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Quantitative proteomics approaches to study leaf senescence in Arabidopsis thaliana Dissertation to obtain the degree Doctor Rerum Naturalium (Dr. rer. nat.) Of the Faculty of Biology, Ruhr-University Bochum Department Medical Proteom-Center submitted by Romano Hebeler from Wolfhagen, Germany Bochum November 2006 Quantitative proteomische Methoden für die Untersuchung von Blatt Senescence in Arabidopsis thaliana Dissertation zur Erlangung des Grades eines Doktors der Naturwissenschaften der Fakultät für Biologie an der Internationalen Graduiertenschule Biowissenschaften der Ruhr-Universität Bochum angefertigt am Medizinischen Proteom-Center vorgelegt von Romano Hebeler aus Wolfhagen, Deutschland Bochum November 2006 TABLE OF CONTENTS TABLE OF CONTENTS 1. Introduction ...................................................................................................................... 1 1.1. Arabidopsis thaliana as a model organism to study leaf senescence...................... 1 1.2. Proteomics ............................................................................................................... 4 1.3. Mass spectrometry................................................................................................... 6 1.3.1. Online nano-HPLC/ESI-MS.............................................................................. 7 1.3.2.
Publié le : lundi 1 janvier 2007
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Quantitative proteomics approaches to study leaf senescence
in Arabidopsis thaliana



Dissertation to obtain the degree
Doctor Rerum Naturalium (Dr. rer. nat.)
Of the Faculty of Biology, Ruhr-University Bochum
Department Medical Proteom-Center





submitted by
Romano Hebeler

from Wolfhagen, Germany




Bochum
November 2006


Quantitative proteomische Methoden für die Untersuchung von
Blatt Senescence in Arabidopsis thaliana


Dissertation zur Erlangung des Grades
eines Doktors der Naturwissenschaften
der Fakultät für Biologie
an der Internationalen Graduiertenschule Biowissenschaften
der Ruhr-Universität Bochum


angefertigt am Medizinischen Proteom-Center


vorgelegt von
Romano Hebeler

aus Wolfhagen, Deutschland




Bochum
November 2006 TABLE OF CONTENTS
TABLE OF CONTENTS

1. Introduction ...................................................................................................................... 1
1.1. Arabidopsis thaliana as a model organism to study leaf senescence...................... 1
1.2. Proteomics ............................................................................................................... 4
1.3. Mass spectrometry................................................................................................... 6
1.3.1. Online nano-HPLC/ESI-MS.............................................................................. 7
1.3.2. Quadrupole time-of-flight (QTOF) tandem mass spectrometry........................ 8
1.4. Quantitative proteomic approaches ....................................................................... 10
1.4.1. Protein quantification by densitometric analysis ............................................ 10
1.4.2. Mass spectrometry-based quantitative protein analysis using stable isotope
labeling...........................................................................................................13
1.4.2.1. Chemical derivatization.........................................................................14
1.4.2.2. Metabolic labeling..................................................................................16
1.4.2.3. Interpretation of mass spectra derived from stable isotope coded peptide
pairs....................................................................................................... 19
1.5. Aim of this work...................................................................................................... 20

2. Materials and methods .................................................................................................. 21
2.1. Reagents and chemicals........................................................................................ 21
2.2. Plant material and growth conditions ..................................................................... 21
152.3. N-labeling ............................................................................................................ 21
2.4. Protein extraction and determination of protein concentrations............................. 22
2.5. Labeling of proteins with CyDyes (minimal labeling).............................................. 23
2.6. Separation of proteins by 2-D gel electrophoresis ................................................. 23
2.7. Image acquisition, analysis, and visualization of proteins...................................... 24
2.8. In-gel digestion of proteins..................................................................................... 25
2.9. Chromatographic separation of peptides and mass spectrometry......................... 26
2.10. Analysis of mass spectrometric data ..................................................................... 27
2.11. Determination of protein expression ratios......................................................... 27
2.12. Statistical analyses............................................................................................. 29
2.12.1. Propagation of error model ............................................................................ 29
2.12.2. Box plot analysis ............................................................................................ 29

