ABSTRACT
JAEGER, FREDERICK HOWARD. Development and Application of Mass Spectrometry
based Tools for Metabolomic and Proteomic Profiling of Transgenic Arabidopsis thaliana.
(Under the direction of Professor Lin He).
The goals of the project presented here are five-fold.
First, application of my
previously described metabolic profiling methodology to elucidate the changes in the
metabolome of heat stressed transgenic Arabidopsis thaliana. Second, method optimization
was undertaken in order to improve the reproducibility and precision of the metabolic
profiling methodology.
Third, development of a new methodology for the analysis of
complex metabolites in order to increase coverage of the metabolome accessible by GC/MS
based analysis. Fourth, the newly develop methodology will be applied to a synthetic
polymer model to explore its efficacy in the field of chemical profiling. Fifth, the proteome
of transgenic Arabidopsis thaliana was explored to look for differences in protein expression.
© Copyright 2013 Frederick Howard Jaeger
All Rights Reserved
Development and Application of Mass Spectrometry based Tools for Metabolomic and
Proteomic Profiling of Transgenic Arabidopsis thaliana
by
Frederick Howard Jaeger
A dissertation submitted to the Graduate Faculty of
North Carolina State University
in partial fulfillment of the
requirements for the degree of
Doctor of Philosophy
Chemistry
Raleigh, North Carolina
2014
APPROVED BY:
_______________________________
Professor Lin He
Committee Chair
______________________________
Professor Edmond Bowden
________________________________
Professor David Muddiman
________________________________
Professor Wendy Boss
DEDICATION
As always, my wife Lava, and my three children Hough, Mason and Lyla are a constant
source of motivation and inspiration. Without these four wonderful people in my life I
honestly do not know what I would be doing or where I would be. The support that your
unconditional love has brought to me has made this work and many other things in my life
possible. I will never be able to thank you enough for the patience and understanding you
have shown me, but I can spend the rest of my life trying. I love you.
ii
BIOGRAPHY
Frederick was born outside of Chicago, Illinois in 1975. He moved throughout the
country several times to live in Nebraska and New Jersey before finally settling in North
Carolina in 1986. After finishing High School he received a Bachelor Degree in Chemistry
from UNCW in 1997. After working for several years, he joined the laboratory of Lin He in
the Chemistry Department of NCSU in 2005 in pursuit of a Master of Science degree, on a
part-time basis and completed the MS degree in 2008. Frederick started his Doctoral studies
in the Fall of 2009, again on a part-time basis.
Frederick has worked in several different industries over the years. Fields worked in
have included pesticides, biotech, polymers, tobacco (for smoking cessation) and currently
human health. He is currently a Senior Laboratory Research Analyst in the Human Vaccine
Institute of Duke University and works in the laboratory of Dr. Munir Alam. Current work
includes the pursuit of an HIV vaccine through vaccine candidate biophysical and antigenic
characterization. Other pursuits include exploration of innate protective factors in human
mucosal fluids for the reduction in global HIV mother to child transmission. Frederick is
married to his high school sweetheart Lava and they have three children Hough, Mason and
Lyla. They reside in Raleigh, NC.
iii
ACKNOWLEDGMENTS
I would like to thank Dr. Robert Bereman for starting me on this journey through
graduate school. He pushed me from someone thinking about school to someone doing
something about it. I will always be grateful. I would like to thank my advisor Dr. Lin He,
for taking a chance on me as a part-time student. You have been a great mentor to me over
the last 8 years, 1st through the master’s work and now through the doctoral project. Your
guidance has helped me to grow as a scientist and I will always greatly appreciate all of your
many efforts on my behalf. Dr. Norman Glassbrook, I would like to thank you for the many
years of support and friendship. You gave me my first shot at running a mass spectrometer
way back at Paradigm, where I was able to cut my teeth in the early days of metabolomics.
Your assistance in connecting me to people on campus with available mass spectrometers
was critical to getting this work done. The proteomics chapter would not have been possible
without your generosity. I do not exaggerate when I say that this work would not have been
possible without your help. I am in your debt. I would like to thank Dr. David Danehower,
Dr. R. Michael Roe and especially John Strider for making their instruments and laboratories
available to me for this work. I believe I left the instruments in better shape than I found
them (I hope so!) but the instrument time was invaluable and your trust in letting me come in
on off hours when my schedule permitted was greatly appreciated. I would like to thank Dr.
Wendy Boss and the following people from her lab, Dr. YangJu Im, Dr. Imara Perera and
Chiu-Yueh Hung for their assistance and the gifts of many many samples. Without these
samples this work would not have been possible. I would like to thank Dr. Ping Zhang for
iv
his invaluable assistance with the NMR and FTIR analyses as well as the rest of the
Reichhold gang, Dave Grandy, Pam Hayes and Eric Thompson for their support and
friendship over the years. I would also like to acknowledge the partial funding for this work
that I received from Reichhold, Inc. I would like to thank Dr. Moses Sekaran and Lee
Jeffries Jr. from the Duke “morning coffee club” for their kind words of encouragement and
support. I would like to thank Dr. Munir Alam for our many discussions and his confidence
in me. I would like to thank my parents Frederick Jaeger Jr. and Gerardine Jaeger for their
unwavering encouragement and support throughout my life. I would also like to thank my
father-in-law Sabah Jirjis for his support and encouragement over the years, especially his
assistance with the kids.
Last but not least I would like to thank my mother-in-law Nakia
Aziz for watching the kids on the weekends when I was either in the lab or writing. You
have put in nearly as many hours into this work as I have and your tireless efforts are greatly
appreciated. Especially with how you approached the task with joy and you always made me
feel that I was actually doing you a favor and not the other way around. You are truly one of
a kind.
v
TABLE OF CONTENTS
LIST OF TABLES ........................................................................................................... ix
LIST OF FIGURES ......................................................................................................... xi
LIST OF SCHEMES ..................................................................................................... xiv
CHAPTER 1: General Introduction
1.1 Biological Importance of Metabolomics ...........................................................1
1.1.1. Plant Metabolomics ..........................................................................2
1.1.2. Animal Metabolomics .......................................................................3
1.1.3. Clinical Metabolomics…………………………………………….. 4
1.2 Conventional Instruments Used in Metabolomics…………………………..... 5
1.2.1. Capillary Electrophoresis Mass Spectrometry…………………….. 6
1.2.2. Gas Chromatography Mass Spectrometry………………………… 6
1.2.3. Liquid Chromatography Mass Spectrometry ....................................7
1.2.4. Nuclear Magnetic Resonance Spectroscopy………………………. 8
1.3 Mass Spectrometers…………………………................................................... 9
1.3.1. Quadrupole Mass Spectrometry ......................................................10
1.3.2. Time of Flight Mass Spectrometry……………………………… .10
1.3.3. Ion Trap Mass Spectrometry………….…..……………………… 11
1.3.4. Fourier Transform Ion Cyclotron Resonance Mass Spectrometry .12
1.4 Data Analysis ...................................................................................................13
1.4.1. Initial Data Processing for Non-targeted and Targeted Data…… ..14
1.4.2. Data Mining for Non-targeted Data…………………………… ....15
1.5 Thesis Layout…………………………........................................................... 16
1.6 References………………………………………………………………… ....18
CHAPTER 2: Expression of Pyrococcus furiosus Superoxide Reductase in
Arabidopsis thaliana and the metabolomic changes upon acute
thermal stress
2.1. Introduction .....................................................................................................38
2.2. Experimental ...................................................................................................39
2.2.1. Materials .........................................................................................39
2.2.2. Sample Preparation .........................................................................39
2.2.3. Analyte Derivatization ....................................................................40
2.2.4. Instrumentation ...............................................................................40
2.3. Results and Discussion ...................................................................................41
2.3.1. L-Glycine, L-Serine and L-Threonine Metabolism ........................43
2.3.2. L-Valine, L-Leucine and L-Isoleucine Biosynthesis ......................44
vi
2.3.3. Alanine, Aspartate and Glutamate Metabolism ..............................44
2.3.4. Citrate Cycle (TCA) ........................................................................45
2.3.5. Antioxidants Ascorbate and a-Tocopherol .....................................46
2.3.6. Phenylalanine and Sinapinic acid ...................................................47
2.3.7. Glutathione Metabolism..................................................................48
2.3.8. Steroid Biosynthesis........................................................................50
2.3.9. Fatty Acid and Unsaturated Fatty Acid Biosynthesis .....................50
2.3.10. Proline .............................................................................................51
2.3.11. Multivariate Analysis of GC/MS Metabolic Data ..........................52
2.4. Conclusions .....................................................................................................53
2.5. References .......................................................................................................54
CHAPTER 3: Using Reverse Phase Liquid Chromatography coupled to a Linear
Ion Trap-Quadrupole Tandem Mass Spectrometer for Bottom Up
Proteomics to Elucidate Changes in the Proteome of Transgenic
Arabidopsis thaliana
3.1. Introduction……………………………………………………………….… 95
3.2. Experimental……………………………...………………………………… 99
3.2.1. Materials………………………………………………………… .99
3.2.2. Sample Preparation……………………………………………… .99
3.2.3. Instrumentation .............................................................................100
3.3. Results and Discussion .................................................................................101
3.4. Conclusions ...................................................................................................105
3.5. References .....................................................................................................106
CHAPTER 4: Comparison in Variation for a Constant Sample Weight
Methodology to a Variable Sample Weight Methodology for a
Trimethylsilyl based Metabolic Profiling Analysis
4.1. Introduction…..……….………………………………………………… ....132
4.2. Experimental…………………………………………………………… .....134
4.2.1. Materials……………………………………………………… ...134
4.2.2. Tissue Preparation.........................................................................134
4.2.3. Analyte Derivatization…………………………………………. .135
4.2.4. Instrumentation ………………………………………………… 135
4.3. Results and Discussion…………………………………………………..... 135
4.4. Conclusions……………………………………………………………… ...139
4.5. References………………………………………………………………… .140
vii
CHAPTER 5: Fatty Acid Methyl Ester Analysis of Arabidopsis thaliana using
Trimethylsulfonium Hydroxide and Gas Chromatography Mass
Spectrometry
5.1. Introduction…..……….………………………………………………… ....161
5.2. Experimental………………………………………………………………. 163
5.2.1. Materials……………………………………………………… ...163
5.2.2. Sample Preparation....................................................................... 163
5.2.3. Instrumentation………………………………………………… .164
5.3. Results and Discussion…………………………………………………..... 164
5.4. Conclusions……………………………………………………………… ...168
5.5. References………………………………………………………………… .168
CHAPTER 6: Rapid Analysis of Liquid Polyester Resins using
Trimethylsulfonium Hydroxide (TMSH) and Gas
Chromatography Mass Spectrometry (GC-MS)
6.1. Introduction…..……….………………………………………………… ....186
6.2. Experimental……………………………………………………………… .188
6.2.1. Materials……………………………………………………… ...188
6.2.2. Solution Preparation......................................................................188
6.2.3. Standards……………………………………………………… ...188
6.2.4. Sample Preparation....................................................................... 189
6.2.5. Instrumentation…………………………………………………. 189
6.3. Results and Discussion…………………………………………………..... 190
6.4. Conclusions……………………………………………………………… ...193
6.5. References………………………………………………………………… .194
viii
LIST OF TABLES
CHAPTER 2: Expression of Pyrococcus furiosus Superoxide Reductase in
Arabidopsis thaliana and the metabolomic changes upon acute
thermal stress
Table 2.1
Table 2.2
Shows how Data was normalize and fold change calculated for
samples C-WT1-4 (Control) and H-WT1-4
(Heat Treated)………………………. .....................................................68
Fold differences comparing heat treated to non-heat treated samples.....69
CHAPTER 3: Using Reverse Phase Liquid Chromatography coupled to a Linear
Ion Trap-Quadrupole Tandem Mass Spectrometer for Bottom Up
Proteomics to Elucidate Changes in the Proteome of Transgenic
Arabidopsis thaliana
Table 3.1
Table 3.2
Table 3.3
Table 3.4
Table 3.5
Table 3.6
Protein and Peptide Information Obtained from Scaffold 2………… ..116
Fragmentation table for the peptide
(R)EAVNVSLANLLTYPFVR(E) .......................................................117
Identified proteins and the number of assigned spectra for the WT and
GFP transgenic plant line. Scaffold viewer was set to 95% min
protein, min peptides 2 and min peptide 95%...................................... .118
Identified proteins and the number of assigned spectra for the 2-6 and
2-8 transgenic plant lines. Scaffold viewer was set to 95% min
protein, min peptides 2 and min peptide 95%........................................120
Identified proteins and the number of assigned spectra for the Hs5-8
and Hs9-7 transgenic plant lines. Scaffold viewer was set to 95% min
protein, min peptides 2 and min peptide 95%........................................122
Identified proteins and the number of assigned spectra for the SOR3
and SOR9 transgenic plant lines. Scaffold viewer was set to 95%
min protein, min peptides 2 and min peptide 95%................................ 124
CHAPTER 4: Comparison in Variation for a Constant Sample Weight
Methodology to a Variable Sample Weight Methodology for a
Trimethylsilyl based Metabolic Profiling Analysis
Table 4.1
Table 4.2
Table 4.3
5972 MS Instrument Reproducibility L-Serine-TMS……………… ...143
5972 MS Instrument Reproducibility Hexadecanoic acid-TMS…… ...144
5972 MS Instrument Reproducibility Sitosterol-TMS……………… ..145
ix
Table 4.4
5972 MS Method Reproducibility Consistent Weights
L-Serine-TMS…....................................................................................146
Table 4.5
5972 MS Method Reproducibility Consistent Weights Hexadecanoic
Acid-TMS………………………………………………………… ......147
5972 MS Method Reproducibility Consistent Weights
Sitosterol-TMS ......................................................................................148
5972 MS Method Reproducibility Variable Weights L-Serine-TMS ...149
5972 MS Method Reproducibility Variable Weights Hexadecanoic
Acid-TMS…………………………………………………………… ..150
5972 MS Method Reproducibility Variable Weights Sitosterol-TMS ..151
Table 4.6
Table 4.7
Table 4.8
Table 4.9
CHAPTER 5: Fatty Acid Methyl Ester Analysis of Arabidopsis thaliana using
Trimethylsulfonium Hydroxide and Gas Chromatography Mass
Spectrometry
Table 5.1
Table 5.2
Table 5.3
Table 5.4
Table 5.5
Table 5.6
Example of how data is collected and normalized using 2 factors WT
Data shown………………………………………………………… ....176
Example of how the comparison of chemical compounds in Arabidopsis
seedling extracts was determined…………………………………… ..178
Method Reproducibility Hexadecanoic Acid, Methyl Ester………… .180
Method Reproducibility Octadecanoic Acid, Methyl Ester………… ..181
Instrument Reproducibility Hexadecanoic Acid, Methyl Ester……… 182
Instrument Reproducibility Octadecanoic Acid, Methyl Ester……… .183
CHAPTER 6: Rapid Analysis of Liquid Polyester Resins using
Trimethylsulfonium Hydroxide (TMSH) and Gas
Chromatography Mass Spectrometry (GC-MS)
Table 6.1
Table 6.2
Table 6.3
Table 6.4
Shows some of the partially etherified by-products generated by this
methodology and how they are accounted for in the formula
Comparison………………………………………………………........198
Resin 1 technique comparisons to formula………………………… ...199
Instrument Precision………………………………………………… ..200
Method Precision…………………………………………………… ...201
x
LIST OF FIGURES
CHAPTER 2: Expression of Pyrococcus furiosus Superoxide Reductase in
Arabidopsis thaliana and the metabolomic changes upon acute
thermal stress
Figure 2.1
Figure 2.2
Figure 2.2A
Figure 2.3
Figure 2.3A
Figure 2.3B
Figure 2.4
Figure 2.4A
Figure 2.5
Figure 2.5A
Figure 2.6
Figure 2.7
Figure 2.8
Figure 2.9
Figure 2.10
Figure 2.10A
Figure 2.10B
Figure 2.11
Figure 2.12
Figure 2.13
Figure 2.14
Figure 2.15
Figure 2.16
A-Representative chromatogram for a WT sample (WT1-4).
B-Representative chromatogram for a Heat Treated sample (WT1-4).
C-Overlay of the Extracted Ion Chromatograms of A (Black) and B
(Green) showing the up regulation of Alanine and the down
regulation of Glycine upon heat treatment............................................. .72
KEGG Pathway for Glycine, Serine and Threonine Metabolism........ ...73
Glycine, Serine and Threonine Metabolism………………………… ....74
KEGG Pathway for Valine, Leucine and Isoleucine Biosynthesis…… .75
KEGG Pathway for Valine, Leucine and Isoleucine Degradation…… ..76
Valine, Leucine and Isoleucine Degradation 1st Enzymatic step
appears to be blocked by heat treatment……………………………… .77
KEGG Pathway for Alanine, Aspartate and Asparagine Metabolism ....78
Alanine, Aspartate and Asparagine Metabolism……………………… .79
KEGG Pathway for Citrate Cycle (TCA)……………………………… 80
Citrate Cycle (TCA) enzymatic reactions………………… ...................81
KEGG Pathway for Ascorbate and Alderate Metabolism……… ...........82
KEGG Pathway for Ubiquinone and Other Terepenoid-Quinone
Biosynthesis………………………………………………………… .....83
KEGG Pathway for Phenylpropanoid Biosynthesis………………… ....84
KEGG Pathway for Glutathione Metabolism……………………..........85
KEGG Pathway for Steroid Biosynthesis…………………………........86
Steroid Biosynthesis………………………………………………… ....87
KEGG Pathway for Brassinosteroid Biosynthesis………………… ......88
KEGG Pathway for Fatty Acid Biosynthesis………………………… ..89
KEGG Pathway for Unsaturated Fatty Acid Biosynthesis.................... ..90
KEGG Pathway for Arginine and Proline Metabolism……………… ...91
Principle component analysis of all metabolite data—Score plot of
PC1 and PC2…………………………………………………………....92
Principle component analysis of identified metabolite data—Score
plot of PC1 and PC2………………………………………………… ....93
KEGG Metabolic Pathways-Arabidospis thaliana (thale cress) .............94
xi
CHAPTER 3: Using Reverse Phase Liquid Chromatography coupled to a Linear
Ion Trap-Quadrupole Tandem Mass Spectrometer for Bottom Up
Proteomics to Elucidate Changes in the Proteome of Transgenic
Arabidopsis thaliana
Figure 3.1
Figure 3.2
Figure 3.3
Figure 3.4
Figure 3.5
Figure 3.6
(A) A typical MS elution profile of digested proteins. (B) A typical
MS/MS elution profile of digested proteins……………………… ......126
Example of spectrum for an identified peptide from the protein
Carbonic Anhydrase 1 for sample 2-8 B1—Shows the b and y type
ions identified……………………………………………………… ....127
Carbonic Anhydrase 1 (CA1) 95% Peptide Probability—Assigned
Spectra……………………………………………………………… ...128
Carbonic Anhydrase 1 (CA1) 95% Peptide Probability—Unique
Peptides……………………………………………………………......129
(A) A typical TIC of wild-type Arabidopsis seedlings;
(B) A zoom-in of RT where stigmasterol-TMS was expected;
(C) The background subtracted mass spectrum of stigmasterol-TMS;
and (D) An EIC of stigmasterol-TMS for the ion m/z= 484............... ..130
(A) A typical TIC of wild-type Arabidopsis seedlings;
(B) A zoom-in of RT where stigmasterol-TMS was expected;
(C) The background subtracted mass spectrum of stigmasterol-TMS;
and (D) An EIC of stigmasterol-TMS for the ion m/z= 484................ .131
CHAPTER 4: Comparison in Variation for a Constant Sample Weight
Methodology to a Variable Sample Weight Methodology for a
Trimethylsilyl based Metabolic Profiling Analysis
Figure 4.1
Figure 4.2
Figure 4.3
Figure 4.4
Figure 4.5
Figure 4.6
Figure 4.7
Figure 4.8
Figure 4.9
Representative chromatogram of an Arabidopsis thaliana sample… ...152
Serine-TMS nine constant weight injections overlaid ………………..153
Hexadecanoic acid-TMS nine constant weight injections overlaid … .154
Sitosterol-TMS nine constant weight injections overlaid…………… .155
Serine-TMS nine variable weight samples overlaid ………………… .156
Hexadecanoic acid-TMS nine variable weight samples overlaid……..157
Sitosterol-TMS nine variable weight samples overlaid……………….158
Overlaid extracted ion chromatograms of two samples with identical
weights using ion 281……………………………………………… ....159
Overlaid extracted ion chromatograms of two samples with different
weights using ion 281……………………………………………… ....160
xii
CHAPTER 5: Fatty Acid Methyl Ester Analysis of Arabidopsis thaliana using
Trimethylsulfonium Hydroxide and Gas Chromatography Mass
Spectrometry
Figure 5.1
Figure 5.2
Chromatogram of 2 week old Arabidopsis thaliana leaves using
TMSH methodology ........................................................................184
Zoom in of chromatogram of 2 month old Arabidopsis thaliana
leaves using TMSH methodology ...................................................185
CHAPTER 6: Rapid Analysis of Liquid Polyester Resins using
Trimethylsulfonium Hydroxide (TMSH) and Gas
Chromatography Mass Spectrometry (GC-MS)
Figure 6.1
Figure 6.2
Figure 6.3
Figure 6.4
Figure 6.5
The effect of increasing levels of the TMSH reagent on resin
Hydrolysis and esterification where a partially etherified
byproduct of DEG monomethyl ether (A) and DEG (B) were
monitored as TMSH concentrations increased ................................202
Chromatogram of a Polyester Resin ................................................203
Chromatogram demonstrating qualitative analysis leading to the
discovery of a contaminant ..............................................................204
Overlay and zoom-in of the chromatograms of two separate batches
of the same product to demonstrate semi-quantitative analysis ......205
NMR spectra of a Polyester Resin...................................................206
xiii
LIST OF SCHEMES
CHAPTER 5: Fatty Acid Methyl Ester Analysis of Arabidopsis thaliana
using Trimethylsulfonium Hydroxide and Gas
Chromatography Mass Spectrometry
Scheme 5.1
Scheme 5.2
Sample preparation flow chart using the newly developed one-pot
Scheme ............................................................................................174
Step-wise TMSH reactions ..............................................................175
CHAPTER 6: Rapid Analysis of Liquid Polyester Resins using
Trimethylsulfonium Hydroxide (TMSH) and Gas
Chromatography Mass Spectrometry (GC-MS)
Scheme 6.1
Scheme 6.2
Scheme 6.3
General Chemical equation for Hydrolysis .....................................195
Chemical Reaction for Methylation (Esterification) .......................196
Chemical Reactions for Unsaturated Polyesters .............................197
xiv
CHAPTER 1
General Introduction
1.1 Biological Importance of Metabolomics
Metabolomics can be defined as the study of all metabolites contained in an organism of
interest. This simple definition encompasses a complex and diverse field of study. Fiehn, an
early pioneer in the field, has split metabolomics into 4 different classifications; (1) Target
compound analysis; (2) Metabolite/Metabolic profiling; (3) Metabolomics and (4) Metabolite
fingerprinting.1 Target compound analysis can be used to look for the primary effect of a
genetic alteration, usually a single change is studied.1 This type of analysis is usually narrow
in focus and limited to a few compounds or a single class of compounds.
Either a
quantitative
desired.
or
semi-quantitative
determination
of
differences
is
Metabolite/Metabolic profiling is used to look at the function or functions of one or more
pathways after a genetic manipulation.1 Either a quantitative or qualitative determination may
be desired with this technique. Metabolomics casts a wider net and aims to look at all of the
metabolites in an organism after a genetic alteration.1 This technique requires both a
qualitative and quantitative determination of metabolites found and looks to elucidate global
changes within the metabolome. Metabolic fingerprinting is to be used as a classification
tool to distinguish between different groups.
These groups can be classified by
predetermined biomarkers and has applications from plant breeding to the clinical setting.
1
The use of one or more of these different techniques can be very powerful in the
identification and/or quantification of metabolites and can be applied to many scientific
problems.
Three of the major areas where metabolomics can be applied are: plant
metabolomics;
animal
metabolomics
and
medical/clinical
metabolomics.
Plant
metabolomics was pioneered in the early part on the millennia by Weckwerth, Trethewey and
Fiehn, and was originally envisioned as a way to identify novel metabolites in plants.2 As
more research was done and more tools developed plant metabolomics was envisioned as a
technique for functional genomics and eventually systems biology.1, 3-5
1.1.1 Plant Metabolomics
An early breakthrough showed the efficacy of the metabolomics as a tool for
functional genomics by showing a difference in the metabolome of a transgenic plant when it
had a silent phenotype.6 Metabolomic tools have been applied to crop breeding and in the
development of genetically modified and functional foods. The cross breeding of crops to
pass along or improve upon desired traits has been going on for thousands of years.7 Now
genetic factors that govern metabolite quality and quantity are being investigated due to their
effects on final crop nutritional value.8-14 It has been shown that small genomic regions
control the overall metabolome and that modes of inheritance differ among metabolites.10, 15
This knowledge is especially important in the face of the need for increased crop yields and
nutritional density to feed the world’s ever increasing population.16 Genetically modified
foods have been a reality for some years now.17 Genetically modified foods, in general, are
able to either increase their yields by increased hardiness to natural stresses or the ability to
2
tolerate different herbicides, pesticides or other chemically based crop pest aids.18-21 Some
genetically modified crops even have the ability to produce a pesticide themselves as is the
case with Bt based genetically modified crops.22 Functional foods on the other are designed
to increase the nutritional value of foods grown. “Golden Rice” was an early success in this
area and was able to add a large amount of vitamin A to rice and was hoped to end vitamin A
deficiencies in the third world.23 Recently, a corn variety was made that was able to increase
the level of 3 different vitamins further showcasing the possibilities in the is area of
research.24 One concern with these genetically modified foods are the unintended
consequences of genetic alteration.25 Concerns over unknown compounds and proteins being
made have been an issue along with the fear of increased or different allergens being
produced.26
1.1.2 Animal Metabolomics
Metabolomics and model organisms such as C. elegans (nematode), D. rerio (zebra
fish) and D. melanogastor (fruit fly) has been used to study various biological questions
pertaining to animals. The use of metabolomics to study zebra fish has given scientists the
ability to study vertebrates and assess the global gene expression changes caused by exposure
to toxic chemicals and/or drugs.27,
28
Additionally, the ability to study embryogenesis has
given insight into the small molecule changes occurring during the earliest stages of
development.29-33 Differences in the organs between the sexes has been shown in a study
looking at the livers of zebrafish which could have wide implications in clinical treatments.34
3
C. elegans has been shown to be useful as a model in the study of endogenous small
molecule signals. Studies have shown that the pheromones called ascarosides are regulators
in development, mating and social behaviours.35-39 Advances in targeted metabolomics have
enabled quantification of these molecules and allowed for absolute comparisons.36 Novel
metabolisms have even been shown in long-lived mutants potentially leading to life span
improvements.40
The fruit fly is another model organism that is useful for studying the effects of aging
and for tracking changes to physiology.14 Originally, metabolomics was employed to study
the relationship between metabolites and the phenotype.14 Studies using fruit flies have
included looking at the affects of heat stress and also on how inbreeding affects the responses
to heat stress when compared to wild-type controls.41-42 The study of cold shock and hypoxia
has also been successfully tackled using metabolomic tools.43-44
1.1.3 Clinical Metabolomics
Use of metabolomics in the clinic has increased in recent years as new technologies
come online. Metabolomic’s power comes in the ability to identify biomarkers of disease
and to even to elucidate the effects of treatment on the human metabolome. The ability to
distinguish between the plasma of normal patients and patients with diseases such as type 2
diabetes and cardiovascular disease has been shown through the use of biomarkers.45,
46
Biomarkers for cancer detection has also been explored with some success. Biomarkers
found in urine were shown to be efficacious in the detection of prostate cancer, which is the
leading cancer for men in the western hemisphere.47 Other cancers including hepatocellular
4
carcinoma, lung cancer, pancreatic cancer and ovarian cancer to name few have had
biomarkers discovered for testing and diagnosis which could lead to early discovery and
improved patient prognosis. 48-52
With the aging population in the developed world a deeper understanding of how
aging affects the metabolome is gaining increased importance.
