Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin,...

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Large scale comparative proteomics analysis of genotypic variation in germination and early seedling growth of chickpea under sub-optimal soil water conditions By Saeedreza Vessal This thesis is presented for the degree of Doctor of Philosophy of The University of Western Australia School of Plant Biology and the UWA Institute of Agriculture Faculty of Natural and Agricultural Sciences The University of Western Australia 2010

Transcript of Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin,...

Page 1: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

Large scale comparative proteomics analysis of

genotypic variation in germination and early seedling

growth of chickpea under sub-optimal soil water

conditions

By

Saeedreza Vessal

This thesis is presented for the degree of Doctor of Philosophy of

The University of Western Australia

School of Plant Biology and the UWA Institute of Agriculture

Faculty of Natural and Agricultural Sciences

The University of Western Australia

2010

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ABSTRACT

The research of this thesis has applied a proteomic approach to identifying proteins

associated with more rapid germination of chickpea (Cicer arietinum L.) seeds under

limiting water supply in soil. A method was developed so that different genotypes

could be compared under strictly controlled environmental conditions such that water

supply could be reproducibly applied at levels less that 10% water holding capacity in a

sand medium. A number of very similar genotypes were grown for seed under the same

field conditions in the same season and the harvested mature seed graded for size,

regular shape, color and weight. Seeds of the same grade and size were compared

across genotypes. The levels of water were established without the use of poly ethylene

glycol (PEG) or other additions such that the levels chosen resulted in very different

rates of radicle emergence among the genotypes. The assay developed in the study

determined that seed size is an effective variable for germination performance,

particularly at extremely decreased water levels. Despite the lack of any physical barrier

for seed water absorption, germination rate and further radicle growth also varied

significantly among chickpea genotypes.

The proteins were extracted at imbibition (0 hr) and at 48 and 72 hr after imbibition,

separated on 2DE-Gels using narrow pH range isoelectric strips (pH 4-7) to gain

excellent resolution of up to 760 Coomassie blue-stained spots. Those spots that were

differentially abundant across genotypes and sampling times were determined using

PDQuest software, excised, and after digestion analyzed by MALDI-TOF/TOF MS/MS.

In addition to a large number of storage proteins, 96 differentially abundant proteins

were identified from database analysis and divided into functional groups including

metabolism (6.3%), respiration and energy (4.2%), defense, stress and detoxification

(25.3%) as well as allergens and nutrient reserves (11.6%), protein processing (7.4%),

nucleic acid (DNA, RNA) processing (7.4%), signaling and transduction (3.2%),

nucleotide sugar metabolism (19.0%) together with a group of unknown proteins

(15.8%). Self Organizing Tree Algorithm (SOTA) clustering analysis with heat map

representation was used to identify like expression profiles for germination responsive

proteins under limited water availability. In the genotype with the most rapid radicle

emergence (cv Rupali) the important proteins appeared to belong to groups of

chaperone-like proteins, LEA and HSP groups but also a number of enzymes involved

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in ROS homeostasis and metabolism. An anaplerotic mechanism to ensure C flow into

the TCA cycle and C4 carboxylic acid metabolism during early germination also

appeared to be important in cv Rupali so that synthetic metabolism was maintained

along with synthesis of reductant and ATP. In the most rapidly germinating genotype

the proteomic analysis provides a basis for further investigation of the genetic

mechanism underlying its superior performance and may lead to the generation of

useful molecular markers for the trait.

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DECLARATION This thesis is entirely composed of my own work, and includes no material previously

written or published by any other person except where corresponding reference has been

made appropriately in the text. I also declare no part of this work has been previously

submitted to qualify for another degree at this or any other institution.

Saeedreza Vessal,

2010

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ACKNOWLEDGEMENTS The research done for this thesis was a journey that would not have come into being

without great support, guidance and assistance of following people and organisations.

I would like to express my gratitude to my fantastic supervisors Prof. Craig Atkins from

School of Plant Biology, UWA and Winthrop Prof. Kadambot Siddique, Chair in

Agriculture and Director of the UWA Institute of Agriculture, UWA for their kind

support, encouragement and guidance for critical thinking throughout this thesis. I

enjoyed a lot for useful discussions with them over research hypotheses and developing

sound experimental approaches for this project. My special thanks a gain to Prof Craig

Atkins for his critical reviewing, comments over entire thesis, especially proteomics

work. I wish also to thank my co-supervisor Dr. Jairo Palta from CSIRO, Plant Industry

for his time to time comments especially for germination assay and related chapter.

I am sincerely thankful for 3.5 years scholarship awarded by Ministry of Science

Research and Technology of Iran to achieve this PhD. Thanks also to UWA Post

Graduate Research School for short term completion scholarship and School of Plant

Biology and the UWA Institute of Agriculture for allocating additional funds for my

research work and also for providing supplementary funds to attend an international

legume conference in Turkey.

I would like to thank the staff at “Proteomic International” for their generosity in

providing long term laboratory facilities, excellent scientific and technical assistance for

peptide extraction and cutting edge of mass spectrometry analysis. In this regard, I wish

to thank Dr Richard Lipscombe, Dr Scott Bringans, Ms Andreja Livk, Dr Ben Atcliffe,

Dr Tammy Casey, Dr Thomas Stoll, Ms Kaye Winfield and of course Dr Reza Zareie

for his help in interpretation of MS/MS spectra and also de novo peptide sequencing. I

also thank “UWA Statistical Consulting Group” staff for their guidelines and fruitful

discussion on statistical procedures used in this study.

Special thanks to Dr Danica Goggin for her generosity in offering the theoretical and

practical knowledge which I needed in the area of protein science. I am truly thankful

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for all her technical support and her patient to answer my frequent questions. I wish also

to thank Dr. Ricada Jost for her technical assistance and scientific discussions.

I wish to thank all the postgraduate students that I have shared some time with them in

the office during my PhD like Chris, Nader, Foteini, Chai, Weihua, Parwinder, Marcal,

Honghua, Shahid. Thanks also to Harsh, Mariana, Hazel, Caren, Mingren, Nazanin and

many other PhD students.

Last but no mean least, I am particularly thankful and indebted to my lovely wife

Malihe and son Erfan as well as my baby girl Mahya for their understanding, support

and patience during doing my PhD studies particularly within the last few months of

thesis writing.

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TABLE OF CONTENTS ABSTRACT ...................................................................................................................... i DECLARATION ............................................................................................................ iii ACKNOWLEDGEMENTS........................................................................................... iv

TABLE OF CONTENTS............................................................................................... vi LIST OF FIGURES AND TABLES ........................................................................... viii TABLES:.......................................................................................................................... x

LIST OF ABBREVIATIONS ...................................................................................... xii CHAPTER 1 .................................................................................................................... 1

1 General Introduction .............................................................................................. 1

CHAPTER 2 .................................................................................................................... 3

2 Literature Review ................................................................................................... 3 2.1 Chickpea ..................................................................................................................... 3

2.1.1 Drought stress ..................................................................................................................... 4 2.1.2 Chickpea adaptation to dry land environments ............................................................... 4 2.1.3 Early plant establishment ................................................................................................... 5

2.2 Seed development and its role in subsequent germination ..................................... 6 2.2.1 Seed storage proteins ............................................................................................................ 9 2.2.2 Environmental and genetic impacts on seed filling and maturation...................................... 9

2.3 Germination characteristics and early seedling growth ....................................... 10 2.3.1 Seed germination ................................................................................................................ 10 2.3.2 Resumption of metabolic activity in parallel with imbibition ............................................. 12 2.3.3 RNA status and synthesis of protein during germination.................................................... 12

2.4 Proteomics ................................................................................................................. 13 2.4.1 Application of proteomics in studies of plant stress ........................................................... 14 2.4.2 Proteomic and transcriptomics analyses of germination ..................................................... 19

2.5 Research objectives .................................................................................................. 22 CHAPTER 3 .................................................................................................................. 24

3 Development of an Assay to Assess Differences in Germination Rate among Chickpea Cultivars under Limited Water Content ................................................... 24

3.1 Introduction .............................................................................................................. 24 3.2 Materials and Methods ............................................................................................ 26

3.2.1 Plant material ...................................................................................................................... 26 3.2.2 Seed mineral analysis .......................................................................................................... 27 3.2.3 Germination assay ............................................................................................................... 27 3.2.4 Germination and pre-emergence seedling growth .............................................................. 29 3.2.5 Statistical analysis ............................................................................................................... 30

3.3 Results ....................................................................................................................... 31 3.3.1 Experiment I- germination conditions ................................................................................ 31 3.3.2 Experiment II- hydro-time effect on imbibition .................................................................. 32

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vii 3.3.3 Experiment III- seed size impact ........................................................................................ 33 3.3.4 Experiment IV- seed water absorption ............................................................................... 38 3.3.5 Mineral analysis ................................................................................................................. 38 3.3.6 Genotypic variation ............................................................................................................ 39

3.4 Discussion ..................................................................................................................42 3.4.1 Hydro-time relationship ..................................................................................................... 44 3.4.2 Seed size effect ................................................................................................................... 44 3.4.3 Genotypic variation ............................................................................................................ 46

3.5 Conclusions ...............................................................................................................47 CHAPTER 4 .................................................................................................................. 48

4 Comparative Proteomic Analysis of Differentially Expressed Proteins in Germinating Seeds of Chickpea under Limiting Water Supply ............................... 48

4.1 Introduction ..............................................................................................................48 4.2 Material and Methods ..............................................................................................50

4.2.1 Plant material and germination conditions ......................................................................... 50 4.2.2 Protein Extraction and Two- dimensional electrophoresis (2-DE) ..................................... 50 4.2.3 Gel Staining, Image Acquisition and Data Analysis .......................................................... 51 4.2.4 Protein Identification .......................................................................................................... 53 4.2.5 Protein Classification and Clustering Analysis .................................................................. 54 4.2.6 Statistical Analysis ............................................................................................................. 54

4.3 Results ........................................................................................................................55 4.3.1 Identification of differentially expressed proteins .............................................................. 59 4.3.2 Functional cataloguing of proteins ..................................................................................... 64 4.3.3 Clustering the abundance of differentially expressed proteins ........................................... 79

4.4 Discussion ..................................................................................................................80 4.4.1 Gel analysis ........................................................................................................................ 81 4.4.2 Seed storage proteins .......................................................................................................... 81 4.4.3 Chaperone-type proteins and the restoration of metabolism .............................................. 83 4.4.4 ROS homeostasis and oxidative stress ............................................................................... 88 4.4.5 Additional proteins associated with germination ............................................................... 92 4.4.6 Clustering analysis ............................................................................................................. 94

4.5 Conclusions ...............................................................................................................96 CHAPTER 5 .................................................................................................................. 98

5 General Discussion ................................................................................................ 98 5.1 Seed related features ................................................................................................99 5.2 Assay system ............................................................................................................102 5.3 Proteins associated with the rapid germination trait in cv Rupali .....................104 5.4 Conclusions .............................................................................................................109

REFERENCES ............................................................................................................ 110

APPENDICES ............................................................................................................. 143 Appendix 1 ...........................................................................................................................144 Appendix 2 ...........................................................................................................................146 Appendix 3 ...........................................................................................................................168

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LIST OF FIGURES AND TABLES

FIGURES:

CHAPTER 2

Figure 1 Legume (soybean) seed development. ............................................................... 8

Figure 2 Critical events during time course of germination and subsequent post-

germination growth. ........................................................................................................ 11

Figure 3 A representative illustration summarising how genetic diversity found

naturally can be used by proteomics, and incorporates other areas such as

transcriptomics, metabolomics, and allele mining in a holistic manner to produce

interesting phenotypes in crops.. ..................................................................................... 15

CHAPTER 3

Figure 1 (A) General view of the container filled with fine sand used for the

experiments ..................................................................................................................... 28

Figure 2 Loss of water from closed experimental containers over 7 days after sufficient

water was added to achieve 5 to 30% of the water-holding capacity (WHC) of 150 g air-

dried sand.. ...................................................................................................................... 30

Figure 3 Effect of water content on the rate of germination of medium-sized seeds of

chickpea cv Rupali.. ........................................................................................................ 32

Figure 4 Seedling growth of different sized seeds of cv Rupali in response to low water

contents (10 and 5% WHC). ........................................................................................... 35

Figure 5 Radicle specific mass (length/dry weight) following germination for 2, 4 or 8

DAS using three seed sizes (L: large; M: medium; S: small) in cv Rupali at critical

water levels (5 and 10% WHC). ..................................................................................... 35

Figure 6 Partitioning of seed dry matter (%) to different parts of the seedling and loss of

mass during germination and seedling growth after (A) 2, (B) 4 and (C) 8 DAS in cv

Rupali under various soil moisture levels. ...................................................................... 37

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Figure 7 Genotypic variation of pre-emergence seedling growth of five chickpea

genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water

supply (10, 7.5, 6.25 and 5% WHC) at 4 and 6 DAS. .................................................... 41

Figure 8 Display of variation in germination and pre-emergence seedling growth of two

contrasting genotypes of KJ850 and cv Rupali 6 DAS. .................................................. 42

CHAPTER 4

Figure 1 Representative gels of proteins from seed of three genotypes after 48 hr

imbibition using broad band 13 cm Immobiline pH 3-11 Dry Strip NL (Non-linear) and

pH 4-7 strips. ................................................................................................................... 52

Figure 2 Pictorial demonstration of varied germination performance at key time points

after imbibition coincident with morphological response of radicle emergence and

further elongation together with corresponding typical protein profiles for each

genotype.. ........................................................................................................................ 55

Figure 4.3 A representative sample of 2-DE gels obtained from three contrasting

genotypes (cv Rupali, KH850 and KJ 850) germinating under critical water supply

(6.25% WHC) at various times of imbibition (0, 48 and 72 hr). .................................... 58

Figure 4 Advanced match set images with protein spots detected on the basis of a set of

defined criteria for validation of each spot using PDQuest software from all genotypes

for (A) 0, (B) 48 and (C) 72 hr imbibition under limited water availability.. ................ 63

Figure 5 Venn diagram analysis of protein spots differentially expressed among the

three genotypes (cv Rupali shown as purple, KH850 shown as green and KJ850 shown

as yellow) in response to limited water availability 72 hr of imbibition. ....................... 64

Figure 6 Distribution of 96 identified proteins into nine major functional classes. ....... 75

Figure 7 Magnified genotype- and time-dependant display for some differentially

expressed protein spots derived from the corresponding 2-DE gels. .............................. 76

Figure 8 Self Organizing Tree Algorithm (SOTA) clustering analysis with heat map

representation of the expression profile for germination responsive proteins under

limited water availability with particular emphasis on sample clustering (genotype over

imbibition time). .............................................................................................................. 77

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Figure 9 Expression patterns for all proteins in each cluster (panels above) and

corresponding functional cataloguing for five selected clusters (pie diagrams above).