3. Results........................................................................................................................... 31
3.1. Identification of differentially expressed proteins from wild type and mutant
A. thaliana using DIGE minimal labeling..................................................................31
I TABLE OF CONTENTS
3.2. Relative protein quantification using an advanced strategy composed of DIGE and
15 N-labeling ............................................................................................................ 36
3.2.1. Description of the strategy ............................................................................. 36
153.2.2. Metabolic incorporation of N atoms into proteins of A. thaliana plants........ 39
153.2.3. Linearity of relative protein quantification using N labeling and mass
spectrometry .................................................................................................. 41
14 153.2.4. Evaluation of the elution times of N and N peptides on reversed-phase
nano HPLC..................................................................................................... 42
153.2.5. Evaluation of potential effects of N-labeling on protein expression levels of
A. thaliana ...................................................................................................... 44
153.2.6. Comparison of DIGE versus N-labeling in reversed labeling experiments.. 46

4. Discussion..................................................................................................................... 52
4.1. Proteins differentially expressed in A. thaliana wild type and old1-1 mutant plants...
............................................................................................................................... 52
4.2. Evaluation of protein quantification using an advanced strategy composed of DIGE
15 and N-labeling ..................................................................................................... 55
154.2.1. Metabolic incorporation of N atoms into proteins of A. thaliana plants............
.......................................................................................................................55
154.2.2. Linearity of relative protein quantification using N-labeling and mass
spectrometry..................................................................................................56
14 154.2.3. Co-elution of N/ N-labeled peptide pairs in reversed-phase chromatography57
154.2.4. Evaluation of potential effects of N-labeling on protein expression levels of
A. thaliana ...................................................................................................... 57
154.2.5. Comparison of DIGE vs. N-labeling in reversed labeling experiments........ 58
154.3. Assets and drawbacks of DIGE and N-labeling in quantitative proteomics......... 62
4.4. Future perspectives...............................................................................................65

5. Summary....................................................................................................................... 68

6. Zusammenfassung........................................................................................................69

7. References.................................................................................................................... 70

8. Supplemental information..............................................................................................81

9. Curriculum vitae............................................................................................................. 89
II LIST OF FIGURES AND TABLES
LIST OF FIGURES

Figure 1: Schematic representation of two common proteomic strategies............................ 5

Figure 2: ns of a quadrupole time-of-flight (QTOF) tandem mass
spectrometer. ......................................................................................................... 9

Figure 3: Nomenclature of peptide fragmentation according to Johnson and Martin.......... 10

Figure 4: Labeling reaction of DIGE minimal fluors............................................................. 12

Figure 5: Representative 2-D gel showing protein spots with altered protein concentrations
in A. thaliana wild type and old1-1 mutant plants revealed by DIGE analysis ..... 32

Figure 6: Experimental setup for the identification of differentially expressed proteins in
A. thaliana wild type and old1-1 mutant plants using a double and reverse labeling
approach .............................................................................................................. 37

15Figure 7: Determination of metabolic N incorporation into proteins of A. thaliana wild type
and old1-1 mutant plants via comparison of theoretically and experimentally
acquired peptide mass spetra.............................................................................. 40

15Figure 8: Linearity of relative protein quantification on the basis of N-labeling and MS
analysis ................................................................................................................ 42

Figure 9: Extracted ion chromatograms (XICs) representing elution profiles of selected
15peptides and their N-labeled counterparts following reversed-phase
chromatography ................................................................................................... 43

15Figure 10: Evaluation of metabolic labeling of A. thaliana wild type and mutant using N... 45

Figure 11: Comparison of relative quantification of proteins based on fluorescent labeling
followed by densitometry and metabolic labeling combined with mass
spectrometry ........................................................................................................ 47