Differences in the
metabolome due to age has been explored and could be important in developing new
treatments to age related disease.53 The impact of metabolomics on human disease discovery
and treatments is just now being felt, with more research the possibility of increased
detection sensitivities and earlier windows of disease discovery from onset are possible and
could have help patients have beneficial outcomes.
1.2 Conventional Instruments Used in Metabolomics
Metabolomics uses sophisticated analytical technologies in order to assess the
metabolites found in a target organism.
Cutting edge sample preparation, metabolite
separation and metabolite analysis are required to gain insight into sometimes small but
significant changes in the metabolome. To date, no single sample preparation and analysis
protocol can ascertain the contents of the metabolome. Multiple analytical approaches must
be cultivated in order to be able to analyze the extremely diverse set of biological properties
contained in the metabolome. Molecules vary widely in their polarity, solubility, molecular
weight as well as many other molecular attributes. Covered here are some of the current
tools used to gain partial pictures of the metabolome.
5
1.2.1 Capillary Electrophoresis Mass Spectrometry
Capillary electrophoresis mass spectrometry (CE/MS) has proven to be a powerful
technique for the analysis of polar metabolites in biological samples. CE/MS separates
molecules on the basis of the charge to mass ratio. CE separations are highly efficient and
require very little sample for the analysis. One major drawback to the technique is its
inherent lack of sensitivity due to the small sample volumes used. Another problem that
CE/MS has is the relative lack of reproducibility primarily due to its sensitivity to small
temperature changes.62 CE/MS has been used for comparison of transgenic versus wild-type
soybeans with the ability to detect differences.63 A study of the rice plant oryza sativa was
able to successfully detect amino acids, purines and amines.64-65 A study of moss extract was
able to analyze for organic acids, sugar phosphates, nucleotides, coenzymes.66 Even
dehydration stressed Arabidopsis thaliana was studied by CE/MS to reveal metabolic
changes.67 On the clinical side, CE/MS based metabolomics has been used to look at urine
samples as well as saliva and cerebral spinal fluid.68-70 While great strides have been made
recently to improve the technology and techniques used, CE/MS still lags behind other
techniques and as of now is still considered a secondary technology.71
1.2.2 Gas Chromatography Mass Spectrometry
Gas chromatography mass spectrometry (GC/MS) has been used in metabolomics
from the very beginning when the terminology was coined.72-73 The strengths of GC/MS are
numerous. Among them are excellent reproducibility, high peak capacity, sensitivity and
excellent ability to separate peaks chromatographically due to the large number of types of
6
columns available.62 Sensitivity and a large dynamic range are also listed among the
techniques advantages. Additional advantages include the ability to derivatize polar, nonvolatile or unstable molecules in order to make them conducive to GC/MS analysis. The
ability to analyze such a wide range of molecules with very different chemistries is rivaled by
few in the field. Many early works in metabolomics were based on GC/MS analyses,
including early work in plant and clinical studies.74-78 Currently, many metabolomic analysis
are still being performed using GC/MS as the main technique for detection, identification and
quantitation of biomolecules. Recent advances have included the advent of 2D GC/MS
where two GC columns are set up in tandem to increase the resolving power before
introduction into the mass spectrometer.79-81
1.2.3 Liquid Chromatography Mass Spectrometry
Liquid chromatography mass spectrometry (LC/MS) is another powerful technique
used for metabolomics. LC/MS is a versatile technique that has the ability to separate and
analyze a wide range of biomolecules. Polar and non-polar molecules as well as small and
large molecular weight molecules are all possible.62 The discovery of electrospray by Fenn
et.al. led to a veritable explosion in the use of LC/MS due to this ionization technique having
the ability to work with many classes of compounds.82 Drawbacks to the methodology
include matrix effects causing difficulties in quantitation.83 Recent plant based metabolic
profiling work include identification of plant natural products for medicinal plants.84 Recent
work has also been done on comparison of transgenic plant samples as well as work done on
the effect of abiotic stresses on the metabolome.85-86 Clinical based metabolomics has made
7
great strides in elucidating the metabolome of humans in different bodily fluids including
plasma and urine.87-88 The discovery of biomarkers for prostate, bladder and kidney cancers
to name a few have shown great promise in becoming screening tools for earlier cancer
detection.89-90 Recently, metabolomic screenings to look into such areas as cognitive
impairment, atherosclerosis and rheumatoid arthritis shows some of the potential for this
powerful technique.91-93
1.2.4 Nuclear Magnetic Resonance Spectroscopy
Nuclear Magnetic Resonance Spectroscopy (NMR) is a relative new comer to the
field of metabolomics being only used in the last decade or two.54 1H NMR has the great
advantage of requiring very little to no sample preparation.55 The technique also has the
advantage of being non-destructive, which allows for further downstream analysis by other
techniques if desired. Quantitation is also possible due to the fact that NMR signal intensity
is drawn from the molar concentration of the sample.55 Drawbacks to the technique include a
lack of sensitivity when compared to Mass Spectrometry.55 Additionally, complex extracts
can be a problem due to signal overlap. Two dimensional (2D) NMR can help to resolve this
issue but with the cost of time, which can reduce the high throughput capabilities of the
technique.55 Currently, NMR based metabolomics techniques have been able perform such
functions as sample quality control in medicinal plants, chemotaxonomy and importantly in
the age of genetically modified crops the ability to determine equivalence among transgenic
plants.55-61 1H NMR based metabolomics have been used to shed light on many different
diseases in a clinical setting. A recent study used NMR to look at changes in the metabolome
8
caused by inflammation for patients with arthritis.94 Four biomarkers recently were identified
in urine that were able to identify patients with bipolar disorder when compared with normal
patients.95 Biomarker research for cancer detection has shown some efficacy in recent years.
Biomarkers for epithelial ovarian cancer, pancreatic cancer, oral cancer and lung cancer have
been identified and shown to be different from normal patients.96-99
1.3 Mass Spectrometers
The discovery and analysis of canal rays (positively charged ions) by Eugene
Goldstein was done in 1886. Wilhelm Wien discovered that strong magnetic and electric
fields deflected these rays and in 1899 was able to separate these rays by their mass to charge
ratio. Building on these techniques J.J. Thompson in 1913 was able to separate the neon
isotopes, therefore giving the world the first example of a mass spectrometer. Since that time
many applications were thought of for the mass spectrometer including the separation of
Uranium-235 from Uranium-238 for use in the first atomic bomb using a Calutron developed
by Ernest Lawrence the discoverer of the cyclotron.119 With many advancements in
technology and the development of new techniques mass spectrometers in the 1950’s were
used for the analysis of organic compounds for the first time.120-121 With this new application
being proven efficacious, the ground work for the field of metabolomics had been laid.
Major advances in mass analyzers and their relative strengths and weaknesses will be
covered in this section.
9
1.3.1 Quadrupole Mass Spectrometer
The quadrupole mass analyzer was first reported in the 1950s by Wolfgang Paul.122
The quadrupole mass analyzer consists of four parallel rods that have a fixed direct current
applied to them as well as alternating radio frequencies applied to them. The combination of
specific currents and radio frequencies allow for ions of certain mass to charge ratios being
selected while other ions develop unstable trajectories and fail to reach the detector. One
great strength of the instrument is the ability monitor a single mass to charge ratio for long
periods of time. The instrument can also scan a larger mass range by alternating the radio
frequency in order to look at many mass to charge ratios over a short time frame. The
quadrupole is a relatively cheap mass analyzer and is very popular due to this fact. The
connecting of multiple quadrupoles in line used for a technique called tandem mass
spectrometry. Tandem mass spectrometry allows for greater identification capabilities by
allowing targeted ions to be further fragmented and identified by the fragmentation pattern
which can distinguish between two molecules of identical molecular weight but differing
compositions makeup. One drawback is the relative low resolution compared to some of the
more powerful mass analyzers available but albeit at much greater cost.
1.3.2 Time of Flight Mass Spectrometers
The time of flight mass analyzer was first demonstrated in 1948 by A.E Cameron and
D.F. Eggers Jr. while working at the top secret Y-12 National security complex, dubbed the
velocitron.123 Ions are accelerated down the flight path by an electric field. This electric field
gives each ion of the same charge the same amount of kinetic energy. Ions that are heavier
10
will travel slower and this difference can be measured and the mass to charge ratio of an ion
calculated due to its flight time. Early time of flight mass spectrometers, while functional
suffered from the primitive electronics available at the time. With the improvement both in
the speed and sophistication of electronic components and the rapid improvements in
computer processing power, the ability to make a fast and accurate time of flight mass
spectrometer became possible in the 1990s. Improvements in techniques and design like the
reflectron, ion gating and orthogonal ion acceleration have led to great improvements in
resolution and mass accuracy.124-126 Time of flight mass spectrometers are capable of high
resolutions only exceeded by a few other mass spectrometers. One big drawback of the
instrument is calibration drift that can happen over the course of a run causing the masses to
shift.
1.3.3 Ion Trap Mass Spectrometers
There are several different types of ion trap mass analyzers. The first ion trap was
developed by Wolfgang Paul in 1953 and was a three dimensional quadrupole design.122 The
quadrupole ion trap works on the same principles as the quadrupole mass analyzer covered
earlier in this chapter except that the ions can be trapped and ejected sequentially. In 1984
the coupling of a dampening gas as well as the development of the mass-selective instability
mode led to the three dimensional ion trap being commercially available for the first time.127
The key improvement in the development of mass-selective instability was that ions of a
specific mass to charge ratio could be selectively ejected for either detection or isolation of
the non ejected ions for further fragmentation.
11
Linear ion traps trap ions in a two dimensional quadrupole field instead of a three
dimensional field.
One of the first examples of a linear ion trap was made 1969 by
Church.128 This is done by confining ions radially using a two dimensional radio frequency
field and axially by having end electrodes with stopping potentials applied.130 Many
advancements have been made over the years to the design and have culminated into the
toroidal shaped traps that we have today.129 A couple of advantages of the linear ion trap is
the increased injection efficiencies and higher ion storage capacities when compared to a
three dimensional ion trap.130 Linear ion traps also have the great advantage of being either
used as a standalone mass spectrometer or as part of a hybrid tandem mass spectrometer.130
The orbitrap mass spectrometer was invented at the turn of the millennium by
Alexander Makarov.131 The orbitrap traps ions electrostatically around a central spindle
shaped electrode.131-132 The image current from the trapped ions are detected while in orbit
and the digitized signal is converted using a Fourier transform from the time domain to the
frequency domain and a mass spectrum of the ions present is produced. Advantages of this
system are its good dynamic range, high sensitivity, high resolution and high mass
accuracy.132 A disadvantage of the system is its high price when compared to other trapping
instruments but if high resolution and mass accuracy are required the orbitrap is relatively
cheap when compared to a Fourier transform ion cyclotron resonance mass spectrometer.
1.3.4 Fourier Transform Ion Cyclotron Resonance Mass Spectrometer
The Fourier transform ion cyclotron resonance mass spectrometer (FT-ICR MS) was
developed in 1974 by Comisarow and Marshall.133 The FT-ICR MS determines the mass to
12
charge ratio of ions in a fixed magnetic filed by their cyclotron frequency.133 Ions are initially
trapped in a Penning trap and then excited with an oscillating electric field to their resonant
cyclotron frequencies. The now phased ions produce a charge and can now be detected by a
pair of electrodes in the instrument. The signal produced is called free induction decay. The
free induction decay signal is then Fourier transformed from the time domain to the
frequency domain in to produce a mass spectrum. The great strength of FT-ICR MS is in its
high mass accuracy and high resolution. No other mass spectrometer to date can match this
instrument in these qualities. One drawback of the instrument is the great expense of
purchasing, operating and maintaining such an instrument.
1.4 Data Analysis
Metabolomic based experiments can be broken into the general categories of targeted
and non-targeted. The non-targeted approach requires looking at all possible metabolites in a
sample including the identified and non-identified. The non-targeted data sets tend to be
very large and complex and some sort of statistical analysis needs to be carried out in order
to simplify the variables and make sense of the results. The targeted approach creates data
sets that are relatively simple when compared to the non-targeted approach but can still
consist of dozens of compounds and multiple variables. Usually, the targeted approach must
go through a non-targeted analysis first in order to identify all compounds within the
specified criteria (i.e. amino acids, monosaccharides, etc.).62 The key steps to data analysis
are as follows: (1) Initial data processing (2) Statistical analysis and (3) Method validation.
13
1.4.1 Initial Data Processing for Non-targeted and Targeted Data
The initial processing of the data is the first step for both the non-targeted and
targeted data sets. The processing can be done manually if no software tools are available
but software gives the advantage of speed and also produces unbiased data sets with no
human error introduced. The initial data processing should be able to do the following two
things: (1) Perform peak detection and (2) Peak identification. Peak detection starts with
finding the peaks in the chromatograms through deconvolution of mass spectral data. Next,
retention times need to be established and peak alignment performed between sample
replicates and samples needing to be compared. A quantification ion for each unidentified
needs to be established and a response collected. The peak should then be identified if
possible by comparing the data to mass spectral databases such as the NIST library for
GC/MS based data or the Golm Metabolome Database.100-101 Additionally, for LC/MS based
analyses databases such as METLIN can be used for identification.102 Many software tools
are available for the automated processing of data but they are usually focused on either
GC/MS or LC/MS data processing. For GC/MS based data the programs Aloutput, Metab,
MetaboliteDetector, MetaQuant, MET-IDEA, MSFACTs, MetAlign and TagFinder are
available with a range of tools for raw data analysis.62,
103-110
Some of these tools like
TagFinder are more comprehensive in their nature and able to do most needed functions,
while programs like MetAlign, which performs data pre-processing, are considered a first
step only before other tools are used to finish the analysis. For LC/MS based data the
programs MetAlign, Mzmine and XCMS are available.106, 111-112 As with the GC/MS based
14
programs MetAlign is a single function tool but the Mzmine and XCMS are more
comprehensive tools.
1.4.2 Data Mining for Non-targeted Data
After initial processing of the data is completed the raw data set now needs to be
mined for the interesting metabolites. Data from identified and unidentified peaks can be
used and the important biological differences extracted.
Many powerful forms of
multivariate analysis tools are available and can be used to simplify the sometimes enormous
data sets. Some of the most popular methods will be outlined below. Principal component
analysis (PCA) is the most frequently used methodology for metabolomic data mining.62, 113
PCA uses the variance in the data to produce a score matrix, a loading matrix and a residual
matrix. The score matrix which can be represented either as a 2D plot or a 3D plot show the
overall variance in the relationship between the samples.
The loading matrix tells the
researcher which metabolites are contributing to the sample variance and by how much,
which can be useful in picking out the metabolites of interest. Hierarchial cluster analysis
(HCA) is another popular methodology used in metabolomic data mining.62,
114
The total
metabolite information of each sample is represented by a single vector. These vectors then
have their distances within multidimensional space calculated. Clustering of these calculated
distances allow the samples to be compared.
Supervised multivariate analysis approaches can also be used for identifying important
individual metabolites in a data set.
The partial least squares regression (PLS) based
technique uses the output from the PCA or HCA non-supervised data analysis and allows the
15
data to be explored in another dimension based on a new data matrix based on another set of
experiments.62,
115
PLS analysis can be simply stated as a technique for finding the
fundamental relations between two data matrices. An additional variant of PLS regression
that can be useful under certain circumstances is orthogonal PLS, which simplifies the results
down to one single component.62,
116
For a supervised data set, if there are no specific
variables a discriminant analysis version of PLS and orthogonal PLS can be used.62,
117-118
With this approach binary vectors are used in the place of specific variables.
1.5 Thesis Layout
The overall goals of this research were four-fold:
(1) applying my previously
developed metabolic profiling methodology to wild-type and transgenic plants under stressed
conditions with the aim of elucidating the changes that occur in the metabolic pathways; (2)
optimizing the methodology to further improve assay reproducibility and throughput;
(3)expanding the developed methodologies to be able to analyze complex metabolites and
then to apply the developed methodology to chemical profiling of synthetic polymers; (4)was
to expand the scope of the study and to see if differences in the proteome could be found in
the wild-type and transgenic plants.
Chapter 2 describes the application of my previously developed metabolic profiling
methodology in the MS degree studies to heat treated wild-type and transgenic Arabidopsis
thaliana plants. Super oxide reductase, an enzyme that removes reactive oxygen species
from plant cells in high efficiency, is present in different forms in the different transgenic
16
lines. The metabolic stress response to acute heat treatment in the wild-type samples was
established, followed by differential metabolite profiling of various transgenic lines.
Chapter 3 uses proteomics tools to look at the wild-type and transgenic Arabidopsis
thaliana plants in an unstressed condition. Using these tools I show the differences in the
proteome for the transgenic plants when compared to the wild-type. For this work I used the
same transgenic plants as were previously used to develop the metabolic profiling
methodology.
Chapter 4 shows the work done to improve the previously developed metabolic
profiling methodology. Compared in this chapter is the variable weight weighing technique
used previously and a constant weight technique. Both methodologies have their pros and
cons but what is established here is the affect on method variance that each technique has.
Chapter 5 describes the development of a new one-pot methodology to look at
complex metabolites not conducive to silyl derivatization. Fatty acid methyl esters (FAMEs)
will be analyzed by chemically separating these compounds into their component parts of
fatty acids and glycerin from their larger complex metabolite form, which is not volatile or
conducive to silyl derivatization. The freed fatty acids will then be esterified into methyl
esters in the presence of methanol to make then easily analyzed by GC-MS. The FAMEs
will be qualitatively analyzed and compared semi-quantitatively across the wild-type and
transgenic lines.
Chapter 6 represents a paradigm shift in the application of the method developed in
Chapter 5. This chapter describes a sample preparation and analysis solution for polyester
resins.
This methodology is compared to several standard polyester resin analysis
17
methodologies including NMR and FTIR as well as older ASTM sample preparation
methods for gas chromatography based analysis.
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CHAPTER 2
Expression of Pyrococcus furiosus Superoxide reductase in Arabidopsis
thaliana and the Metabolomic Changes upon Acute Thermal Stress
2.1 Introduction
The ultimate aim of this project is to provide a valid technique capable of elucidating
the complex metabolic changes associated with heat treatment of Arabidopsis thaliana.
Understanding of these complex metabolic changes and how genomic variants differ from
the wild type can lead to plant genomic designs that increase heat tolerance, which in turn
could lead to better crop yields in the face of rising global temperatures.66
Previous studies using GC/MS based technologies have been done to analyze plants
undergoing various forms of abiotic stresses.64, 65 These studies, while successful have used
either complex partitioning procedures or have required the use of expensive isotopically
labeled reagents.64, 65 Additionally, these studies limited their heat treatments to much milder
conditions than in the current study. For this study I used my previously established one-pot
sample preparation procedure as well as using a higher temperature for heat treatment which
could give us more insight into an extreme heat exposure.54
For this study I looked at the effects of heat treatment on two different transgenic
plant lines and compare their responses to a wild-type reference. The transgenic line SOR3
has a gene expressed from the archael hyperextremeophile Pyrococcus furiosus to produce
the enzyme superoxide reductase.2 This enzyme is responsible for eliminating reactive
oxygen species, which can cause damage to cells when present in high levels. Superoxide
38
reductase has three major benefits when compared to superoxide dismutase.
First,
superoxide reductase is a more efficient enzyme than the endogenous superoxide
dismutase.2,67,70 Second, superoxide reductase does not produce oxygen as a byproduct,
which is important because the oxygen byproduct from superoxide dismutase can lead to
more reactive oxygen species being formed.2,67-69 Third, superoxide reductase is stable over
a very large range of 4-100°C, when compared to super oxide dismutase.2,67,70
The 2nd transgenic plant line is CA6. This plant line expresses inactive superoxide
reductase and is used as a 2nd control along with wild-type to show the difference for plants
expressing active super oxide reductase.
2.2 Experimental
2.2.1 Materials
Acetonitrile (ACN), tetrahydrofuran (THF) and benzene of HPLC grade or better
were purchased from EMD Chemicals.
Trimethylsilyl dimethylamine (TMSDMA) and
pentadecane were purchased from Sigma-Aldrich, Inc. Zirconium oxide grinding media
were purchased from Zircoa, Inc.
2.2.2 Sample Preparation
Arabidopsis thaliana seedlings (250+ Seedlings) were grown to development stage
1.02 for a plate based platform.1 Two separate batches of wild-type (WT), two batches of
SOR3 and three batches of CA6 were grown for this study. Each batch was separated into
39
two sub batches in which one half was heat treated and designated as H and one half was not
and designated as C. Heat treated samples were heated directly to 45°C for 2h.2 The samples
were immersed in liquid nitrogen and ground using the traditional mortar and pestle
technique and frozen at -80°C.3 Seedlings were then lyophilized overnight and stored at
-80°C until analysis.
2.2.3 Analyte Derivatization
300 μL of an 80:20 solvent mixture (3:1:1, ACN: THF: Benzene): TMS-DMA
extraction-derivatization solution (internal standard included) was added to 4.4-7.4mg of
ground tissue (Scheme 3.1).4 After vortexing the solution for 10 sec and heating at 70°C for
40 min using a digital heat block (VWR, Germany), the mixture was centrifuged for 10 min
at 9500×g. The supernatant was analyzed directly.
2.2.4 Instrumentation
All mixtures were analyzed using a HP5890 Gas Chromatograph coupled to a
HP5972
quadrupole
Mass
Spectrometer
(Hewlett-Packard,
Palo
Alto,
CA).
Chromatographic separations were achieved with a RTX-5ms 5% Diphenyl/ 95% Dimethyl
Polysiloxane GC capillary column 30 m × 0.25 mm × 0.25 um (Restek, Bellefonte, PA). The
helium carrier gas was set to 1.2 mL per minute. One microliter of derivatized sample was
injected into the instrument in splitless mode. The injection port was 280°C with an initial
oven temperature of 50°C along with a 20°C per minute ramp to 320°C, hold for 10 min.
40
The mass spectrometer was set in the scan mode from 50 amu to 550 amu. Data were
collected using HP/Agilent’s Chemstation software (Agilent, Palo Alto, CA) and analyzed in
Excel (Microsoft, Redmond, WA).
2.3 Results and Discussion
In the current study I seek to elucidate the affects of heat stress on the metabolome of
wild-type (WT) and transgenic Arabidopsis thaliana seedlings. The previously discussed gas
chromatography mass spectrometry (GC-MS) based metabolic profiling methodology was
used as the main tool in the study to semi-quantitatively monitor metabolites identified for
the changes of their expression levels due to heat stress.54 The samples used in this work
were seedling batches grown side by side in order to minimize fluctuations in metabolites
caused by small differences in growing conditions. Regardless, natural variation in plants
has been shown to be as high as 30%; two growth batches are therefore examined throughout
to reduce unexpected operational errors.55
Figure 2.1(A) shows a typical untreated elution chromatogram for the plant samples
analyzed and Figure 2.1(B) shows a typical Heat Treated chromatogram.
Chemical
components to be tracked were found by searching the Total Ion Chromatogram (TIC) of the
plant samples for peaks and then comparing the spectra to the reference spectra found in the
NIST library. A compound was considered positively identified if a match quality of ≥90
was obtained using the probability-based matching (PBM) algorithm developed by Fred
McLafferty and co-workers.53 A match quality of <90 but >60 was deemed to be partially
identified and a match quality of <60 was determined to be an unknown. For this work,
41
partially identified compounds will be tracked but the indentifying information was limited to
compound class (i.e. monosaccharide, disaccharide, etc.). When a match quality of <60 was
obtained the compound was considered to be unknown and therefore not tracked for this
study. Ultimately, 73 compounds were selected for tracking across multiple compound
classes, including: amino acids, fatty acids, small organic acids, vitamins, phenylpropanoids,
sterols and brassinosteroids.
Quantification of positively identified compounds was carried out by normalizing the
peak area of a unique ion species in its Extracted Ion Chromatogram (EIC) to the initial
sample weight. An ion was considered unique if no co-eluting compounds were detected and
if the signal to noise ratio was >3 Fold. Table 2.1 shows representative calculations for six
compounds from the samples C-WT1-4 and H-WT1-4. The naming scheme for samples is as
follows; C is for Control and H is for Heat treated, plant line, either, WT, CA6 or SOR3,
growth batch then finally aliquot. The sample C-WT1-4 means the control sample for the
wild type plant line from growth batch 1 and aliquot 4. Table 2.2 shows a summary of the
changes in the expression level of the heat treated over non- treated samples. Figure 2.1(C)
shows a representative overlay of the EICs for an untreated and heat treated sample. This
figure shows the relative up regulation of Alanine and the relative down regulation of
Glycine upon heat treatment. Second, the differences found between the heat treated and non
heat treated samples will be compared for each line with WT being considered the baseline to
see if the separate lines had differing responses to heat treatment.