Details of each cluster and corresponding proteins are presented in Appendix 3. ......... 78

CHAPTER 5

Figure 5.1 A proposed scheme for the essential role of ROS and chaperone proteins in

rapid germination of chickpea under severe limitation of water availability. .............. 108

TABLES:

Table 1 Summary of recent studies using proteomic analysis of plant responses to

abiotic stresses (drought, salinity) and various aspects of seed germination…………..17

CHAPTER 2

Table 2 Some proteins frequently reported in drought stress proteomics studies...…...20

Table 1 Distribution of seed size among genotypes of chickpea. Seeds of similar size

were grouped into small, medium and large for each genotype..………………………27

CHAPTER 3

Table 2 The effect of the number of chickpea cv Rupali seeds sown per container on the

rate of germination and water uptake at limiting levels of water supply (5, 10 and 15%

WHC)…………………………………………………………………………………..34

Table 3 Imbibition of seeds at 2 DAS for two levels of water supply (10 and 7.5%

WHC). The imbibition value was calculated as the % increase in initial seed dry weight

(numbers below each genotype)………………………………………………………..38

Table 4 Mineral analysis of macro- and micro element contents of seed samples among

the genotypes studied……….………………………………………………………….39

Table 1 Frequency of differentially expressed protein spots detected from image

analysis of gels among three genotypes (cv Rupali, KH850 and KJ850) at three time

points of imbibition from image analysis..……………………………………………..56

CHAPTER 4

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Table 2 The representative seed storage proteins identified from LudwigNR and MSDB

databases across all time points and chickpea genotypes………..…………..………...60

Table 3 Differentially abundant proteins identified by MALDI TOF/TOF analysis with

details of protein identities and their relative expression density profile during

germination over various times of imbibition in three genotypes under limiting soil

water content (6.25% WHC)………...…………………………………………………65

Table 4 Analysis of peptides recovered from a diffuse area that comprised a number of

spots too small to be sampled separately……..………………………………………...74

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LIST OF ABBREVIATIONS ºC degree Celsius

µ micro

2-DE two dimensional gel electrophoresis

ABA abscisic acid

ACN acetonitrile

ANOVA analysis of variation

AS anthranilate synthase

BLAST basic local alignment search tool

BSA bovine serum albumin

CaM Calmodulin

cDNA complementary DNA

CHAPS (3-[(3-cholamidopropyl) dimethyl-ammonio]-1-propannesulfonate

CHCA α-Cyano-4-hydroxycinnamic acid

cm centimetre

CRT calreticulin

CSIRO commonwealth scientific and industrial research organisation

cv cultivar

D days

DAS days after sowing

DNA Deoxyribonucleic acid

dpi dot per inch

dTDP Deoxythymidine Diphosphate

DTT dithiothreitol

EDTA ethylenediamine tetraacetic acid

ER endoplasmic reticulum

EST expressed sequence tag

Exp experimental

g Gram

g acceleration due to gravity

G genotype

Gl/RibDH glucose/ribitol dehydrogenase

GLP germin like protein

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GPX glutathione peroxidase

GST glutathione S-transferase

H2O2 hydrogen peroxide

ha hectare

HAS hour after sowing

hr hour

HSP heat shock proteins

ICP-AES inductively-coupled atomic emission spectrometry

IEF isoelectric focusing

IPG immobilized pH gradient

kDa kilodalton

kg kilogram

kVh kilovolt hour

L large

LC-MS/MS liquid chromatography-tandem mass spectrometry

LEA late embryogenesis abundant

log2 base 2 logarithm

M molar

m medium

MALDI-TOF-MS matrix-assisted laser desorption/ionization time of flight mass

spectrometry

MAS marker-assisted selection

MD malate dehydrogenase

MDH mitochondrial NAD-dependent malate dehydrogenase

mg milligram

min minute

ml millilitre

mm millimetre

mM millimolar

mRNA messenger ribonucleic acids

MS mass spectrometry

Mw molecular weight

N nitrogen

n number

NCBI national centre for biotechnology information

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NL non linear

O-2 superoxide radical

OH- hydroxyl radical

P2O5 phosphorus pentoxide

PAGE polyacrylamide gel electrophoresis

PEPC phosphoenolpyruvate carboxylase

PEPCK phosphoenolpyruvate carboxykinase

PER1 1-Cys peroxiredoxin 1

pH the power of hydrogen

pI isoelectric point

PI protease inhibitors

PR pathogenesis related

Prs proteins

Prx peroxiredoxins

p-value probability value

Qb-SNARE soluble N-ethylmaleimide-sensitive-factor attachment protein

receptor

QTL quantitative trait loci

ROS reactive oxygen species

RPs responsive protein spots

S sedimentary coefficient

SA salicylic acid

SDS sodium dodecylsulphate

SE standard error

sHSPs small heat shock proteins

SOD superoxide dismutase

SOTA self- organizing tree algorithm

SRL seed reserve loss

T time

TAG triacylglyceride

TBE tris-borate-EDTA

TCA trichloroacetic acid

TCA cycle tricarboxylic acid cycle (Krebs cycle)

TFA trifluoroacetic acid

Thr theoretical

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TREX transcription and export

Tris tris (hydroxymethyl) amino methane

Trp tryptophan

Trx thioredoxin

V volt

vol volume

WHC water holding capacity

Ψb water potential

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CHAPTER 1

CHAPTER 1

1 General Introduction

Chickpea (Cicer arietinum L.) is an important cool-season food legume which is third in

terms of production among all food legumes (FAO, 2010). The crop is mostly cultivated

in dryland areas under rainfed conditions with variable soil water availability (Toker et

al., 2007) and in recent decades has been successfully grown in developed countries

such as Australia and Canada. In most growing areas, chickpea relies on limited stored

soil moisture remaining either from the post-rainy season or from winter and spring

rainfall. In these environments, growth and productivity of chickpea is adversely

influenced by a number of abiotic and biotic stresses of which drought is the most

prevalent and frequent. Poor early establishment of the crop mainly due to inadequate

soil moisture in the seed bed is one of the most important reasons for yield reduction

(Saxena et al., 1993; Turner et al., 2001; Sabaghpour et al., 2003).

In non-dormant seeds, water absorption is the first prerequisite towards initiating the

complex process of germination. Maximum germination in chickpea occurs when soil

moisture is at field capacity (Sharma, 1985). However, since chickpea is usually planted

in drying soil with declining soil water content germination and subsequent seedling

growth may be compromised. Therefore, under such adverse conditions, rapid

germination leads to better seedling emergence and crop growth, yield and water use

efficiency (Dahiya et al., 1997; Hanley and May, 2006; Hanley and Fegan, 2007).

Seedling emergence and early growth varies considerably under decreased soil moisture

among chickpea genotypes (Hosseini et al., 2009) and cultivars that are able to

germinate under the conditions of limited soil moisture are likely to show more

vigorous establishment with greater yield potential.

In general, plant responses to water stress have been investigated using adult plants.

There is limited information about the response to limited water supply at the time of

seed germination (Rajjou et al., 2006), and there are few reports for chickpea that

systematically deal with possible genotypic variation for rapid germination under

suboptimal soil water conditions. Mechanisms at both the molecular and biochemical

level which might account for differences among genotypes for germination under

limited soil water content have not been described.

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CHAPTER 2

The main objectives of the studies reported in this thesis were to design experimental

protocols that would reliably demonstrate differences in germination rate between

closely related cultivars of chickpea and to identify potential mechanisms that might

account for differential germination in response to limited water availability using

comparative functional proteomic analysis.

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CHAPTER 2 3

CHAPTER 2

2 Literature Review

2.1 Chickpea

Chickpea, a member of the Fabaceae family, is the world’s second most cultivated food

legume with the third highest global production among food legumes. It is grown in

diverse agro-climatic conditions from arid and semi-arid areas of the Mediterranean

basin and West Asia to the Indian subcontinent (the highest producer with 75% of

global production), eastern Africa, Australia, and North and South America. In 2008,

estimated world chickpea production was about 8.8 million tonnes from 11.6 million

hectares (FAO, 2010).

In recent decades, chickpea production has emerged in new regions including Australia

and North America, and has also moved into marginal land of traditional chickpea

production regions in India, Pakistan, Iran, Turkey, Mexico and Ethiopia. In Australia,

chickpea is a relatively new crop with the first cultivar released (Tyson) in 1978.

Chickpea is well-adapted to cool-season growing regions from summer-dominant

rainfall zones of northern New South Wales (NSW) and southern Queensland to

Mediterranean-type climates in the south and west of the country (Siddique and Sykes,

1997).

Chickpea seeds contain 20–30% protein, approximately 40% carbohydrates and 3–6%

oil (Gil et al., 1996). Moreover, the seeds are a good source of calcium, magnesium,

potassium, phosphorus, iron, zinc and manganese (Ibrikci et al., 2003). In contrast to

soybean, chickpea does not contain high levels of isoflavones, but contains more

beneficial carotenoids such as α-carotene. In comparison to other grain legumes, anti-

nutritive components are very low or absent (Williams and Singh, 1987). Thus,

chickpea is regarded as a functional food or nutriceutical crop (Charles et al., 2002;

McIntosh and Topping, 2000). Not only is it a cheap source of protein and energy in the

developing world, chickpea is also becoming an important food for wealthy populations

to alleviate major food-related health problems (Millan et al., 2006). In addition,

chickpea's ability to fix atmospheric nitrogen plays an important role in soil fertility

especially in dry, rainfed cropping systems (Yadav et al., 2007).

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CHAPTER 2 4

Chickpea genotypes are divided into desi and kabuli types; desi chickpeas generally

have small, dark-coloured seeds, whereas kabuli types produce large, cream-coloured

seeds. Kabuli × desi crosses are used in many breeding programs to combine genes; for

example, genes for cold tolerance, resistance to ascochyta blight and prolonged

vegetative growth. These traits are more frequently found in kabuli types, while genes

for heat and drought tolerance, resistance to fusarium wilt and early flowering are

usually prevalent in desi types (Williams and Singh, 1987).

2.1.1 Drought stress

Despite extensive adaptation, productivity of chickpea is currently very low (world

average is around 0.76 t/ha) and has been steady with small fluctuations in recent years

(FAO, 2010). The main reasons for only slight improvement are a combination of biotic

and abiotic stresses that reduce yield and yield stability. Water stress (drought), after

diseases like ascochyta blight and fusarium wilt, is the most important constraint for

yield improvement and adoption by farmers (Clarke et al., 2004; Millan et al., 2006).

Drought is a real challenge given the current trend in climate change. Global chickpea

losses resulting from abiotic stresses are estimated to be 6.4 million tonnes, accounting

for 40–60% of average losses (Toker et al., 2007).

2.1.2 Chickpea adaptation to dry land environments

Research has shown that drought reduces chickpea productivity by 25 to 100%

depending on genotype and type of drought experienced (Toker et al., 2007). The reason

being is that most areas under chickpea cultivation are in arid and semi-arid zones, of

which approximately 90% are rain-fed (Leport et al., 1999). Chickpea can extract more

soil water than some other grain legume species (Zhang et al., 2000; Siddique et al.,

2001) and therefore has some characteristics consistent with drought resistance.

However, poor early vigour and late phenology, compared with other legume crops like

field pea (Pisum sativum L.) or faba bean (Vicia faba L.), limits yield potential in major

chickpea-growing areas. For instance, in South Asia and northern Australia, chickpea is

predominantly grown in the post-rainy season on receding stored soil moisture. In the

Mediterranean, West Asia, North Africa and Central Asia, chickpea is traditionally

grown in spring on stored soil moisture from winter and early spring rainfall. In these

environments, chickpea is subjected to declining soil residual moisture, increasing

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temperatures and vapour pressure deficits during flowering and maturity (Singh et al.,

1997; Siddique et al., 2000). Consequently, both plant establishment and post-emergent

growth in the growing season are severely affected by drought stress. Breeding for

adaptation to dryland environments could, therefore, lead to stable and higher yields in

chickpea.

Studies by plant breeders and molecular biologists have acknowledged that plant

responses to abiotic stresses are multigenic (Panjabi-Sabharwal et al., 2009).

Morphological and physiological traits corresponding to drought resistance in chickpea

are inherited with different patterns (monogenic and polygenic) and gene action

(additive or non-additive). In this regard, Sabaghpour et al. (2003) demonstrated that

seedling vigour in chickpea is controlled by two genes with duplicate dominant

epistasis. They also found that root length density and root dry weight are likely to be

under polygenic control in recombinant inbred lines.

2.1.3 Early plant establishment

While poor plant establishment can result from a number of biotic and abiotic stress

factors, in chickpea there is some evidence that lack of adequate soil moisture in the

seed bed plays an important role. The adverse effect of inadequate soil moisture on

plant stand establishment has a major impact on reducing yield in rainfed chickpea

crops in India and Syria (Sharma, 1985; Saxena et al., 1993). The risk of temporary

drought, though small, could be significant, especially when the crop is sown early; for

example, delayed planting of spring chickpea led to crop failure due to lack of adequate

soil moisture (Saxena et al., 1993).

Early vigour is considered one of the most important drought escape mechanisms for

improving plant establishment in grain legumes (Thomson and Siddique, 1997; Turner

et al., 2001). In recombinant inbred lines of chickpea, high growth vigour was

negatively correlated with days to first flower, first pod and maturity (Sabaghpour et al.,

2003) and selection for this trait improved chickpea production.

In most environments, chickpea is sown into dry soil under variable soil water

conditions and where low soil water content is a limiting factor for germination and

subsequent crop establishment. Under limited soil water contents, water absorption by

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seeds takes longer delaying germination and resulting in poor seedling establishment.

Therefore, rapid germination and better seed emergence and seedling establishment

under low soil moisture would improve crop performance (Dahiya et al., 1997). Better

establishment increases water use and nutrient use efficiencies through faster growing

radicles and roots that penetrate deeper into the soil thereby accessing more water

resources enabling the newly established crop to survive under moderate drought

conditions until rainfall occurs (Kikuzawa and Koyama, 1999; Hanley and May, 2006).

Chickpea cultivars vary considerably in their ability to germinate and establish under

limited soil moisture conditions (Saxena et al., 1993; Hosseini et al., 2009). Rapid seed

germination and early establishment is an indispensible prerequisite for chickpea

productivity in adverse dryland environments (Calcagno and Gallo, 1993).

2.2 Seed development and its role in subsequent germination

Seed development is initiated by double fertilisation which differentiates the three main

components of seed, i.e. the embryo, endosperm and seed coat (Figure 2.1 adapted from

Le et al., 2007; Go et al., 1994; Miller et al., 1999; Moïse et al., 2005). This process is

genetically programmed and exerted through a cascade of metabolic changes. Seed

components are genetically and physiologically heterogeneous with different origins,

playing distinct roles in seed formation and later in germination (Weber et al., 2005;

Finch-Savage and Leubner-Metzger, 2006; Le et al., 2007). In legumes, like many other

plants, the endosperm is absorbed by embryo tissues during development and, therefore,

is not contained within the mature seed (Figure 2.1). Instead, cotyledons develop during

a complex process involving sucrose absorption, regulated cell division, cell expansion

and endopolyploidization, accumulation of storage reserves and acquisition of

desiccation tolerance, and, in some seeds, dormancy (Borisjuk et al., 1998; Borisjuk et

al., 2004; Gallardo et al., 2008). The zygote divides immediately after fertilisation,

resulting in differentiation of the embryo proper and the suspensor. The suspensor is

responsible for early support and nourishment of the embryo proper but disappears later

in development (Yeung and Meinke, 1993). The embryo proper (axis) is the newly

generated tissue with meristemic axes that develop into root and shoot organs in the

growing seedling after germination (Goldberg et al., 1994; Laux et al., 2004).

