisation (DCI)
thermally labile compounds, molecular weight information
• Field Ionisation (FI) / Field Desorption
soft ionisation, molecular weight information, reduced
background
Ionisation Methods
• Ionisation via
bombardment of
the sample with a
stream of high
energy electrons
• Impact of the high
energy electrons
with the vaporised
sample molecules
causes ejection of
(multiple) electrons
from the analyte
and a radical cation
M+• is formed
Electron Impact
M + e-  M+• + 2e-
Analyzers for MS/MS - Triple Quadrupole
Q1 collision Q2
cell
Best combined with an upstream separation device, e.g.,
liquid chromatography or capillary electrophoresis
Mass analyzers
Ionisation
of peptides
Ion acceleration
by high voltage
Time
Of
Flight
Field free drift region
Detection of ions
For GC
or LC
The time needed for an accelerated ion to transverse
a field-free drift zone is directly related to the mass of
an ion / peptide. The longer the flight path the better
the resolution.
2D GC-ToFMS
Tandem MS (MS/MS)
58.2
134.6
178.8
MS/MS instruments select a
single ion from a spectrum
obtained by MS1
121.2
primary scan
This ion is fragmented by
collision with an inert gas
The mass of the secondary
fragment ions is measured
by MS2. For peptides, the
amino acid sequence is
deduced from the mass
differences of the ions
121.2
178.8
daughter ion scan
134.6
58.2
Tandem Mass Spectrometry
S#: 1707 RT: 54.44 AV: 1 NL: 2.41E7
F: + c Full ms [ 300.00 - 2000.00]
RT: 0.01 - 80.02
100
90
80
1409
LC
NL:
1.52E8
1991
1615
2149
1621
1411
2147
1387
60
1593
1995
1655
1435
50
1987
1445
1661
40
2001 2177
1937
1779
30
2155
2205
2135
2017
1095
80
75
70
65
60
55
801.0
50
45
40
35
Scan 1707
638.9
25
2207
1105
85
30
1307 1313
20
MS1
90
Relative Abundance
70
95
Base Peak F: +
c Full ms [
300.00 2000.00]
1611
Relative Abundance
638.0
100
1389
2329
872.3
1275.3
15
1707
687.6
10
2331
10
1173.8
20
944.7
783.3
1048.3
1212.0
1413.9
1617.7
1400
1600
1742.1
1884.5
5
0
200
400
600
800
1000
m/z
0
5
10
15
20
25
30
35
40 45
Time (min)
50
55
60
65
70
75
1200
1800
2000
80
S#: 1708 RT: 54.47 AV: 1 NL: 5.27E6
T: + c d Full ms2 638.00 [ 165.00 - 1925.00]
850.3
100
95
687.3
90
85
Ion
Source
588.1
80
75
70
MS/MS
65
Relative Abundance
collision
MS-2
MS-1
cell
60
55
851.4
425.0
50
45
949.4
40
326.0
35
524.9
30
25
20
Scan 1708
589.2
226.9
1048.6
1049.6
397.1
489.1
15
10
629.0
5
0
200
400
600
800
1000
m/z
1200
1400
1600
1800
2000
Analyzers: Quadrupole vs. ToF
Quadrupole
- poor resolution
ToF
- high resolution
- better peak
separation
accurate mass
by ToF
Elemental Composition Report
Mass
Calc. Mass mDa
29.0027 29.0027
29.0140
29.0265
29.0391
0.0
-11.3
-23.8
-36.4
ppm
-1.4
-388.7
-822.3
-1255.9
Formula
CHO
H N2
C H3 N
C2 H5
ToF: resolves co-eluting compounds
Peak finding
software
- mass spectral
deconvolution
(further resolves coeluting
and/or low abundant
analytes)
2D GC-MS
Linear dynamic range: 104-106
1D GC
- Analytes Coelute in
complex samples
2D GC
- separates coeluting
peaks in 2nd dimension
Spectral comparison with libraries
NIST, Wiley
chromatogram
Selected peak
Mass-spectrum
Spectral match
Library hits
Comparison of EI and FI spectra
13
56
74.04
87.05
100
56
detective work
56
%
43
EI+
143.11
75.04
55.05
Fragmentation
298.29
255.23
31
199.17
101.06
129.09
157.12
185.16
213.19
241.22
267.27
269.25
299.29
0
298.29
100
12
Intact ion
FI+
Methyl Stearate
%
299.30
CH3(CH2)16COOCH3
300.31
0
m/z
60
80
100
120
140
160
180
200
220
240
260
280
300
GC/MS – a routine technology -
Challenges
(1) Automation of sample preparation, wet chemistry, data processing after
an increasing number of data is obtained,
(2) Extension of the analytical scope – e.g., combined analyses of a sample
using multiple platforms,
(3) Combined analyses with proteome and transcriptome studies
(4) Profiling trace compounds, or signaling molecules in the presence of
(very) abundant ‘bulk’ metabolites,
(5) Increasing accuracy in multi-parallel metabolite quantification
(6) Combining metabolite and flux analyses
(7) Establishing quantitative repeatability, arrive with an unambiguous
nomenclature,
(8) Comparability between analytical platforms, and of work done by
different labs.