A further analysis was performed in order to make sense of the changes using known
metabolic pathways and to understand the responses that plants have to heat stress. Since the
42
known pathways are extremely complicated and interconnected to each other as shown in
Figure 2.16, the pathways were separated using the divisions suggested by the Kyoto
Encyclopedia of Genes and Genomes (KEGG), in order to make biological sense of the
changes found in the plants.56
2.3.1 L-Glycine, L-Serine and L-Threonine Metabolism
The average reduction in L-Glycine detected for the heat treated samples of WT, CA6
and SOR3 is 68%, 67%, and 78%, respectively. The universal accumulation of L-Glycine in
the system across different genetic lines is consistent with the expected activation of
photorespiratory pathways.43 Similarly, with acute heat treatment L-Threonine increases
55%, 70% and 55% for WT, CA6, and SOR3 plants, respectively. Additionally, the amino
acid L-Threonine, which was potentially accumulated directly from L-Glycine through the
enzyme threonine aldolase, showed a large increase. This was not unexpected since LThreonine has been shown in the past to accumulate under many different abiotic stresses. 44
L-Serine, having a role in sulfur assimilation, on the other hand, shows a reduction after heat
treatment: 15% for WT, 7% for CA6 and 25% for SOR3.45 The decrease in expression is
largest for SOR transgenic species which was 1.7 fold more than WT and 3.7 fold more than
CA6. Pyruvate, known to be involved in fatty acid synthesis, as well as being a major
contributor to respiration pathways, also had a relatively nominal change in amounts after
heat treatment with WT increasing 15%, and SOR3 decreasing 5% but a larger difference for
CA6 of 23% was found.46-48 This pathway is shown in Figure 2.2. The direct relationship
between these compounds and their enzymatic conversion is shown in Figure 2.2(A).
43
2.3.2 L-Valine, L-Leucine and L-Isoleucine Biosynthesis
These three amino acids, which can be classified as branched-chain amino acids49,
have been deemed essential in the development of bacteroids and symbiotic nitrogen
fixation50. It has been shown in the past that accumulation of these branched-chain amino
acids is a response to many form of abiotic stress, including Osmotic and temperature
stresses is expected.51-52 The L-Valine, L-Leucine and L-Isoleucine biosynthesis pathway
was also looked at for this study6. L-Valine, L-Leucine and L-Isoleucine are known to
increase tolerance to drought, salt, and heat stress; not surprisingly they all show very large
increases in the extracts after acute heat treatment. Specifically, L-Valine showed a 245%
increase in the WT line, a 300% increase in the CA6 line and a 275% increase in the SOR3
transgenic line. L-Leucine had a 510% increase for the WT line, a 780% increase for the
CA6 line and a 545% increase for the SOR3 line. Additionally, L-Iso-Leucine had a 480%
increase in the WT line, a 573% increase in the CA6 line and a 515% increase in the SOR3
line of plants. This pathway is shown in Figure 2.3. In this case, the pathway has been
strongly upregulated due to the stress of acute heat exposure.
Figure 2.3(A) shows
degradation. Figure 2.3(B) shows the 1st enzymatic step and the possible stopping point for
the pathway upon heat treatment.
2.3.3 Alanine, Aspartate and Glutamate Metabolism
The Alanine, Aspartate and Glutamate pathway was looked at for this study.7
Aspartate is down regulated in this pathway upon heat treatment with the WT line showing a
25% decrease, the CA6 line showing a 40% decrease and the SOR3 line also showing a 40%
44
decrease. Conversely, both Alanine and Asparagine show large increased levels after acute
heat treatment. With Alanine have an increase of 130% for the WT line, 183% for the CA6
line and 135% for the SOR3 line. Which is not surprising since the accumulation of Alanine
though is considered to be a universal stress signal.71 It has been shown in previous work that
under stresses as various as low frequency magnetic fields, hypoxic stress, low and high
temperature stress, water stress, salt stress and nutrient stress the accumulation of Alanine as
a stress response have been reported.71-77 Asparagine showed an increase of 145% for the
WT line, 123% for the CA6 line and 110% for the SOR3 line.
This pathway is shown in
Figure 2.4. Figure 2.4(A) shows the direct relationship between Aspartate and Asparagine
but leaves a question as to possible routes of accumulation for Alanine due to the fact that
Pyruvate levels do not change.
2.3.4 Citrate Cycle (TCA)
The effect of heat treatment on the compounds Malate, Fumarate and Succinate was
studied via the Citrate Cycle pathway.8 The compounds Fumarate and Succinate show
moderate increases after acute heat treatment. Malate on the other hand shows a moderate
decrease post treatment. Fumarate increased 45% for the WT line, 33% for the CA6 line and
30% for the SOR3 line. Similarly, Succinate increased 40% for the WT line, 63% for the
CA6 line and 35% for the SOR 3. The decrease in Malate was 30% for WT, 40% for CA6
and 55% for the SOR3 line. This pathway is shown in Figure 2.5. The direct conversion of
Malate to Fumarate via the Fumarate hydratase enzymatic reaction and subsequent further
downstream conversion to Succinate via Succinate dehydrogenase is the immediate response
45
to acute heat treatment.
Figure 2.5(A) shows the enzymatic reactions for these three
compounds and shows that Malate is possibly being consumed as the pathway pushes
towards fumarate and Succinate upon heat treatment. Malate was shown to decrease more
for the transgenic SOR when compared to the WT and CA6 lines.
2.3.5 Antioxidants Ascorbate and a-Tocopherol
The antioxidants Ascorbate, otherwise known as Vitamin C, and a-Tocopherol,
otherwise known as Vitamin E were looked at for this study via the Ascorbate and Alderate
Metabolism pathway and the Ubiquinone and Other Terepenoid-Quinone Biosynthesis
pathway.9-10 Vitamin C in WT plants increased 105%, the CA6 line increased a more
moderate 33% and the SOR3 line increased 75%. In this study, Vitamin E accumulation was
increased 20% for WT plant line, 40% for CA6 line and 40% for SOR3 line. The pathway
for Vitamin C is shown in Figure 2.6 and the pathway for Vitamin E is shown in Figure 2.7.
Ascorbate, which plays a role in sweeping out reactive oxygen species as well as cell
signaling, is greatly enhanced as a result of acute heat stress. 11-15 The WT plant increased
105%, the CA6 line increased a more moderate 33% and the SOR3 line increased 75%. The
Vitamin E family, of which a-tocopherol is the primary form found in Arabidopsis leaves, is
a powerful antioxidant.16 Vitamin E has been shown to be effective in scavenging the ROS
species singlet Oxygen (1O2).17 In previous studies, it has been shown that Vitamin E
accumulation in response to heat stress is expected and can be as much as 5 fold higher under
extended stress conditions.18
46
2.3.6 Phenylalanine and Sinapinic acid
The compounds sinapinic acid, also known as sinapic acid, which is a member of the
Phenylpropanoid family, was tracked through this study as well as the amino acid
phenylalanine as shown in Figure 2.8. Both of these compounds are in the Phenylpropanoid
Biosynthesis pathway.19 For Sinapinic acid the WT line increased 20%, the CA6 line
increased 23% and the SOR3 line increased 5%.
In this study, a significant accumulation of
L-Phenylalanine was found to have taken place in response to the acute heat stress. The WT
plant line was shown to increase 415%, while the CA6 line increased 513% and the SOR3
line increased 465%. Phenylpropanoid compounds have been implicated in plant signaling,
acting as antioxidants due to their radical scavenging abilities, as well as antibiotics and
pigments
among
other
functions
and
roles.28-30
Additionally,
biosynthesis
of
phenylpropanoids has been shown to take place in response to biotic and abiotic stresses.26-27
Phenylalanine which is the starting material for the phenylpropanoid biosynthesis pathway,
as well as being significant in multiple other biosynthetic pathways including Flavonoid
Biosynthesis, amongst others.19,
22-25
The large accumulation of this compounds is not
surprising although it has been shown that many of the compounds in this pathway normally
do not remain as free acids due to their rapid conversion to sugar, carbohydrate and organic
acid conjugates.26 Sinapinic acid on the other hand, and its corresponding esters have been
proven to play an important role in UV-B resistance in Arabidopsis.20-21 Sinapinic acid
increase only nominally for the SOR transgenic line but the increases for WT and CA6 were
both significantly larger, by 4 fold and 4.6 fold respectively.
47
2.3.7 Glutathione Metabolism
The compounds L-Pyroglutamic acid, L-Glutamic acid and L-Glutamine were
analyzed for this study and their relationship elucidated in the Glutathione Metabolism
pathway, as shown in Figure 2.9.31 In this case, acute heat stress had no effect on the
amounts of L-Pyroglutamic acid in Arabidopsis thaliana. The WT line had a 0% change in
amount, the CA6 line had a 3% increase and the SOR3 line had a 5% increase. All of these
changes are well within the method’s variance so there is no change in the compound.
Interestingly, for L-Glutamic acid the affect of acute heat stress did cause an
inconsistent response in 2 of the 3 plant lines. The WT line had one homogenate show a
30% decrease and the other two shows a 100% increase, in the SOR3 line, one homogenate
showed a nominal 10% decrease and the other samples showed a 130% increase and finally
the CA6 line showed a more consistent response with a 3 homogenates increasing with the
average being 43%.
As was the case with L-Glutamic acid, there was an inconsistent response to the
amounts of L-Glutamine post acute heat stress in 2 of the 3 plant lines. In the WT line one
homogenate showed a 60% decrease and the other showed a 50% increase, in the SOR3 line
one homogenate showed a 20% decrease and one showed a 30% increase and as before the
CA6 line showed a more consistent response where all 3 homogenates increase, with the
average being 37%.
L-Pyroglutamic acid which is the cyclic lactam of L-Glutamic acid has been called at
one time, the forgotten amino acid, but is now generally considered to be an amino acid
derivative or metabolite due to its non zwitterionic nature.32-33 L-Pyroglutamic acid is
48
considered to be an important source of L-Glutamic acid in Arabidopsis thaliana and any
change in accumulation would directly affect L-Glutamic acid and Glutamine biosynthesis.34
In this case, acute heat stress had no effect on the amounts of L-Pyroglutamic acid in
Arabidopsis thaliana.
L-Glutamic acid, an amino acid, which is the next step in the Glutathione Metabolism
pathway, is considered to have a central role in higher plants in amino acid metabolism.35
There is also evidence that L-Glutamic acid is a potential signaling molecule in plants, with
evidence of there being a role in Nitrogen systems.35-36 Additionally, many studies have
noted that there appears to be homeostasis of L-Glutamic acid in plants, with the most likely
reason being its key role in many critical plant functions.37-40 Interestingly, the affect of
acute heat stress did caused an inconsistent response in 2 of the 3 plant lines.
L-Glutamine, an amino acid, is the next step in line in the Glutathione Metabolism
pathway. L-Glutamine unlike L-Glutamic acid has not achieved homeostasis in plants and
has shown large variances in accumulation in response to stress or time of day.37-40
L-Glutamine plays an important role in the biosynthesis of other amino acids through the
utilization of its amide-nitrogen.41 Additionally, along with other amino acids, L-Glutamine
helps control Nitrogen status during growth and development.42 As was the case with LGlutamic acid, there was an inconsistent response to the amounts of L-Glutamine post acute
heat stress in 2 of the 3 plant lines. It should also be noted that the homogenate samples that
decreased in L-Glutamine also decreased in L-Glutamic acid and the samples that increased
also did so for both molecules. The inconsistencies in response to acute heat stress in the
Glutathione Metabolism pathway poses an interesting possibility. Are there 2 (or more)
49
responses to acute heat stress for this pathway where neither is dominant over the other but
both equally possible and if yes, what are the necessary conditions to tip the scale in one
direction or the other?
2.3.8 Steroid Biosynthesis
The compounds Campesterol, Sitosterol and Stigmasterol were analyzed for this
study and their relationship elucidated in the Steroid Biosynthesis pathway, as shown in
Figure 2.10.57 Brassinosteroids have been shown to play a role in abiotic stress response and
signaling.78 Stresses in which brassinosteroids have been shown to play a protective role
include osmotic, oxidative, heavy metal, saline and thermal stress among others.78-82
Campesterol upon heat treatment the WT samples were reduced an average of 30%, the
SOR3 line reduced 20% and the CA6 line was also reduced 20%. Sitosterol is reduced 35%
for WT, the SOR3 line was reduced by 20% and the CA6 line was also reduced by 20%. The
compound Stigmasterol was reduced 20% for WT and a more moderate 10% for SOR3 and
7% for the CA6 line. Figure 2.10(A) shows the enzymatic reactions for the three compounds
Campesterol, Sitosterol and Stigmasterol which shows the possibility that the sterols are
being consumed in order to feed the Brassinosteroid Biosynthesis pathway. This pathway is
shown in Figure 2.10(B).63
2.3.9 Fatty Acid and Unsaturated Fatty Acid Biosynthesis
The Fatty Acids Palmitic acid and Stearic acid were analyzed for this study and their
relationship shown in the Fatty Acid Biosynthesis pathway, as shown in Figure 2.11.59 Fatty
50
acids have been shown to be important compounds of cellular membranes.83 Additionally,
fatty acids have been found to be important for suberin and cutin waxes that form protective
barriers for plants from the environment.83 In response to stresses, they have also been shown
the ability to remodel membrane fluidity and to release the unsaturated fatty acid linolenic
acid.84-85 Unsaturated fatty acids have been shown to be either upregulated or down regulated
when exposed to different abiotic stresses.83 Specifically, glycerolipid composition of
unsaturated fatty acids was found to decrease as a result of heat stress.83, 86-88 What has not
been explored until the current study if how heat stress affects free fatty acids. The Saturated
Fatty Acid Palmitic acid showed nominal change after heat treatment. WT was reduced by
10%, SOR3 was reduced by 10% and CA6 increased by 13%. The Saturated Fatty Acid
Stearic Acid showed modest change after heat treatment. WT was reduced by 20%, SOR3
was increased by 5% and CA6 was increased by 13%. The Unsaturated Fatty Acids Linoleic
Acid and Linolenic Acid were analyzed for this study and their relationship shown in the
Unsaturated Fatty Acid Biosynthesis pathway, as shown in Figure 2.12.60 The Unsaturated
Fatty Acid Linoleic acid showed a large increase after heat treatment. WT increased by 45%,
SOR3 increased by 55% and CA6 increased by 77%. The Unsaturated Fatty Acid Linolenic
Acid also increased by a large amount. WT increased by 55%, SOR3 increased by 65% and
CA6 increased by 83%.
2.3.10 Proline
The amino acid Proline was also studied via the Arginine and Proline Metabolism
pathway as shown in Figure 2.13.58 Proline has been shown to accumulate during abiotic
51
stress.89-90 Additionally, there is evidence that Proline is involved in signaling of stress and
also influencing adaptive responses.89, 91-92 Proline accumulation during abiotic stresses has
been observed to play a crucial role against the oxidative damage caused by reactive oxygen
species, and has even been shown to modify the activity of the antioxidative enzymes.89, 93-96
For WT Proline increased 50% after heat treatment, SOR3 increase 30% and CA6 increased
by 67%.
2.3.11 Multivariate Analysis of GC/MS Metabolic Data
In order to examine the relationship between the untreated and heat treated samples
across the three different genetic strains Principal Component Analysis was performed both
on the 40 identified metabolites as well as the total data set with partially identified
compounds included for a total of 71 compounds tracked. Principal Component Analysis,
which is an unsupervised technique, has the ability to simplify complex data sets and show
their inherent relationships. 61-62 Figure 2.14 shows the Principal Component Analysis of the
total data set collected including partially identified compounds. Figure 2.15 shows the
principal component analysis for the narrowed list of identified compounds that were used
for pathway analysis.
The 1st and 2nd principal components for the total data set account for 72.66% of the
variance and clearly show the differentiation between the non-treated and heat treated
samples. The 1st and 2nd principal components for the identified compounds account for
78.3% of the variance, and also clearly shows the differentiation between the non-treated and
heat treated samples.
52
The analysis done on the partial and complete data sets show that the heat treated
samples can clearly be separated from the heat treated samples but that the untreated
transgenic samples are not so easily differentiated which agrees with previous work on these
strains that shows them to be largely similar in their metabolic profile and that most
differences were due to biological variation.54
2.4 Conclusions
In this chapter I described the application of my previously developed GC-MS based
metabolic profiling methodology to the analysis of heat treated transgenic Arabidopsis
thaliana. This method was able to track and compare multiple analytes across multiple
classes of molecules and compare the differences found between the heat treated and nontreated samples as well as the differences between the SOR transgenic line and its two
control lines. As expected, many different pathways were affected, sometimes dramatically,
by the heat treatment of the samples.
Additionally, significant differences were observed
between the SOR transgenic line and its controls for several compounds across several
pathways. These differences suggest that their regulation in stressed plants is affected by
exposure to reactive oxygen species and that the reduction of these by the enzyme SOR
altered the plants defense response after heat treatment.
53
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Table 2.1 Shows how Data was normalize and fold change calculated for samples C-WT1-4
(Control) and H-WT1-4 (Heat Treated).
Heat Treated ?
Compound Name
N
Y
Pyruvic acid
Pyruvic acid
N
Y
R.T.
Ion Area Counts(AC) Sample Weight(WT)
AC/WT
Fold Change
5.56 217
5.56 217
1629675
1185827
5.6
5.4
291013
219598
0.8
L-Alanine
L-Alanine
5.686 190
5.686 190
9675854
17237909
5.6
5.4
1727831
3192205
1.8
N
Y
L-Glycine-Di-TMS
L-Glycine-Di-TMS
5.824 131
5.824 131
5016326
873123
5.6
5.4
895773
161689
0.2
N
Y
B-Alanine
B-Alanine
6.362 176
6.362 176
2566764
2131017
5.6
5.4
458351
394633
0.9
N
Y
L-Valine
L-Valine
6.573 218
6.573 218
6349005
17752465
5.6
5.4
1133751
3287494
2.9
N
Y
L-Leucine
L-Leucine
6.982 158
6.982 158
8832853
47138460
5.6
5.4
1577295
8729344
5.5
68
Table 2.2 Fold differences comparing heat treated to non-heat treated samples
Compound Name
Pyruvic acid
WT1-4
WT3-4
SOR3-2-4
SOR3-1-4
CA6-2-4
CA6-3-4
CA6-1-4
Fold Change Fold Change Fold Change
Fold Change
Fold Change
Fold Change
Fold Change
0.8
1.5
0.9
1.0
1.0
1.3
1.4
L-Alanine
1.8
2.8
2.2
2.5
2.3
2.7
3.5
L-Glycine-Di-TMS
0.2
0.4
0.2
0.2
0.2
0.4
0.3
B-Alanine
0.9
1.3
1.0
0.8
1.2
1.2
0.9
L-Valine
2.9
4.0
3.6
3.9
3.5
3.8
4.7
L-Leucine
5.5
6.7
5.7
7.2
5.2
6.2
9.0
Phosphate-TMS
0.9
1.5
1.0
1.1
1.1
1.3
1.7
L-Proline
1.5
1.5
1.2
1.4
1.4
1.5
2.1
L-Iso-Leucine
5.4
6.2
5.8
6.5
5.5
6.3
8.4
L-Glycine-TRI-TMS
0.1
0.6
0.2
0.3
0.2
0.4
0.5
Succinic acid
1.1
1.7
1.2
1.5
1.3
1.8
1.6
Glyceric acid
0.3
0.5
0.5
0.5
0.5
0.5
0.5
2-Butenedioic acid
1.8
1.1
1.4
1.2
1.6
1.2
1.2
L-Serine
0.6
1.1
0.8
0.7
0.9
0.9
1.0
L-Threonine
1.3
1.8
1.6
1.5
1.6
1.6
1.9
2,5-Diaminovalerolactam
0.9
2.0
0.6
0.7
1.3
0.7
1.0
Malic acid
0.2
1.2
0.2
0.7
0.3
0.7
0.8
L-Aspartic acid
1.0
0.5
0.9
0.3
0.6
0.5
0.7
5-oxo-proline
0.8
1.2
1.1
1.0
0.9
1.1
1.1
L-Glutamic acid
0.7
2.0
0.9
2.3
1.1
1.3
1.9
Phenylalanine
3.5
6.8
5.2
6.1
5.7
6.0
6.7
L-Asparagine
1.5
3.4
1.6
2.6
2.0
1.9
2.8
Phosphate sugar 1
0.4
3.3
1.3
1.5
0.8
1.1
1.3
L-Glutamine
0.4
1.5
0.8
1.3
1.1
1.8
1.2
mono sugar 1
2.0
0.5
1.2
0.5
1.3
1.0
0.7
69
Table 2.2 continued
WT1-4
WT3-4
SOR3-2-4
SOR3-1-4
CA6-2-4
CA6-3-4
CA6-1-4
Fold Change Fold Change Fold Change
Fold Change
Fold Change
Fold Change
Fold Change
2,3-bis(trimethyl silyl)oxy propyl bis0.7
2.0
1.1
1.1
0.9
1.1
1.5
(trimethylsilyl)oxy propyl ester
Compound Name
mono sugar 2
125.5
118.6
129.1
120.6
132.6
132.7
176.6
mono sugar 3
1.0
1.5
1.4
1.4
1.3
1.5
1.7
mono sugar 4
1.3
1.5
1.5
1.6
1.4
1.5
1.9
mono sugar 5
0.7
0.8
0.8
0.7
0.7
1.0
1.0
Citric acid
0.8
2.9
0.6
0.9
1.0
1.3
2.0
mono sugar 6
1.0
1.9
1.3
1.1
1.2
1.5
1.5
mono sugar 7
0.8
0.8
0.8
1.0
0.9
1.1
1.5
mono sugar 8
0.7
1.3
0.8
0.9
1.0
1.1
1.6
mono sugar 9
0.7
3.1
0.7
0.6
1.1
1.1
1.2
mono sugar 10
0.7
1.0
0.9
0.7
1.0
1.1
1.4
mono sugar 11
1.0
1.2
0.9
1.0
1.2
1.1
1.6
mono sugar 12
0.9
1.4
1.2
1.2
1.2
1.3
1.5
mono sugar 13
0.5
2.2
1.1
1.7
1.1
1.5
2.0
Phosphate sugar 2
1.2
0.8
1.1
0.6
1.2
1.2
1.2
mono sugar 14
0.9
1.5
1.0
1.2
1.1
1.2
2.0
mono sugar 15
1.1
2.0
1.1
1.4
1.0
1.4
1.5
L-Ascorbic acid
1.8
2.3
1.8
1.7
1.0
1.1
1.9
mono sugar 16
0.5
2.2
1.1
1.7
1.1
1.6
1.8
mono sugar 17
0.9
1.4
0.9
1.0
1.2
1.3
1.9
D-Gluconic acid
0.9
1.3
0.9
1.4
1.0
1.2
1.8
Hexadecanoic acid
1.0
0.8
1.0
0.8
1.1
1.0
1.3
Phosphate sugar 3
0.9
0.9
1.0
0.9
1.0
1.1
1.3
mono sugar 18
2.5
4.2
4.0
2.9
3.0
4.3
4.9
70
Table 2.2 continued
Compound Name
Phosphate sugar 4
WT1-4
WT3-4
SOR3-2-4
SOR3-1-4
CA6-2-4
CA6-3-4
CA6-1-4
Fold Change Fold Change Fold Change
Fold Change
Fold Change
Fold Change
Fold Change
0.4
2.1
0.8
0.7
0.8
0.9
0.9
Myo-Inositol
2.3
7.1
2.3
8.2
3.1
3.9
8.8
mono sugar 19
1.4
0.4
1.1
0.4
1.1
0.7
0.5
Phytol-TMS
0.6
0.6
0.9
0.4
0.8
0.7
0.5
Phosphate sugar 5
2.0
1.0
1.6
0.8
1.4
1.0
0.9
Linoleic acid
1.2
1.7
1.6
1.5
1.6
1.6
2.1
Phosphate sugar 6
1.0
4.6
1.5
2.4
1.5
2.0
2.5
Linolenic acid
1.3
1.8
1.6
1.7
1.6
1.8
2.2
Stearic acid
0.9
0.7
1.1
1.0
1.0
1.2
1.2
Phosphate sugar 7
0.3
7.1
1.1
2.5
1.0
1.5
2.5
Sinapinic acid
0.9
1.5
1.1
1.0
1.1
1.1
1.5
Dissach 1
0.9
0.8
1.0
0.9
1.0
1.1
1.3
Myo-inositol-monophosphate
0.6
2.0
1.1
1.7
1.0
1.3
1.7
Dissach 2
1.4
2.2
1.3
1.5
1.5
1.6
3.0
Dissach 3
0.6
2.0
0.9
0.9
1.0
1.4
1.3
Dissach 4
0.5
1.6
1.1
1.1
1.1
1.1
0.4
Vitamin E
0.8
1.6
1.5
1.3
1.2
1.4
1.6
Dissach 5
2.8
5.0
3.2
4.4
3.1
4.6
7.6
Campesterol
0.5
0.9
0.8
0.8
0.7
0.8
0.9
Dissach 6
0.7
1.6
0.7
1.0
1.2
1.3
1.7
Stigmasterol
0.6
1.0
0.9
0.9
0.8
0.9
1.1
Sitosterol
0.5
0.8
0.8
0.8
0.7
0.8
0.9
Dissach 7
4.2
17.4
2.7
14.1
4.9
9.1
17.6
Dissach 8
1.1
4.9
1.0
3.9
2.1
2.6
5.9
71
3.0x108
A
3.0x105
2.6x108
Response
Response
2.2x108
1.8x108
1.4x108
2.2x105
1.0x105
6.0x104
2.0x107
2.0x104
0
Minutes
L-Glycine-TMS
1.4x105
7
4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0
L-Alanine-TMS
1.8x105
1.0x108
6.0x10
C
2.6x105
5.60 5.62 5.64 5.66 5.68 5.70 5.72 5.74 5.76 5.78 5.80 5.82 5.84
Minutes
3.4x108
3.0x108
B
Response
2.6x108
2.2x108
1.8x108
1.4x108
1.0x108
6.0x107
2.0x107
4.0 5.0 6.0 7.0 8.0 9.0 10.0 11.0 12.0 13.0 14.0 15.0 16.0 17.0
Minutes
Figure 2.1 A-Representative chromatogram for a WT sample (WT1-4). B-Representative
chromatogram for a Heat Treated sample (WT1-4). C-Overlay of the Extracted Ion
Chromatograms of A (Black) and B (Green) showing the up regulation of Alanine and the
down regulation of Glycine upon heat treatment.
72
Figure 2.2 KEGG Pathway for Glycine, Serine and Threonine Metabolism
73
threonine dehydratase
AT3G10050
Serine
Pyruvate
Serine/Glycine
hydroxymethyltransferase
AT4G13930
threonine aldolase
AT1G08630
Threonine
Glycine
Figure 2.2(A) Glycine, Serine and Threonine Metabolism
74
Figure 2.3 KEGG Pathway for Valine, Leucine and Isoleucine Biosynthesis
75
Figure 2.3(A) KEGG Pathway for Valine, Leucine and Isoleucine Degradation
76
Leucine
BCAT
4-Methyl-2-oxopentanoate
Valine
IsoLeucine
BCAT
BCAT
3-Methyl-2-oxobutanoate (S)-3-Methyl-2-oxopentanoate
BCAT= Branched Chain Amino Acid Amino Transferase
Figure 2.3(B) Valine, Leucine and Isoleucine Degradation 1st Enzymatic step appears to be
blocked by heat treatment.