The seed coat, which originates maternally from ovule integuments, surrounds the

embryo sac (Weber et al., 2005). This not only protects the embryo but also plays a

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significant role in transferring nutrients from the maternal plant to the embryo as the

seed develops (Borisjuk et al., 2004; Moïse et al., 2005). The impact of the seed coat in

developing seeds is attributed in part to the action of invertases that determine both

composition and concentration of sugars in vacuoles of the developing embryo (Weber

et al., 1995; Weber et al., 2005). Seed coats in pea for example are critical for unloading

amino acids, mostly glutamine, threonine and alanine from phloem but also in

metabolism of amino acids (Lanfermeijer et al., 1992; Rochat et al., 1995). Solutes are

unloaded into the apoplast that lies between the maternal seed coat and the growing

embryo and are taken up by the cotyledons. A number of transporters highly specific for

hexoses or particular amino acids are expressed in cotyledons and especially in the

epidermis that in some species are differentiated into transfer cells (Fieuw and Patrick,

1993; Offler and Patrick, 1993). The amino acid transporter, VfAAP1, and peptide

transporter, VfPTR1, are expressed in growing cotyledons of Vicia faba and engage in

seed protein accumulation (Miranda et al., 2001; Miranda et al., 2003). Although the

nature of protein reserves is genetically controlled (Gallardo et al., 2008), nitrogen

uptake and its availability plus other nutrients such as sulfur determine the rate of seed

protein accumulation in legumes (Golombek et al., 2001; Salon et al., 2001) and

ultimately seed size.

Phosphoenolpyruvate carboxylase (PEPC) has been investigated in relation to seed

storage protein synthesis, particularly in legumes such as pea, soybean and Vicia

(Hedley et al., 1975; Smith et al., 1989; Golombek et al., 1999). This enzyme is

ubiquitous and broadly regulated in plants and, through the anaplerotic reaction in the

citric acid cycle, provides carbon skeletons for biosynthesis of seed amino acids (Weber

et al., 2005; Gallardo et al., 2008). Via the catalytic action of the enzyme, respiratory

HCO3- is converted to PEP, which is then metabolised to malate or aspartate and other

intermediate products within the citric acid cycle. Rolletschek et al. (2004) showed that

PEPC over expression in Vicia seeds caused a further diversion of carbon flow from

sucrose or starch to organic or free amino acids, which led to larger seeds having 20%

more storage protein. This clearly indicates that the quality and quantity of seed protein

reserves is under genetic control and a promising target for molecular plant breeding.

Seed size is a controversial factor for determining germination and early seedling

growth; its effect is dependent on species and seed bed conditions such as soil water

status, soil properties, soil texture and so forth (Marshall, 1986; Evans and Etherington,

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1990; Hanley et al., 2007; Tobe et al., 2007; Daws et al., 2008; Kaydan and Yagmur,

2008; Urbieta et al., 2008; Loha et al., 2009). There is some evidence that small seeds

germinate better under certain water deficit situations since they can absorb more water

and initiate germination-related processes due to a greater ratio of seed surface area to

seed mass (Saxena et al., 1993; Kikuzawa and Koyama, 1999).

Figure 2.1 Legume (soybean) seed development. A) Schematic display of the life cycle. B)

Representation of seed development with various embryo morphologies and developmental events

(Adapted and modified from Le et al., 2007).

QUIESCENT

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2.2.1 Seed storage proteins

Seed storage proteins have three specific characteristics in addition to their abundance

and economic importance, and were the first proteins to be characterised (Shewry et al.,

1995). Firstly, storage proteins are synthesized in large amounts in particular tissue at

specific developmental stages. In this way, they act as a nitrogen sink, and in some

cases as a sink for sulfur. Secondly, these proteins are present as structurally distinct

'protein bodies' in mature seeds. Lastly, storage proteins exhibit considerable

polymorphism within a single genotype or among various genotypes as a consequence

of their expression from multigene families and/or not only post-transcriptional but

post-translational modification, proteolytic activity and glycosylation.

Globulins, which are present not only in dicotyledons but also in monocotyledon seeds

and fern spores, are the most broadly dispersed group of storage proteins (Templeman et

al., 1987). Based on their sedimentation coefficient, globulins are divided into two

groups: 7S vicilin-type globulin and 11S legumin-type globulin. Considerable variation

exists in their structures partially due to post-translational modification (Gatehouse et

al., 1984; Shewry et al., 1995).

2.2.2 Environmental and genetic impacts on seed filling and maturation

The interaction between genetic and environmental factors for seed growth and the

accumulation of reserves is determined in part by the ability of the embryo to store

different compounds, but also by other processes during a plant's development and life

cycle (Le et al., 2007; Gallardo et al., 2008). Any unfavourable environmental condition

may alter seed composition and quality and this, in turn, appears to affect germination

and establishment performance even within one genotype (Buitink et al., 2006; Fait et

al., 2006). In fact, activation of some stress tolerance genes such as late embryogenesis

abundant proteins (LEA) and heat shock proteins (HSP) during seed maturation and

desiccation plays a critical role in seed performance (Bettey and Finch-Savage, 1998;

Clerkx et al., 2004). LEA proteins become more abundant in seeds during late

maturation, preparing seeds for desiccation and their quiescent status (Tunnacliffe and

Wise, 2007; Catusse et al., 2008b). LEA proteins are believed to prevent protein

aggregation through a chaperone-type of activity (Grelet et al., 2005) as the water stress

associated with desiccation increases (Goyal et al., 2005). HSPs also act as chaperones

to prevent irreversible protein aggregation (Basha et al., 2004). In addition to these

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proteins, there are some other protective molecules like non-reducing di- and oligo-

saccharides that may be important during the final stages of seed maturation by

enhancing tolerance to desiccation (Hoekstra et al., 2001).

Availability of nutrients such as sulphur can influence seed composition and in turn

germination; for example, by changing the ratio of legumin to vicilin (Tabe and Droux,

2001). Nutrient status can also affect seed maturation through a complex interaction

regulatory network including hormonal and signalling pathways (Radchuk et al., 2007).

It has also been shown that metabolic preparation for germination and subsequent

seedling establishment in Arabidopsis commences during late seed filling and

maturation (Fait et al., 2006); therefore germination is influenced by genetics of the

plant maternal and environmental factors. A recent genetic study using QTL analysis in

pea has also determined the interaction of genetics and growing environments for

controlling seed protein content (Burstin et al., 2007).

2.3 Germination characteristics and early seedling growth

2.3.1 Seed germination

Seed germination is a complex and only partially understood process. Many challenges

and questions remain to be addressed and answered (Bewley, 1997; Buitink et al., 2006;

Finch-Savage and Leubner-Metzger, 2006). The process essentially begins with water

absorption by dry seed (imbibition) so that metabolism can be re-initiated. Three

successive stages are recognised (Figure 2.2); rapid primary uptake (stage I), followed

by steady state (stage II), followed by another sharp increase in water uptake (stage III)

when part of the embryonic axis, usually the radicle, starts elongation. In fact

germination sensu restricto, by definition, is terminated by radicle protrusion through

surrounding layers of the seed. Any further radicle growth after protrusion is considered

seedling growth. Among a range of environmental stimuli which influence germination

such as temperature, light, oxygen, smoke, allelopathic effects and so on (Finch-Savage

and Leubner-Metzger, 2006), availability of water is the most important factor by which

germination is restricted. This is because the resumption of metabolic networks and

mobilisation of reserves, especially storage proteins or de novo synthesis of essential

molecules, all require water (Bewley and Black, 1994).

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Figure 2.2 Critical events during time course of germination and subsequent post-germination

growth. Time required for completion of events would vary, depending on germination

conditions and plant species (Adapted from Bewley 1997).

One of the difficulties with germination studies is that populations of seed are unable to

complete the process of germination synchronously due to subtle physiological

differences within individual seeds for threshold requirements to initiate germination

(Bradford, 2005). Despite these individual differences, the behaviour of seed

populations can be predicated based on the bio-time concept, which applies a

mathematical model to estimate germination responses to factors that affect the process

(Bradford, 1990; Finch-Savage and Phelps, 1993; Bradford, 2002; Finch-Savage et al.,

2005). The hydrothermal model is a population-based model proposed in the 1980s,

which is still being improved using mathematical and physiological amendments.

Briefly, based on the model, there is a base temperature and water potential below

which a seed cannot complete germination. Above the threshold, germination progress

(rate) is a function of water potential and temperature. These thresholds are calculated

from their distribution within the seed population and are considered constant for each

population of seed. On the other hand, threshold models would have biological meaning

which reflects internal and/or external physical constraints upon embryo growth

depending on the genetic background of a species (Ni and Bradford, 1992; Alvarado

and Bradford, 2005; Finch-Savage and Leubner-Metzger, 2006). In other words, the

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sensitivity of seed to these cardinal requirements may differ as a result of even small

differences in genetic make-up; for example, natural allelic variation in main regulatory

genes within or among species.

2.3.2 Resumption of metabolic activity in parallel with imbibition

Soon after water enters desiccated seed cells at stage I during imbibition, some solutes

including low molecular weight metabolites begin to rapidly leak into the surrounding

medium because of sudden perturbation in the structure of cells, especially cell

membranes (Bewley, 1997). It has been proposed that within a short time of water

ingress the gel-like cell membranes in dry seeds are transformed into the hydrated

liquid-crystalline state of normal cells, whereby leakage rapidly becomes restricted from

both cell and organelle membranes (Crowe and Crowe, 1992). Although the mechanism

of this transition is not well-defined, it has been proposed that one of phospholipids, N-

acylphosphatidylethanolamine (NAPE), is a potential regulatory factor in increasing

membrane stability (Sandoval et al., 1995).

It is generally assumed that all enzymes and structures required for resumption of

metabolic processes accumulate in dry seeds during late stages of desiccation and

therefore, through rehydration, seeds resume full metabolic activity within a few hours

(Bewley, 1997; Nakabayashi et al., 2005). Of those metabolic activities, resumption of

respiratory activity is evident during the earliest stages of imbibition. Subsequently the

rate of respiration declines and again peaks with associated radicle emergence from

surrounding seed layers (Bewley and Black, 1994). Lack of oxygen mostly due to the

relative impermeability of enclosed layers of the seed during germination causes ethanol

production in some plant species (Morohashi and Shimokoriyama, 1972).

2.3.3 RNA status and synthesis of protein during germination

The subject of transcriptional and post-transcriptional control of physiological events

during germination dates back more than four decades (Holdsworth et al., 2008) when

Dure and Waters (1965) reported the presence of stored mRNA in dry seeds of cotton.

This is supported by a number of studies in a diverse range of plant species (Peumans et

al., 1979; Comai and Harada, 1990; Ishibashi et al., 1990; Almoguera and Jordano,

1992). Stored mRNA is the remaining messages accumulated during the last stages of

late embryogenesis and seed maturation, and might be used temporarily as a template

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for proteins synthesis in early germination (Bewley, 1997; Holdsworth et al., 2008). In

accordance with this, a recent comprehensive genome-wide expression analysis detected

more than 12,000 stored mRNA species in dry Arabidopsis seed thought to be necessary

for late embryogenesis and seed germination (Nakabayashi et al., 2005). Initial protein

synthesis can, therefore, be resumed within minutes following imbibition (Figure 2.2).

In addition, de novo mRNAs play a major role in progression of germination because

they encode enzymes which are required for sustaining the usual cellular metabolism

and mobilisation of storage reserves (Bewley and Black, 1994; Bewley, 1997).

In a recent study, the role of stored and newly-synthesized mRNA was investigated by

application of transcriptional and translational inhibitors during germination in

Arabidopsis (Rajjou et al., 2004). The experiment proved that the transcription process

was not a limiting step in early germination (up to protrusion of a radicle) when α-

amanitin (transcriptional inhibitor targeting RNA polymerase II) was applied to non-

dormant seed, although subsequently the rate and uniformity of germination was

considerably slowed and seedling growth was blocked. The effect of the inhibitor later

in germination indicated that de novo transcripts must be synthesized for new enzymes

involved in reserve mobilisation and resumption of metabolic activity that is necessary

for further progress of germination and seedling establishment. However, germination

was completely inhibited in the presence of a translational inhibitor (cycloheximide).

The experiment demonstrated the role of stored mRNA and proteins in Arabidopsis

germination which is mainly programmed during seed maturation.

In addition to storage protein, mature dry seeds contain many functional proteins

engaged in a variety of cellular activities including DNA replication and transcription,

signal transduction, solute transport, protein synthesis, respiration and metabolism,

disease/defense mechanisms and so on (Gallardo et al., 2001; Gallardo et al., 2002a;

Gallardo et al., 2002b; Finnie et al., 2006; Rajjou et al., 2006; Catusse et al., 2008c).

2.4 Proteomics

In the post-genomic era, large-scale analysis of proteins (proteomics) bridges the gap

between genomics/transcriptomics and the phenotype (Figure 2.3). These tools provide

direct information about gene expression and ultimately will lead to advances in

molecular plant breeding. A growing database of genomic information from a diverse

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range of species, together with the availability of powerful mass spectrometric (MS/MS)

techniques, have allowed detailed information about the identity of expressed genes to

be gathered. This in turn provides a view of significant metabolic processes and

regulatory mechanisms involved in plant development and responses to endogenous and

environmental cues (Pandey and Mann, 2000; Tyers and Mann, 2003). In this context,

rapid development and application of diverse bioinformatics and computational tools

has been a driving force to integrate proteomic information with physiological and other

-omics data. This capacity, indeed, paves the way for analysing the complexities of

signal transduction, regulatory mechanisms and metabolic network integration

responsible for critical phenotypic traits (Kitano, 2002; Salekdeh and Komatsu, 2007;

Weckwerth, 2008).

2.4.1 Application of proteomics in studies of plant stress

Stress is a ubiquitous and real challenge for all living organisms. This situation is

serious for plants because of an inability to physically move to avoid stressful

environments (Kuiper, 1998). Plant breeding for abiotic stress tolerance has been

hampered in part by the variable nature of stresses and the sensitivity of plant phenotype

but also to the difficulty of transferring stress tolerance features found in some plant

material to elite genotypes because of the genetic complexity of traits (Morgante and

Salamini, 2003; Ashikari and Matsuoka, 2006). A combination of marker-assisted

selection (MAS) and quantitative trait loci (QTL) has proved to be an efficient way for

increasing stress tolerance (Ren et al., 2005; Salvi and Tuberosa, 2005) but at the same

time these approaches require further understanding of mechanisms and genes involved

in the complex process of tolerance to stress in the plant (Figure 2.3).

The value of proteomics lies in the ability to resolve a large number of proteins that are

altered in their abundance in response to the imposition of a particular stress. While

protein abundance may be altered by post-translational events, differences relate

directly to gene expression and as such can identify mechanisms that might confer

tolerance/sensitivity to a stress. Further pertinent information, such as patterns of

mRNA expression or metabolomics data, can be useful for verifying the association of

candidate proteins with certain traits (Figure 2.3) (Cánovas et al., 2004; Salekdeh and

Komatsu, 2007).