Figure 12: Evaluation of relative quantitative results obtained in two reciprocally labeled sets
15of experiments using DIGE and N-labeling ....................................................... 49

Figure 13: Correlation between the average ratios obtained by DIGE and metabolic labeling
............................................................................................................................. 51

III LIST OF FIGURES AND TABLES
LIST OF TABLES

Table 1: The most commonly used staining methods for visualization of proteins separated
by gel electrophoresis .......................................................................................... 11

15Table 2: Number of N atoms incorporated into individual amino acids and the resulting
mass shifts ........................................................................................................... 18

Table 3: Voltage gradient for protein separation by IEF..................................................... 24

Table 4: Differentially regulated proteins derived from A. thaliana wild type and old1-1
mutant plants as determined by DIGE experiments............................................. 35

14 15Table 5: Overview of double and reverse labeling using CyDyes and N/ N-isotopes
applied to soluble protein extracts from A. thaliana wild type and old1-1 mutant
plants ................................................................................................................... 38



IV ABBREVIATIONS
ABBREVIATIONS

1-D/2-D PAGE one- or two-dimensional polyacrylamide gel electrophoresis
ACN acetonitrile
aqua dest. distilled water
BVA Biological Variation Analysis
CID collisional-induced dissociation
DIGE Difference Gel Electrophoresis
DIA ce In-gel Analysis
DTT dithiothreitol
ESI electrospay ionization
FA formic acid
GST glutathione S-transferase
HPLC high pressure/performance liquid chromatography
IEF isoelectric focusing
ICAT isotope-coded affinity tag
I.D. inner diameter
iTRAQ isobaric tag for relative and absolute quantification
LC liquid chromatography
m/z mass-to-charge ratio
MALDI matrix-assisted laser desorption/ionization
MS mass spectrometry
MS/MS tandem mass spectrometry
Mr molecular weight
old onset of leaf death
p.a. pro analysis
pI isoelectric point
PMF peptide mass fingerprinting
PSD post source decay
RP reversed phase
RuBisCO ribulose 1,5-bisphosphate carboxylase/oxygenase
SAG senescence associated genes
SDS sodium dodecyl sulfate
S/N signal-to-noise
s.d. standard deviation
TFA trifluoroacetic acid
TOF time-of-flight
vs. versus
Units of the International System of Units (SI) are not separately listed.
V Introduction
1. Introduction