77
Figure 2.4 KEGG Pathway for Alanine, Aspartate and Asparagine Metabolism
78
ASN(1-3)
Asparagine
Aspartate
ASN(1-3)=asparagine synthetase (1-3)
?
Alanine
?
Figure 2.4(A) Alanine, Aspartate and Asparagine Metabolism
79
Figure 2.5 KEGG Pathway for Citrate Cycle (TCA)
80
FUM1, FUM2
Fumaric acid
Malic acid
SDH1-(1-2)
SDH2-(1-3)
Succinic acid
FUM(1-2)= fumarate hydratase 1 or 2
SDH1-(1-2)=succinate dehydrogenase flavoprotein subunit 1 or 2
SDH2-(1-3)=succinate dehydrogenase flavoprotein subunit 1-3
Figure 2.5(A) Citrate Cycle (TCA) enzymatic reactions
81
Figure 2.6 KEGG Pathway for Ascorbate and Alderate Metabolism
82
Figure 2.7 KEGG Pathway for Ubiquinone and Other Terepenoid-Quinone Biosynthesis
83
Figure 2.8 KEGG Pathway for Phenylpropanoid Biosynthesis
84
Figure 2.9 KEGG Pathway for Glutathione Metabolism
85
Figure 2.10 KEGG Pathway for Steroid Biosynthesis
86
Brassinosteroid
Biosynthesis
Pathway
DWF1
Campesterol
DWF1
CYP710A(1-3)
Sitosterol
Stigmasterol
DWF1=∆24-sterol reductase
CYP710A(1-4)= cytochrome P450, family 710, subfamily A enzymes 1-4
Figure 2.10(A) Steroid Biosynthesis
87
Figure 2.10(B) KEGG Pathway for Brassinosteroid Biosynthesis
88
Figure 2.11 KEGG Pathway for Fatty Acid Biosynthesis
89
Figure 2.12 KEGG Pathway for Unsaturated Fatty Acid Biosynthesis
90
Figure 2.13 KEGG Pathway for Arginine and Proline Metabolism
91
Observations (axes F1 and F2: 72.66 %)
10
C‐WT1‐4
C‐SOR1‐2‐4
5
C‐CA6‐3‐4
F2 (23.01 %)
C‐CA6‐2‐4
H‐CA6‐3‐4
C‐SOR3‐2‐4
0
H‐SOR1‐2‐4
H‐WT3‐4
H‐CA6‐1‐4
H‐CA6‐2‐4
C‐CA6‐1‐4
C‐WT3‐4
‐5
H‐SOR3‐2‐4
H‐WT1‐4
‐10
‐15
‐10
‐5
0
5
10
15
F1 (49.65 %)
Figure 2.14 Principle component analysis of all metabolite data—Score plot of PC1 and PC2
92
Observations (axes F1 and F2: 78.30 %)
8
C‐WT1‐4
6
4
F2 (26.94 %)
C‐CA6‐3‐4
H‐CA6‐3‐4
C‐SOR1‐2‐4
C‐CA6‐2‐4
2
H‐WT3‐4H‐SOR1‐2‐4
0
H‐CA6‐1‐4
C‐SOR3‐2‐4
‐2
H‐CA6‐2‐4
C‐CA6‐1‐4
C‐WT3‐4
‐4
H‐WT1‐4
H‐SOR3‐2‐4
‐6
‐10
‐8
‐6
‐4
‐2
0
2
4
6
8
10
F1 (51.36 %)
Figure 2.15 Principle component analysis of identified metabolite data—Score plot of PC1
and PC2
93
Figure 2.16 KEGG Metabolic Pathways-Arabidopsis thaliana (thale cress)
94
CHAPTER 3
Using Reverse Phase Liquid Chromatography coupled to a Linear Ion
Trap-Quadrupole Tandem Mass Spectrometer for Bottom Up Proteomics
to Elucidate Changes in the Proteome of Transgenic Arabidopsis thaliana
3.1 Introduction
Previously I was able to establish two complementary methods for doing
metabolomic studies on transgenic Arabidopsis thaliana plant material.1 While multiple
differences in the metabolome were found and quantified among the transgenic materials
analyzed, what has not been explored are differences in the proteome of these samples. In
order to elucidate the proteins found in the plants, techniques from the field of proteomics
must be employed. The field of Proteomics has been around since just before the millennia
(although the study of proteins has been around much longer) large scale 2-D gels were used
in complex protein analysis at the time and MS has only become the major workhorse for
proteomics at the turn of the century because of development of ESI, MALDI and high
resolution MS analyzers.
Proteomics was at first envisioned as only an extension of
genomics.3, 4 In the infancy of plant proteomics the coupling of one and two dimensional
SDS-PAGE gels to mass spectrometry was common and gave a good start to understanding
what is in the proteome.28-31 Advancements in capillary column based reverse phase
separations using High Performance Liquid Chromatography (HPLC) has greatly improved
the separation of complex protein and
tryptic peptide mixtures.32-35 The discovery,
commercialization and subsequent advancement in the soft ionization techniques
95
electrospray ionization (ESI) and matrix assisted laser desorption ionization (MALDI)
coupled to mass spectrometry has led to a veritable explosion in this field.5-7
Two main approaches can be taken with mass spectrometry based proteomics, either
a “top down” approach where the protein is left intact prior to analysis or a “bottom up”
approach, also known as shotgun proteomics, where proteins are enzymatically digested prior
to analysis.8,9 In Top down proteomics the intact protein is analyzed directly in the mass
spectrometer. The intact protein goes through one of many forms of dissociation techniques
in the mass spectrometer in the gas-phase and the resulting fragments are then analyzed.
Common dissociation techniques include collision-induced dissociation (CID), electrontransfer dissociation (ETD) and electron-capture dissociation (ECD).
CID is the most common dissociation technique used.42-45 In CID, molecular ions are
accelerated to a high kinetic energy and collided with a neutral gas, such as argon. The
resulting collision converts kinetic energy to internal energy which causes bond breakage,
with fragmentation in the peptide backbone usually occurring at the amide bond.46 The
resulting cleavage primarily results in b and y ions and post-translational modifications
(PTMs) are not usually preserved.46 In ECD low energy electrons are captured by multiply
charged precursor ions to produce odd electron ions.
These ions fragment to produce
primarily c and z type ions. Additionally, ECD has the great advantage of preserving
PTMs.36-37 ETD, like ECD, transfers an electron to multiply charged intact proteins to induce
fragmentation and produce c and z ions. The difference with ETD is that it uses a singlycharged anion to accomplish this feat. As with ECD, PTMs are preserved.38-39
96
In “bottom up” proteomics either a single protein or mixture of proteins (shotgun
proteomics) are enzymatically digested into peptides before analysis on the mass
spectrometer.8, 9 Prior to analysis on the mass spectrometer the complex mixture of peptides
needs to be separated either by a single or a multi-dimensional separation technique in order
to simplify what is introduced on the system and not overwhelm the mass spectrometer.40-41
The first step in bottom up proteomics is choosing the proteolytic digestion strategy.
There are a large number of enzymes available that give different cleavage specificities.47
Enzymes may be used either individually or multiple enzymes can be used in parallel in
order to increase sequence coverage percentage.48-51 Common enzymes used include Trypsin,
chymotrypsin, Lys-C, GluC and AspN. After enzyme digestion the peptides can then be
introduced on the mass spectrometer by either going through a single or multi-dimensional
separation in order to reduce the complexity of the sample.40-41 Peptide separations are
usually carried out using isoelectric focusing (IEF), strong cation exchange (SCX) and
reverse-phase (RP) chromatography.47, 52-58
The separated mixture is now ionized in real time using electrospray ionization (ESI)
or spotted onto a target plate for a MALDI based analysis. Common mass spectrometers
used for proteomics are based on the following technologies: quadrupole; ion trap; time of
flight (TOF); orbitrap; and ion cyclotron resonance (ICR).47 Quadrupole mass spectrometers
are a beam type instrument that have a resolution around 1-2K and a mass accuracy of 1 part
per thousand (ppt).47,
59
Linear ion trap mass spectrometers are a trapping type mass
spectrometer (Paul Trap) that has a resolution also around 1-2K and a mass accuracy of 1
part per thousand (ppt).47,
60
The TOF based mass spectrometer is another beam type
97
instrument with an increased resolution of 10-50K and an increased mass accuracy of 510ppm.47, 61 The orbitrap mass spectrometer is based on the Kingdon trap and offers an even
higher resolution of 7.5-240K and a mass accuracy of 500-10ppm.
47, 62-64
The ICR based
mass spectrometer is a trapping based system that uses a Penning trap and offers a much
better resolution of 100-500K and the highest mass accuracy around 100ppb.47, 65
Proteins analyzed using either the “bottom up” or “top down” approach with tandem
mass spectrometry yield data that can then be searched and identified using huge searchable
databases such as SwissProt or NCBInr using search algorithms such as SEAQUEST,
MASCOT or X!Tandem.10-14
In the present study I will be using “bottom up” proteomics in order to compare
several transgenic plant lines to a Wild-Type plant line using a linear ion trap mass
spectrometer and electrospray ionization. Bottom up proteomics was chosen for this study
due to its ability to handle very complex mixtures of proteins. Bottom up proteomics needs
no off-line separation or purification, therefore the mixtures can be automated more easily.
Additionally, since a relatively low resolution linear ion trap quadrupole mass spectrometer
was being used for this study with no ECD or ETD capabilities possible, bottom up
proteomics was the appropriate choice.
This proteomic analysis combined with the
previously done metabolomic experiment should give us a more complete picture of what
similarities and differences can be found in these transgenic lines.
98
3.2 Experimental
3.2.1 Materials
1-Methyl-2-pyrrolidone (C1P), 1-Octyl-2-pyrrolidone (C8P), Hydroxyethyl disulfide
(HED), Diethylamine (DEA), N-Methyl-2-Pyrrolidone (NMP) were all purchased from
Sigma-Aldrich. HPLC grade Acetonitrile and ACS grade acetic acid purchased from VWR.
Reverse Osmosis deionized water, purified in lab. Zirconium oxide grinding media were
purchased from Zircoa, Inc. Trypsin digestion enzyme Proteomics grade Sigma-Aldrich.
Trypsin digestion solution was made by adding 20ug of trypsin to 500uL of 50mM Acetic
acid in water.
3.2.2 Sample Preparation
Arabidopsis thaliana plants were grown and the leaves were harvested at
development stage 9.7 on a soil-based platform.2 The samples were immersed in liquid
nitrogen and stored at -80C until grinding. The leaves were vacuum desiccated prior to
mechanic grinding, during which leaves were placed into a 20-mL polypropylene
scintillation vial. Zirconium oxide grinding media were then added to the vial and the cap
was tightened securely. A 14.4V reciprocating saw (Black & Decker, Hunt Valley, MD)
with a custom vial holder was used to homogenize the tissue for approximately 60 sec.
9-11mg of each sample was weighed out and normalized by volume with a
CXP/DEA/HED extraction solution to achieve a 10mg/mL concentration. A Bradford assay
was used to check the relative protein concentration of the samples and adjusted if necessary.
99
After a 1 hour extraction at 37C the sample was dried using a speedvac for 1 hour. Added
10uL of NMP and 10uL of water to sample tube. Vortex and spin for 30seconds. Using the
Trypsin digestion solution add 25uL to sample tube along with 100uL of a 100mM N-Methyl
morpholine acetate solution. Mix vigorously with a pipette tip until it foams. An overnight
digestion at 37°C using trypsin was then performed.
3.2.3 Instrumentation
All mixtures were analyzed using a Thermo Surveyor HPLC coupled with a Thermo
LTQ linear ion trap mass spectrometer (Thermo Fisher, San Jose, CA). Chromatographic
separations were achieved with a Synergi Hydro-RP C18 column, 150mm x 0.50mm I.D.,
4um particle size and 80A pore size (Phenomenex, Torrance, CA). A gradient from 95:5 to
19:81 A:B (50mM Acetic acid/H2O:Acetonitrile) was used at a flow rate of 60uL/min. The
mass spectrometer was operated in positive ion mode with an electrospray ionization source.
The mass spectrometer was operated in data dependent MS/MS scan mode, scanning from
m/z 420-2000Da with MS/MS spectra acquired on the four most abundant ions in the
precursor scan. Identification of the proteins in the samples was performed by searching the
tandem mass spectra against an 2 Arabidopsis protein databases in fastA format using
Bioworks Browser software version 3.3 (Thermo Fisher, San Jose, CA). The following
settings were used for the database search: Peptide tolerance 2.0Da; Fragment tolerance
1.0Da; Digestion enzyme-Trypsin; Searched databases-1.subset_arabidopsis_NCBI_45794
2.arabidopsis.NCBI.fasta.hdr; search algorithms-Sequest and X! tandem; fixed modifications
DeStreak (Cysteine mercaptoethanol) (mass76.00) (AA-C). Data was organized for analysis
100
using Scaffold 2 version 2.6.02 (Proteome Software, Portland, OR). Scaffold analysis was
done with the following filters: Min Protein 99.0%; Min# peptides-2 and Min peptide 95%.
3.3 Results and Discussion
Figure 3.1 shows representative mass spectral data for MS and MS/MS
chromatograms for a WT sample. For this study, samples from two separate growth batches
for each transgenic line and control were prepared and analyzed by LC/MS/MS.
The
subsequent Arabidopsis protein database search in scaffold view (99.0% minimum protein,
minimum number of peptides 2 and 95% minimum peptide) identified a minimum of 62 and
a maximum of 68 unique proteins identified. Proteins were considered identified if they
were present in both replicates of the plant line studied. The three paired transgenic plant
lines (6 total) were profiled and compared to a wild-type control and an additional green
fluorescent protein (GFP) modified control.
Figure
3.2
shows
the
spectrum
for
the
representative
peptide
(R)EAVNVSLANLLTYPFVR(E) which comes from the protein Carbonic Anhydrase 1.
The b and y ions are shown as well as the parent ion for this peptide.
The peptide
information that can be obtained through Scaffold 2 is found in Table 3.1. The Peptide
identification probability was found to be 95% or higher which is the highest match possible
using the Scaffold 2 data analysis program.
Other identification metrics include the
SEQUEST XCorr score which is the cross correlation value from the search, and in this case
was found to be 3.05 and is considered a good value since a value greater than 2.0 is
considered good.66 Another metric is the SEQUEST deltaCN score which is the delta
101
correlation value which tells you the difference between correlation scores for the 1st and 2nd
hits in the database, in this case the score was 0.43 which is a good score since a score about
0.1 is considered to be good.66 The X! Tandem –log(e) score is also used to rate the strength
of the identification.67 In this case the score for our peptide was 5.55 which shows the
strength of the match considering that a score greater than 2.0 is considered good.67
Additional information that can be gathered through Scaffold 2 are the number of tryptic
termini which shows you if the peptides were cleaved properly for a tryptic digested sample.
A score of 2 shows a properly digested peptide as was the case for our example peptide.
Information on the observed mass, the actual mass and the charge of the peptide are also
available. The last piece of information shown in Table 3.1 is the error calculated for the
observed mass and the in silico calculated mass which in this case was 0.57 AMU and
300PPM.
Table 3.2 shows the fragmentation table for this peptide.
Included are the
expected b ions, b ions plus 2H, b ions with the loss of ammonia, b ions with the loss of
water as well as the y ions, y ions plus 2H, y ions with the loss of ammonia and y ions with
the loss of water. The cells with the green background were the ions found in the current
mass spectrum. This peptide and the quality of the identification are typical of the peptides
found in this study.
Table 3.3 shows the number of assigned spectra for each of the 65 proteins found in
the WT and GFP control lines. The number of spectra assigned can be as high as 44 for the
oxygen-evolving enhancer protein 1-1 or as low as 1 for some of the least abundant proteins.
Table 3.4 shows the data collected for the 1st set of transgenic plant lines 2-6 and 2-8. Again
the highest number of assigned spectra belongs to the abundant protein oxygen-evolving
102
enhancer protein 1-1 with a total of 44. The transgenic lines 2-6 and 2-8 were inserted with
the coding region of the human type I Inositol Polyphosphate 5-phosphatase cDNA to
express the human type I inositol polyphosphate 5-phosphatase enzyme.15 This enzyme
specifically hydrolyzes inositol-1,4,5-trisphosphate, which has been linked to signaling
events involved in gravitropic responses.16 The 2-6 and 2-8 lines had 68 proteins identified.
Table 3.5 shows the 2nd pair of transgenic plant lines Hs5-8 and Hs9-7 where the
number of assigned spectra range from 42 down to 1. The transgenic lines Hs5-8 and Hs9-7
express the human type I alpha phosphatidylinositol 4-phosphate 5 kinase enzyme, which
catalyzes the synthesis of phosphatidylinositol-4,5-bisphosphate and results in an increase in
phosphatidylinositol-4,5-bisphosphate and also an increase in inositol-1,4,5-trisphosphate.15
The Hs5-8 and Hs9-7 lines had 62 proteins identified.
Table 3.6 shows the final pair of transgenic plant lines SOR3 and SOR9 where the
number of assigned spectra ranges from 45 down to 1. The transgenic lines, SOR3 and
SOR9, are modified to express the superoxide reductase enzyme from the extremophile
Pyrococcus furiosus. These plants are a GFP-fusion under the control of the CaMV 35S
promoter using the Gateway vector construct, pK7WGF2.17 This group has shown that with
the overexpression of the superoxide reductase enzyme that an enhancement in heat tolerance
is observed in stressed plants but no morphological differences are observed in non-stressed
plants.17 The SOR3 and SOR9 lines had 68 proteins identified.
Upon first inspection of the enormous amount of data, it is apparent that the
proteomic profiles of these samples qualitatively are remarkably similar. This is not too
surprising when you take into account that these samples are phenotypically and
103
morphologically similar to each other under non stressed conditions.15-17 Quantitatively, it is
normal to see 30% of more in natural biological variation within samples, therefore,
differences will only be considered to be real if they are seen in all samples for a given
transgenic line and in both of the duplicate growth batches analyzed.18
After a close inspection of the proteomic data for all transgenic and control plant
lines, it was shown that the protein Carbonic Anhydrase 1(EC 4.2.1.1) was completely
abrogated in the SOR3 and SOR9 transgenic plant lines. Whereas the other transgenic and
control plant lines showed a minimum of four assigned spectra and a maximum of 9 assigned
spectra for this protein. Figure 3.3 shows the number of assigned spectra for each sample
using the 95% peptide probability filter in place. Figure 3.4 shows the number of unique
peptides identified for each sample, again using the 95% peptide probability filter in place.
The difference between the number of assigned spectra and the number of unique peptides is
due to there being the possibility of multiple spectral identifications being assigned to a
single peptide. The maximum sequence coverage was seen in the 2-6 and 2-8 transgenic
lines, and Figure 3.5 shows the sequence coverage for the sample 2-8 B1 where 21% of the
sequence was identified, which is a strong positive identification for this protein. In Table
3.1, the protein identification information for Carbonic Anhydrase 1 for sample 2-8 B1 is
shown.
Protein identification probability is 100%.
The numbers of unique peptides
identified in this case are 4 and the number of unique spectra was found to be 5 and the total
number of spectra was 8.
The zinc metalloenzyme Carbonic Anhydrase has been shown to catalyze the
hydration of CO2 to HCO3- and the reverse dehydration of HCO3- back to CO2 as shown in
104
Figure 3.6.19 Carbonic Anhydrase was found to be present in plants by Bradfield in 1947 but
has also been found to have multiple isoforms and to have activity in all microorganisms,
plants and animals.19-22 In plants, carbonic Anhydrase has been implicated in many biological
processes including respiration, CO2 transfer and fixation, pH regulation, ion exchange and
biosynthesis.23-27 The link between the loss of Carbonic Anhydrase expression in the super
oxide reductase expressing transgenic line poses an interesting question. What is the effect
of superoxide reductase on the transgenic plants that the expression of carbonic Anhydrase is
severely reduced or eliminated entirely? One possibility is that in the SOR mutant the SOR
enzyme and the still available Superoxide Dismutase enzyme have either sequestered all of
the available zinc in the plants or has somehow retarded its uptake. It has been shown that
the activity levels of Carbonic Anhydrase are a great indicator of overall zinc levels.68 SOR
has been shown to only use Iron as its metal cofactor so it is not possible for it to use any
available zinc.69 On the other hand SOD uses the metals copper and zinc.70-71 It may be
possible that the SOD enzyme has used up the available zinc in the plant but it is more likely
that the uptake of zinc was retarded due to SOD being outcompeted by the higher order
kinetics of SOR and its ability to work at much higher temperatures therefore, eliminating the
need for SOD and its metal cofactor zinc.
3.4 Conclusions
In this study, using “bottom up” proteomics, I looked at the differences in the proteins
expressed over six different transgenic lines as compared to wild type and GFP fused
controls. I was able to show that qualitatively the transgenic lines were very similar to their
105
controls in their unstressed states, although, one major difference was found. The enzyme
Carbonic Anhydrase was found to be down regulated in the super oxide reductase expressing
transgenic lines, so as to be rendered undetectable by the mass spec based assay employed in
this study. While the mechanism for such a change has not been investigated here in this
study, one possibility is that SOR is outcompeting SOD due to its ability to effectively
operate at elevated temperatures and therefore down regulating SOD and its cofactor metal
zinc.
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115
Table 3.1 Protein and Peptide Information Obtained from Scaffold 2
Protein Information
Protein group
Protein identification probability
Protein molecular weight (AMU)
Number of unique peptides
Number of unique spectra
Number of total spectra
CA1 (CARBONIC ANHYDRASE 1)
100.00%
37,432.70
4
5
8
Peptide Information
Peptide sequence
Peptide identification probability
SEQUEST XCorr score
SEQUEST DCn score
X! Tandem ‐log(e) score
Number of Tryptic Termini
Observed peptide mass (AMU)
Actual peptide mass (AMU)
Spectrum charge
Actual minus calculated peptide mass (AMU)
Actual minus calculated peptide mass (PPM)
(R)EAVNVSLANLLTYPFVR(E)
95.00%
3.05
0.43
5.55
2
953.81
1,905.61
2
0.57
300
116
Table 3.2 Fragmentation table for the peptide (R)EAVNVSLANLLTYPFVR(E)
b
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
b Ions
130.05
201.09
300.16
414.2
513.27
600.3
713.38
784.42
898.46
1,011.55
1,124.63
1,225.68
1,388.74
1,485.80
1,632.86
1,731.93
1,906.04
b+2H
300.65
357.2
392.71
449.74
506.28
562.82
613.34
694.88
743.4
816.94
866.47
953.53
b‐NH3
397.17
496.24
583.27
696.36
767.39
881.44
994.52
1,107.60
1,208.65
1,371.72
1,468.77
1,615.84
1,714.91
1,889.02
b‐H2O
112.04
183.08
282.15
396.19
495.26
582.29
695.37
766.41
880.45
993.54
1,106.62
1,207.67
1,370.73
1,467.78
1,614.85
1,713.92
1,888.03
AA
E
A
V
N
V
S
L
A
N
L
L
T
Y
P
F
V
R
y Ions
1,906.04
1,777.00
1,705.96
1,606.90
1,492.85
1,393.78
1,306.75
1,193.67
1,122.63
1,008.59
895.5
782.42
681.37
518.31
421.26
274.19
175.12
y+2H
953.53
889
853.49
803.95
746.93
697.4
653.88
597.34
561.82
504.8
448.26
391.71
y‐NH3
1,889.02
1,759.97
1,688.94
1,589.87
1,475.83
1,376.76
1,289.73
1,176.64
1,105.60
991.56
878.48
765.39
664.35
501.28
404.23
257.16
158.09
y‐H2O
1,888.03
1,758.99
1,687.95
1,588.88
1,474.84
1,375.77
1,288.74
1,175.66
1,104.62
990.58
877.49
764.41
y
17
16
15
14
13
12
11
10
9
8
7
6
5
4
3
2
1
117
Table 3.3 Identified proteins and the number of assigned spectra for the WT and GFP
transgenic plant line. Scaffold viewer was set to 95% min protein, min peptides 2 and min
peptide 95%.
# Identified Proteins
1 Oxygen‐evolving enhancer protein 1‐1, chloroplastic
2 Ribulose bisphosphate carboxylase large chain
3 ATPase beta subunit
4 Probable fructose‐bisphosphate aldolase 2, chloroplastic
5 Oxygen‐evolving enhancer protein 3‐2, chloroplastic
6 Ribulose bisphosphate carboxylase/oxygenase activase, chloroplastic
7 PSBQ/PSBQ‐1/PSBQA; calcium ion binding
8 putative photosystem II type I chlorophyll a b binding protein.