Some metabolites are very abundant –
how to quantify,
and how to analyze low abundance
(a) Typical ES- mass spectrum for
polar extract green tomato
(L. esculentum) fruit.
Major identifiable peaks:
179 (hexose sugars, [M)H])),
191 (citric/iso-citric acid, [M)H])),
215 (hexose sugars, [M+Cl])),
237 (HEPES buffer, [M)H])),
475 (HEPES buffer, [2M)H])).
(b) Typical ES+ mass spectrum for
polar extract of green tomato
(L. esculentum) fruit.
Major identifiable peaks:
147 (glutamic acid, [M+H]+),
203 (hexose sugars [M+Na]+),
219 (hexose sugars, [M+K]+),
239 (HEPES buffer, [M+H]+),
261 (HEPES buffer, [M+Na]+),
277 (HEPES buffer, [M+K]+).
Dunn et al. (2005) Evaluation of automated
electrospray-TOF MS for metabolic
fingerprinting of the plant metabolome.
Metabolomics 1, 137.
Quantification
Relationship between concentration of
metabolite standard added to a plant
extract and molecular ion intensity.
(a) ES-;
open circle - pyruvate,
open triangle - oxalate,
closed circle - fumarate,
open triangle - oxalate,
closed square - malate,
open diamond - ascorbate.
(b) ES+;
open circle - alanine,
open diamond - proline,
closed triangle - GABA,
closed diamond - aspartate,
closed square - leucine.
Analytical and Biological Variations
Considerable
differences in amounts
between individual plants!
Considerable
analytical variation!
Considerable
variation even within a single
organ (e.g., tip and base of leaf)!
Considerable
variation over time
(diurnal, developmental)!
Peak intensity for 13
selected metabolite
ions measured in each
of three fruit extracts
of two tomato species
Lycopersicon esculentum - white fill; L. pennellii - grey fill;
1 malic acid,
2 citric acid,
4 C4 sugars,
5 hexoses,
7 fumaric acid,
8 ascorbic acid,
10 leucine/isoleucine, 11 asparagine,
3 GABA,
6 pyruvic acid,
9 valine,
12 glutamine, 13 tyrosine.
For clarity, the responses for 3–8 are increased by a factor of 10, and
those for 9–13 increased by a factor of 50. Values are ion intensity (cps),
calculations employed the summed ion intensity for 180 scans and are
presented as the means of three replicate extracts ± standard deviation.
Goodacre et al (2004)
Trends Biotech. 22, 245.
Technologies for metabolome analysis.
General strategies for metabolome analysis. CE, capillary electrophoresis; DIESI, direct-infusion
ESI, which can be linked to Fourier transform ion cyclotron resonance mass spectrometry (FTICR-MS); NMR, nuclear magnetic resonance; RI, refractive index detection; UV, ultraviolet
(b) Example of an FT-IR spectrum of a biofluid. In this experiment, 10 ml of rat urine was dried
and analysed on a Bruker IFS66 instrument between 400 and 600 cm21, with 4 cm21
resolution and 256 co-adds.
(c) Capillary gas chromatography–
time-of-flight–mass spectrometry (GCTOF-MS) analysis of human serum. In
a 15 min run, 722 peaks could be
discriminated.
Types of database for metabolomics
• Databases storing detailed metabolite profiles, including raw data
and detailed metadata (i.e. data about the data) [73].
• Single species-based databases that will store ‘relatively’ simple
metabolite profiles [73].
• Databases storing complex metabolite profile data from many
species in many different physiological states [73].
• Databases listing all known metabolites for each biological
species.With suitable metadata, these databases could be extended
to contain temporal and spatial information.
• Databases such as KEGG [74], compiling established biochemical
facts.
• Databases that integrate genome and metabolome data with an
ability to model metabolic fluxes [75,76].
References in Goodacre et al. (2004)
73. Mendes, P. (2002) Emerging bioinformatics for the metabolome.
Brief. Bioinform. 3, 134–145
74. Kanehisa, M. et al. (2002) The KEGG databases at GenomeNet.
Nucleic Acids Res. 30, 42–46
75. Famili, I. et al. (2003) Saccharomyces cerevisiae phenotypes can be
predicted by using constraint-based analysis of a genomescale
reconstructed metabolic network. Proc. Natl. Acad.
Sci. U. S. A. 100, 13134–13139
76. Fo¨rster, J. et al. (2003) Genome-scale reconstruction of the
Saccharomyces cerevisiae metabolic network. Genome Res.
13, 244–253
Deposited on web - April 3
Metabolomics of volatile signals in
Inter-species (and Inter-kingdom)
Communication.
Plant Volatiles – Chemical Defense Mechanisms
Symbiotic,
antibiotic,
and
defense
relationships
Acacias
–
sugar composition
adjusted to
desired
ant species
Heil et al. (2005) Postsecretory hydrolysis of nectar sucrose and
specialization in ant/plant mutualism. Science 308 (5721)
Plants provide sugars for which particular ant species have no catabolic enzyme.