1.1. Arabidopsis thaliana as a model organism to study leaf
senescence

Aging is a universal event in life. Aging increases entropy and finally leads to the death of
living cells and organisms. It is known that single gene mutations significantly alter lifespan,
revealing that aging is subjected to genetic control. Lim et al. have suggested that a
differentiation should be made between the terms senescence and aging (Lim et al., 2003).
They referred to senescence as the process that leads to the death of the leaf, while aging
itself occurs throughout development. Leaf senescence as a genetically regulated program
exhibits sequential events at the morphological, physiological, and molecular levels. Specific
signatures of its stages can be identified. In higher plants, aging is most obviously illustrated
by the visible yellowing during the senescence of leaves. As the last part of leaf
development, leaf senescence has evolved as an essential process to maximize the
reutilization of nutrients that have been accumulated in the senescing leaves (Bleecker,
1998). This correlates with massive physiological and biochemical changes including
degradation of RNA and proteins as well as the drop in chlorophyll content and, therefore,
photosynthetic activities.
Leaf senescence requires active gene expression; this was anticipated by the observation of
a great number of genes being up-regulated during this process. These investigations led to
the isolation of so-called senescence associated genes (SAGs) (Gan and Amasino, 1997;
Biswal and Biswal, 1999). Expression analysis of SAGs stressed that the processes during
leaf senescence exhibit enormous complexity (Park et al., 1998; Weaver et al., 1998;
Yoshida et al., 2001). More than 1,000 SAGs have been identified in various plant species
and their expression profiles under various conditions during development have been
observed (Nam, 1997; Lin and Wu, 2004; Buchanan-Wollaston et al., 2005). Many genes
from diverse biochemical pathways are involved in the complex developmental phase of leaf
senescence.
There appear to be no common regulatory mechanisms controlling SAG expression, since
no shared cis-elements in the promoter regions of the SAGs have been found. Leaf
senescence overlaps with other biological processes because many SAGs have also been
found to be up-regulated during other processes (e.g., as a result of pathogen attack). It
seems that senescence is modulated by a large array of genetic loci. Even if the senescence
syndrome may phenotypically look similar, the causal molecular basis could be very different
and knock-out of one pathway in the senescence network does not necessarily affect the
overall appearance (He et al., 2001). Therefore, mutational analysis approaches may not be
1 Introduction
the best way to study the regulation of leaf senescence (Bleecker and Patterson, 1997;
Quirino et al., 2000). However, it has been shown that single gene mutations can significantly
alter the lifespan of organisms ranging from yeast to mammals (Oh et al., 1997; Kirkwood
and Austad, 2000; Jing et al., 2002; Jing et al., 2005) demonstrating that genetic analysis
can be a very powerful tool to elucidate the senescence regulatory pathway.
Age-related changes (ARCs) occur during aging as a result of the differential regulation of
developmental processes. Certain ARCs must have taken place in the leaf before
senescence can be initiated. One example of a process dependent on ARCs in Arabidopsis
thaliana is age-related resistance (ARR) (Kus et al., 2002). The mechanisms that organize
ARCs and therefore aging in plants are still unclear. The photosynthetic capacity of the leaf is
one of the parameters for aging. If full leaf expansion has reached, CO fixation rates drop. 2
The senescence program is launched (Thomas and Howarth, 2000). This indicates that a
shift in metabolic flux may serve as a universal signal for the induction of leaf senescence
(Woo et al., 2002). Numerous other stress-inducing conditions such as drought, darkness,
ozone, and pathogen attack can accelerate leaf senescence as well (Lim and Nam, 2005).
Plant hormones, especially cytokinin and ethylene, are a further group of plant endogenous
components that play important roles in the regulation of the onset of senescence. Changes
in cytokinin levels can result in delay (Gan and Amasino, 1995; Ori et al., 1999) as well as
acceleration of senescence (Masferrer et al., 2002). For the regulation of the onset of leaf
senescence, ethylene has been shown to be a key hormone. The role of ethylene in ce has been verified by several studies. Both ethylene-insensitive mutants ethylene
resistant 1-1 (etr1-1) and ethylene insensitive 2 (ein2) exhibit increased leaf longevities
(Grbi ć and Bleecker, 1995; Oh et al., 1997). However, both constitutive ethylene response 1
(ctr1) mutants that show constitutive expression of ethylene-regulated genes and
Arabidopsis plants grown in continuous presence of exogenous ethylene did not show early
senescence (Kieber et al., 1993; Grbić and Bleecker, 1995). Therefore, ethylene is neither
necessary nor sufficient for the occurrence of senescence. These studies imply that ethylene
does not directly regulate the onset of leaf senescence but acts to vary the timing of leaf
senescence. Ethylene induces senescence only when certain ARCs, controlled by leaf age,
have taken place (Grbi ć and Bleecker, 1995; Jing et al., 2002).
In a study by Jing et al., (Jing et al., 2002; Jing et al., 2005), ethylene treatment of
mutagenized Arabidopsis plants was used as a screening system, which allowed for the
isolation of mutants with onset of leaf death ( old) phenotypes that exhibit altered leaf
senescence. Ethylene-treated mutagenized plants that show yellow cotyledons before 17
days or no yellowing after 24 days were considered as early or late leaf senescence mutant,
respectively. Several genetic loci were selected for characterization of their senescence
syndromes by analyses of the chlorophyll degradation, ion leakage, and SAG expression
2

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