9 Glutamine synthetase, chloroplastic/mitochondrial
10 ATP synthase subunit alpha, chloroplastic
11 Oxygen‐evolving enhancer protein 2‐1, chloroplastic
12 transketolase, putative
13 LHCB2.2 (Photosystem II light harvesting complex gene 2.2)
14 GLP1 (GERMIN‐LIKE PROTEIN 1)
15 Photosystem I reaction center subunit IV A, chloroplastic
16 Sedoheptulose‐1,7‐bisphosphatase, chloroplastic
17 Ribulose bisphosphate carboxylase small chain 1A, chloroplastic
18 CA1 (CARBONIC ANHYDRASE 1); carbonate dehydratase/ zinc ion binding
19 Chlorophyll a‐b binding protein CP26, chloroplastic
20 Myrosinase
21 ESM1 (EPITHIOSPECIFIER MODIFIER 1); carboxylesterase
22 PGK1 (PHOSPHOGLYCERATE KINASE 1)
23 GAPC (GLYCERALDEHYDE‐3‐PHOSPHATE DEHYDROGENASE C SUBUNIT)
24 ATP synthase beta chain 1, mitochondrial
25 Chain A, A. Thaliana Cobalamine Independent Methionine Synthase
26 Ribulose bisphosphate carboxylase small chain 3B, chloroplastic
27 2‐Cys peroxiredoxin BAS1, chloroplastic; Thiol‐specific antioxidant protein A
28 TGG2 (GLUCOSIDE GLUCOHYDROLASE 2)
29 Glyceraldehyde‐3‐phosphate dehydrogenase A, chloroplastic
30 PSAD‐1 (photosystem I subunit D‐1)
31 Apocytochrome f
32 CPN60A (chloroplast / 60 kDa chaperonin alpha subunit)
33 GLP3 (GERMIN‐LIKE PROTEIN 3)
34 ATP synthase gamma‐subunit
35 Plastocyanin major isoform, chloroplastic; DNA‐damage‐repair/toleration protein DRT112
36 putative chlorophyll binding protein
37 PSI type III chlorophyll a/b‐binding protein
38 Glyceraldehyde‐3‐phosphate dehydrogenase B, chloroplastic
39 Ribulose bisphosphate carboxylase small chain 1B, chloroplastic
40 Thioredoxin M‐type 1, chloroplastic
41 fructose‐1,6‐bisphosphatase, putative
42 uridylyltransferase‐related
Accession Number
M.W.
gi|19883896
gi|3914541
gi|5881701 (+2)
gi|75306047
gi|18206249 (+1)
gi|12643259 (+2)
gi|15233587 (+1)
gi|21592428
gi|11386828 (+1)
gi|190358242
gi|18206371
gi|18411711 (+1)
gi|15224465 (+3)
gi|15218535 (+2)
gi|18203441 (+1)
gi|1173345 (+3)
gi|27735223
gi|30678347 (+3)
gi|28558077 (+1)
gi|585536
gi|15231805 (+3)
gi|15230595
gi|15229231 (+1)
gi|186521400 (+3)
gi|55670130 (+2)
gi|20141686 (+1)
gi|14916972 (+2)
gi|18420974 (+3)
gi|20455490
gi|15235503 (+3)
gi|193805978
gi|15226314 (+2)
gi|15242028 (+2)
gi|166632 (+2)
gi|12644265 (+3)
gi|12083258 (+3)
gi|110736814 (+3)
gi|20455491
gi|132090
gi|11135105 (+3)
gi|15232408 (+2)
gi|18394414 (+2)
35 kDa
53 kDa
54 kDa
43 kDa
25 kDa
52 kDa
24 kDa
28 kDa
47 kDa
55 kDa
28 kDa
80 kDa
29 kDa
22 kDa
15 kDa
42 kDa
20 kDa
30 kDa
30 kDa
61 kDa
44 kDa
50 kDa
37 kDa
60 kDa
84 kDa
20 kDa
29 kDa
63 kDa
42 kDa
23 kDa
35 kDa
62 kDa
22 kDa
41 kDa
17 kDa
28 kDa
29 kDa
48 kDa
20 kDa
20 kDa
45 kDa
31 kDa
WT A1
WT B1
44
36
27
12
9
10
6
7
6
3
9
9
7
1
5
4
4
5
7
3
3
5
3
4
4
1
4
3
1
3
4
4
4
3
4
3
4
2
2
4
2
1
GFP A1
39
37
28
9
8
8
10
8
8
8
7
7
6
3
6
4
5
5
7
4
4
5
6
5
5
1
3
3
7
3
5
5
2
3
4
3
4
3
3
2
1
3
GFP B1
36
35
27
13
8
8
7
10
9
7
6
8
7
4
6
5
6
6
4
4
6
5
2
6
5
3
4
2
5
2
7
3
4
2
4
4
4
2
3
3
1
1
34
24
30
9
8
9
10
10
8
5
6
8
7
1
5
6
6
7
4
4
6
6
2
4
5
3
5
3
4
2
4
5
3
4
5
2
4
2
2
1
3
3
118
Table 3.3 Cont’d
# Identified Proteins
LHCB4.2 (LIGHT HARVESTING COMPLEX PSII)
FLA8 (Arabinogalactan protein 8)
Glutathione S‐transferase PM24; auxin‐binding protein
Oxygen‐evolving enhancer protein 1‐2, chloroplastic
CLPC (HEAT SHOCK PROTEIN 93‐V); ATP binding / ATPase
Probable fructose‐bisphosphate aldolase 1, chloroplastic
Chlorophyll a‐b binding protein 165/180, chloroplastic
unknown protein
PSII 47KDa protein [Capsella bursa‐pastoris]
ATP synthase epsilon chain, chloroplastic; ATP synthase F1 sector epsilon subunit
ATP synthase subunit beta, chloroplastic; ATP synthase F1 sector subunit beta
Malate dehydrogenase 1, mitochondrial
Glutamate‐1‐semialdehyde 2,1‐aminomutase 1, chloroplastic
hypothetical protein
H+‐transporting ATP synthase‐like protein
60S acidic ribosomal protein P2‐2
binding / catalytic/ coenzyme binding
chlorophyll A‐B binding protein CP29 (LHCB4)
COR15A (COLD‐REGULATED 15A)
ATGSTF9 (Arabidopsis thaliana Glutathione S‐transferase (class phi) 9)
thioglucoside glucohydrolase [Arabidopsis lyrata subsp. lyrata]
Ferredoxin‐dependent glutamate synthase 1, chloroplastic
Chain A, A Putative Udp‐Glucose Pyrophosphorylase
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
Accession Number
M.W.
gi|15231990 (+2)
gi|18406799 (+3)
gi|1170091 (+1)
gi|11134146 (+1)
gi|18423214 (+3)
gi|75313518
gi|115767 (+1)
gi|18394049 (+3)
gi|139387278 (+1)
gi|114591 (+2)
gi|190358687
gi|11133715 (+3)
gi|1170028 (+3)
gi|110740665 (+3)
gi|14596171 (+1)
gi|12229934 (+2)
gi|18404496 (+3)
gi|15241005 (+2)
gi|15227963 (+1)
gi|15224581 (+2)
gi|74473455
gi|12643970 (+2)
gi|150261506 (+2)
31 kDa
43 kDa
24 kDa
35 kDa
103 kDa
43 kDa
28 kDa
16 kDa
56 kDa
14 kDa
54 kDa
36 kDa
50 kDa
24 kDa
24 kDa
11 kDa
35 kDa
31 kDa
15 kDa
24 kDa
54 kDa
180 kDa
52 kDa
WT A1
WT B1
1
3
2
1
4
3
2
2
3
2
1
2
2
2
2
2
2
3
1
1
0
0
1
GFP A1
2
2
2
2
1
3
2
2
2
2
1
1
2
3
2
2
1
2
1
0
0
0
0
GFP B1
2
1
0
1
3
0
2
2
3
2
0
1
1
2
3
0
1
0
2
1
1
1
1
2
2
2
1
3
0
1
2
2
2
0
1
1
2
2
0
0
1
1
1
1
1
3
119
Table 3.4 Identified proteins and the number of assigned spectra for the 2-6 and 2-8
transgenic plant lines. Scaffold viewer was set to 95% min protein, min peptides 2 and min
peptide 95%.
# Identified Proteins
1 Oxygen‐evolving enhancer protein 1‐1, chloroplastic
2 Ribulose bisphosphate carboxylase large chain
3 ATPase beta subunit
4 Probable fructose‐bisphosphate aldolase 2, chloroplastic
5 Oxygen‐evolving enhancer protein 3‐2, chloroplastic
6 Ribulose bisphosphate carboxylase/oxygenase activase, chloroplastic
7 PSBQ/PSBQ‐1/PSBQA; calcium ion binding
8 putative photosystem II type I chlorophyll a b binding protein.
9 Glutamine synthetase, chloroplastic/mitochondrial
10 ATP synthase subunit alpha, chloroplastic
11 Oxygen‐evolving enhancer protein 2‐1, chloroplastic
12 transketolase, putative
13 LHCB2.2 (Photosystem II light harvesting complex gene 2.2)
14 GLP1 (GERMIN‐LIKE PROTEIN 1)
15 Photosystem I reaction center subunit IV A, chloroplastic
16 Sedoheptulose‐1,7‐bisphosphatase, chloroplastic
17 Ribulose bisphosphate carboxylase small chain 1A, chloroplastic
18 CA1 (CARBONIC ANHYDRASE 1); carbonate dehydratase/ zinc ion binding
19 Chlorophyll a‐b binding protein CP26, chloroplastic
20 Myrosinase
21 ESM1 (EPITHIOSPECIFIER MODIFIER 1); carboxylesterase
22 PGK1 (PHOSPHOGLYCERATE KINASE 1)
23 GAPC (GLYCERALDEHYDE‐3‐PHOSPHATE DEHYDROGENASE C SUBUNIT)
24 ATP synthase beta chain 1, mitochondrial
25 Chain A, A. Thaliana Cobalamine Independent Methionine Synthase
26 Ribulose bisphosphate carboxylase small chain 3B, chloroplastic
27 2‐Cys peroxiredoxin BAS1, chloroplastic; Thiol‐specific antioxidant protein A
28 TGG2 (GLUCOSIDE GLUCOHYDROLASE 2)
29 Glyceraldehyde‐3‐phosphate dehydrogenase A, chloroplastic
30 PSAD‐1 (photosystem I subunit D‐1)
31 Apocytochrome f
32 CPN60A (chloroplast / 60 kDa chaperonin alpha subunit)
33 GLP3 (GERMIN‐LIKE PROTEIN 3)
34 ATP synthase gamma‐subunit
35 Plastocyanin major isoform, chloroplastic; DNA‐damage‐repair/toleration protein DRT112
36 putative chlorophyll binding protein
37 PSI type III chlorophyll a/b‐binding protein
38 Glyceraldehyde‐3‐phosphate dehydrogenase B, chloroplastic
39 Ribulose bisphosphate carboxylase small chain 1B, chloroplastic
40 Thioredoxin M‐type 1, chloroplastic
41 fructose‐1,6‐bisphosphatase, putative
42 uridylyltransferase‐related
Accession Number
M.W.
gi|19883896
gi|3914541
gi|5881701 (+2)
gi|75306047
gi|18206249 (+1)
gi|12643259 (+2)
gi|15233587 (+1)
gi|21592428
gi|11386828 (+1)
gi|190358242
gi|18206371
gi|18411711 (+1)
gi|15224465 (+3)
gi|15218535 (+2)
gi|18203441 (+1)
gi|1173345 (+3)
gi|27735223
gi|30678347 (+3)
gi|28558077 (+1)
gi|585536
gi|15231805 (+3)
gi|15230595
gi|15229231 (+1)
gi|186521400 (+3)
gi|55670130 (+2)
gi|20141686 (+1)
gi|14916972 (+2)
gi|18420974 (+3)
gi|20455490
gi|15235503 (+3)
gi|193805978
gi|15226314 (+2)
gi|15242028 (+2)
gi|166632 (+2)
gi|12644265 (+3)
gi|12083258 (+3)
gi|110736814 (+3)
gi|20455491
gi|132090
gi|11135105 (+3)
gi|15232408 (+2)
gi|18394414 (+2)
35 kDa
53 kDa
54 kDa
43 kDa
25 kDa
52 kDa
24 kDa
28 kDa
47 kDa
55 kDa
28 kDa
80 kDa
29 kDa
22 kDa
15 kDa
42 kDa
20 kDa
30 kDa
30 kDa
61 kDa
44 kDa
50 kDa
37 kDa
60 kDa
84 kDa
20 kDa
29 kDa
63 kDa
42 kDa
23 kDa
35 kDa
62 kDa
22 kDa
41 kDa
17 kDa
28 kDa
29 kDa
48 kDa
20 kDa
20 kDa
45 kDa
31 kDa
2‐6 A1
2‐6 B1
44
29
22
11
5
10
12
10
7
3
10
7
6
2
3
2
6
8
4
3
4
4
3
5
2
2
2
3
5
3
0
3
4
3
3
2
3
2
2
2
4
1
2‐8 A1
44
30
35
13
5
10
9
13
8
4
8
4
7
5
5
3
5
8
4
3
6
2
4
4
4
5
6
4
2
2
2
3
3
4
3
4
4
2
2
3
3
4
2‐8 B1
35
30
26
14
4
10
10
8
7
5
8
7
6
1
5
4
6
5
3
4
5
2
4
5
3
1
4
4
3
3
4
5
5
6
4
0
3
2
2
0
3
2
28
24
21
12
8
10
10
5
7
5
9
1
7
0
2
4
7
8
3
5
4
3
3
5
4
1
4
4
2
5
3
2
5
4
3
1
4
2
2
2
3
1
120
Table 3.4 Cont’d
# Identified Proteins
LHCB4.2 (LIGHT HARVESTING COMPLEX PSII)
FLA8 (Arabinogalactan protein 8)
Glutathione S‐transferase PM24; auxin‐binding protein
14‐3‐3‐like protein GF14 chi; General regulatory factor 1
Oxygen‐evolving enhancer protein 1‐2, chloroplastic
CLPC (HEAT SHOCK PROTEIN 93‐V); ATP binding / ATPase
Probable fructose‐bisphosphate aldolase 1, chloroplastic
Chlorophyll a‐b binding protein 165/180, chloroplastic
unknown protein
PSBY (photosystem II BY)
PSII 47KDa protein [Capsella bursa‐pastoris]
ATP synthase epsilon chain, chloroplastic; ATP synthase F1 sector epsilon subunit
ATP synthase subunit beta, chloroplastic; ATP synthase F1 sector subunit beta
Malate dehydrogenase 1, mitochondrial
Glutamate‐1‐semialdehyde 2,1‐aminomutase 1, chloroplastic
hypothetical protein
binding / catalytic/ coenzyme binding
chlorophyll A‐B binding protein CP29 (LHCB4)
putative Lhca2 protein
COR15A (COLD‐REGULATED 15A)
EMB2726 (EMBRYO DEFECTIVE 2726); translation elongation factor
ATGSTF9 (Arabidopsis thaliana Glutathione S‐transferase (class phi) 9)
putative protein
thioglucoside glucohydrolase [Arabidopsis lyrata subsp. lyrata]
putative protein
Probable glycerophosphoryl diester phosphodiesterase 3
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
Accession Number
M.W.
gi|15231990 (+2)
gi|18406799 (+3)
gi|1170091 (+1)
gi|152112421
gi|11134146 (+1)
gi|18423214 (+3)
gi|75313518
gi|115767 (+1)
gi|18394049 (+3)
gi|15220530 (+3)
gi|139387278 (+1)
gi|114591 (+2)
gi|190358687
gi|11133715 (+3)
gi|1170028 (+3)
gi|110740665 (+3)
gi|18404496 (+3)
gi|15241005 (+2)
gi|17529286 (+2)
gi|15227963 (+1)
gi|18417320 (+2)
gi|15224581 (+2)
gi|7523401
gi|74473455
gi|4678944
gi|115502214 (+3)
31 kDa
43 kDa
24 kDa
30 kDa
35 kDa
103 kDa
43 kDa
28 kDa
16 kDa
19 kDa
56 kDa
14 kDa
54 kDa
36 kDa
50 kDa
24 kDa
35 kDa
31 kDa
28 kDa
15 kDa
104 kDa
24 kDa
32 kDa
54 kDa
60 kDa
84 kDa
2‐6 A1
2‐6 B1
1
2
2
1
2
2
1
1
1
1
2
2
0
2
2
1
1
1
2
1
2
2
1
0
0
0
2‐8 A1
3
2
1
1
3
2
4
1
2
2
2
2
0
1
3
3
0
1
4
4
3
1
1
1
1
0
2‐8 B1
1
2
1
1
3
1
2
1
1
0
1
2
1
2
3
3
3
2
0
2
2
1
2
1
2
1
2
2
1
1
2
1
1
3
0
0
1
1
1
2
1
2
4
1
2
2
3
1
2
2
2
1
121
Table 3.5 Identified proteins and the number of assigned spectra for the Hs5-8 and Hs9-7
transgenic plant lines. Scaffold viewer was set to 95% min protein, min peptides 2 and min
peptide 95%.
# Identified Proteins
1 Oxygen‐evolving enhancer protein 1‐1, chloroplastic
2 Ribulose bisphosphate carboxylase large chain
3 ATPase beta subunit
4 Probable fructose‐bisphosphate aldolase 2, chloroplastic
5 Oxygen‐evolving enhancer protein 3‐2, chloroplastic
6 Ribulose bisphosphate carboxylase/oxygenase activase, chloroplastic
7 PSBQ/PSBQ‐1/PSBQA; calcium ion binding
8 putative photosystem II type I chlorophyll a b binding protein.
9 Glutamine synthetase, chloroplastic/mitochondrial
10 ATP synthase subunit alpha, chloroplastic
11 Oxygen‐evolving enhancer protein 2‐1, chloroplastic
12 transketolase, putative
13 LHCB2.2 (Photosystem II light harvesting complex gene 2.2)
14 GLP1 (GERMIN‐LIKE PROTEIN 1)
15 Photosystem I reaction center subunit IV A, chloroplastic
16 Sedoheptulose‐1,7‐bisphosphatase, chloroplastic
17 Ribulose bisphosphate carboxylase small chain 1A, chloroplastic
18 CA1 (CARBONIC ANHYDRASE 1); carbonate dehydratase/ zinc ion binding
19 Chlorophyll a‐b binding protein CP26, chloroplastic
20 Myrosinase
21 ESM1 (EPITHIOSPECIFIER MODIFIER 1); carboxylesterase
22 PGK1 (PHOSPHOGLYCERATE KINASE 1)
23 GAPC (GLYCERALDEHYDE‐3‐PHOSPHATE DEHYDROGENASE C SUBUNIT)
24 ATP synthase beta chain 1, mitochondrial
25 Chain A, A. Thaliana Cobalamine Independent Methionine Synthase
26 Ribulose bisphosphate carboxylase small chain 3B, chloroplastic
27 2‐Cys peroxiredoxin BAS1, chloroplastic; Thiol‐specific antioxidant protein A
28 TGG2 (GLUCOSIDE GLUCOHYDROLASE 2)
29 Glyceraldehyde‐3‐phosphate dehydrogenase A, chloroplastic
30 PSAD‐1 (photosystem I subunit D‐1)
31 Apocytochrome f
32 CPN60A (chloroplast / 60 kDa chaperonin alpha subunit)
33 GLP3 (GERMIN‐LIKE PROTEIN 3)
34 ATP synthase gamma‐subunit
35 Plastocyanin major isoform, chloroplastic; DNA‐damage‐repair/toleration protein DRT112
36 putative chlorophyll binding protein
37 PSI type III chlorophyll a/b‐binding protein
38 Glyceraldehyde‐3‐phosphate dehydrogenase B, chloroplastic
39 Ribulose bisphosphate carboxylase small chain 1B, chloroplastic
40 Thioredoxin M‐type 1, chloroplastic
41 fructose‐1,6‐bisphosphatase, putative
Accession Number
M.W.
gi|19883896
gi|3914541
gi|5881701 (+2)
gi|75306047
gi|18206249 (+1)
gi|12643259 (+2)
gi|15233587 (+1)
gi|21592428
gi|11386828 (+1)
gi|190358242
gi|18206371
gi|18411711 (+1)
gi|15224465 (+3)
gi|15218535 (+2)
gi|18203441 (+1)
gi|1173345 (+3)
gi|27735223
gi|30678347 (+3)
gi|28558077 (+1)
gi|585536
gi|15231805 (+3)
gi|15230595
gi|15229231 (+1)
gi|186521400 (+3)
gi|55670130 (+2)
gi|20141686 (+1)
gi|14916972 (+2)
gi|18420974 (+3)
gi|20455490
gi|15235503 (+3)
gi|193805978
gi|15226314 (+2)
gi|15242028 (+2)
gi|166632 (+2)
gi|12644265 (+3)
gi|12083258 (+3)
gi|110736814 (+3)
gi|20455491
gi|132090
gi|11135105 (+3)
gi|15232408 (+2)
35 kDa
53 kDa
54 kDa
43 kDa
25 kDa
52 kDa
24 kDa
28 kDa
47 kDa
55 kDa
28 kDa
80 kDa
29 kDa
22 kDa
15 kDa
42 kDa
20 kDa
30 kDa
30 kDa
61 kDa
44 kDa
50 kDa
37 kDa
60 kDa
84 kDa
20 kDa
29 kDa
63 kDa
42 kDa
23 kDa
35 kDa
62 kDa
22 kDa
41 kDa
17 kDa
28 kDa
29 kDa
48 kDa
20 kDa
20 kDa
45 kDa
Hs5‐8 A1 Hs5‐8 B1 Hs9‐7 A1 Hs9‐7 B1
36
31
29
10
8
12
6
10
8
10
10
4
8
7
4
2
5
4
7
5
5
3
4
7
4
2
3
4
3
5
4
4
1
1
1
5
4
2
3
4
3
36
18
25
9
8
8
8
7
7
9
8
4
6
5
6
3
4
4
6
4
4
5
6
4
4
0
5
5
4
3
7
2
4
3
2
4
3
3
2
2
2
42
40
32
13
10
8
9
10
7
6
6
6
7
5
2
1
7
6
5
5
3
4
8
5
5
3
4
5
3
3
5
3
3
5
4
4
3
2
3
3
3
36
31
27
11
8
8
8
9
7
7
7
8
4
8
4
3
5
5
5
4
6
4
5
4
4
4
1
6
4
3
4
3
4
2
4
2
4
4
4
3
3
122
Table 3.5 Cont’d
# Identified Proteins
LHCB4.2 (LIGHT HARVESTING COMPLEX PSII)
FLA8 (Arabinogalactan protein 8)
Glutathione S‐transferase PM24; auxin‐binding protein
Oxygen‐evolving enhancer protein 1‐2, chloroplastic
CLPC (HEAT SHOCK PROTEIN 93‐V); ATP binding / ATPase
Probable fructose‐bisphosphate aldolase 1, chloroplastic
Chlorophyll a‐b binding protein 165/180, chloroplastic
unknown protein
PSBY (photosystem II BY)
PSII 47KDa protein [Capsella bursa‐pastoris]
ATP synthase epsilon chain, chloroplastic; ATP synthase F1 sector epsilon subunit
Malate dehydrogenase 1, mitochondrial
Glutamate‐1‐semialdehyde 2,1‐aminomutase 1, chloroplastic
hypothetical protein
H+‐transporting ATP synthase‐like protein
ATGSTF9 (Arabidopsis thaliana Glutathione S‐transferase (class phi) 9)
putative protein
thioglucoside glucohydrolase [Arabidopsis lyrata subsp. lyrata]
Peroxidase 34; Atperox P34
Ferredoxin‐dependent glutamate synthase 1, chloroplastic
Catalase‐3
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
Accession Number
M.W.
gi|15231990 (+2)
gi|18406799 (+3)
gi|1170091 (+1)
gi|11134146 (+1)
gi|18423214 (+3)
gi|75313518
gi|115767 (+1)
gi|18394049 (+3)
gi|15220530 (+3)
gi|139387278 (+1)
gi|114591 (+2)
gi|11133715 (+3)
gi|1170028 (+3)
gi|110740665 (+3)
gi|14596171 (+1)
gi|15224581 (+2)
gi|7523401
gi|74473455
gi|25453220
gi|12643970 (+2)
gi|21903384
31 kDa
43 kDa
24 kDa
35 kDa
103 kDa
43 kDa
28 kDa
16 kDa
19 kDa
56 kDa
14 kDa
36 kDa
50 kDa
24 kDa
24 kDa
24 kDa
32 kDa
54 kDa
39 kDa
180 kDa
57 kDa
Hs5‐8 A1 Hs5‐8 B1 Hs9‐7 A1 Hs9‐7 B1
2
2
4
2
3
1
2
2
3
3
2
1
0
0
1
1
0
1
2
0
2
3
2
1
0
5
1
2
2
3
1
2
0
1
1
0
0
1
1
1
0
1
2
2
3
1
3
0
2
2
1
1
1
1
1
1
5
1
1
2
2
1
0
1
4
2
2
2
1
1
4
5
1
2
2
1
1
3
1
0
1
2
3
0
123
Table 3.6 Identified proteins and the number of assigned spectra for the SOR3 and SOR9
transgenic plant lines. Scaffold viewer was set to 95% min protein, min peptides 2 and min
peptide 95%.
# Identified Proteins
1 Oxygen‐evolving enhancer protein 1‐1, chloroplastic
2 Ribulose bisphosphate carboxylase large chain
3 ATPase beta subunit
4 Probable fructose‐bisphosphate aldolase 2, chloroplastic
5 Oxygen‐evolving enhancer protein 3‐2, chloroplastic
6 Ribulose bisphosphate carboxylase/oxygenase activase, chloroplastic
7 PSBQ/PSBQ‐1/PSBQA; calcium ion binding
8 putative photosystem II type I chlorophyll a b binding protein.