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Metabolomics

Genomics/Transcriptomics

Comprativeanalysis

Metabolomics

Metabolomics

Genomics/Transcriptomics

Genomics/Transcriptomics

Comprativeanalysis

Comprativeanalysis

Figure 2.3 A representative illustration summarising how genetic diversity found naturally can

be used by proteomics, and incorporates other areas such as transcriptomics, metabolomics, and

allele mining in a holistic manner to produce interesting phenotypes in crops. The impact of

environmental and developmental changes on plants is examined by systematic approaches of

genomic, proteomic and metabolomic analysis. Quantitative expression profiles of proteins,

post-translational modification regulation and interaction among protein networks are

considered informative data that can be coupled with microarray data to disclose gene

expression regulatory control. In the meantime, linkage between proteomic data and

metabolomics will explain those mechanisms by which proteins adjust metabolic networks. All

this holistic information can be used for crop improvement later by transformation or marker-

assisted selection approaches (Adapted from Salekdeh and Komatsu 2007).

Among the most constraining abiotic stresses, drought and salinity have been the most

studied using a proteomic approach. These studies are listed along with some recent

germination-related proteomic studies in Table 2.1. The subject of the majority of these

investigations is plant growth features, range and length of stress and phenotyping of

plants. For example, the first comparative proteomic study in chickpea, recently

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published by Bhushan et al. (2007), identified 134 differentially-expressed proteins,

some of which were predicted to be novel water deficit-responsive proteins. This study

compared the effect of dehydration on leaves of three-week-old seedlings in control

plants and plants that had recovered from dehydration for up to 192 h. They recognised

more than 100 extracellular matrix proteins, potentially involved in a variety of

functions such as cell wall modification, signalling, metabolism, and defence and

detoxification. The findings are significant and could explain some aspects of molecular

mechanisms for dehydration tolerance in chickpea.

In another study, two contrasting sugar beet genotypes from differing genetic

backgrounds were grown under drought conditions in the field. Proteomic analysis of

the plants using two-dimensional gel electrophoresis (2-DE), followed by liquid

chromatography-tandem mass spectrometry (LC-MS/MS) found a predominance of

several predominant proteins involved in regulating reactive oxygen species (ROS)

levels, protein folding and stability. This report emphasised the essential role of

oxidative damage and ROS in protecting plant functions to overcome the effect of

drought (Hajheidari et al., 2005).

Salinity stress, which can be mechanistically regarded as being relatively close to

drought stress, has accumulated a considerable volume of plant proteomic research

(Table 1). Wang et al. (2009a) created a high resolution proteome map for Salicornia

europaea in an attempt to elucidate the salt tolerance mechanism of this species which

might be applied to generate a salt-tolerant crop. They examined a profile of 1880

proteins, of which 196 showed significant variation when salt was applied. From these,

111 salinity-responsive proteins were identified by mass spectrometry. The majority

were engaged in energy production and ion homeostasis. Using these results and a

hierarchical cluster analysis, they created a schematic representation of the potential

mechanisms involved in phenotyping S. europaea.

Proteomic analysis has been the method of choice to compare contrasting genotypes in a

number of reports involving analysis of stress. Comparison of protein profiling analysis

between susceptible and tolerant cultivars in wheat revealed several sHSPs (small heat

shock proteins) with significant differential expression levels. Some of these proteins

were only found in tolerant genotypes (Fang), suggesting a role consistent with thermo-

tolerance phenotypes (Skylas et al., 2002). In another comparative investigation on

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Table 2.1 Summary of recent studies using proteomic analysis of plant responses to abiotic

stresses (drought, salinity) and various aspects of seed germination.

Plant Tissue/growth stage Treatment Proteomic

approach RP1 Specific key results Reference

Drought stress Chickpea leaf Dehydration

induced on seedlings up to 192 hr

2-DE/MALDI-TOF/TOF

134 Response of extracellular matrix proteome involved in various cellular function

(Bhushan et al., 2007)

Lupin stem Progressive WD2 (0, 3 & 13 d)/ (1 d recovery)

2-DE/Q-TOF 27 Constitutional high-abundance of APX & PR10 & simultaneous expression of proteases & protease inhibitors

(Pinheiro et al., 2005)

Maize root Seedling under constant level of WD

2-DE/ HPLC-EQ TOF MS

152 Protein abundance related to apoplastic ROS production of the elongation zone of water-stressed roots

(Zhu et al., 2007)

Rice leaf sheath Progressive WD between 2 to 6 d

2-DE/Sequencing

12 Up-regulation of actin depolymerising factor

(Ali and Komatsu,

2006) Soybean primary root seedlings

grown under WD up to 72 hr

2-DE/ MALDI-TOF MS/MS

27 Abundance of several enzymes related to isoflavonoid biosynthesis & increase in the abundance of ferritin proteins in the elongation zone

(Yamaguchi et al., 2010)

Sugar beet

leaf Progressive WD up to 100 d

2-DE/LC MS-MS

79 Proteome reaction to very long drought of crop planted in the filed

(Hajheidari et al., 2005)

Wheat seed Progressive WD for 2 months

2-DE/MALDITOF- TOF

120 >60% of drought responsive proteins were Trx (thioredoxin) targets, consistent with the link between drought & oxidative stress

(Hajheidari et al., 2007)

Salinity stress

Barley seed/ germination

2.5% NaCl solution for 10 d

2-DE/ MALDI-TOF MS

11 Over expression of 6-phosphogluconate dehydrogenase and Glc/RibDH in tolerant lines

(Witzel et al., 2010)

Rice root 150 mM NaCl, 24 to 72 hr

2-DE/MALDI TOF

54 6 novel salt responsive proteins like a-NAC, COX6b-1, UGP

(Yan et al., 2005)

Rice young panicle 70 mM NaCl for 10 d

2-DE/MALDI TOF-TOF

13 Higher constitutive high expression and/or up-regulation of ROS

(Dooki et al., 2006)

Salicornia europaea

shoot Up to 800 mM NaCl for 12 d

MALDI TOF/TOF MS

111 Energy production and ion homeostasis associated proteins played important roles for salt tolerance ability

(Wang et al., 2009a

Tobacco leaf 100 mM NaCl for 20 d

2-DE/ ESI-Q-TOF

20 Increase of two chitinases and a germin-like protein and de novo expression of two lipid transfer proteins

(Dani et al., 2005)

Wheat root and shoot 125 mM NaCl for 10 d

2-DE NA Similar expression after short-term stress in salt-resistant and salt-sensitive genotypes

(Saqib et al., 2006)

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1RP, number of responsive proteins; 2WD, water deficit tolerant (Khazar 1) and sensitive (Arvand and Afgani) wheat genotypes, a number of

proteins were found to play a major role in response to drought (Hajheidari et al., 2007).

Among those proteins, GST and Trx were up-regulated, specifically and significantly in

the tolerant genotype. A number of other potential candidate proteins were found to be

associated with drought response including HSP17, dehydroscobate reductase and

elongation factor-1 alpha (Hajheidari et al., 2007). These kinds of investigations are

essential in attempting to develop candidate markers for drought tolerance or other

stresses. Further proteome analysis in mapping populations and clustering responsive

proteins will help to develop the genetic basis of those protein to be used as reliable

markers for application in plant breeding. Other approaches such as transcript profiling

and/or expression and silencing of corresponding candidate genes through gene

transformation may help to confirm the function of these proteins under stress.

A number of different proteins have been identified in plants by a number of research

groups and shown to have expression profiles related to the nature of stresses, plant

Table 2.1: Continued

Germination Arabidopsis germinating

seeds/ seedling 0.5 mM Salicylic acid for 24hr

2-DE/ ESI-Q-TOF

28 Improvement of salt stress by SA, detection of carbonylated (oxidized) & new-synthesized proteins

(Rajjou et al., 2006)

Barley germinating embryos

18 hr germinated seeds

SDS-PAGE/LC-MS/MS

61 Differing function of plasma membrane and aleurone layer on germination

(Hynek et al., 2009)

Cress germinating seed/ endosperm

10 mM cis-S(1)-ABA

2-DE/ ESI-Q-TOF

144 Over-expression of energy and stress proteins, suggesting regulation role of endosperm in radicle protrusion

(Muller et al., 2010)

Medicago trancatula

germinating seeds/ radicle

PEG medium for seed incubation

2-DE/MALDI-TOF and Q-TOF

15 Different protective role of DT-linked LEA proteins at high and low hydration levels

(Boudet et al., 2006)

Rice germinating seeds

0.2-1.5 mM copper for 4 days

2-DE/MALDI TOF MS

25 Up-regulation of some antioxidant and stress-related proteins like peroxiredoxin, DnaK-type chaperone; inhibitory effect of alpha-amylase or enolase on germinations

(Ahsan et al., 2007)

Subclover/ T.subterraneum

germinating seeds

Distilled water germinated seeds for up to 36 hr

2-DE/MALDI TOF MS

61 Relevance of Convicilins catabolism during early seedling growth to environmental condition

(Sahnoun-Abid et al., 2009)

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species, duration and severity of stress. The variation even at the same stress level and

plant species can be due to various treatment levels and/or different sampling time

courses. Table 2.2 provides a short list of drought stress proteins reported by some

researchers. The actual number may increase considerably as the real functions for

many genes are not yet available, and some genes with apparently unrelated function

may soon be proven to relate to particular traits (Salekdeh and Komatsu, 2007).

Of those proteins listed in Table 2.2, oxidative stress defence proteins such as

superoxide dismutase (SOD) are the most common, not only in drought but also in other

stresses. The detoxification pathway associated with SOD can be found in nearly all

cellular organelles, including chloroplasts, mitochondria, peroxoisomes, apoplast and

cytosol (Mittler, 2002). ROS has also been identified as a key player in seed

germination by regulating cell growth, protecting against pathogens and more

importantly by controlling the redox status of cells (El-Maarouf-Bouteau and Bailly,

2008). sHSPs are another series of proteins which are considerably up-regulated during

heat or drought stress (Table 2.2). Their chaperone function protects other proteins by

facilitating protein folding and assembly. HSP response is a common response when

gene expression or protein translation is altered in cells by any means (Vierling, 1991;

Basha et al., 2004). The HSPs molecular mass varies from 15–20 kDa up to 80–100

kDa in different classes (Vierling, 1991; Sun et al., 2002; Laksanalamai and Robb,

2004). Up-regulation of a large number of pathogenesis-related proteins (PR) in various

plant tissues has also been documented in reaction to biotic and abiotic stresses (Table

2.2) (Park et al., 2004). Although the role of this class of protein is not fully understood,

it has been shown that the increased level of expression of the PR10 gene in Brassica

napus was associated with better germination and seedling growth under salinity

tolerance (Srivastava et al., 2004).

2.4.2 Proteomic and transcriptomics analyses of germination

A large body of research into seed germination has been achieved using post genomic

tools with the model plant Arabidopsis (Gallardo et al., 2001; Gallardo et al., 2002a;

Nakamura, 2005; Fait et al., 2006; Rajjou et al., 2006). These studies have mainly

focused on a range of aspects to reveal the mechanisms and regulatory networks

involved in the transition from quiescence to the active state following imbibition.

However, a number of recent studies have dealt with some of the physiological aspects

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Table 2.2 Some proteins frequently reported in drought stress proteomics studies.

1 Up, spot density up-regulated in response to drought stress; 2 Down, spot intensity down-regulated in response to drought stress.

of germination in important crop plants such as rice and barley (Table 2.1). The

response of plants to abiotic stresses has been broadly examined in vegetative tissues

and organs, but less often in seeds (Table 2.1). An important proteomic-based study in

germinating seed and seedlings of Arabidopsis evaluated the impact of the growth

regulator salicylic acid (SA) on the process (Rajjou et al., 2006). The analysis revealed a

specific role for SA in promoting the re-initiation of a late maturation program during

early stages of germination that enhanced the seed’s ability to respond to environmental

stress. Among processes influenced by SA were those related to protein translation, seed

metabolism priming, over-expression of antioxidant enzymes, and seed storage

mobilisation. All of these modifications may be the reason for improved seedling

vigour. Furthermore, the occurrence of oxidative stress following SA treatment has been

revealed by characterisation of the carbonylated proteome. Lastly, the proteome analysis

data demonstrated a close interaction between SA and ABA signal transduction for seed

vigour.

Changes in protein abundance in different compartments of seeds such as embryo tissue

during germination of barley was investigated using protein patterns on 2-DE, followed

Protein name Protein symbol

Expression level

Reference

2-Cys peroxiredoxin Up1 (Haslekas et al., 2003; Wood et al., 2003; Hajheidari et al., 2005; Ali and Komatsu, 2006)

Dehydroascorbate reductase

DHAR Up (Salekdeh et al., 2002; Hajheidari et al., 2005)

Late embryogenesis abundant

LEA Up (Goyal et al., 2005; Grelet et al., 2005; Boudet et al., 2006; Tunnacliffe and Wise, 2007; Dalal et al., 2009)

Heat shock proteins sHSP Up (Basha et al., 2004; Hajheidari et al., 2007)

Superoxide dismutase [Cu-Zn], cytosolic

SOD (Cu-Zn) Up (Abbasi and Komatsu, 2004; Bhushan et al., 2007)

Pathogenesis related protein

PR Up Down2

(Pinheiro et al., 2005; Ali and Komatsu, 2006)

Malate dehydrogenase MD Up (Bhushan et al., 2007; Wang et al., 2009a Protease inhibitors PI Up (Pinheiro et al., 2005; Clemente and Domoney, 2006;

Yamaguchi et al., 2010)

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by MS protein identification at different times from 4 to 72 h post-imbibition (Bønsager

et al., 2007). The analysis indicated that some proteins—for example, Hsp70, LEA and

ABA-induced proteins—were the earliest to appear, later declining in abundance or

disappearing in embryo tissue, suggesting that these proteins are most likely engaged in

desiccation processes. Since changes occurring in the protein profile were recorded as

early as 4 h after imbibition, the authors concluded that germination is programmed

during seed maturation.

In a recent study, a multi-faceted methodology consisting of proteomics, QTL and

transcript mapping revealed important genes for germination under saline conditions

(Witzel et al., 2010). The mapping population came from two contrasting tolerant lines

(DOM × REC) that had been studied by proteome profiling. Divergent stress responses

found among parents and segregants revealed six differentially abundant protein spots.

Of these, 6-phosphogluconate dehydrogenase and glucose/ribitol dehydrogenase

(Gl/RibDH) were expressed more highly in tolerant lines. Over-expression of Gl/RibDH

in yeast also allowed transformants to grow on saline media. In contrast, QTL analysis

of the population germinating at differing levels of salinity resulted in the identification

of 5H and 7H regions of two chromosomes consistent with the salinity response. A

complementary transcript map was also applied to the corresponding genomic regions

of those identified proteins which proved to be co-located on the QTL map of HSP70

and Gl/RibDH on chromosome 5H. This study clearly showed how proteomics may be

employed to identify potential genes involved in plant responses to various stresses

when parents showing a contrasting phenotype for a specific trait are available.

Transcript profiling of protein genes may help to unravel some aspects of the complex

developmental process. This approach has been applied by Hernandez-Nistal et al.