“Tri-trophic” Interactions
Plant
predator’s
Plant
predator
-
predator
Herbivore
parasitic
Insect
“Tri-trophic” Interactions
forced regurgitating
feeding damage
maize, cotton, etc.
e.g. Spodoptera littoralis
parasitic wasps
Schnee et al. (2006) The products of a single maize
sesquiterpene synthase form a volatile defense
signal that attracts natural enemies of maize herbivores.
PNAS 103, 1129
JA biosynthesis – abbreviated
From plant signaling
to insect response via
VOC – volatile
organic compounds
Jasmonates
Terpenes
Farmer & Ryan (early 90s) –
volatile signals from plant to plant
Turlings TCJ, Loughrin JH, McCall PJ, Rose USR,
Plants respond to caterpillar feeding
Lewis WJ, Tumlinson JH (1992) How caterpillardamaged plants protect themselves by attracting
parasitic wasps. PNAS 92, 4169.
Healthy, undamaged maize seedlings
1
C6
6 hours after start
of caterpillar feeding
5
C10
Some peak IDs (LC-MS):
1,2,3 – 3-hexenal; 2-hexenal;
3-hexenol
5- linalool; 9 – β-farnesene;
10 - nerolidol
C15
IS1,2 – internal standards
9
10
C15
Feeding on cotton
• Change in composition & amount over time of attack.
• Signaling compounds (or degradation products)
are present at low levels only.
jasmone
pinene
1st day
linalool
indole
farnesene
3rd day
Emitted compounds by cotton
Start - 2 p.m.
5 caterpillars on 6w-old cotton
A – LOX products from cotton
B – constitutive cotton volatiles
C – induced compounds in cotton
Emissions by infected corn over time
Leaves scratched, then added
caterpillar regurgitate
LOX-products from maize
Induced complex
compounds
Recognition – timing, composition and nature of compounds
Signals in
caterpillar “spit”
induce
plant
biodefense
WMD
by recruiting
allied forces
Based on
Isoprene &
Isoprenoid metabolism
acetoacetyl-CoA + acetyl-CoA > HMG-CoA > mevalonate >>>> isopentenyl-PP
C4 + C2
>
C6
>
C5 + CO 2
Isoprene
Isopentenyl-PP
Dimethylallyl-PP
C5
C5
Geranyl-PP
C15 – farnesyl-PP
Cyclic sesq.
(cadinene)
C20 - Geranyl-geranyl-PP
Sesquiterpene type –
phytol (retinol, retinal)
6β-acetoxy-24-methyl12, 24-dioxoscalaran-25-al
(pacific sponge)
C25 – Sesterterpines > abundant, non-volatile
C30 - Triterpenes > steroid source structure, abundant, non-volatile
C40 - Carotenes > carotenoid source structure, abundant, non-volatile
Induction of sesquiterpene synthases
maize
Wasps fly straight to damaged leaf from downwind, not to a wounded leaf,
but to wounded leaves treated with regurgitated midgut sap of insect.
Gene to Product
maize
What happens when the gene is expressed in Arabidopsis ?
A single transgene/ protein generates the entire spectrum!
… but will the wasps know?
Let the wasps chose!
Wt and transformed Arabidopsis – wasps in central compartment
• naïve wasps
wt
• trained on Arabidopsis
tr
• trained on maize
Side result –
wasps must learn by
trial & error, i.e.,
there are other cues;
signals that connect
wasp & caterpillar
P < 0.01
One could use the contraption for other experiments
Western
Corn rootworm
Diobrotica
v. virgifera
parasitic
nematodes
Metabolomics
to the
Rescue!
A major problem in US agriculture –
is there a natural biodefense strategy (i.e., no chemicals)?
One could use the contraption for other experiments
Maize
Western
Corn rootworm
Nematode
trap
Rasmann et al. (2005)
Nature 434, 731.
Trimorphic interaction involving a entomopathogenic nematode
Experiments similar to the wasp
predation experiment
• Identification of attractant
• Why is US maize not protected
• Does it work in the field
• Isoprenoids in the soil?
2 – β-caryophyllene
Attraction to / by authentic
β-caryophyllene
Olfactometer arms spiked with
authentic β-caryophyllne
Absence of β-Car.
in some (mostly US)
maize lines
Reproductive success and
β-caryophyllene
Pactol – low amounts
Graf – high amounts
healthy
fungal infections
nematode presence
All six containers received
the same number of nematodes
Added β-caryo.
Emergence of adults is reduced
when nematodes are attracted
A - Detection in a column of wet sand 10 cm from release point
B – detection in air space above a column of sand
(note the scale)
β-caryophylline diffuses readily (at least in and out of sand)
Sesquiterpene hydrocarbons in maize
A – leaf inducible, B – ubiquitous; C – root specific
Terpene synthases in maize
• Heterologous expression
• GC-MS with isotopic tracers
• GC-MS of different lines
• Mutational analysis
Sesquiterpene spectrum as affected by mutational analysis of the gene