9 Glutamine synthetase, chloroplastic/mitochondrial
10 ATP synthase subunit alpha, chloroplastic
11 Oxygen‐evolving enhancer protein 2‐1, chloroplastic
12 transketolase, putative
13 LHCB2.2 (Photosystem II light harvesting complex gene 2.2)
14 GLP1 (GERMIN‐LIKE PROTEIN 1)
15 Photosystem I reaction center subunit IV A, chloroplastic
16 Sedoheptulose‐1,7‐bisphosphatase, chloroplastic
17 Ribulose bisphosphate carboxylase small chain 1A, chloroplastic
18 CA1 (CARBONIC ANHYDRASE 1); carbonate dehydratase/ zinc ion binding
19 Chlorophyll a‐b binding protein CP26, chloroplastic
20 Myrosinase
21 ESM1 (EPITHIOSPECIFIER MODIFIER 1); carboxylesterase
22 PGK1 (PHOSPHOGLYCERATE KINASE 1)
23 GAPC (GLYCERALDEHYDE‐3‐PHOSPHATE DEHYDROGENASE C SUBUNIT)
24 ATP synthase beta chain 1, mitochondrial
25 Chain A, A. Thaliana Cobalamine Independent Methionine Synthase
26 Ribulose bisphosphate carboxylase small chain 3B, chloroplastic
27 2‐Cys peroxiredoxin BAS1, chloroplastic; Thiol‐specific antioxidant protein A
28 TGG2 (GLUCOSIDE GLUCOHYDROLASE 2)
29 Glyceraldehyde‐3‐phosphate dehydrogenase A, chloroplastic
30 PSAD‐1 (photosystem I subunit D‐1)
31 Apocytochrome f
32 CPN60A (chloroplast / 60 kDa chaperonin alpha subunit)
33 GLP3 (GERMIN‐LIKE PROTEIN 3)
34 ATP synthase gamma‐subunit
35 Plastocyanin major isoform, chloroplastic; DNA‐damage‐repair/toleration protein DRT112
36 putative chlorophyll binding protein
37 PSI type III chlorophyll a/b‐binding protein
38 Glyceraldehyde‐3‐phosphate dehydrogenase B, chloroplastic
39 Ribulose bisphosphate carboxylase small chain 1B, chloroplastic
40 Thioredoxin M‐type 1, chloroplastic
41 fructose‐1,6‐bisphosphatase, putative
Accession Number
M.W.
gi|19883896
gi|3914541
gi|5881701 (+2)
gi|75306047
gi|18206249 (+1)
gi|12643259 (+2)
gi|15233587 (+1)
gi|21592428
gi|11386828 (+1)
gi|190358242
gi|18206371
gi|18411711 (+1)
gi|15224465 (+3)
gi|15218535 (+2)
gi|18203441 (+1)
gi|1173345 (+3)
gi|27735223
gi|30678347 (+3)
gi|28558077 (+1)
gi|585536
gi|15231805 (+3)
gi|15230595
gi|15229231 (+1)
gi|186521400 (+3)
gi|55670130 (+2)
gi|20141686 (+1)
gi|14916972 (+2)
gi|18420974 (+3)
gi|20455490
gi|15235503 (+3)
gi|193805978
gi|15226314 (+2)
gi|15242028 (+2)
gi|166632 (+2)
gi|12644265 (+3)
gi|12083258 (+3)
gi|110736814 (+3)
gi|20455491
gi|132090
gi|11135105 (+3)
gi|15232408 (+2)
35 kDa
53 kDa
54 kDa
43 kDa
25 kDa
52 kDa
24 kDa
28 kDa
47 kDa
55 kDa
28 kDa
80 kDa
29 kDa
22 kDa
15 kDa
42 kDa
20 kDa
30 kDa
30 kDa
61 kDa
44 kDa
50 kDa
37 kDa
60 kDa
84 kDa
20 kDa
29 kDa
63 kDa
42 kDa
23 kDa
35 kDa
62 kDa
22 kDa
41 kDa
17 kDa
28 kDa
29 kDa
48 kDa
20 kDa
20 kDa
45 kDa
SOR3 A1 SOR3 B1 SOR9 A1 SOR9 B1
42
23
25
12
6
9
7
10
5
5
6
3
7
8
5
1
5
0
7
4
4
5
4
4
6
1
4
4
1
3
4
3
3
4
3
2
4
2
3
0
2
39
25
24
10
6
9
6
8
4
6
5
3
5
8
5
3
4
0
6
5
5
3
5
4
3
3
3
3
1
3
6
4
7
2
2
5
4
3
3
1
2
43
32
34
13
10
10
12
9
6
4
10
5
6
9
6
2
6
0
4
6
4
3
2
5
2
1
7
3
0
3
3
4
3
1
3
1
3
2
2
3
2
46
39
33
10
11
9
8
7
6
6
7
4
8
8
6
4
4
0
4
5
3
6
4
4
2
2
6
4
2
5
0
4
4
2
2
3
2
2
4
2
3
124
Table 3.6 Cont’d
# Identified Proteins
LHCB4.2 (LIGHT HARVESTING COMPLEX PSII)
FLA8 (Arabinogalactan protein 8)
Glutathione S‐transferase PM24; auxin‐binding protein
Oxygen‐evolving enhancer protein 1‐2, chloroplastic
CLPC (HEAT SHOCK PROTEIN 93‐V); ATP binding / ATPase
Probable fructose‐bisphosphate aldolase 1, chloroplastic
Chlorophyll a‐b binding protein 165/180, chloroplastic
unknown protein
PSBY (photosystem II BY)
PSII 47KDa protein [Capsella bursa‐pastoris]
ATP synthase epsilon chain, chloroplastic; ATP synthase F1 sector epsilon subunit
Malate dehydrogenase 1, mitochondrial
Glutamate‐1‐semialdehyde 2,1‐aminomutase 1, chloroplastic
hypothetical protein
60S acidic ribosomal protein P2‐2
binding / catalytic/ coenzyme binding
chlorophyll A‐B binding protein CP29 (LHCB4)
putative Lhca2 protein
EMB2726 (EMBRYO DEFECTIVE 2726); translation elongation factor
KIN2 (COLD‐RESPONSIVE 6.6)
putative protein
thioglucoside glucohydrolase [Arabidopsis lyrata subsp. lyrata]
putative protein
Peroxidase 34; Atperox P34
curculin‐like (mannose‐binding) lectin family protein
glutathione S‐transferase 4 [Brassica juncea]
putative nucleic acid‐binding protein
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
Accession Number
M.W.
gi|15231990 (+2)
gi|18406799 (+3)
gi|1170091 (+1)
gi|11134146 (+1)
gi|18423214 (+3)
gi|75313518
gi|115767 (+1)
gi|18394049 (+3)
gi|15220530 (+3)
gi|139387278 (+1)
gi|114591 (+2)
gi|11133715 (+3)
gi|1170028 (+3)
gi|110740665 (+3)
gi|12229934 (+2)
gi|18404496 (+3)
gi|15241005 (+2)
gi|17529286 (+2)
gi|18417320 (+2)
gi|15237236 (+1)
gi|7523401
gi|74473455
gi|4678944
gi|25453220
gi|15219201
gi|31790099
gi|13194798 (+3)
31 kDa
43 kDa
24 kDa
35 kDa
103 kDa
43 kDa
28 kDa
16 kDa
19 kDa
56 kDa
14 kDa
36 kDa
50 kDa
24 kDa
11 kDa
35 kDa
31 kDa
28 kDa
104 kDa
7 kDa
32 kDa
54 kDa
60 kDa
39 kDa
49 kDa
24 kDa
28 kDa
SOR3 A1 SOR3 B1 SOR9 A1 SOR9 B1
2
2
2
1
1
1
2
2
2
1
2
2
1
0
1
3
0
0
0
0
1
2
1
0
0
1
0
3
2
2
0
1
1
1
4
2
1
2
2
2
1
2
3
2
0
1
0
1
2
1
0
1
0
0
2
2
3
1
0
2
3
3
0
0
1
2
2
1
0
0
2
1
1
2
1
0
0
2
1
1
1
1
1
2
1
2
2
2
2
1
1
1
2
1
1
2
1
0
3
1
2
2
1
0
1
0
0
1
125
RT: 0.00 - 42.03
100
21.42
95
A
90
NL:
1.15E7
TIC F:
ITMS + c
ESI Full ms
MS FHJ15
23.26
25.05
85
25.31
20.29
80
28.53
75
26.11
20.15
70
RelativeAbundance
65
19.79
60
55
28.82
29.40
50
45
17.28
16.44
40
29.61
16.17
35
30.42
30.86
15.81
1.93
30
25
32.24
33.05
15.17
20
2.13
2.76
15
14.88
3.60
10
3.89
4.53 6.51
7.74
5
34.64
35.59
14.66
13.22
21.49
100
40.38
NL:
5.10E5
TIC F:
ITMS + c
ESI Full
ms2 MS
FHJ15
23.20
21.42
95
90
B
85
80
75
17.29
70
19.29
27.62
RelativeAbundance
65
60
55
17.03
24.70
50
28.40
45
25.11
16.18
40
35
16.14
30
25
29.84
16.11
20
15
15.39
15.10
2.42
10
15.03
5
2.21
2.49
3.33
0
0
5
6.18
12.35
10
29.91
13.61
15
20
Time (min)
25
30
31.83
36.18
35
41.19
40
Figure 3.1 (A) A typical MS elution profile of digested proteins. (B) A typical MS/MS
elution profile of digested proteins
126
y4
100%
1,905.61 AMU , +2 H (Parent Error: 300 ppm)
E
A
Relative Intensity
R
V
V
P
N
F
V
S
L
A
Y
N
L
T
L
L
L
N
T
A
Y
L
P
V
S
F
N
V
V
R
A
E
75%
b13
y5
50%
y6
b13-H 2O
25%
b11
y7
y2
0%
0
250
b4
y3
b5
500
b6
b7
b8
750
b9
b10
y8
1000
m/z
y9
b12
y12
y10
y11
1250
b16
y13
b14
1500
b15
y14
1750
Figure 3.2 Example of spectrum for an identified peptide from the protein Carbonic
Anhydrase 1 for sample 2-8 B1—Shows the b and y type ions identified.
127
0
SOR9 B1
SOR9 A1
SOR3 B1
SOR3 A1
HS9-7 B1
HS9-7 A1
HS5-8 B1
HS5-8 A1
2-8 B1
2-8 A1
2-6 B1
2-6 A1
GFP B1
GFP A1
WT B1
WT A1
95% PEPTIDE PROBABILITY--ASSIGNED SPECTRA
95% PEPTIDE PROBABILITY--ASSIGNED SPECTRA
10
8
6
4
2
Figure 3.3 Carbonic Anhydrase 1 (CA1) 95% Peptide Probability—Assigned Spectra
128
0
SOR9 B1
SOR9 A1
SOR3 B1
SOR3 A1
HS9-7 B1
HS9-7 A1
HS5-8 B1
HS5-8 A1
2-8 B1
2-8 A1
2-6 B1
2-6 A1
GFP B1
GFP A1
WT B1
WT A1
95% PEPTIDE PROBABILITY--UNIQUE PEPTIDES
95% PEPTIDE PROBABILITY--UNIQUE PEPTIDES
6
5
4
3
2
1
Figure 3.4 Carbonic Anhydrase 1 (CA1) 95% Peptide Probability—Unique Peptides
129
gi|30678347 (100%), 29,504.5 Da
CA1 (CARBONIC ANHYDRASE 1); carbonate dehydratase/ zinc ion binding [Arabidopsis thaliana]
5 unique peptides, 6 unique spectra, 9 total spectra, 57/270 amino acids (21% coverage)
MGT E AY D E A I
E A L K K L L I EK
E E L K T V A A AK
V EQ I T AAL QT
G T S SDKK A F D
K F K K EKY E T N
P AL Y G E L AKG
QS PK YM V F AC
SD SR VC P SH V
L D F Q PGD A F V
P V E T I K QG F I
VRN I ANM V P P
F DK VK Y GG VG
A A I EY A V L H L
K V EN I V V I GH
S ACGG I K G L M
S F P L DGNN S T
D F I EDWVK I C
L P AK SK V I S E
L GD S A F EDQC
GRC ER E AVN V
S L AN L L T Y P F
VR EG L VK G T L
A L K GGY Y D F V
K G A F E L WG L E
F GL S E T S S VK
D V A T I L H WK L
Figure 3.5 Carbonic Anhydrase 1 (CA1) sequence with the coverage for sample 2-8 B1
Highlighted
130
O
+
H
C
H
O
+
H
+
O
C
CA1
O
H
O
Figure 3.6 Reversible Carbon Dioxide conversion to Bicarbonate enzymatically controlled
through Carbonic Anhydrase 1 in Arabidopsis thaliana.
131
CHAPTER 4
Comparison in Variation for a Constant Sample Weight Methodology to a
Variable Sample Weight Methodology for a Trimethylsilyl based
Metabolic Profiling Analysis.
4.1 Introduction
Previously I was able to establish a simple, fast and reproducible method for doing
metabolomic studies on Arabidopsis thaliana plant material.2 This method has the versatility
to be used as two different types of methods. First, the method can be used as a shotgun
metabolomics screening tool, otherwise known as metabolite fingerprinting, which allows
you to track many different classes of metabolites semi quantitatively, including sugars,
amino acid, fatty acids and sterols to name a few.2, 7-10 Second, the method can be used for
targeted metabolomic analysis, which has the ability for quantitation of a single metabolite
when coupled with the proper standards and calibration curves.2, 11-14
One of the challenges in MS profiling of biological systems is quantitative analysis,
where technical variations from sample preparation and analyte ionization, in addition to
biological variation, contribute to data fluctuation. It has been suggested that variation in
absolute sample amounts (weight) could affect measurement reproducibility from run to run,
sample to sample, because issues involved with extraction capacity and ionization efficiency,
etc. At the same time, intentionally varying sample amounts from run to run to identify
systematic errors is a common practice in analytical method development. Here I have
systematically evaluated two sample preparation methods for comparative studies:
the
variable weight method and the constant weight method.
132
The first method has the advantage of finding artifactual peaks that do not vary their
response with sample weight and are produced by an outside source rather than the sample.
Possible sources of these artifactual peaks include solvents, glassware, syringes and the
instrumentation used among others. These artifacts if not identified as such can mislead the
scientist into thinking there is another peak of interest that needs to be identified and
characterized. This could cause the loss of precious resources and time. One big drawback
for the variable weight method is the change in the sample to solvent ratio.6 The currently
used methodology requires 400uL of an extraction/derivatization solution be added to each
sample. It is possible the extraction efficiency would be lowered due to there being too much
sample for the solvent to extract effectively. Additionally, it is possible for the derivatization
solution to be consumed before all possible analytes are derivatized. It has also been shown
that the derivatization solution should remain in excess for the derivatization reaction to not
reverse itself.3-5 These possibilities would all result in reported numbers to be artificially low
and not comparable to other samples without these issues.
The 2nd method explored is the constant weight method. This method is where all
weights are held at a predetermined fixed value. This has the advantage of keeping all
variables constant so differences in the chromatograms can be seen visually when they are
overlaid and the differences do not have to be calculated to be found, but only calculated to
be quantified. The can make the initial sample screening and peak picking work much
simpler and faster.
Drawbacks to the method include having artifacts included in the final peak list when
they are not initially identified using mass spec and resources may be expelled trying to
133
identify an extraneous compound. An additional drawback is the increased time it takes in
sample preparation. It can take much more time to zero in on an exact weight than to keep
the samples to a sample range as in the previous method.
4.2 Experimental
4.2.1 Materials
Acetonitrile (ACN), tetrahydrofuran (THF) and benzene of HPLC grade or better
were purchased from EMD Chemicals.
Trimethylsilyl dimethylamine (TMSDMA) and
pentadecane were purchased from Sigma-Aldrich, Inc. Zirconium oxide grinding media
were purchased from Zircoa, Inc.
4.2.2 Tissue Preparation
Arabidopsis thaliana plants were grown and harvested at development stage 3.9 on a
soil-based platform.1 The samples were immersed in liquid nitrogen and stored at -80°C until
grinding. The leaves were vacuum desiccated prior to mechanic grinding, during which
leaves were placed into a 20-mL polypropylene scintillation vial. Zirconium oxide grinding
media were then added to the vial and the cap was tightened securely. A 14.4V reciprocating
saw (Black & Decker, Hunt Valley, MD) with a custom vial holder was used to homogenize
the tissue for approximately 60 sec.
134
4.2.3 Analyte Derivatization
400 μL of an 80:20 solvent mixture (3:1:1, ACN: THF: Benzene): TMS-DMA
extraction-derivatization solution (internal standard included) was added to 5-15mg of
ground tissue.4 After vortexing the solution for 10 sec and heating at 60 °C for 30 min using
a digital heat block (VWR, Germany), the mixture was centrifuged for 10 mins at 9500×g.
The supernatant was analyzed directly.
4.2.4 Instrumentation
All mixtures were analyzed using a HP5890 Gas Chromatograph coupled to a
HP5972
quadrupole
Mass
Spectrometer
(Hewlett-Packard,
Palo
Alto,
CA).
Chromatographic separations were achieved with a RTX-5ms 5% Diphenyl/ 95% Dimethyl
Polysiloxane GC capillary column 30 m × 0.25 mm × 0.25 um (Restek, Bellefonte, PA). The
helium carrier gas was set to 1.2 mL per minute. One microliter of derivatized sample was
injected into the instrument in splitless mode. The injection port was 280°C with an initial
oven temperature of 50°C along with a 20°C per minute ramp to 320°C, hold for 10 min.
The mass spectrometer was set in the scan mode from 50 amu to 550 amu. Data were
collected using HP/Agilent’s Chemstation software and analyzed in Microsoft excel.
4.3 Results and Discussion
A representative chromatogram of an Arabidopsis thaliana sample produced using
this methodology is shown in Figure 4.1. As has been shown in the past, this chromatogram
135
represents ~200 visible peaks, and through an extracted ion chromatogram, compounds of
interest can have their instrument response quantified.2 In this study, three molecules will be
tracked, they are representive of different classes of molecules found in Arabidopsis thaliana
and are located in the early, middle and later parts of the generated chromatogram. Serine is
an essential amino acid that shows up around 7.6 minutes in the chromatogram and is
representative of compounds that are found in the early part of the analysis. Hexadecanoic
acid is a commonly found fatty acid that shows up around 11.4 minutes in the chromatogram
and is representative of compounds found in the middle part of the analysis. Sitosterol is a
commonly found sterol that shows up around 17.1 minutes in the chromatogram and is
representative of compounds found in the later part of the analysis.
In order to do a comparison of the reproducibility of the two variations of the same
method, an instrumental baseline of variation must be established 1st. This allows us to
determine the variation inherent to the method while subtracting out the variation caused by
the equipment available. Table 4.1 shows the data collected for the compound Serine in
order to establish a baseline instrumental variation in the data collected. A single 10.0mg
sample was made and the supernatant was injected 18 times in to the instrument. Using an
extracted ion chromatogram the height of each peak was recorded and normalized to weight
and to the response for the pentadecane internal standard that was added to each sample
which accounts for any injection differences. In this case the relative standard deviation was
determined to be 7.8%. Table 4.2 shows the data collected for the compound Hexadecanoic
acid and the relative standard deviation was established as 7.5%. Additionally, Table 4.3
136
shows the relative standard deviation calculated for Sitosterol and was determined to be
6.9%.
Now that the baseline instrument variation has been established the two sample
weighing techniques can be compared.
In order to establish the weighing method’s
contribution to the overall variation the baseline instrumental variation will be subtracted out.
Table 4.4 shows the data that was collected for Serine for the nine different preparations of
the same sample weighed out at 10.0mg. The calculated relative standard deviation for this
molecule was determined to be 9.2%. After the instrument variation is subtracted out, the
contribution to variation using the constant weight method is 1.4%. Figure 4.2 shows the
chromatogram for the nine overlaid samples where it should be noted that the profiles look
similar.
Tables 4.5 and 4.6 shows the data that was collected for the compounds hexadecanoic
acid and Sitosterol respectively, for the nine different preparations of the same sample
weighed out at 10.0mg. The calculated relative standard deviation for these two molecules
was determined to be 8.0% and 7.8% respectively.
After the instrument variation is
subtracted out, the contribution to variation using the constant weight method is 0.5% and
0.8% respectively. Figures 4.3 and 4.4 show the chromatograms for the nine overlaid
samples where again it is noted that the profiles look visually similar.
Table 4.7 shows the data that was collected for the compound serine for the weight
variation methodology. The nine different weights used ranged from a low of 4.2mg to a
high of 12.1mg, a difference of nearly 3 fold. Since the amount of extraction/derivatization
solution is being held constant then the relative solvent to sample ratio ranges from 95.2ul
137
per mg down to 33.1ul per mg, again a roughly 3 fold difference.
Serine for this
methodology showed a variation of 13.2%. When subtracting out the inherent instrument
variability established earlier variation attributed to the variable weight methodology is 5.4%.
Figure 4.5 shows the nine overlaid samples and as expected there is a large variation in raw
signal shown.
Tables 4.8 and 4.9 show the data that was collected using the variable weight
methodology for the compounds hexadecanoic acid and Sitosterol. The relative standard
deviation for these two compounds was determined to be 13.6% and 8.3% respectively.
After subtracting the instrument variability the variation attributed to the variable weight
methodology for these two compounds was 5.7% and 1.8% respectively. Figures 4.6 and 4.7
shows the nine overlaid samples for these two compounds and as shown earlier for serine
there is large amount of raw signal variation as expected.
Figure 4.8 shows two overlaid extracted ion chromatograms using ion 281 for two
10.0mg samples. Shown in this window are a known compound, in this case 3,5-dimethoxy4-trimethylsiloxy cinnamic acid, and also an unknown compound that the NIST library failed
to identify. Since the overlaid chromatograms show do major differences using identical
tissue sources, it can be inferred that of these compounds belong to the Arabidopsis sample.
The unknown can now be investigated to try and gain more information as to its identity.
Methods employed could include mass fragment pattern analysis, the running of standards
not found in the NIST library and the use of separation techniques to isolate the compound
for possible secondary analysis including FTIR and NMR.
138
Figure 4.9 shows two overlaid extracted ion chromatograms using ion 281 for two
samples of different weights but from the same tissue source. The black line was generated
by a sample weighing 8.1mg and the green line was generated by a sample weighting
12.1mg, which is nearly 50% larger. It is easily seen that the green chromatogram for 3,5dimethoxy-4-trimethylsiloxy cinnamic acid is much larger than the black chromatogram
which is as expected for such a sample mass difference. What is interesting to note is that
the unknown compound does not change in signal intensity for the green chromatogram
when compared to the black chromatogram. This allows us to determine that this compound
is an artifact of the methodology and can thus be ignored and no additional resources will be
expended trying to ascertain its identity.
4.4 Conclusions
This chapter reports the comparison of the variation of two different methods for
weighting samples in a trimethylsilyl based metabolic profiling analysis.
For this
comparison the variation of three representative compounds were tracked. It was shown that
for all three compounds, Serine, Hexadecanoic acid and Sitosterol, the variation was less for
the constant weight methodology. The variation for serine went from 5.4% down to 1.4% for
a 3.8 fold improvement. The variation for hexadecanoic acid went from 5.7% down to 0.5%
for a 11.4 fold improvement. Finally, Sitosterol went from 1.8% down to 0.8% for a 2.3 fold
improvement. The only drawback to using the constant weight methodology versus the
variable weight methodology is the possible addition of unknown artifactual peaks in the list
139
of peaks of interest, which can be avoided (mitigated) by the use of external standards or
ratio comparison instead of absolute measurements, etc.
4.5 References
1. Boyes, D., Zayed, A.M., Ascenzi, R., McCaskill, A.J., Hoffman, N.E., Davis, K.R.,
Gorlach, J. Growth stage-based phenotypic analysis of Arabidopsis: a model for high
throughput functional genomics in plants. Plant Cell, 13, 1499-1510 (2001)
2. Jaeger, F. Simplified Plant Sample Preparation for use in Gas Chromatography-Mass
Spectrometry (GC-MS) Based Metabolomic Profiling and Targeted Analyte
Quantitation. NCSU Master’s Thesis (2008)
3. Sigma-Aldrich Derivatization Reagents Brochure (2013)
4. Danielson, N.D., Gallagher, P.A. and Bao, J.J. Chemical Reagents and Derivatization
Procedures in Drug Analysis. Encyclopedia of Analytical Chemistry Meyers,
R.A.(Ed.) 7042-7076 John Wiley & Sons Ltd. Chichester (2000)
5. Knapp, D.R. Handbook of Analytical Derivatization Reactions John Wiley and Sons
Ltd. (1979)
6. Nollet, L.M.L. Handbook of Food Analysis: Residues and other food component
analysis. Vol. 2. CRC Press (2004)
7. Vaclavik, L., Mishra, K.B. and Hajsbra, J. Mass spectrometry-based metabolomic
fingerprinting for screening cold tolerance in Arabidopsis thaliana accessions. Anal
Bioanal Chem, 405, 2671-2683 (2013)
140
8. Konig, S., Feussner, K., Schwarz, M., Kaever, A., Iven, T., Landesfeind, M., Ternes,
P., Karlovsky, P., Lipka, V. and Feussner, I. Arabidopsis mutants of sphingolipid
fatty acid a-hydroxylases accumulate ceramides and salicylates. New Phytologist,
196: 1086–1097 (2012)
9. Houshyani, B., Kabouw, P., Muth, D., de Vos, R.C.H., Bino, R.J. and Bouwmeester,
H.J. Characterization of the natural variation in Arabidopsis thaliana metabolome by
the analysis of metabolic distance Metabolomics, 8: 131–145 (2012)
10. Kral'ova, K., Jampilek, J., Ostrovsky, I. Metabolomics - Useful Tool for study of
Plant Responses to Abiotic Stresses. Chemia I Inzynieria Ekologiczna s, 19, 133-161
(2012)
11. Renault, H., EL Amrani, A., Berger, A., Mouille, G., Soubigou-Taconnat, L.,
Bouchereau, A. and Deleu, C. g-Aminobutyric acid transaminase deficiency impairs
central carbon metabolism and leads to cell wall defects during salt stress in
Arabidopsis Roots Plant. Cell and Environment, 36, 1009–1018 (2013)
12. Frolov, A., Henning, A., Bottcher, C., Tissier, A. and Strack, D. An UPLC-MS/MS
Method for the Simultaneous Identification and Quantitation of Cell Wall Phenolics
in Brassica napus Seeds. J. Agric. Food Chem., 61, 1219–1227 (2013)
13. Izquierdo Zandalinas, S., Vives-Peris, V., Gomez-Cadenas, A., and Arbona, V. A
Fast and Precise Method To Identify Indolic Glucosinolates and Camalexin in Plants
by Combining Mass Spectrometric and Biological Information J. Agric. Food Chem.,
60, 8648–8658 (2012)