(2006) to investigate the elongation of embryonic axes during chickpea germination.

They found that among four known cDNAs encoding α-expansins in the cell wall, only

iso-form Ca-EXPA1 was expressed highly in embryonic axes. In addition, the

transcription pattern of Cap28 encoding a glutamic acid-rich protein defined a role for

this protein in development of embryonic axes during chickpea seed germination.

The studies mentioned above have shown how physiological changes observed during

the complex process of germination can be studied and analysed by transcriptional and

proteomic means. Recent reviews and research (Holdsworth et al., 2008; Catusse et al.,

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2008a) have emphasized that controlling mechanisms at both levels (RNA and protein)

should occur in seeds to cope with the variable environment and to maximise the

germination performance.

2.5 Research objectives

A previous investigation with 20 chickpea genotypes in a glasshouse study found highly

significant variation for seedling emergence and early growth (Hosseini et al., 2009).

While there may be a number of contributing factors to this variation, the rate of radicle

emergence following imbibition is likely to be critical. The present study extends these

findings by using a proteomic approach to identify proteins that are differentially

abundant in tissues of three chickpea genotypes selected on the basis of their rate of

radicle emergence following imbibition under conditions of limiting soil moisture.

Before a strict comparison of germination among genotypes could be attempted, an

assay system for germinating seeds in a sand medium was developed that provided

highly reproducible conditions of controlled soil moisture levels in a range likely to

limit imbibition. In this way, it was hoped that proteins expressed at elevated levels in

the most rapidly germinating genotype could be identified and linked to a mechanism

that permitted germination at severely limiting water supply. While a number of factors

will influence the levels of proteins recovered at any point in time, in a broad sense their

relative abundance reflects variation in gene expression. Knowledge of the genetic

mechanism for more rapid germination under field conditions could be used as a

breeding trait to enhance the use of chickpea.

Chapter 3 describes germination of the five chickpea genotypes under strictly controlled

and reproducible limiting levels of soil moisture. The studies establish a number of

variables (such as seed size) that need to be taken into consideration to ensure valid

comparisons and the levels of variance within the carefully selected seed lots. Under

these conditions, significant differences in the rate of germination at limiting soil

moisture among genotypes were recorded. Chapter 4 exploits the assay system to make

reproducible comparisons on the spectrum of proteins present in dry seed and which

change in abundance or appear for the first time as imbibition proceeds in each

genotype. The sophisticated proteomics tools, including 2D gel separation and mass

spectrometry, together with available genomic databases identified groups of proteins

that might constitute a mechanism for rapid germination in response to limiting soil

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moisture. This rapid germination can be attributed to the rate of seed imbibition

followed by resumption of metabolic pathways related to germination. Variation in

germination, indeed, determines seedling development and consequently uniformity of

early crop establishment. This feature can strongly influence final productivity of

chickpea as it is mostly cultivated in dryland environments where water availability

fluctuates, particularly in rain-fed conditions. Presently, the issue has not been analysed

in chickpea; hence this thesis attempts to build a reliable and reproducible framework to

investigate genotypic variation within chickpea genotypes for rapid seed germination

and subsequent pre-emergence growth in response to sub-optimal soil water contents.

More importantly, to our knowledge, there has been no such molecular investigation to

disclose or suggest any possible mechanism and/or involvement of candidate genes in

the initiation of rapid germination under restricted soil water content between

contrasting genotypes even in other crop plants.

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CHAPTER 3

3 Development of an Assay to Assess Differences in Germination Rate among Chickpea Cultivars under Limited Water Content

3.1 Introduction

Chickpea (Cicer arietinum L.) is largely cultivated on marginal areas under rainfed

conditions where water availability is the limiting factor for growth and yield.

Inadequate soil moisture contributes up to 60% of yield losses in chickpea, depending

on the region and climate of the growing area (Saxena et al., 1993). The negative impact

of sub-optimal seedbed moisture is an important determinant of yield reduction in rain

fed chickpea, especially when planted late in spring, as occurs in many arid (Sharma,

1985) and semi-arid tropical regions (Saxena et al., 1993). Water shortage at sowing

affects yield by causing poor germination and crop establishment (Finch-Savage et al.,

2005; Urbieta, 2008) and subsequently reducing growth (Saxena et al., 1993).

Attempts have been made to describe seed germination under various water and

temperature conditions. The hydrothermal time model, initially suggested by

(Gummerson, 1986) and further developed by (Bradford, 1990; Rowse et al., 1999;

Alvarado and Bradford, 2002; Bradford, 2002; Finch-Savage et al., 2005) has been used

to quantify germination in response to water and temperature conditions. According to

the model, germination rates in a population of seed are proportional to the amount by

which temperature (T) and water potential (Ψ) exceed a physiological threshold (Tb, Ψb)

for these ambient factors, below which germination will not occur. The model is

described as:

HTT = [Ψ–Ψb (G)] (T–Tb) t(G) Equation 3.1

where HTT is a constant value, within a seed lot Tb does not essentially change,

however, Ψb varies between seeds (G) (Finch-Savage and Leubner-Metzger, 2006), and

therefore a median value (Ψb 50) is often used for a population of seed. To describe the

effect of decreased water potential on germination, Equation 1 can be simplified to the

hydro-time model (Equation 2):

Hydrotime = (Ψ–Ψb)time Equation 3.2

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The hydrothermal model provides a useful framework for quantifying germination and

seedling growth, but it does not explain differences among and within genotypes. For

instance, a previous unfavourable maternal environment can result in changes in seed

metabolism (Bradford, 2002; Finch-Savage and Leubner-Metzger, 2006; Elkoca et al.,

2007; Samarah and Abu-Yahya, 2008) which, in turn, affects seed nutrition and storage

reserves. This results in variable responses of seed germination and post-germination

growth of seeds with the same genetic background (Egli, 2005; Weber et al., 2005),

which is not considered by the model.

Water uptake by the seed is the most critical requirement for germination. The size and

shape of seeds in relation to soil features and topography (Kikuzawa and Koyama,

1999) are also important for successful early establishment (Hanley, 2007; Daws et al.,

2008; Kaya et al., 2008). The size and shape is predominantly maternally controlled

(Biere, 1991; Weber et al., 2005) and is regulated by the seed coat through physical

restriction, transient storage, and by modifing of nutrient supply for the growing embryo

and cotyledons (Weber et al., 1995). Variation in seed size exists among chickpea

genotypes (Hosseini et al., 2009). Indeterminate growth habit, continuous flowering

during seed set under different conditions (Upadhyaya, 2003) and the trade-off between

size and number contribute to final seed size within a genotype (Eriksson, 1999).

The soil moisture requirement for chickpea germination is well below field capacity and

few studies have found differences among genotypes (Saxena et al., 1993). Establishing

and maintaining constant soil water content is critical in germination experiments. A

commonly used method is to expose seeds to various concentrations of polyethylene

glycol (PEG) in solid media in Petri dishes (Bradford, 1990; Willenborg et al., 2005;

Soltani et al., 2006; Kaydan and Yagmur, 2008; Tobe, 2009). Some difficulties are

associated with using this technique including lack of total seed contact, condensation

on the container resulting from any small changes in temperature, difficulty in removing

PEG from the seed for measurements, and, more importantly, failure to form real water

potentials rather than osmotic potentials in the natural medium of soil. Several attempts

have been made to compare germination differences among genotypes using osmotic

solutions like PEG and NaCl, but results from these studies cannot be easily

extrapolated to soil conditions (Saxena et al., 1993; Kaydan and Yagmur, 2008). In a

recent study the percentage of emerged chickpea seedlings under low soil moisture

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content varied among genotypes (Hosseini et al., 2009). This suggests that there may be

sufficient genetic variation within species to identify accessions that can germinate and

grow under low soil moisture conditions.

Four experiments were conducted to develop a reliable assay to investigate genotypic

variation within chickpea genotypes for seed germination and subsequent pre-

emergence growth in response to sub-optimal soil water content. To achieve this goal,

uniform seed stocks were established for different genotypes, grown under the same

environmental conditions in the same season together with a rapid, simple methodology

using a range of extremely low water contents in a natural germination medium.

3.2 Materials and Methods

3.2.1 Plant material

Three chickpea genotypes (cv Rupali, cv Norwin and K850) with different germination

rates (Hosseini et al., 2009) (Table 3.1) were sourced from the Australian Temperate

Field Crops Collection, Horsham, Victoria Australia, and additional seeds of K850 from

CSIRO Plant Industry, WA, Australia. To minimise environmental and maternal seed

source effects, all genotypes were multiplied under identical conditions in a single

location at the University of Western Australia research station. The soil was a light-

textured sand fertilised with 20 kg/ha of N and 40 kg/ha of P2O5. Growing plants were

monitored weekly and any morphologically unusual plants removed. Three

morphologically-distinct plants types were identified in the progeny of one genotype

(K850) so the seed from each of these was collected separately and labelled KH850,

KL850 and KJ850. Following harvest at maturity, seed was stored under dry conditions

at 4ºC.

Seeds from each genotype were sorted into three size classes: small, medium and large

(Table 3.1). Within each genotype, 400 individual seeds were weighed. The SE of the

mean air-dry weight was around 25%. However, this variation decreased to around 10%

within each size class (Table 3.1) following selection. On this basis, seed of one

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genotype could be compared with seed of an equivalent weight class in another

genotype, e.g. large seeds of cv Rupali were compared with medium seeds of KJ850.

3.2.2 Seed mineral analysis

Samples of each seed class for each genotype were analysed for macro- and micro-

element contents at the Western Australian Chemistry Centre laboratories in Perth WA.

Nitrogen (N) content was determined by a dry combustion method using a LECO CHN-

1000 element analyser (LECO, St Joseph. MI, USA). The LECO analyser was

calibrated using 10% EDTA, and was checked for accuracy using a rice flour standard

with a known N concentration of 1.38 %. The concentrations of other elements were

measured using inductively-coupled atomic emission spectrometry (ICP-AES)

replicated five times (Table 3.4).

Table 3.1 Distribution of seed size among genotypes of chickpea. Seeds of similar size were

grouped into small, medium and large for each genotype. Data are means ± SE (n=400).

Genotype Mean seed weight (mg)

Overall a Small Medium Large

cv Rupali 168.4 ± 44.2 108.4 ± 10.3 156.1 ± 21.8 233.7 ± 28.4

cv Norwin 148.0 ± 29.7 100.0 ± 17.9 150.9 ± 16.8 202.8 ± 8.8

KH850 169.6 ± 42.5 105.0 ± 13.5 149.2 ± 17.0 217.5 ± 26.0

KL850 141.9 ± 35.2 100.1 ± 21.2 146.0 ± 18.3 195.0 ± 11.1

KJ850 257.8 ± 69.7 167.2 ± 31.0 247.2 ± 24.4 342.3 ± 34.8 a This is the mean of a normal distribution of seed weights in the random sampling from the seed

population.

3.2.3 Germination assay

A small container system filled with washed river sand (50–250 µm particle size) was

used to maintain constant water content in the sand during germination (Figure 3.1, A).

The system consisted of a plastic container (505060 mm) with a tightly closed, but

not sealed, lid to minimise water loss, with five needle-sized holes (1 mm diameter) in

both the container base and the lid for gaseous exchange. Before the plastic containers

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were filled, the sand was dried at 60°C for 36 hr. The water holding capacity (WHC)

was measured by saturating and weighing the sand in each of the five replicate

containers after 36 hr to calculate gravimetric water drainage. During this period, water

loss due to evaporation was not considered significant (Figure 3.2). It proved difficult to

create a homogenous distribution of water in the soil in situ, so the corresponding

volumetric percentage of WHC for each water level was calculated. The water and 150

g soil were mixed together in a bowl, and then quickly transferred to the containers.

Two to eight seeds, depending on the experiment, were planted equidistant from one

another, with their embryonic axis facing downwards, exactly 10 mm beneath the soil

surface. There were four replications per experiment arranged in a randomised block

design on shelves in a temperature-controlled incubator at 18.5 ± 0.4°C in the dark.

Figure 3.1 (A) General view of the container filled with fine sand used for the experiments; (B)

seedling growth 8 days after sowing (DAS); (C) as per (B) but with sand removed to show the

extent of radicle growth.

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3.2.4 Germination and pre-emergence seedling growth

Three preliminary experiments were conducted to define (i) suitable sub-optimal water

regimes for measuring germination and early growth, (ii) optimum seed number per

container, and (iii) the impact of variation in seed size on germination rate within one

genotype (cv Rupali) before comparing rates of germination for all genotypes.

In the first experiment, seeds of cv Rupali were ranked by eye and eight uniform seeds

planted in four replicate containers to which water was added to establish 5, 10, 15, 20,

30, 40, 50, 60, 70, 80, 90 or 100% WHC. At 2, 4, 6 and 8 days after sowing (DAS)

germinated seeds and seedlings were removed, brushed clean of adhering sand grains

with a small paint brush, and the radicle and epicotyl length, if any, measured (Figure

3.1, B and C).

In the second experiment, the amount of water available to individual seeds during

germination was altered by sowing different seed numbers per container and/or

changing the water content; the competition for space amongst seeds or seedlings was

determined by the extent of radicle deformation. The amount of water available to

individual seeds during germination was manipulated by increasing seed numbers from

two to eight in each container. Water levels likely to limit germination (5, 10 and 15%

WHC) were then applied. These water levels were estimated from the first experiment.

Germination (%) was recorded at 2, 4 and 6 DAS and seed or seedling water uptake

estimated as:

[(Wi–Wd)/Wd]100 Equation 3.3

where Wi is imbibed seed or seedling weight and Wd is dry seed weight. The growth of

seedlings was measured as per the first experiment.

In the third experiment, four replicates of four seeds in each class were exposed to 5 or

10% WHC at 2, 4 and 6 DAS. In addition to previous measurements, partitioning (%) of

mobilised seed reserves into seedling tissues (i.e. radicle and epicotyl) were measured

following the modified method used by Soltani et al. (2006). Briefly, five replicates of

10 seeds each were weighed (W1) and dried at 60°C for 36 hr, then reweighed (W2) to

calculate initial seed water content at 60°C [(W1–W2)/W2]. Oven-dried weights of

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remaining seed, radicle and epicotyl were determined separately after drying for the

same duration and temperature. The difference between total seedling dry weight and

initial dry weight was considered as seed reserve loss (SRL).

Germination and early seedling growth was assessed in all genotypes in response to a

range of WHC (5, 6.25, 7.5 and 10%) which were found previously to be critical for

germination of seeds of cv Rupali. Replication and measurements were similar to

previous experiments, except that only 4 and 6 DAS were considered. The containers

from all experiments were maintained at a constant temperature of 18.5 ± 0.5°C in the

dark.

3.2.5 Statistical analysis

Data from all experiments were analysed separately using Gerry’s Stats Tools (Gerry

LaBute, 2005) and Excel 2003, and the p-value for mean comparisons was calculated

using the Student T-test.

166

168

170

172

174

176

178

180

start day1 day2 day3 day4 day7Time after addition of water

Med

ium

and

con

tain

er

wei

ght

(g)

30% WHC 20% WHC 15% WHC 10% WHC 5% WHC

Figure 3.2 Loss of water from closed experimental containers over 7 days after sufficient water

was added to achieve 5 to 30% of the water-holding capacity (WHC) of 150 g air-dried sand.