141
14. Delatte, T.L., Schluepmann, H., Smeekens, S.C.M., de Jong, G.J. and Somsen, G.W.
Capillary electrophoresis-mass spectrometry analysis of trehalose-6-phosphate in
Arabidopsis thaliana seedlings. Anal Bioanal Chem, 400: 1137–1144 (2011)
142
Table 4.1 5972 MS Instrument Reproducibility L-Serine-TMS
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Compound Name Height NF 1 (Wt) Wt corrected counts NF 2 (ISTD) Wt and ISTD corrected counts
L-serine
617633
10.0
61763.3
0.94
57996.4
L-serine
610897
10.0
61089.7
0.95
58259.3
L-serine
761427
10.0
76142.7
0.90
68453.6
L-serine
697848
10.0
69784.8
0.97
67527.4
L-serine
673861
10.0
67386.1
1.03
69329.4
L-serine
612314
10.0
61231.4
0.99
60600.2
L-serine
624607
10.0
62460.7
1.02
63873.7
L-serine
531206
10.0
53120.6
1.08
57482.6
L-serine
673284
10.0
67328.4
1.05
70926.0
L-serine
564860
10.0
56486.0
1.06
59691.0
L-serine
589580
10.0
58958.0
1.02
60052.3
L-serine
662522
10.0
66252.2
1.00
66116.0
L-serine
576646
10.0
57664.6
1.05
60581.4
L-serine
666060
10.0
66606.0
0.99
65961.2
L-serine
729028
10.0
72902.8
0.97
70460.0
L-serine
580594
10.0
58059.4
0.99
57413.0
L-serine
545654
10.0
54565.4
1.04
56677.6
L-serine
601315
10.0
60131.5
1.04
62268.5
Average Response=
62981.6
SD
4904.6
RSD
7.8
143
Table 4.2 5972 MS Instrument Reproducibility Hexadecanoic acid-TMS
Sample
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Compound Name
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Height NF 1 (Wt) Wt corrected counts NF 2 (ISTD) Wt and ISTD corrected counts
741302
10.0
74130.2
0.94
69609.1
733112
10.0
73311.2
0.95
69914.5
699261
10.0
69926.1
0.91
63809.7
682728
10.0
68272.8
0.90
61378.4
724276
10.0
72427.6
0.97
70084.7
704664
10.0
70466.4
1.03
72498.5
634863
10.0
63486.3
0.99
62831.9
644303
10.0
64430.3
1.02
65887.9
608620
10.0
60862.0
1.08
65859.6
665927
10.0
66592.7
1.06
70371.2
672783
10.0
67278.3
1.02
68527.0
591730
10.0
59173.0
1.00
59051.4
588627
10.0
58862.7
1.05
61840.1
590442
10.0
59044.2
0.99
58472.6
596559
10.0
59655.9
0.97
57656.9
605514
10.0
60551.4
0.99
59877.2
675105
10.0
67510.5
1.04
70123.8
683057
10.0
68305.7
1.04
70733.2
Average Response=
65473.8
SD
4927.4
RSD
7.5
144
Table 4.3 5972 MS Instrument Reproducibility Sitosterol-TMS
Replicate
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
Compound Name Height NF 1 (Wt) Wt corrected counts NF 2 (ISTD) Wt and ISTD corrected counts
Sitosterol
321369
10.0
32136.9
0.95
30647.9
Sitosterol
318618
10.0
31861.8
0.91
29074.9
Sitosterol
307453
10.0
30745.3
0.97
29750.8
Sitosterol
322670
10.0
32267.0
1.03
33197.5
Sitosterol
341806
10.0
34180.6
0.99
33828.3
Sitosterol
348126
10.0
34812.6
1.02
35600.1
Sitosterol
334250
10.0
33425.0
1.08
36169.7
Sitosterol
334396
10.0
33439.6
1.05
35226.4
Sitosterol
281730
10.0
28173.0
1.06
29810.0
Sitosterol
333191
10.0
33319.1
1.06
35209.6
Sitosterol
334077
10.0
33407.7
1.02
34027.8
Sitosterol
339926
10.0
33992.6
1.00
33922.7
Sitosterol
325313
10.0
32531.3
1.05
34176.8
Sitosterol
341796
10.0
34179.6
0.99
33848.7
Sitosterol
335705
10.0
33570.5
0.97
32445.6
Sitosterol
352980
10.0
35298.0
0.99
34905.0
Sitosterol
335906
10.0
33590.6
1.04
34890.9
Sitosterol
356125
10.0
35612.5
1.04
36878.1
Average Response=
33533.9
SD
2307.0
RSD
6.9
145
Table 4.4 5972 MS Method Reproducibility Consistent Weights L-Serine-TMS
Sample
1
2
3
4
5
6
7
8
9
Compound Name
L-serine
L-serine
L-serine
L-serine
L-serine
L-serine
L-serine
L-serine
L-serine
Height NF 1 (Wt) Wt corrected counts NF 2 (ISTD) Wt and ISTD corrected counts
257940
10.0
25794.0
1.14
29479.7
346172
10.0
34617.2
0.98
33762.0
324442
10.0
32444.2
0.97
31609.0
424598
10.0
42459.8
0.92
39119.4
374813
10.0
37481.3
0.95
35654.5
389016
10.0
38901.6
1.00
38990.7
393102
10.0
39310.2
0.94
36971.6
339619
10.0
33961.9
1.07
36362.4
350625
10.0
35062.5
1.06
37233.5
Average Response=
35464.8
SD
3271.4
RSD
9.2
146
Table 4.5 5972 MS Method Reproducibility Consistent Weights Hexadecanoic Acid-TMS
Sample
1
2
3
4
5
6
7
8
9
Compound Name
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Height NF 1 (Wt) Wt corrected counts NF 2 (ISTD) Wt and ISTD corrected counts SD RSD
137351
10.0
13735.1
1.14
15697.7
162477
10.0
16247.7
0.98
15846.3
177427
10.0
17742.7
0.97
17285.9
191423
10.0
19142.3
0.92
17636.3
172480
10.0
17248.0
0.95
16407.4
167393
10.0
16739.3
1.00
16777.6
189851
10.0
18985.1
0.94
17855.6
185726
10.0
18572.6
1.07
19885.3
178458
10.0
17845.8
1.06
18950.8
Average Response=
17371.5 1393.5 8.0
147
Table 4.6 5972 MS Method Reproducibility Consistent Weights Sitosterol-TMS
Sample
1
2
3
4
5
6
7
8
9
Compound Name Height NF 1 (Wt) Wt corrected counts NF 2 (ISTD) Wt and ISTD corrected counts
Sitosterol
148635
10.0
14863.5
1.14
16987.3
Sitosterol
164532
10.0
16453.2
0.98
16046.7
Sitosterol
170737
10.0
17073.7
0.97
16634.2
Sitosterol
172574
10.0
17257.4
0.92
15899.7
Sitosterol
172576
10.0
17257.6
0.95
16416.5
Sitosterol
181327
10.0
18132.7
1.00
18174.2
Sitosterol
176069
10.0
17606.9
0.94
16559.4
Sitosterol
180851
10.0
18085.1
1.07
19363.4
Sitosterol
181752
10.0
18175.2
1.06
19300.6
Average Response=
17264.7
SD
1342.3
RSD
7.8
148
Table 4.7 5972 MS Method Reproducibility Variable Weights L-Serine-TMS
Sample
1
2
3
4
5
6
7
8
9
Compound Name Height NF 1 (Wt) Wt corrected counts NF 2 (ISTD) Wt and ISTD corrected counts
L-serine
367936
9.7
37931.5
0.96
36288.0
L-serine
176283
4.4
40064.3
1.05
41871.1
L-serine
392480
11.0
35680.0
0.97
34598.1
L-serine
221758
4.2
52799.5
0.97
51421.4
L-serine
443392
10.4
42633.8
1.01
43265.4
L-serine
384399
8.9
43190.9
1.01
43434.3
L-serine
415292
8.1
51270.6
0.96
48977.0
L-serine
514091
12.1
42486.9
1.08
45764.0
L-serine
391681
10.4
37661.6
1.02
38244.2
Average Response=
42651.5
SD
RSD
5623.5 13.2
149
Table 4.8 5972 MS Method Reproducibility Variable Weights Hexadecanoic Acid-TMS
Sample
1
2
3
4
5
6
7
8
9
Compound Name
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Hexadecanoic acid
Height NF 1 (Wt) Wt corrected counts NF 2 (ISTD) Wt and ISTD corrected counts SD RSD
238089
9.7
24545.3
0.96
23481.7
99241
4.4
22554.8
1.05
23571.9
325100
11.0
29554.5
0.97
28658.4
149034
4.2
35484.3
0.97
34558.1
299244
10.4
28773.5
1.01
29199.7
260747
8.9
29297.4
1.01
29462.5
195657
8.1
24155.2
0.96
23074.6
332639
12.1
27490.8
1.08
29611.3
301848
10.4
29023.8
1.02
29472.8
Average Response=
27899.0 3807.5 13.6
150
Table 4.9 5972 MS Method Reproducibility Variable Weights Sitosterol-TMS
Sample
1
2
3
4
5
6
7
8
9
Compound Name
Sitosterol
Sitosterol
Sitosterol
Sitosterol
Sitosterol
Sitosterol
Sitosterol
Sitosterol
Sitosterol
Height NF 1 (Wt) Wt corrected counts NF 2 (ISTD) Wt and ISTD corrected counts
158722
9.7
16363.1
0.96
15654.1
69692
4.4
15839.1
1.05
16553.4
187113
11.0
17010.3
0.97
16494.5
78063
4.2
18586.4
0.97
18101.3
199964
10.4
19227.3
1.01
19512.1
150018
8.9
16856.0
1.01
16951.0
153114
8.1
18903.0
0.96
18057.3
225886
12.1
18668.3
1.08
20108.2
189597
10.4
18230.5
1.02
18512.5
Average Response=
17771.6
SD
1479.1
RSD
8.3
151
Abundance
1.7e+07
1.5e+07
1.3e+07
1.1e+07
9000000
7000000
5000000
3000000
1000000
6.00
8.00
10.00
12.00
14.00
16.00
18.00
20.00
22.00
Time
Figure 4.1 Representative chromatogram of an Arabidopsis thaliana sample
152
Abundance
1100000
1000000
900000
800000
700000
600000
500000
400000
300000
200000
100000
7.30
7.35
7.40
7.45
7.50
7.55
7.60
7.65
Time
7.70
7.75
7.80
7.85
7.90
7.95
Figure 4.2 Serine-TMS nine constant weight injections overlaid
153
Abundance
750000
650000
550000
450000
350000
250000
150000
50000
11.10
11.20
11.30
11.40
11.50
11.60
11.70
11.80
Time
Figure 4.3 Hexadecanoic acid-TMS nine constant weight injections overlaid
154
Abundance
340000
300000
260000
220000
180000
140000
100000
60000
20000
0
16.80
17.00
17.20
17.40
Time
17.60
17.80
18.00
Figure 4.4 Sitosterol-TMS nine constant weight injections overlaid
155
Abundance
800000
700000
600000
500000
400000
300000
200000
100000
0
7.30
7.35
7.40
7.45
7.50
7.55
7.60
Time
7.65
7.70
7.75
7.80
7.85
7.90
7.95
Figure 4.5 Serine-TMS nine variable weight samples overlaid
156
Abundance
380000
340000
300000
260000
220000
180000
140000
100000
60000
20000
11.00
11.10
11.20
11.30
11.40
Time
11.50
11.60
11.70
11.80
Figure 4.6 Hexadecanoic acid-TMS nine variable weight samples overlaid
157
Abundance
120000
110000
100000
90000
80000
70000
60000
50000
40000
30000
20000
10000
0
16.60
16.80
17.00
17.20
Time
17.40
17.60
17.80
18.00
Figure 4.7 Sitosterol-TMS nine variable weight samples overlaid
158
Abundance
13000
3,5-dimethoxy-4-trimethylsiloxy
cinnamic acid
12000
11000
10000
9000
8000
Unknown
7000
6000
5000
4000
3000
2000
1000
0
12.30
12.35
12.40
12.45
12.50
12.55
12.60
Time
Figure 4.8 Overlaid extracted ion chromatograms of two samples with identical weights
using ion 281.
159
Abundance
24000
3,5-dimethoxy-4-trimethylsiloxy
cinnamic acid
22000
20000
18000
16000
14000
12000
Unknown
10000
8000
6000
4000
2000
0
12.30
12.35
12.40
12.45
12.50
12.55
12.60
Time
Figure 4.9 Overlaid extracted ion chromatograms of two samples with different weights
using ion 281.
160
CHAPTER 5
Fatty Acid Methyl Ester Analysis of Arabidopsis thaliana using
Trimethylsulfonium Hydroxide and Gas Chromatography
Mass Spectrometry
5.1 Introduction
Another method improvement explored in my study is to explore different
derivatization chemistry to expand the usage of Gas Chromatography Mass Spectrometry
(GC-MS) to larger complex metabolites not amenable to silyl derivatization, such as Fatty
Acid Methyl Esters (FAMEs) should be looked at for additional information about pathways
affected by transgenic mutations or by some form of stress.
Many methodologies have been used in the past for compositional analysis of
FAMEs, including the analysis of oils in plant tissues and fatty acids in bacteria.1 The
foundation of these methodologies is the hydrolysis of compounds with ester bonds and
esterification of the freed carboxylic acids to form methyl, ethyl or butyl esters, thus making
them conducive for GC-MS analysis.
Various extraction schemes, including liquid
extraction, supercritical fluid extraction, solid phase extraction and microwave extraction
among others, are critical in isolating compounds of interest.1 A number of analytical
techniques including high performance liquid chromatography (HPLC), capillary
electrophoresis (CE), and GC-based systems have been used subsequently in order to
qualitatively and quantitatively analyze the extracted FAMEs.1-4
Additional GC based
methods have been explored; albeit coupled with a more extensive lipid extraction step.5
Trimethylsulfonium Hydroxide (TMSH) based methodologies in the past have yielded
161
positive results, however, the assistance of extensive sample preparation protocols was
required.6, 7
GC-MS based methodologies in the past have been shown to be effective in analyzing
FAMEs in Plant based materials.8-16, 38 These multi-stepped analyses usually start out with
some form of caustic digestion using the reagents potassium hydroxide (KOH), sodium
hydroxide (NaOH), sodium methoxide (NaOMe) and tetramethyl guanidine among others, in
order to perform hydrolysis of the oils present which frees the fatty acids from the glycerin
backbone.8-16, 38 The second step of this process is to esterify the free fatty acids found in the
solution to make them amenable to GC-MS analysis as well as to make them more stable and
thermally labile.36 Many methods use the highly toxic chemical Boron Tri-Fluoride (BF3) in
the presence of Methanol to achieve this reaction.8-10,
16
These reported methods, while
effective, are time consuming and use toxic, dangerous chemicals.
This study will examine FAMEs by chemically separating these compounds into their
component parts of fatty acids and glycerin from their larger complex metabolite form, which
is not volatile or conducive to silyl derivatization.
The freed fatty acids will then be
esterified into methyl esters in the presence of methanol to make then easily analyzed by GCMS.
This methodology, used in addition to the previously described trimethylsilyl
derivatization based metabolic profiling protocol can then be used to obtain a more complete
picture of small molecule activity in plants. Using the model organism Arabidopsis thaliana,
an in-situ hydrolysis and esterification reaction using trimethylsulfonium hydroxide (TMSH)
will be performed. This preparation coupled with GC-MS technology will allow us to
analyze FAMEs and related derivatives in a qualitative and semi-quantitative manner. A
162
one-step, in-situ hydrolysis and esterification reaction is proposed herein as a simple and
effective alternative to the traditional methods.
5.2 Experimental
5.2.1 Materials
Acetonitrile (ACN), stabilized tetrahydrofuran (THF), and benzene (EMD
Chemicals). TMSH was purchased in a 0.2M solution in methanol (TCI America) and
pentadecane (Sigma-Aldrich, Inc.). All reagents are HPLC grade or equivalent. 1.0mL
Gastight syringe was purchased from SGE. Zirconium oxide grinding media were purchased
from Zircoa, Inc.
5.2.2 Sample Preparation
Arabidopsis thaliana plants were grown and the leaves were harvested at
development stage 9.7 on a soil-based platform.35 The plants were vacuum desiccated prior
to mechanic grinding, during which seedlings from one batch were placed into a 20-mL
polypropylene scintillation vial. Zirconium oxide grinding media were then added to the vial
and the cap was tightened securely. A 14.4V reciprocating saw (Black & Decker, Hunt
Valley, MD) with a custom vial holder was used to homogenize the tissue for approximately
60 sec. 300 μL of a 90:10 solvent mixture (3:1:1, ACN: THF: Benzene): TMSH solution
(Pentadecane internal standard included) was added to 5-15mg of ground tissue. After
vortexing the solution for 10 sec and heating at 60 °C for 30 min using a digital heat block
163
(VWR, Germany), the mixture was centrifuged for 10 mins at 9500×g. The supernatant was
transferred to the appropriate glass vials and analyzed directly.
5.2.3 Instrumentation
All samples were analyzed using a 6890 Plus Gas Chromatograph coupled to an
Agilent 5973N quadrupole Mass Spectrometer (Agilent, Palo Alto, CA), upgraded with an
inert ion source and enhanced electronics package.
Chromatographic separations were
achieved with a BPX-50 Phenyl polysilphenylene-siloxane GC capillary column 30 m × 0.25
mm × 0.25 μm (SGE, Australia). The helium carrier gas was set to 1.1 mL per min. One
microliter of sample was injected into the instrument in splitless mode. The injection port
temperature was set at 280 °C with an initial oven temperature of 50 °C. A 20 °C per minute
ramp was applied until the oven temperature reached 320 °C where it was held for 5 min.
The mass spectrometer was set in scan mode from 30 amu to 550 amu. Data were gathered
using Agilent’s Chemstation software.
5.3 Results and Discussion
Using the sample preparation flow chart outlined in scheme 5.1, four transgenic
plants were profiled and compared to a wild-type control and an additional green fluorescent
protein (GFP) modified control. These four transgenic plants can be separated in two distinct
groups. The first group consists of the SOR3 and SOR9 lines which are modified to express
the superoxide reductase enzyme from the extremophile Pyrococcus furiosus. These SOR
plants are “a GFP-fusion under the control of the CaMV 35S promoter using the Gateway
164
vector construct, pK7WGF2”.18 This group has shown that with the overexpression of the
superoxide reductase enzyme an enhancement in heat tolerance is observed in stressed plants
but no morphological differences are observed in non-stressed plants.18
The second group of transgenic lines, HS5-8 and HS9-7, are a GFP-fusion type that
express the human type I-alpha-phosphatidylinositol-4-phosphate-5-kinase enzyme, which
catalyzes the synthesis of phosphatidylinositol-4,5-bisphosphate and results in an increase in
phosphatidylinositol-4,5-bisphosphate and also an increase in inositol-1,4,5-trisphosphate.19
Under normal growing conditions these two transgenic lines do not phenotypically show any
changes in plant morphology when compared to wild-type plants. However, it is predicted
that the changes in the level of phosphatidylinositol-4,5-bisphosphate and inositol-1,4,5trisphosphate could “affect lipid and protein interactions and alter cell structure and
membrane biosynthesis”.20, 21
For this study, 3 to 5 leaves were taken from 6 to 8 different plants of each line,
pooled together and homogenized using the mechanical grinding method described earlier.37
Due to limited plant samples, two samples of each genetic line, grown in different batches,
were analyzed in duplicate for this study. Samples from the 2 biological replicates were
weighed out in duplicate for a total of 4 data points for each compound. Figure 5.1 shows a
chromatogram obtained with this newly described methodology, in which, 56 separate peaks
are found. Using the NIST library and the raw chromatogram, 11 peaks were positively
assigned identification with a confirmation being performed using an extracted ion
chromatogram search. Of the 11 positively identified peaks 10 are FAMEs and 1 is glycerin
which is the main byproduct of hydrolysis of oils. Additionally, 1 partially identified FAME
165
was added for analysis with identification to be determined at a later date with standards.
The identification threshold was set at a match quality of ≥ 90 using the probability-based
matching (PBM) algorithm developed by McLafferty and co-workers.17 The identities of 41
peaks remain uncertain with a match quality ≤ 89. The identification of the 11 FAMEs
compares well with some previous attempts to track FAMEs is Arabidopsis, where only 6
compounds were identified and tracked.38
Figure 5.2 shows an enlarged region of a representative chromatogram from a WT
sample in which 6 identified FAME peaks of interest reside as well as Phytol which will not
be tracked for this study.
The 6 FAMEs, that range from the saturated fatty acids
Hexadecanoic acid, Methyl Ester and Octadecanoic acid, Methyl Ester, to the unsaturated
fatty acids 9-Hexadecenoic acid, Methyl Ester, 9, 12-Octadecadienoic acid, Methyl Ester, 7,
10, 13-Hexadecatrienoic acid, Methyl Ester and 9, 12, 15-Octadecatrienoic acid, Methyl
Ester. The ability to identify, track and compare is critical for oil compositional analysis.
Scheme 5.2 demonstrates the chemical reaction of TMSH with a representative oil
called Tri-Palmitin which consists of glycerin covalently bonded with 3 Hexadecanoic acid
molecules. TMSH reacts with this complex molecule and converts it into its separate parts
by the hydrolysis of Tri-Palmitin and then the subsequent esterification of the carboxylic
acids which takes place in the presence of heat and MeOH.
Table 5.1 shows the WT data for the 12 compounds of interest as well as how the
data collected was normalized so intraharvest and %intraharvest difference can be calculated.
Table 5.2 shows the final data comparison chart where comparisons are made between the
WT and transgenic data. Among the many compounds being tracked it is interesting to note
166
that under these non-stressed conditions no significant fluctuation in their expression levels
between the WT and transgenic lines were observed. The only changes that were potentially
significant were where the GFP line showed a 9% and 23% increase in Hexadecanoic acid
and Heptadecanoic acid respectively. These increases however, were just slightly above the
combined variance for WT and GFP.
The absence of significant change is not very
surprising for the following 2 reasons. First, the natural genetic variation of plant samples is
known to cause a certain amount of fluctuation in metabolite contents. Additionally, it is
well-known that small fluctuations in growing conditions, such as small differences in
moisture levels, nutrient levels and light levels, manifest into large variations in the plant
samples with differences up to 30% have been routinely cited.22 Second, these transgenic
plant lines have been observed in the past to show no phenotypical or morphological
differences to WT samples under non-stressed conditions.18, 20, 21
In order to further assess this methodology, the method and instrument reproducibility
must be quantified. Table 5.3 shows the normalized data for Hexadecanoic acid, Methyl
Ester and Table 5.4 shows the normalized data for Octadecanoic acid, Methyl Ester for 5
separate preparations of the same plant sample in order to ascertain the method
reproducibility. The reproducibility of this methodology was very good with the RSDs being
2.4% and 6.3% respectively.
The instrument reproducibility was also calculated by
analyzing the same preparation 5 times, the normalized data for Hexadecanoic acid, Methyl
Ester is shown in Table 5.5 and Table 5.6 shows the normalized data for Octadecanoic acid
Methyl Ester. The instrument reproducibility was also very good with the RSD being 2.0%
and 4.6% respectively. The instrument and method reproducibility are very close with the
167
difference being only 0.4% for Hexadecanoic acid, Methyl Ester and 1.7% for Octadecanoic
acid, Methyl Ester.
5.4 Conclusion
In this chapter a one-step, in-situ hydrolysis and esterification reaction is shown as a
simple and effective alternative to the traditional FAME analysis methods. This method was
able to track 11 different FAMEs as well as glycerin.
Using Arabidopsis thaliana from four
different transgenic lines and comparing them to two control lines, WT and GFP, it was
proven that the FAME content of these non-stressed plants were identical. Additionally, a
RSD of between 2.4% to 6.3% was established for instrument reproducibility and a RSD of
between 2.0% and 4.6% was established for instrument reproducibility.
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Sjostrom, M., Antii, H., Moritz, T. High-throughput data analysis for detecting and
identifying differences between samples in GC/MS-based metabolomic analyses.
Anal. Chem., 77, 5635-5642 (2005)
172
33. Shellie, R.A., Welthagen, W., Zrostlikova, J., Spranger, J., Ristow, M., Fiehn, O.,
Zimmermann, R. Statistical methods for comparing comprehensive two-dimensional
gas chromatography-time-of-flight mass spectrometry results: Metabolomic analysis
of mouse tissue extracts. J. Chromatogr A, 1086, 83-90 (2005)
34. Miura, D., Tanaka, H., Wariishi, H. Metabolomic differential display analysis of the
white-rot basidiomycete Phanerochaete chrysosporium grown under air and 100%
oxygen. FEMS Microbiol. Lett., 234, 111-116 (2004)
35. Boyes, D., Zayed, A.M., Ascenzi, R., McCaskill, A.J., Hoffman, N.E., Davis, K.R.,
Gorlach, J. Growth stage-based phenotypic analysis of Arabidopsis: a model for high
throughput functional genomics in plants. Plant Cell 13, 1499-1510 (2001)
36. Erickson, D.R. Edible Fats and Oil Processing: Basic Principles and Modern
Practices: World Conference Proceedings The American Oil Chemists Society p.408
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37. Jaeger, F.H. Simplified Plant Sample Preparation for use in Gas ChromatographyMass Spectrometry (GC-MS) Based Metabolomic Profiling and Targeted Analyte
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173
Harvest Arabidopsis thaliana
plant tissue
Vortex samples
for 10sec.
Vacuum desiccate
plant samples
Grind plant samples w/ ceramic
media for 60sec.
Heat sample for
30min. at 60 °C
Centrifuge sample for
10min. at 9500g
Weigh 5 -15mg of
Plant tissue into vials
Remove supernatant and place
in instrument vial for GC/MS analysis
Add - 300uL of
extraction/ derivatization
solution
Scheme 5.1 Sample preparation flow chart using the newly developed one-pot scheme.
174
Scheme 5.2 Step-wise TMSH reactions
175
Table 5.1 Example of how data is collected and normalized using 2 factors-WT Data shown.
Wild-Type Data
Compound Name
WTA-1
Glycerin
WTA-2
Glycerin
WTB-1
Glycerin
WTB-2
Glycerin
WTA-1
WTA-2
WTB-1
WTB-2
Tetradecanoic
Tetradecanoic
Tetradecanoic
Tetradecanoic
acid
acid
acid
acid
WTA-1
WTA-2
WTB-1
WTB-2
Unknown Fatty
Unknown Fatty
Unknown Fatty
Unknown Fatty
WTA-1
WTA-2
WTB-1
WTB-2
7,10,13-Hexadecatrienoic
7,10,13-Hexadecatrienoic
7,10,13-Hexadecatrienoic
7,10,13-Hexadecatrienoic
WTA-1
WTA-2
WTB-1
WTB-2
WTA-1
WTA-2
WTB-1
WTB-2
R.T. Ion
5.52
61
5.52
61
5.52
61
5.52
61
Hexadecanoic
Hexadecanoic
Hexadecanoic
Hexadecanoic
Heptadecanoic
Heptadecanoic
Heptadecanoic
Heptadecanoic
10.886
10.886
10.886
10.886
acid 1
acid 1
acid 1
acid 1
acid
acid
acid
acid
acid
acid
acid
acid
acid
acid
acid
acid
74
74
74
74
Height NF 1 (Wt) NF 2 (ISTD) Normalized counts Interharvest Diff. %Interharvest Diff.
9856
11.9
0.89
738
6428
11.9
0.99
534
11833
16.2
1.05
765
8387
16.2
1.10
567
Average Response=
651
117
18
16683
14530
12967
12325
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
1250
1208
838
834
1032
228
22
11.44
11.44
11.44
11.44
84
84
84
84
6538
3451
4546
3323
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
490
287
294
225
324
115
35
11.881
11.881
11.881
11.881
79
79
79
79
28254
22943
51129
47346
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
2117
1908
3304
3203
2633
723
27
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
25548
24612
23915
22440
24129
1310
5
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
517
461
415
409
451
50
11
11.968
11.968
11.968
11.968
12.469
12.469
12.469
12.469
74
74
74
74
74
74
74
74
341050
295994
370076
331678
6907
5549
6424
6038
176
Table 5.1 Continued
Wild-Type Data
Compound Name
WTA-1
9,12-Octadecadienoic acid
WTA-2
9,12-Octadecadienoic acid
WTB-1
9,12-Octadecadienoic acid
WTB-2
9,12-Octadecadienoic acid
R.T. Ion
12.843
67
12.843
67
12.843
67
12.843
67
WTA-1
WTA-2
WTB-1
WTB-2
12.884
12.884
12.884
12.884
WTA-1
WTA-2
WTB-1
WTB-2
WTA-1
WTA-2
WTB-1
WTB-2
9,12,15-Octadecatrienoic
9,12,15-Octadecatrienoic
9,12,15-Octadecatrienoic
9,12,15-Octadecatrienoic
Octadecanoic
Octadecanoic
Octadecanoic
Octadecanoic
Eicosanoic
Eicosanoic
Eicosanoic
Eicosanoic
acid
acid
acid
acid
acid
acid
acid
acid
WTA-1
WTA-2
WTB-1
WTB-2
Docosanoic
Docosanoic
Docosanoic
Docosanoic
acid
acid
acid
acid
WTA-1
WTA-2
WTB-1
WTB-2
Tetracosanoic
Tetracosanoic
Tetracosanoic
Tetracosanoic
acid
acid
acid
acid
acid
acid
acid
acid
12.95
12.95
12.95
12.95
13.852
13.852
13.852
13.852
79
79
79
79
74
74
74
74
74
74
74
74
Height NF 1 (Wt) NF 2 (ISTD) Normalized counts Interharvest Diff. %Interharvest Diff.