The containers were maintained at 18.5°C in the dark.

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3.3 Results

3.3.1 Experiment I- germination conditions

The small plastic containers filled with washed sand and closed to reduce water loss

provided a convenient and reproducible system to measure rates of germination of

chickpea genotypes under conditions of constant temperature in the dark. The

progressive loss of weight from these containers in the absence of seeds indicated that

the same amount of water was lost when at time zero the sand water content was

adjusted from 5 to 30% WHC (Figure 3.2). The average rate of water loss was 0.07

g/day at all levels of initial water content and, as a consequence, at the lowest initial

water content (5% WHC or 1% by weight) the loss was only 4%/day.

Germination was inhibited in containers when the water content of the sand was 100%

WHC. Germination was also inhibited at values below 20% WHC, particularly at the

lowest level of water supplied (i.e. 5%) (Figure 3.3).

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Figure 3.3 Effect of water content on initial germination and post germination growth of

medium-sized seeds of chickpea cv Rupali. The length of the emerged radicle and epicotyl was

measured 2, 4, 6 and 8 DAS. The water content in the sand was established at sowing and

ranged from 100% to 5% WHC. Containers were closed and maintained at 18.5°C in the dark.

Data are means ± SE (n=18).

3.3.2 Experiment II- hydro-time effect on imbibition

There was only a small difference in water uptake by seeds at 15 and 10% WHC (Table

3.2). Decreasing seed numbers in each container at this time had no impact on water

absorption at 10 and 15% WHC, resulting in 100% germination. A similar response

occurred at 4 DAS, after which the rate of imbibition was significantly different in

containers with more than two seeds due to greater competition among germinated

seeds for water. At 5% WHC (Table 3.2), and as time after sowing increased from 2 to 6

DAS, the absorption of water increased in all containers. The increment was nearly 37%

0

10

20

30

40

50

60

70

80

2 4 6 8

Time of imbibition (days)

Epic

otyl

leng

th (m

m)

100% WHC50% WHC20% WHC10% WHC5% WHC

0

20

40

60

80

100

120

140

160

Rad

icle

leng

th (m

m)

100% WHC50% WHC20% WHC10% WHC5% WHC

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more with 4 and 8 seeds per container resulting in increased germination rate from 0 to

38% and 33 to 100%, respectively. The same pattern was observed when seed numbers

decreased, which increased available water per seed. For instance, seeds with nearly

74% water uptake at 2 and 6 DAS had similar germination status (Table 3.2).

3.3.3 Experiment III- seed size impact

The impact of variations in seed size on germination rates demonstrated that even

though seeds in the small class were half the weight of those in the large class, there was

no significant difference between classes in the length of the radicle that emerged up to

8 DAS at 10% WHC (Figure 3.4). However, radicles of smaller seeds weighed

significantly less. The epicotyl emerged after the radicle but was not apparent until 2

DAS. At 4 and 8 DAS, small seeds had longer epicotyls but there was little difference in

epicotyl dry weight between seed sizes. At 5% WHC, seed size significantly impacted

the rate of germination. No epicotyls emerged from any seeds at 5% WHC, but at 8

DAS radicles from the smallest seed class were 12 times longer than those of the largest

seed class. Differences in radicle weight were less obvious but again the heaviest

emerged from the smallest seeds. Using these data, a general estimate of specific mass

(thickness) of the radicle was determined by expressing radicle length per unit dry

weight (mm/mg) (Figure 3.5). This showed that longer radicles produced by smaller

seeds were less dense and probably thinner than those produced by larger seeds.

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Table 3.2 The effect of the number of chickpea cv Rupali seeds sown per container on germination percentage and water uptake at limiting levels of

water supply (5, 10 and 15% WHC). Germination was scored as the emergence of a radicle 2, 4 and 6 DAS. Water absorption was measured as the %

increase in initial dry seed weight (means ± SE [n=24]). Any losses of seed weight as a consequence of respiration and leaching were ignored. The

numbers in bold rows indicate the correlation of critical imbibition and germination rate which is consistent with the hydro-time concept presented in

Equation 2 (Finch-Savage et al., 2005).

Seed number per pot

WHC (%)

2 DAS 4 DAS 6 DAS Germination

(%) Water absorption

per seed (%) Germination

(%) Water absorption per seed (%)

Germination (%)

Water absorption per seed (%)

8 15 100 101.7 ± 0.5 100 180.1 ± 1.3 100 246.8 ± 0.7 10 100 95.2 ± 0.7 100 142.5 ± 4.4 100 158.5 ± 3.3 5 0 54.6 ± 0.9 0 54.8 ± 5.8 38 74.3 ± 1.2

4 15 100 104.3 ± 0.4 100 171.5 ± 8.4 100 324.8 ± 6.0 10 100 95.5 ± 1.2 100 170.7 ± 1.7 100 252.1 ± 5.2 5 33 74.6 ± 0.2 75 80.3 ± 1.3 100 101.1 ± 0.3

2 15 100 100.6 ± 1.3 100 183.1 ± 11.6 100 329.8 ± 0.8 10 100 97.2 ± 1.3 100 180.5 ± 2.9 100 308 ± 1.4 5 100 85.7 ± 0.4 100 101.3 ± 4.0 100 143.1 ± 0.6

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0

0.5

1

1.5

2

2.5

3

L10 M10 S10 L5 M5 S5

Seed size and % WHC

Spec

ific

radi

cle

leng

th (m

m/m

g) 2 DAS4 DAS8 DAS

0

20

40

60

80

100

120

140

160

L10 M10 S10 L5 M5 S5

Rad

icle

len

gth

(m

m)

2 DAS4 DAS8 DAS

0

20

40

60

80

100

120

140

L10 M10 S10 L5 M5 S5

Rad

icle

dry

wei

gh

t (m

g)

2 DAS4 DAS8 DAS

0

5

10

15

20

25

30

35

L10 M10 S10 L5 M5 S5

Seed size & % WHC

Ep

ico

tyl d

ry w

eig

ht

(mg

)

2 DAS4 DAS8 DAS

05

1015202530354045

L10 M10 S10 L5 M5 S5

Seed size & % WHC

Ep

ico

tyl l

eng

th (

mm

)

2 DAS4 DAS8 DAS

Figure 3.4 Seedling growth of different sized seeds of cv Rupali in response to low water

contents (10 and 5% WHC). Radicle and epicotyl lengths were measured at 2, 4 and 8 DAS.

The corresponding dry weights were measured after oven drying at 60°C for 36 h (means ± SE

[n=12]).

Figure 3.5 Radicle specific mass (length/dry weight) following germination for 2, 4 or 8 DAS

using three seed sizes (L: large; M: medium; S: small) in cv Rupali at critical water levels (5 and

10% WHC) (means ± SE [n=12]).

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In all seed size classes, the mobilised resource fraction decreased significantly in

response to water availability in the sand, with a substantial reduction in large and

medium sized seeds compared with small ones (Figure 3.6). Large seed failed to

produce any seedling tissues and some seed resource (weight) was lost. The loss

increased up to five-fold from 2 to 6 DAS, although these values were significantly

lower than in small germinated seeds (P≤0.01). Furthermore, the conversion efficiency

of mobilisation to seedling organs was 1.5 to 2 times higher in small seeds compared

with larger ones at various times after sowing. Large seeds had no emergent tissue at

low WHC and their mobilised reserves and/or breakdown was inhibited under both

moderate and low water availability.

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Figure 3.6 Partitioning of seed dry matter (%) to different parts of the seedling and loss of mass

during germination and seedling growth after (A) 2, (B) 4 and (C) 8 DAS in cv Rupali under

various soil moisture levels. The differences between total seedling dry weight and original seed

weight were calculated and assumed to be due to the loss of CO2 in respiration and leaching of

any material from seeds into sand. Data are means ± SE (n=12).

2.02.6

3.4

0.0 0.11.0

3.02.4

1.9

0.6

1.9

2.5

0.0

1.0

2.0

3.0

4.0

5.0

6.0

L10 M10 S10 L5 M5 S5

Seed size & water content (FC%)

Par

titi

on

ing

(%

)

Reseve loss

Epi

Rad

3.2 3.55.0

0.01.8

4.3

1.1 1.3

2.4

0.0

0.0

0.0

3.23.7

5.7

2.4

3.9

4.4

0.0

2.0

4.0

6.0

8.0

10.0

12.0

14.0

16.0

L10 M10 S10 L5 M5 S5

Seed size & water content (FC%)

Par

titi

on

ing

(%

)

10.6 12.6

19.0

0.24.4 6.5

3.1

5.3

7.2

0.0

0.00.0

3.6

6.1

6.0

3.0

3.54.2

0.0

5.0

10.0

15.0

20.0

25.0

30.0

35.0

L10 M10 S10 L5 M5 S5

Seed size & soil water content (FC%)

Par

titi

on

ing

(%

)

C

B

A

(% WHC)

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3.3.4 Experiment IV- seed water absorption

Imbibition of seeds of each genotype was measured as the increase in weight at 10 and

7.5% WHC (Table 3.3). The rates of water uptake were consistent with water content of

the sand, but at each water level there was no significant difference between genotypes.

However, the pattern of response for each genotype varied slightly depending on water

supply.

Table 3.3 Imbibition of seeds at 2 DAS for two levels of water supply (10 and 7.5% WHC).

The imbibition value was calculated as the % increase in initial seed dry weight (numbers below

each genotype). Seed size was 150 mg for all genotypes.

WHC (%)

Genotypes

cv Rupali cv Norwin KH850 KL850 KJ850

10 103.34 ± 1.18 100.7 ± 0.07 98.7 ± 0.41 97.14 ± 1.11 104.16 ± 1.33

7.5 96.19 ± 0.73 94.45 ± 0.47 93.01 ± 0.11 91.7 ± 0.08 88.85 ± 0.36

3.3.5 Mineral analysis

The analysis of seed mineral reserves indicated that there were small but in some cases

significant differences among genotypes (Table 3.4). In micronutrient content, however,

the concentration of all elements except Na were significantly higher in cv Rupali

compared with other genotypes. This was more evident for N content, with almost 30%

higher N than the others when compared with KJ850. Among the micro elements,

KJ850 had significantly lower Fe concentration and to some extent lower Zn content

compared to the other genotypes.

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Table 3.4 Mineral analysis of macro- and micro element contents of seed samples among the genotypes studied.

Genotypes cv Rupali a cv Norwin b KH850 b KL850 b KJ850 a

Macro (%)

N 4.03±0.19 3.02 3.22 2.77 3.09±0.12 P 0.46±0.01 0.42 0.38 0.38 0.41±0.01 K 0.90±0.03 0.71 0.86 0.72 0.76±0.02 Na 0.32±0.02 0.54 0.31 0.44 0.43±0.05 Ca 0.17±0.01 0.14 0.16 0.18 0.13±0.01 Mg 0.14±0.4 0.11 0.12 0.11 0.12±0.01 S 0.19±0.01 0.17 0.18 0.16 0.18±0.00

Micro (mg/Kg)

B 11.25±0.5 8.00 10.00 11.00 11.8±0.50 Cu 7.23±0.41 6.40 6.10 5.90 7.2±0.15 Fe 57.75± 4.65 87.00 78.00 65.00 51±1.73 Mn 27.25±2.06 21.00 30.00 33.00 25.75±1.71 Mo <1.0 <1.0 <1.0 <1.0 <1.0 Zn 58.00±2.16 61.00 54.00 48.00 49.50±2.38

a The analyses were obtained from 5 separate biological seed samples with 2 technical

replicates.

b The analyses resulted from pooled biological seed samples with 2 technical replicates.

3.3.6 Genotypic variation

Lowering WHC below 5% could prevent germination. Moderate germination and

growth occurred at 10%WHC, especially at 4 and 6 DAS. The response of other

genotypes was unclear, so water contents of 7.5 and 6.5% WHC were included in the

experiment to define variations in germination response among genotypes more closely.

There was also variation in the rate of early seedling growth between genotypes

depending on WHC and DAS (Figure 3.7). The genotypes were grouped into three

categories, based on their germination response to limited water as rapid (cv Rupali),

moderate (cv Norwin, KH850, KL850) and low germination (KJ850). As water content

of the sand decreased, there was little effect on the rate of seedling growth in the

moderate group, but mixed responses were observed in the other groups (Figure 3.7A,

B, E & F). With decreasing water availability in the sand at 4 DAS, the difference

between rapid and low germination genotypes increased progressively. The emergent

radicle length in cv Rupali, for example, was up to four times more than KJ850 at 6.25%

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WHC (P≤0.0001) and no germination occurred at 5% WHC for KJ850 despite

considerable radicle growth in cv Rupali (Figure 3.7A). The same trend was observed at

the highest WHC (P≤0.01). Although epicotyls were only present at 10% WHC with no

statistical difference between genotypes at 4 DAS (Figure 3.7D), a significant difference

in growth of the epicotyl was observed between cv Rupali and the other genotypes,

especially KJ850 at 6 DAS (Figure 3.7D). At 7.5 and 6.5% WHC, there was some small

but inconsistent epicotyl growth. This trend was similar for radicle length and

corresponding dry weights with the exception of KJ850 which had relatively higher

weights per unit length, with a greater radicle thickness or density (Figure 3.7E, F). On

the whole, cv Rupali performed better over a wide range of reduced water availability in

the sand compared with the others. KJ850, with low pre-emergence seedling growth and

failure to germinate at very low WHC, was the most sensitive genotype to water supply

in this range (Figure 3.8).

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Figure 3.7 Genotypic variation of pre-emergence seedling growth (before appearance from the

soil) of five chickpea genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to

critical water supply (10, 7.5, 6.25 and 5% WHC) at 4 and 6 DAS (means ± SE [n=12]). (A, B)

emergent radicle growth (mm); (C, D) epicotyl growth (mm) and (E, F) dry weight (mg) of

corresponding radicle 4 and 6 DAS, respectively.

0

10

20

30

40

50

60

70

Rup 1

0

Nor

10

KH

10

KL 1

0

KJ 1

0

Rup 7

.5

Nor

7.5

KH

7.5

KL 7

.5

KJ 7.5

Rup 6

.25

Nor

6.2

5

KH

6.2

5

KL 6

.25

KJ 6

.25

Rup 5

Nor

5

KH

5

KL 5

KJ 5

Genotypes & soil water content (FC%)

Rad

icle

len

gth

(m

m)

0

10

20

30

40

50

60

70R

up 1

0

Nor

10

KH

10

KL 1

0

KJ 1

0

Rup 7

.5

Nor

7.5

KH

7.5

KL 7

.5

KJ 7.5

Rup 6

.25

Nor

6.2

5

KH

6.2

5

KL 6

.25

KJ 6

.25

Rup 5

Nor

5

KH

5

KL 5

KJ 5

Genotypes & soil water content

Rad

icle

len

gth

(m

m)

0

5

10

15

20

25

Rup 1

0

Nor

10

KH

10

KL 1

0

KJ 1

0

Rup 7

.5

Nor

7.5

KH

7.5

KL 7

.5

KJ 7.5

Rup 6

.25

Nor

6.2

5

KH

6.2

5

KL 6

.25

KJ 6

.25

Rup 5

Nor

5

KH

5

KL 5

KJ 5

Genotypes & soil water content (FC%)

Ep

ico

tyl le

ng

th (

mm

)

0

5

10

15

20

25

Rup 1

0

Nor

10

KH

10

KL 1

0

KJ 1

0

Rup 7

.5

Nor

7.5

KH

7.5

KL 7

.5

KJ 7.5

Rup 6

.25

Nor

6.2

5

KH

6.2

5

KL 6

.25

KJ 6

.25

Rup 5

Nor

5

KH

5

KL 5

KJ 5

Genotypes & soil water content (FC%)

Ep

ico

tyl le

ng

th (

mm

)

0

5

10

15

2025

30

35

40

45

Rup 1

0

Nor

10

KH

10

KL 1

0

KJ 1

0

Rup 7

.5

Nor

7.5

KH

7.5

KL 7

.5

KJ 7.5

Rup 6

.25

Nor

6.2

5

KH

6.2

5

KL 6

.25

KJ 6

.25

Rup 5

Nor

5

KH

5

KL 5

KJ 5

Genotypes & soil water content (FC%)

Rad

icle

dry

weig

ht

(mg

)

0

5

10

15

2025

30

35

40

45

Rup 1

0

Nor

10

KH

10

KL 1

0

KJ 1

0

Rup 7

.5

Nor

7.5

KH

7.5

KL 7

.5

KJ 7.5

Rup 6

.25

Nor

6.2

5

KH

6.2

5

KL 6

.25

KJ 6

.25

Rup 5

Nor

5

KH

5

KL 5

KJ 5

Genotype & soil water content (FC%)

Rad

icle

dry

weig

ht

(mg

)

A B

C D

E F

4 DAS 6 DAS

Genotype & critical % WHC Genotype & critical % WHC

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Figure 3.8 Display of variation in germination and pre-emergence seedling growth of two

contrasting genotypes of KJ850 and cv Rupali 6 DAS. (A, B) Representative germination

phenotypes at 10% WHC; (C, D) at 6.25% WHC and (E, F) at 5% WHC. Germination of KJ850

was completely inhibited even after 6 days at 5% WHC while all seeds in the other genotype

formed radicles more than 6 mm long. Orange scale bar represents 10 mm.

3.4 Discussion

The interaction of maternal environment x genetic factors, which causes variation in

seed germination and subsequent early establishment of seedlings, was minimised. This

was possible by producing seeds of all genotypes at the same field site, under the same

environmental and agronomic conditions at the same time. The sandy soil was prepared

so that fertilizer application was as uniform as possible and watering regimes were

likewise uniform. Variables such as water deficit during grain filling, which often

influence seed vigour during germination and early seedling growth (Biere, 1991;

Bettey et al., 2000; Weber et al., 2005; Hanley, 2007) were eliminated. High

temperatures before harvest (Egli, 2005) and temperature fluctuations during seed

storage, which affect germination capability (Huarte and Benech-Arnold, 2005) were

also avoided. In this way the seed stocks harvested were as far as possible comparable

among the genotypes used. Furthermore, the harvested seed stocks were graded and

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selected into size classes, eliminating any seeds that were unusual in shape and colour.

In this way it was hoped that germination rates could be assessed and compared among

genotypes with experimental material that was as comparable as possible. Despite these

considerations there were a number of significant differences in the elemental

composition of seeds of different genotypes and presumably this resulted from genetic

variation among the genotypes for nutrient uptake. Phosphorus efficiency in soybean

(Wang et al., 2009b) and seed nitrate content in Arabidopsis (Chopin et al., 2007) for

example depend on the activity of specific regulated transporters and it was these

nutrients that varied most among the genotypes.

The outstanding features of the assay that was developed were its (i) simplicity in

operation and (ii) reproducibility of results by creating low uniform moisture levels

similar to those in the field. The natural physical contact of seeds with sand particles

resulted in normal growth of seedlings. In addition, the system provided a homogenous

distribution of low levels of water across the sand for absorption by each seed. This also

resulted in normal architecture of the radicle, a criterion which is critical for measuring

the partitioning of seed reserves and growth parameters. In most germination studies,

poly ethylene glycol (PEG) has been used to reduce water content because it is

convenient (Bradford, 1990; Willenborg et al., 2005; Soltani et al., 2006; Kaydan and

Yagmur, 2008; Tobe, 2009). However, this method does not permit complete and

uniform seed contact with the water-containing medium and causes condensation on the

container resulting from small changes in temperature. It is difficult to remove PEG

from seeds for measurements, and, more importantly, the conditions fail to provide real

water potentials, rather than osmotic potential, in the natural medium of soil

(Etherington and Evans, 1986).

Germination was not significantly affected by the amount of water at early stages (up to

2 DAS) due to a lower demand for water. Differences became evident with time

especially at 100% and less than 20% WHC (Figure 3.3). It is likely that the main

limiting factors at high and low water availability were hypoxia and progressive water

shortage respectively. Reduced oxygen concentration in the seed bed, caused by

excessive water, may have impacted germination and seedling emergence. The extent of

sensitivity is also genetically controlled (Finch-Savage et al., 2005 and references

therein). Therefore, the results indicate that the likely competitive response to limiting

water among the genotypes studied may occur at less than 20% WHC.

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3.4.1 Hydro-time relationship

Time to germination was delayed under low water availability. This lag phase in

initiating germination was directly proportional to the number of seeds and the

imbibition interval. Imbibition and the rate of germination were slowed as moisture

content in the sand decreased. A similar pattern has been reported in other plants

(Bradford, 1990; Evans and Etherington, 1990; Finch-Savage and Phelps, 1993;

Alvarado and Bradford, 2002; Phil, 2003; Finch-Savage et al., 2005; Urbieta, 2008) and

it is consistent with the hydro-thermal time model. Since temperature was constant

throughout the study, this model could be converted to a hydro-time model, where water

is the only variable such that progress towards complete germination depends only on

water potential.

The hydro-time value is assumed to be constant. However, as the seed threshold water

potential (Ψb) differs between individual seeds within a population, germination rate

has, in turn, a distribution relating to the distribution of Ψb. For this reason, a median

threshold (Ψb50) is considered for the whole seed population in most studies, as it tends

to be constant. Interestingly, in this study, the germination rate and timing were

predictable and consistent with the hydro-time model (Table 3.2, bold rows). Seed

imbibition gradually increased either by the elapse of time or by decreasing the number

of seeds per pot. When it exceeded Ψb50, between 75 and 80% of initial seed mass,

germination was completed in nearly 50% of seeds. Therefore water availability

between 5 and 10% WHC would provide an appropriate condition to detect subtle

variations in germination among cultivars if they existed. It has been suggested that

Ψb50 may have biological meaning (Finch-Savage and Leubner-Metzger, 2006) and that

Ψb50 is related to endogenous and exogenous physical limitation for embryo growth

depending on species or genetic background, or may even change due to various

environmental stresses and the growth conditions for maternal plants during seed

production (Alvarado and Bradford, 2002; Bradford, 2002; Phil, 2003). Consequently

all these variables may alter endogenous hormone concentrations or affect other

biochemical mechanisms controlling Ψb50 (Finch-Savage and Leubner-Metzger, 2006).

3.4.2 Seed size effect

Variation in seed size can lead to variation in germination and seedling fitness but the

relationship was dependent on available water. When the amount of water was

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extremely limited, seed size played a more significant role in germination and

subsequent seedling growth; smaller seeds were more successful at absorbing enough

water to reach the threshold water uptake to initiate germination. This kind of response

in small seeds has been observed in other studies where limited water was available for

germination (Saxena et al., 1993; Kikuzawa and Koyama, 1999). The reason is

attributed to a larger surface area to volume ratio for smaller seeds compared with larger

seeds. This is then important when water is scarce, as there is less driving force from the

water gradient between the medium and the centre of the seed. Moreover, through

further elongation of the emergent radicle, which occurs earlier in small seeds, contact

with soil particles progressively increases. As a consequence, small seeds have the

advantage of greater access to water to germinate and grow. This is consistent with the

findings of Kikuzawa and Koyama (1999) who also found that small-seeded species had

rapid water imbibition and extended their radicle further into deeper layers of soil to

access relatively high and stable water contents before the soil above becomes too dry.

Seedlings also develop a relatively larger root to shoot ratio to avoid dehydration

(Hendrix et al., 1991). In other stresses, such as salinity, where water availability is

critical for seed germination, seed size plays a similar role. For instance, Kaya et al.

(2008), found that among the chickpea cultivars studied smaller seeds germinated and

grew more rapidly than medium and large seeds in response to various level of salt

stress. It has also been suggested that the cost of chickpea production could be reduced

by using small seeds and shallower sowing without compromising grain yield (Gan et

al., 2003). In contrast, Kaydan et al., (2008) observed that large-seeded triticale had

better germination and seedling growth than smaller seeds under normal and osmotic

stress conditions.

Heterotrophic growth of pre-emergent seedlings can be directly attributed to mobilised

seed reserves and conversion efficiency of mobilisation from seed storage tissues to

emergent tissues (Soltani et al., 2006). This provides the required energy to fuel further

seedling growth up to the stage at which the seedling becomes photosynthetically

competent (Pritchard, 2002). In the present study, the relative mobilised reserves and

efficiency were significantly reduced by water deficit and the extent of this influence

depended on seed size. This may be attributed to the greater imbibition rate in small

seed (Bewley, 1997) which is partly in agreement with the findings of Soltani et al.

(2006) that water deficit reduced mobilised reserves in wheat and chickpea with the

same sized seed. They also pointed out that this feature could potentially be targeted in

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future plant improvement programs in response to water deficit or salinity stresses.

Another interesting finding of this study was the loss of reserves during germination in

all cases, even in large seeds that imbibed but did not germinate. This can be partly

related to increased respiration concurrent with water uptake, but is also the result of

rapid leakage of solutes and low molecular metabolites into the surrounding imbibition

solution due to cell membrane perturbation from the gel state to the stable configuration

during initial water imbibition (Bewley, 1997). Following this initial step, leakage is

restricted or merely limited to radicle exudation.

3.4.3 Genotypic variation

The contrasting germination and subsequent growth among genotypes, especially

between cv Rupali and KJ850 indicated that these two genotypes should have different

Ψb requirements to complete germination as there was no difference in their rate and

extent of imbibition. They did differ in their elemental composition and particularly in

N content but also to a lesser degree in P content and some of the microelements.

Adequate levels of nutrients are critical for plant growth and development especially

during periods of rapid cell expansion and division such as during germination or rapid

seedling growth (Rengel and Graham, 1995; Hegeman and Grabau, 2001; Naegle et al.,

2005; Ozturk et al., 2006) and it is possible that cv Rupali had a nutritional advantage

following imbibition. Whether or not this nutrient difference influenced events early in

germination is not known but it seems more likely that N supply especially might

become more critical after radicle emergence and during later seedling growth.

Hosseini et al. (2009) showed that emergence time and post-emergent seedling growth

among various chickpea genotypes (including cv Rupali and Norwin) was considerably

diverse under low soil moisture. These authors suggested that screening chickpea

germplasm for rapid emergence under reduced soil water should be targeted in future

breeding programs. Similarly, genotypic variation has been reported in other

germination studies including Turkish chickpea cultivars in response to salinity (Kaya et

al., 2008), oat genotypes for osmotic potential (Willenborg et al., 2005) and cool season

grasses to soil water content (Gazanchian et al., 2006). The evidence suggests that, seed

Ψb, which reflects water status of the seed for germination to be initiated can be

determined by endogenous hormone concentrations. This should be controlled by

particular genes but at the same time influenced by previous environmental constraints

during seed development (Finch-Savage and Leubner-Metzger, 2006). So far, a few

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candidate genes have been proposed that might initiate germination and play a role in

the establishment of Ψb threshold (Chen and Bradford, 2000; Hernandez-Nistal et al.,

2006; Holdsworth et al., 2008). However, detailed biochemical and potential molecular

mechanism(s) by which Ψb can be regulated or modulated are yet to be identified and

analysed at the level of gene expression.

3.5 Conclusions

This study developed an assay system that provided rapid and reproducible germination

at low soil water content. The assay allowed comparison of different chickpea

genotypes in their ability to complete germination without the difficulties of controlling

accurate water levels and also in a rapid and reproducible fashion. The system consisted

of small pots filled with sand to simulate soil conditions. The assay system showed that

seed size is an effective prerequisite for germination performance, particularly at low

soil water contents. Small seeds within a single genotype successfully germinated and

even entered early stages of seedling growth but large seeds failed to do so. In addition,

small seeds were more efficient in mobilising seed reserves to seedling tissues. Despite

the lack of any physical barrier for seed water absorption, germination rate and further

radicle growth varied significantly among the chickpea genotypes used in which cv

Rupali was the most competent genotype compared with others especially KJ850. This

evidence leads to the proposition that the physiological threshold of water potential (Ψb)

for initiation of germination should be under the control of genes with corresponding

biochemical and molecular processes (e.g. at proteome and transcriptome levels) which

could be manipulated. In the next chapter, large scale proteome analysis is used to

further analyse the likely mechanism and identify candidate genes responsible for the

different performance of contrasting genotypes for germination under critically low

water contents.

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CHAPTER 4

4 Comparative Proteomic Analysis of Differentially Expressed Proteins in Germinating Seeds of Chickpea under Limiting Water Supply

4.1 Introduction

During seed germination there is resumption of a number of metabolic activities that

ceased as the seed matured and was desiccated together with the expression of a new

suite of proteins and consequent metabolism (Bewley, 1997; Rajjou et al., 2004). If the

ingress of water during imbibition is adequate within a few hours seeds enter full

metabolic activity with de novo mRNA and protein synthesis completing germination

and initiating radicle emergence/elongation (Nakabayashi et al., 2005). Thus both the

initial functional molecular components present in dry seeds, such as stored RNA and

proteins, together with the newly transcribed RNA and expressed proteins are equally

important to the germination process and establishment of a new seedling. The initial

metabolic activity is critical to the rate at which germination proceeds and the rate at

which the nutrient reserves of the seed storage reserves are mobilized.

Although to date gene expression at the transcript level has been used in studies of the

response to drought stress (Seki et al., 2002; Varshney et al., 2009) expressed sequence

tags alone are insufficient without information as to their function (Watson et al., 2003;

Bhushan et al., 2007). Moreover, the level of protein synthesis is not always predictable

from mRNA level most likely because of post-translational modifications and

widespread changes in protein turnover (Gygi et al., 1999) or even effects of

microRNAs and other regulators that modulate gene expression (Selbach et al., 2008).

For these reasons proteomic analysis provides a more accurate assessment of plasticity

of gene expression and biochemical processes associated with the physiological state of

tissues and cells. In recent years, plant responses to dehydration or drought have been

investigated using proteomic analyses in some important crops such as maize (Riccardi,

1998; Alvarez et al., 2008), rice (Ali and Komatsu, 2006), wheat (Hajheidari et al.,

2007), soybean (Yamaguchi et al., 2010), lupin (Pinheiro et al., 2005) and chickpea

(Bhushan et al., 2007). However, these studies have been restricted to relatively mature

plants or seedling organs at some later point of their life cycle rather than on the key

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developmental stages of seed germination. In these studies, expression a number of

proteins (associated with metabolism, antioxidant enzymes, signalling, cell defence and

rescue) were shown to be directly or indirectly involved in the plant response. The

metabolic pathways identified were considered important parts of the plant’s response

to stress. Proteomics has also been used to successfully investigate aspects of

germination, mainly in the model plant Arabidopsis thaliana including the role of

gibberellins (Gallardo et al., 2002a), mechanisms of seed dormancy (Chibani et al.,

2006), the effect of salicylic acid (Rajjou et al., 2006) and of seed priming (Gallardo et

al., 2001).