48885
11.9
0.89
3662
42592
11.9
0.99
3542
84047
16.2
1.05
5431
70419
16.2
1.10
4764
Average Response=
4350
907
21
131445
114111
237097
222707
455189
405209
393041
351670
9167
7621
8533
8744
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
9847
9488
15322
15068
12431
3196
26
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
34099
33693
25399
23793
29246
5412
19
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
687
634
551
592
616
58
9
14.681
14.681
14.681
14.681
74
74
74
74
2624
2572
3933
2834
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
197
214
254
192
214
28
13
15.53
15.53
15.53
15.53
74
74
74
74
1759
1843
2823
2204
11.9
0.89
11.9
0.99
16.2
1.05
16.2
1.10
Average Response=
132
153
182
149
154
21
14
177
Table 5.2 Example of how the comparison of chemical compounds in Arabidopsis seedling
extracts was determined
Line
Compound Name
WT Glycerin
GFP
HS5-8
HS9-7
SOR3
SOR9
WT Tetradecanoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
WT Unknown Fatty acid 1
GFP
HS5-8
HS9-7
SOR3
SOR9
WT 7,10,13-Hexadecatrienoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
WT Hexadecanoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
WT Heptadecanoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
Normalized Area Counts %Interharvest Diff. % Change
651
18
537
21
-18
569
20
-13
502
38
-23
768
26
18
430
26
-34
1032
1137
1056
1067
1168
1045
22
8
26
15
18
20
10
2
3
13
1
324
100
19
17
73
15
35
154
24
119
148
136
-69
-94
-95
-77
-95
2633
2243
2369
2122
3646
2711
27
14
17
5
12
25
-15
-10
-19
38
3
24129
26212
23756
23479
26586
22609
5
1
20
13
17
23
9
-2
-3
10
-6
451
556
514
507
517
487
11
11
27
17
15
17
23
14
12
15
8
178
Table 5.2 Continued
Line
Compound Name
WT 9,12-Octadecadienoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
Normalized Area Counts %Interharvest Diff. % Change
4350
21
4252
14
-2
3393
14
-22
3414
5
-22
4173
19
-4
3031
38
-30
WT 9,12,15-Octadecatrienoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
12431
11092
10508
9945
15338
10907
26
13
8
2
14
28
-11
-15
-20
23
-12
WT Octadecanoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
29246
33232
31493
30513
32603
30440
19
5
26
17
17
19
14
8
4
11
4
WT Eicosanoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
616
603
602
569
648
582
9
3
25
26
16
21
-2
-2
-8
5
-6
WT Docosanoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
214
246
163
189
268
176
13
21
27
8
39
33
15
-24
-12
25
-18
WT Tetracosanoic acid
GFP
HS5-8
HS9-7
SOR3
SOR9
154
143
122
126
171
112
14
20
20
8
32
29
-7
-21
-18
11
-27
179
Table 5.3 Method Reproducibility Hexadecanoic Acid, Methyl Ester
Sample
TMSH A
TMSH B
TMSH C
TMSH D
TMSH E
Compound Name
Hexadecanoic acid, Methyl Ester
Hexadecanoic acid, Methyl Ester
Hexadecanoic acid, Methyl Ester
Hexadecanoic acid, Methyl Ester
Hexadecanoic acid, Methyl Ester
Area NF 1 (Wt) Wt corrected counts
171830
5.0
171830
167172
5.0
167172
177600
5.0
177600
172969
5.0
172969
176395
5.0
176395
173193
Average Response=
SD
4118
RSD
2.4
180
Table 5.4 Method Reproducibility Octadecanoic Acid, Methyl Ester
Sample
TMSH A
TMSH B
TMSH C
TMSH D
TMSH E
Compound Name
Octadecanoic acid, Methyl Ester
Octadecanoic acid, Methyl Ester
Octadecanoic acid, Methyl Ester
Octadecanoic acid, Methyl Ester
Octadecanoic acid, Methyl Ester
Area NF 1 (Wt) Wt corrected counts
32797
5.0
32797
40641
5.0
36433
34822
5.0
34822
37918
5.0
37918
38197
5.0
38197
Average Response=
36033
SD
2255
RSD
6.3
181
Table 5.5 Instrument Reproducibility Hexadecanoic Acid, Methyl Ester
Sample
TMSH A1
TMSH A2
TMSH A3
TMSH A4
TMSH A5
Compound Name
Hexadecanoic acid, Methyl Ester
Hexadecanoic acid, Methyl Ester
Hexadecanoic acid, Methyl Ester
Hexadecanoic acid, Methyl Ester
Hexadecanoic acid, Methyl Ester
Area
NF 1 (Wt) Wt corrected counts SD RSD
171830
5.0
171830
178642
5.0
178642
170072
5.0
170072
173574
5.0
173574
170913
5.0
170913
173006 3408 2.0
Average Response=
182
Table 5.6 Instrument Reproducibility Octadecanoic Acid, Methyl Ester
Sample
TMSH A1
TMSH A2
TMSH A3
TMSH A4
TMSH A5
Compound Name
Octadecanoic acid, Methyl Ester
Octadecanoic acid, Methyl Ester
Octadecanoic acid, Methyl Ester
Octadecanoic acid, Methyl Ester
Octadecanoic acid, Methyl Ester
Area
NF 1 (Wt) Wt corrected counts SD RSD
32797
5.0
32797
31400
5.0
31400
34939
5.0
34939
34587
5.0
34587
32171
5.0
32171
Average Response=
33179 1534 4.6
183
Area Counts
1 .6 x 1 0
7
1 .4 x 1 0
7
1 .2 x 1 0
7
1 .0 x 1 0
7
8 .0 x 1 0
6
6 .0 x 1 0
6
4 .0 x 1 0
6
2 .0 x 1 0
6
0 .0
5
10
15
20
M i n u te s
Figure 5.1 Chromatogram of 2 week old Arabidopsis thaliana leaves using TMSH
methodology
184
7
1.8x107
1
1.6x107
Area Counts
1.4x107
5 6
1.2x107
1.0x107
2
8.0x106
3
4
6.0x106
4.0x106
2.0x106
0
10
10.5
11
11.5
12
Minutes
Legend: 1.Hexadecanoic acid, Methyl Ester
2. 9-Hexadecenoic acid, Methyl Ester
3. 7, 10, 13-Hexadecatrienoic acid, Methyl Ester
4. Phytol
5. Octadecanoic acid, Methyl Ester
6. 9, 12-Octadecadienoic acid, Methyl Ester
7. 9, 12, 15-Octadecatrienoic acid, Methyl Ester
Figure 5.2 Zoom in of chromatogram of 2 month old Arabidopsis thaliana leaves using
TMSH methodology
185
CHAPTER 6
Rapid Analysis of Liquid Polyester Resins using Trimethylsulfonium
Hydroxide (TMSH) and Gas Chromatography Mass Spectrometry
(GC-MS)
6.1 Introduction
The needs for small molecule profiling are beyond biological and biomedical
research. Chemical industry relies on routine QC methods to monitor and quantify the
presence of low-mass species during chemical production and characterization. For example,
the ability to quickly, accurately and cost effectively analyze liquid polyester resins is vital to
companies in the resin business for a variety of reasons. First and foremost is the ability to
evaluate resin quality. A company must be able to evaluate the contents of a resin produced,
be it from a small lab cook during R&D or up to a large scale production batch. In addition a
large component of a company’s reputation is based on its ability to deliver resin to a
customer within specification. If a suspect or off-specification resin batch is produced in the
plant then the ability to look for a possible formula mischarge or contamination before it
reaches the customer is invaluable to a company’s bottom line. Also, the ability to defend
oneself from potential erroneous claims and litigation by providing evidence that the resin
supplied to the customer was in specification and made according to formula.
A good analytical methodology can be called upon to do several different things by
an analyst. First, the method can be called upon to perform a qualitative analysis. A
qualitative analysis can quickly tell the analyst if all of the right components are there, if
something is missing or if a contamination is present. Second, an analyst may require a
186
semi-quantitative analysis. This type of analysis allows an analyst to compare a control
sample, be it a lab cook or known good plant batch to a suspect or problematic sample.
Third, a methodology may need to perform an absolute quantitative analysis on some or all
components present in a sample. This type of analysis is useful when no reference sample is
available for semi-quantitative analysis or if a contamination needs to be characterized.
Initial attempts to analyze polyester resins have relied first on multi-step ASTM based
methodologies that were coupled to a GC. Problems with these methodologies include the
amount of time it takes to perform, the man hours involved along with the low sample
through-put.1-5
Other well characterized methods have included the use of NMR spectroscopy. NMR
methods are very effective in garnering the molar ratios of acids and glycols in liquid
polyester resins with minimal sample preparation and quick turnaround time under ideal
conditions. Unfortunately, because of the extreme costs involved in their procurement,
operation and maintenance, these instruments are cost-prohibitive for most companies. NMR
spectroscopy has the further drawback of not easily being able to identify and quantify
components that contain multiple glycols and halogenated species among others.
I describe here a much simplified sample preparation solution for rapid qualitative,
semi-quantitative or quantitative analysis of polyester resins. This methodology can be
performed in a single step and is based on an in situ hydrolysis and esterification using
TMSH as the reagent. This methodology has the advantages of simplicity, speed, small
sample size and an extremely high sample through-put when compared to the existing ASTM
based methodologies.1-5
187
6.2 Experimental
6.2.1 Materials
Acetonitrile (ACN), stabilized tetrahydrofuran (THF), and benzene (EMD Chemicals).
TMSH was purchased in a 0.2M solution in methanol (TCI America) and pentadecane
(Sigma-Aldrich, Inc.). All reagents are HPLC grade or equivalent. 1.0mL Gastight syringe
(SGE). 10mL and 100mL Class A volumetric flasks (VWR Scientific). 100mL graduated
cylinder (VWR Scientific).
6.2.2 Solution Preparation
In a 500 mL glass bottle place 300mL of Acetonitrile (ACN), 100mL Stabilized
Tetrahydrofuran (THF) and 100mL Benzene to make the extraction solution (ES). In a
100mL volumetric flask place 10mL of the TMSH solution and bring to volume with the ES
solution to make the reaction solution (RS). Weigh 100mg of pentadecane into a 10mL
volumetric flask and bring to volume with ES to make the internal standard solution (ISTD).
6.2.3 Standard Preparation
In a 10mL volumetric flask weigh 100mg of the standard of interest then bring to volume
with RS. Place volumetric flask onto a 60°C hot plate for 30 minutes. This will produce a
10mg/mL standard.
In another 10mL volumetric flask place 1.0mL of each 10mg/mL
standard of interest. This will make a 1.0mg/mL stock solution (SS) that all further dilutions
will be made from. An example curve point preparation using the SS and a 10mL volumetric
188
flask: using the equation C1 x V1 = C2 x V2 a 10µg/mL curve point is desired. 1.0mg/mL x
XµL = 10µg/mL x 10mL where X= 100µL. Place the calculated amount of SS into a
volumetric flask along with 10uL of ISTD and bring to volume with ES. Repeat calculation
for each curve point desired.
6.2.4 Sample Preparation
In a 10mL volumetric flask weigh 100mg of the liquid polyester resin of interest. Add 10µL
of the ISTD to the same volumetric flask and bring to volume with RS. Place volumetric
flask onto a 60°C hot plate for 30 minutes. This will produce a 10mg/mL sample.
6.2.5 Instrumentation
All GC samples were analyzed using a 6890 Plus Gas Chromatograph coupled to an Agilent
5973N quadrupole Mass Spectrometer (Agilent, Palo Alto, CA), upgraded with an inert ion
source and enhanced electronics package. Chromatographic separations were achieved with
a BPX-50 Phenyl polysilphenylene-siloxane GC capillary column 30 m × 0.25 mm × 0.25
μm (SGE, Australia). The helium carrier gas was set to 1.1 mL per min. One microliter of
sample was injected into the instrument in splitless mode. The injection port was set at 280
°C with an initial oven temperature of 50 °C. A 20 °C per minute ramp was applied until the
oven temperature reached 320 °C where it was held for 5 min. The mass spectrometer was
set in scan mode from 30 amu to 550 amu. Data were gathered using Agilent’s Chemstation
software.
189
All NMR samples were analyzed using a Bruker Avance III 400MHz NMR. Samples
were dissolved in deuterated chloroform and a deuterated water exchange was performed if
necessary. Samples were analyzed using both 1D and 2D 1H and
13
C techniques where
necessary. Data were gathered using Bruker’s Topspin 2.1 software package.
6.3 Results and Discussion
An unsaturated polyester resin is formed by a condensation reaction of dibasic acids
and polyhydric alcohols to form a thermosetting polymer of varying chain lengths.7 Analysis
on a liquid polyester resin using a GC based analysis is impossible without some sort of
chemical digestion to break down the polymer chain to its monomeric components. This is
mainly caused by the fact that large molecules are not volatile or thermally stable enough to
be put into the GC system, get through the analytical column and then get to the detector.
The methodology presented here uses an in situ hydrolysis and subsequent esterification in
order to accomplish this break down. A hydrolysis of an ester under basic conditions as
demonstrated in Scheme 6.1 forms an alcohol and the salt of a carboxylic acid.6 The acids
then undergo esterification as demonstrated in Scheme 6.2 by having our reagent (TMSH) in
the presence of an excess of methanol. This method results in our getting the polyester’s
glycols in original form and getting the acids in the form of a methyl ester derivative. Please
note that maleic anhydride will be seen mostly as fumaric acid dimethyl ester along with a
small amount of maleic acid dimethyl ester because of isomerization. Partially etherified byproducts of the glycols are also produced if there is too much excess TMSH; this
phenomenon is demonstrated in Figure 6.1 where the same resin is analyzed using increasing
190
amounts of TMSH. This phenomenon can affect the quantitative outcome of the analysis
unless these by-products are taken into account in an additional standard curve. Most, if not
all of these byproducts have commercially available standards.
Table 6.1 shows a
breakdown of a resin analysis and what some of these partially etherified by-products are that
need to be accounted for when performing a formula comparison. The calculated amounts of
each partially etherified by-product are added back to the total of their corresponding glycol
on a per mole basis.
Figure 6.2 shows the chromatogram of a resin that has undergone the in situ
hydrolysis and esterification process. Present in the chromatogram are all of the glycols and
partially etherified glycols along with the acids of interest.
Other peaks found in the
chromatogram are other monomers and additives not of interest for this analysis (i.e. styrene,
etc.). This chromatogram generated by the GC instrument can now be used either for
qualitative analysis, semi-quantitative analysis or if coupled to a standard curve can now be
used for quantitative analysis.
Figure 6.5 shows a 1D Proton NMR spectra of the same polyester resin shown in
Figure 6.2. Present in this spectra are all of the components in the resin as well as their
corresponding proton ratios located at the bottom in red. One thing to note from this spectra
is how the chemical shifts for propylene glycol(PG), ethylene glycol(EG) and Dietheylene
glycol(DEG) occupy the same space. This makes the relative ratios of these glycols difficult
to accurately measure and usually leaves and analyst making an educated guess as to their
true amounts.8
191
Solving a resin performance issue through a qualitative analysis is trouble-shooting in
its most basic form. In a real world example, Figure 6.3 shows a chromatogram of a
problematic resin batch produced in the lab. The analyst was told that the resin was a PGMaleic-Ortho based resin that was off-specification. After hydrolysis and esterification of
the resin and a comparison to the formula, a dicylcopentadiene alcohol (DCPD-OH) peak
was found that was determined to be the cause of the issues.
Figure 6.4 shows an overlay of two chromatograms of two separate batches of the
same resin product but made by two different chemists.
It is easily seen that one
chromatogram shows a significant difference in the levels of adipic acid along with
orthophthalic acid. One of the resins is considered a control and therefore using raw area
counts a semi-quantitative determination of differences can be obtained. In this case adipic
acid and orthophthalic acid was found to be 15% and 14% less respectively when compared
to the control.
In a direct comparison of the 3 methodologies of interest in Table 6.2 from freshly
made resins, it was shown that the molar ratio data generated with the new TMSH based
methodology closely matched the formula and NMR data and was a marked improvement
over the old style ASTM based methodology. Table 6.2 shows a table comparing the
techniques of interest to the formula of the supplied resin. It can be seen that the new
methodology gives comparable results to the NMR data generated but at a fraction of the
cost. The new methodology also was able to improve on the accuracy of the old ASTMbased methodology while minimizing the time required to perform. The new methodology
also was able to limit the glassware used to one volumetric flask per sample.
192
A partial validation on the methodology was performed using a polyester resin with a
formula of 106 moles Diethylene Glycol, 22.2 moles Adipic acid, 44.4 moles isophthalic acid
and 33.4 moles Maleic acid.
Table 6.3 shows the results of an instrument precision
experiment where 7 to 8 replicate injections of a single method preparation was analyzed.
RSD for the instrument ranged from 1.7% to 4.1% for the 4 compounds tracked. Table 6.4
shows the results of a method precision experiment where 7 to 8 separate preparations were
analyzed. RSD for the method ranged from 2.7% to 4.4%. The limit of detection (LOD) for
some of the most common glycols including ethylene glycol, propylene glycol and
diethylene glycol was found to be as low as 100ppm and acids including Maleic acid, Adipic
acid, Isophthalic acid and orthophthalic acid were found to be 1ppm or better.
The
coefficient of determination (R2) for standard curves using the most common glycols and
acids was found to be consistently 0.995 or better.
6.4 Conclusion
I have presented here a single step, in situ, GC-MS based methodology that can
prepare a liquid polyester resin sample for analysis in as little as 30 minutes.
This
methodology is a marked improvement over past ASTM and GC based methodologies and
also an improvement over NMR based techniques. This single step methodology has the
advantages of simplicity, speed and an extremely high sample through-put.
This methodology was found to be reproducible, with RSDs ranging from 2.7% to
4.4% for the example resin. This methodology additionally is sensitive enough to detect
193
many common acids and glycols in the low ppm range. Furthermore, it was demonstrated
that this methodology can be used for qualitative, semi-quantitative and quantitative analysis.
6.5 References
1. ASTM Method D 2455 Standard Test Method for Identification of Carboxylic acids in
Alkyd Resins Revision / Edition: 89, Chg: W/ E1, Date: 11/00/96
2. Jankowski, S.J., Garner, P. Determination of Carboxylic acids Present as Esters in
Plasticizers and Polymers by Transesterification and Gas Chromatography. Anal. Chem., 37,
1709 (1965)
3. ASTM Method D 2456 Identification of Polyhydric Alcohols in Alkyd Resins Revision /
Edition: 91, Chg: W/ E1, Date: 09/00/97
4. Esposito, G.G. Identification of Polyhydric Alcohols in Synthetic Resins by Programmed
Temperature Gas Chromatography. Anal. Chem., 34, 1173 (1962)
5. Kirby, J.R., Baldwin, A.J., and Heidner, R.H. Determination of Diethylene Glycol in
Polyethylene Terephthalate. Anal. Chem., 37, 1306 (1965)
6. Kappelmeier, C.P.A. in Kappelmeier, C.P.A. ed., Chemical Analysis of Resin-Based
Coating Materials, Interscience Publishers, Inc., New York, 1959, pp. 18.
7. Ash, M. and Ash, I; Handbook of Filers, Extenders and Diluents 2nd Edition, Synapse
Information Resources, Inc., New York, 2007, pp317
8. Spyros, A. Characterization of unsaturated polyester and alkyd resins using one- and twodimensional NMR spectroscopy. Appl Polym Sci, 88, 1881 (2003)
194
Scheme 6.1 General Chemical equation for Hydrolysis
195
Scheme 6.2 Chemical Reaction for Methylation (Esterification)
CH3OH + RCO2H → RCO2CH3 + H2O 196
Scheme 6.3 Chemical Reactions for Unsaturated Polyesters
O
H O C H 2C H 2O
H
EG
H O C H 2C H 2
HO
+
_
O
O
C CH = CH C
OH
FUMARIC ACID
H 2O
O
O
C
CH = CH C
OH
ESTER GROUP
197
Table 6.1 Shows some of the partially etherified by-products generated by this methodology
and how they are accounted for in the formula comparison.
Compounds Quantified
Formula**
Resin 1 Liquid**
Ethylene Glycol
81
88.1
Ethylene Glycol Half-Ether
*
*3.8
12.9
15.1
*
*1.1
15.9
19.9
Diethylene Glycol Half-Ether
*
*1.1
Fumaric Acid, Dimethyl Ester
*
*24.7
23.6
25
*
*0.2
76.4
75
Propylene Glycol
Propylene Glycol Half-Ether
Diethylene Glycol
Maleic Acid, Dimethyl Ester
Isophthalic Acid, Dimethyl
Ester
Orthophthalic Acid, Dimethyl
Ester
*Compounds not found in formula, but present in sample due to impurities, partial methylation f rom TMSH
reaction or isomerization. Totals for these compounds have been added back to their corresponding glycols on
a per Mole basis
**Total moles of acids are set equal to 100, with amount of glycols adjusted accordingly
198
Table 6.2 Resin 1 technique comparisons to formula.
Compound
Formula*
NMR*
Old Method Resin 1
Liquid*
TMSH Method Resin 1
Liquid*
81
61.2
112.4
88.1
Diethylene Glycol
15.9
27.1
35.1
19.9
Propylene Glycol
12.9
12.9
16.3
15.1
Orthophthalic Acid
76.4
77.8
73.9
75
Maleic Acid
23.6
22.2
26.1
25
Ethylene Glycol
*Total moles of acids are set equal to 100, with amount of glycols adjusted accordingly
199
Table 6.3 Instrument Precision
Sample
Replicate 1
Replicate 1
Replicate 1
Replicate 1
Replicate 1
Replicate 1
Replicate 1
Replicate 1
Area Counts
Area Counts
Area Counts Area Counts
Diethylene Glycol Fumaric Acid/Maleic Acid Adipic Acid Isophthalic Acid
107208206
17736869
8994007
102159626
17316247
9275141
22682158
102003522
17467152
9050954
25435910
106222786
18459690
9376189
25379816
108324994
18649865
9120890
23437327
106630550
18536661
9269387
24301366
107994984
19138423
9163420
24461113
109372468
18546165
9453337
24201049
Mean=
STDEV=
106239642
2749726
18231384
644573
9212916
158832
24271248
987242
RSD%=
2.6%
3.5%
1.7%
4.1%
200
Table 6.4 Method Precision
Sample
Prep 1
Prep 2
Prep 3
Prep 4
Prep 5
Prep 6
Prep 7
Prep 8
Area Counts
Area Counts
Area Counts Area Counts
Diethylene Glycol Fumaric Acid/Maleic Acid Adipic Acid Isophthalic Acid
81305462
13265040
9226088
87570273
14546386
9462546
15756353
88294248
14601213
8914230
16410882
84494789
13505779
8652356
14978445
86370173
13483882
9056247
15162869
89764640
14580944
9138958
16828409
89455916
14224313
8956488
16377781
85622023
13668093
8882828
15476342
Mean=
STDEV=
86609691
2814647
13984456
561482
9036218
245205
15855869
698848
RSD%=
3.2%
4.0%
2.7%
4.4%
201
6
2.0x10
A
6
1.8x10
6
Area Counts
1.6x10
O
6
1.4x10
OH
O
6
1.2x10
6
1.0x10
5
8.0x10
TMSH
5
6.0x10
Concentration
5
4.0x10
5
Legend:
Red line = 10%TMSH
Blue line = 20%TMSH
Yellow line = 30%TMSH
Black line = 40%TMSH
2.0x10
0.0
6.5
6.6
6.7
Minutes
6
9x10
B
6
8x10
6
Area Counts
7x10
HO
6
6x10
OH
TMSH
Concentration
O
6
5x10
6
4x10
6
3x10
6
2x10
6
1x10
0
8.5
8.6
8.7
8.8
Minutes
Figure 6.1 The effect of increasing levels of the TMSH reagent on resin hydrolysis and
esterification where a partially etherified byproduct of DEG monomethyl ether (A) and DEG
(B) were monitored as TMSH concentrations increased.
202
7
1.4x10
11
7
1.2x10
7
Area Counts
1.0x10
6
8.0x10
6
6.0x10
6
4.0x10
10
6
2.0x10
0.0
8
5
2
34
1
5
6
9
7
10
15
20
Minutes
Legend: 1. Propylene Glycol Half-Ether
2. Ethylene Glycol Half-Ether
3. Propylene Glycol
4. Ethylene Glycol
5. Fumaric Acid, Dimethyl Ester
6. Diethylene Glycol Half Ether
7. Maleic Acid, Dimethyl Ester
8. Butanedioic acid, Methoxy-, Dimethyl Ester
9. Diethylene Glycol
10. Isophthalic Acid, Dimethyl Ester
11. Orthophthalic Acid, Dimethyl Ester
Figure 6.2 Chromatogram of a Polyester Resin
203
Ortho
Styrene
Fumaric
AMS
DCPD-OH
Octoate
PG
4.00
5.00
6.00
7.00
8.00
9.00
10.00
11.00
Figure 6.3 Chromatogram demonstrating qualitative analysis leading to the discovery of a
contaminant.
204
Orthophthalic acid ∆14%
Adipic acid ∆15%
11.26 11.27 11.28 11.29 11.30 11.31 11.32 11.33 11.34 11.35 11.36
9.06
9.08
6.00
9.10
9.12
9.14
9.16
7.00
9.18
9.20
9.22
8.00
9.24
9.26
9.00
10.00
11.00
12.00
13.00
14.00
Figure 6.4 Overlay and zoom-in of the chromatograms of two separate batches of the same
product to demonstrate semi-quantitative analysis.
205
Orthophthalic acid CH
EG, DEG and PG CH2
Fumaric acid CH
Maleic acid CH
PG CH3
PG CH
4.0
3.5
3.0
2.5
2.0
1.5
1.0
0.5
ppm
27.39
4.5
8.15
5.0
58.23
5.5
186.65
6.0
7.43
6.5
3.36
0.95
20.00
7.0
52.35
7.5
78.57
8.0
76.75
8.5
Figure 6.5 NMR spectra of a Polyester Resin
206