Comparison of protein profiling analysis between susceptible and tolerant cultivars

(contrasting genotypes) for heat (Skylas et al., 2002) water deficit (Riccardi, 1998) and

other stresses (Salekdeh and Komatsu, 2007) has been applied to identify potential

candidate protein markers involved in stress tolerance. These markers might then be

applied in plant breeding by their application to mapping populations (Salekdeh and

Komatsu, 2007). In wheat, for example, comparative proteomic analysis between

drought tolerant and sensitive genotypes has demonstrated a specific and significant

over-expression of some proteins such as glutathione S-transferase (GST), thioredoxin

(Trx), heat shock protein 17 (HSP17) and dehydroscobate reductase in a tolerant

genotype (Hajheidari et al., 2007).

There has been no large scale proteomic analysis reported that has dealt with

germination under sub-optimal water supply with particular emphasis on genotypic

variation for this trait. Potentially a detailed protein profile could provide a useful

foundation for further investigation of mechanisms involved in the initiation of

germination and more particularly of likely metabolic events that permit rapid

germination.

In the previous chapter significant variation among genotypes in threshold water

potential for seed to initiate germination and for a radicle to emerge was identified.

These data and the assay that was developed provide the basis for the present study

which exploits a temporal functional proteomic analysis in an attempt to reveal potential

mechanisms responsible for the differential germination performance observed among

contrasting genotypes under conditions of limited available water.

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4.2 Material and Methods

4.2.1 Plant material and germination conditions

Among the five genotypes studied in the previous chapter, two, with contrasting

germination responses (cv Rupali and KJ850) and a genotype with an intermediate

germination response (KH850) to limiting water supply were selected for detailed

proteomic analysis. To ensure a valid comparison between these genotypes seeds of the

same size for each were selected and matched with the large seed class in cv Rupali

(Table 3.1). All seeds were sown in sand containing a level of water equivalent to

6.25% of WHC and incubated at 18.5 ºC ± 0.4 according to the detailed methods

described in Chapter 3.

4.2.2 Protein Extraction and Two- dimensional electrophoresis (2-DE)

Total soluble protein was extracted using the method developed by Goggin et al (2010).

Briefly, 2.5 g imbibed tissue from a pooled sample of 5 seeds, excluding the radicle,

were ground to a powder in liquid nitrogen and immediately placed in a centrifuge tube

with two volumes of extraction buffer (8 M urea, 2% (v/v) Triton X-100, 5 mM DTT)

and vortexed. After 30 min incubation at room temperature with gentle rocking the

tubes were centrifuged for 10 min at 12000 g. Protein recovered in the supernatant was

precipitated in methanol chilled to -80°C, incubated overnight at -80°C and then

centrifuged for 30 min at 10000 g. The pellet was allowed to air-dry, re-suspended in

minimal IEF buffer (8 M urea, 2% [w/v] CHAPS, 60 mM DTT, 2% [v/v] IPG buffer)

for 10 min with rocking, and centrifuged at 12000 g for 30 min. The concentration of

protein in the supernatant was determined by Peterson- Lowry assay (Peterson, 1983)

using crystalline BSA as standard.

Iso-electric focusing was conducted with samples containing 500 µg of protein in 250

µl of 2-DE rehydration buffer on 13 cm Immobiline gel strips (GE Healthcare Bio-

Science). This protein solution (250 µl) was loaded onto the IPG strips (13 cm) with

non-linear pH 3-11 or linear pH 4-7 gradients and electro-focused in a Multiphor™ II

Electrophoresis Unit (Amersham Biosciences) under the following conditions: 300 V

for 1 min, Gradient from 300 V to 3500 over 1.5 hr and constant 3500 V for 4 hr (total

= 17 kVh). The focused IPG strips were reduced in 10 ml equilibrium buffer (50 mM

Tris pH 8.8, 6 M urea, 30% [v/v] glycerol, 2% [w/v] SDS, 0.002% [w/v] bromophenol

blue) with 65 mM DTT, followed by alkylation using the same buffer but containing

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135 mM iodoacetamide for 15 min per each step separately. For SDS-PAGE, the

developed strips were placed on gels containing 12.5% [w/v] polyacrylamide.

The initial analysis of all 2-DE gels indicated that there was considerable crowding and

overlay of resolved proteins towards the acidic end of the gradient in the pH 3-11 gels.

This area comprised approximately 90% of all protein spots with nearly the same

pattern recorded for each genotype (Figure 4.1). The proteins were much better

resolved on pH 4-7 gels and, although around 40 and 20 kDa there was some crowding

and a number of proteins were not completely resolved, only the narrow range strips

were used for the study.

4.2.3 Gel Staining, Image Acquisition and Data Analysis

Protein spots on the SDS-PAGE gel were stained overnight by the “Blue Silver”

technique followed by de-staining overnight in 2% [v/v] acetic acid. This is a recently

developed sensitive colloidal coomassie blue G-250 staining method (Candiano et al.,

2004).. The gels were then digitalized at 800 dpi (dot per inch) resolution using a GS-

800 Calibrated Densitometer (Bio-Rad). PDQuest Software (Version 8.0.1, Bio-Rad) to

analyse the images and create master gel images following the user’s manual (PDQuest

8.0 2D Image Analysis, Bio-Rad) from three biological replicate gels for individual

genotypes at each of the time points after sowing. Values for experimental molecular

mass and isoelectric point (pI), were determined using standard molecular mass markers

with digitalized images generated by the software. To ensure the requisite quality of

spots that were resolved on the standard gel several parameters were checked manually

and applied. Only those spots on replicate gels were selected that were recorded on at

least two replicates with consistent shape and size. The average quantity of these high

quality spots was used on the standard gels and for further analysis. The correlation

coefficients between replicate gels were at least 0.8 to make a first level match set.

Then the second level match sets for each time course were developed to compare

genotypes over the time courses.

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Figure 4.1 Representative gels of proteins from seed of three genotypes after 48 hr imbibition

using broad band 13 cm Immobiline pH 3-11 Dry Strip NL (Non-linear) and pH 4-7 strips. The

gel images on the left for all genotypes showed a region of low resolution with crowded and

overlapping spots. The poor resolution with pH 3-11 isoelectric focusing strips was

substantially overcome by using narrow pH range (4-7) strips.

kDa

6.5 _

14.4 _

21.5 _

31.0 _

45.0 _

116 _ 200 _

pH 11 pH 3

6.5 _

14.4 _

21.5 _

31.0 _

45.0 _

116 _ 200 _

6.5 _

14.4 _

21.5 _

31.0 _

45.0 _

116 _ 200 _

6.5 _

14.4 _

21.5 _

31.0 _

45.0 _

116 _ 200 _

pH 7 pH 4

6.5 _

14.4 _

21.5 _

31.0 _

45.0 _

116 _ 200 _

6.5 _

14.4 _

21.5 _

31.0 _

45.0 _

116 _ 200 _

Rupali

KH 850

KJ850

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4.2.4 Protein Identification

Protein spots were identified by MALDI-TOF/TOF MS/MS according to the standard

techniques described by Bringans et al., (2008). First, candidate Coomassie gel spots

were excised and after initial washing with water, de-stained by washing three times for

45 min with 25 mM ammonium bicarbonate in acetonitrile (ACN):water (50:50) and

vacuum-dried. Each de-stained excised gel piece was subsequently digested with 10 µl

trypsin digestion solution (12.5 µg/ml trypsin [Roche, Cat. No. 11418475001], 25mM

ammonium bicarbonate) and incubated overnight at 37ºC. Extraction of the resultant

peptides was achieved by two 20 min incubations of 10-20 µl ACN with 1% (w/v)

Trifluoroacetic acid (TFA); the volume depending on the size of the gel pieces. Large

pieces were subjected to three extractions which were pooled, then dried by rotary

evaporation and stored at -20 ºC before further MS analysis.

Extracted dried peptides were reconstituted with 2 µl ACN: water (30:70) and further

diluted 1 to 10 with matrix solution (containing 10 mg/ml α-Cyano-4-hydroxycinnamic

acid [CHCA]). A total of 0.6 µl reconstituted peptide sample was first spotted in

duplicate on a 384-well Opti-TOF plate, followed by 0.6 µl matrix solution. The

spotted samples were analysed using MALDI-TOF/TOF MS/MS on a 4800 MALDI-

TOF/TOFTM analyzer (Applied Biosystems, MDS SCIEX). The MS/MS spectra were

analysed automatically with the DeNovo Explorer version 3.6 (Applied Biosystems).

All the derived peptide sequences in the peak list were searched against LudwigNR

(released date April 13, 2009; 8,777,915 sequences and 3,084,943,043 residues) and

MSDB database using Mascot search engine version 2.2.1 (Matrix Science) for protein

identification. A homology search was carried out as the chickpea genome has not been

sequenced. The MS/MS ion search parameters were as follow: Taxonomy, Viridiplantae

(Green Plant, 630487 sequences); Enzyme, Trypsin; Fixed modification of cysteine,

Carbamidomethylation (C); Allowance missed cleavage, 1; Variable modification,

Oxidation (M); Peptide and MS/MS tolerance, 0.6 kD each; Mass value, Monoisotopic.

Based on the size of the data base and p<0.05, individual ion scores more than 43

(threshold) were indicated as having identity or extensive homology by the Mascot

algorithm. Final protein scores were obtained from ion scores as a non-probabilistic

basis for ranking protein hits. In case the peptides matched with different members of a

family, or the identified protein was found to have various accession number and

names, the best match was chosen as the identity by considering other parameters, such

as higher Mascot score, expression pattern on 2-D gel and putative function of the

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protein. Details for analysis of spectra and also peptide view with MS/MS

fragmentation and its corresponding table of matched fragment ions has been provided

in Appendix 2 and 3 for only those proteins identified using a single peptide. An extra

built-in submission for BLAST search was also performed to further verify the matches

at www.ncbi.nlm.nih.gov/BLAST/.

4.2.5 Protein Classification and Clustering Analysis

The function of identified proteins was sought by comparison against standard protein

data bases at InterPro, Pfam and UniProt to minimize any redundancy in functional

assignment. Additional literature searches, if they were available, were also used to

divide the proteins into different classes based on protein function and likely metabolic

role.

SOTA (Self- organizing tree algorithm) clustering of the expression data was conducted

on log2 transformed fold abundance values across genotype time points using

MultiExperimental Viewer (MeV) version 4.5.1 (The Institute for Genomic Research).

The parameter for the hierarchical clustering was the Pearson correlation assessed as

metric distance with maximum cycle of 10 and a maximum cell diversity of 0.8 (Romijn

et al., 2005).

4.2.6 Statistical Analysis

In order to differentiate those spots showing statistically different abundance, the

constitutional Student’s t-test in PDQuest software at p<0.05% was initially used on

each match set. This analysis aimed at significant expression changes for a given spot

at a specific time point (0, 48 and 72 hr). The results generated by the software were

further validated by extracting the intensity of every spot and subjecting it to additional

statistical analysis for all time points as follows (Sharma et al., 2008): the intensity of

each spot was normalized further by dividing the average density of the given spot on

the sum of the spot across time courses and then multiplied by 100 to determine

normalized spot density (density %) (Wang et al., 2009a). This data transformation

enabled cross point comparisons and produced more smooth and normalized data for

parametric statistical testing. A two-way ANOVA as a factorial design was then

performed using GenStat software (version 12.0) to compare the effect of time and

genotype and the interaction between them. For convenience, the results of this

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statistical analysis have been displayed as mean ± SE for each spot with p-value for

each factor (genotype and time) and their interaction in the data sets.

4.3 Results

The comparison of germination by three genotypes (cv Rupali, KH850 and KJ850)

under a range of sub-optimal water supplies revealed significant differences between

genotypes and identified 6.25% WHC as the critical water supply (Figure 3.7 and 3.8).

Figure 4.2 Pictorial demonstration of varied germination performance at key time points after

imbibition coincident with morphological response of radicle emergence and further elongation

together with corresponding typical protein profiles for each genotype. At the 0 hr time point,

only dry seeds were used to extract protein without sowing in the sand but for the other two time

points each genotype was directly exposed to 6.25% WHC in the sand medium.

This WHC level did not permit germination in KJ850 (sensitive) up to 72 hr after

sowing (HAS) while at the same time cv Rupali germinated and formed an elongated

Genotype 0 48 72 Imbibition time course (hr)

KJ850

KH850

Rupali

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radicle up to 20 mm. The third genotype used, KH850, provided a germination

response intermediate to these two lines. Accordingly, this provided a range of delayed

germination morphology among genotypes because of limited water availability; they,

therefore, could be compared at each of the three time points chosen (Figure 4.2). In

fact, delayed germination at 48 and 72 hr coincided with radicle emergence in cv Rupali

and KH850, respectively, providing comparable phenology of germination events for

further comparison at the proteome level.

Table 4.1 Frequency of differentially expressed protein spots detected from image analysis of

gels among three genotypes (cv Rupali, KH850 and KJ850) at three time points of imbibition

from image analysis. These spots were subsequently used for MALDI TOF/TOF mass

spectrometric analysis and protein identification.

Imbibition

time course

(hr)

Initial differentially

expressed protein spots a

High quality spots b

MS/MS analysed spots c

Identified spots Non

significant hits f storage

proteins d Functional proteins e

0 709 208 172 101 17 54

48 844 312 246 128 41 77

72 640 240 211 117 38 56

Total 2193 760 629 346 96 187

a Protein spots were judged to be different in expression by built-in statistical analysis in the

PDQuest software. b These number of protein spots were established to be differentially expressed after manually

checking the characteristics of each spot and removing artifacts (specks or debris etc. stained

by blue silver) or inconsistent (absence on at least two replicates) spots within the replicate

gels. c Includes those spots that could be excised from the gels and used for further downstream

MS/MS identification. d Refers to the proteins identified as seed storage proteins from the database analysis. e All defined non-storage proteins were considered as functional proteins. Additionally unknown

proteins and putative uncharacterized protein were grouped in this category. f The MS/MS spectra resulting from these protein spots could not be matched with any peptides

in the protein database.

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Using a 13 cm pH 4-7 isoelectric focusing strip and a recently developed extraction

protocol for seed proteins (Goggin et al., 2010) combined with “Blue Silver” staining, a

total average number of 1600 resolved spots was initially detected on each individual 2-

DE gel with some obvious changes within genotypes or time of imbibition (Figure 4.1

and 4.3). For each genotype at each specific time of 0, 24 and 72 hr imbibition, three

replicate gels coming from separate extractions were run to obtain a combination of

nine treatments of genotype time as a factorial design. The replicate gel images were

then digitally combined with the software to construct a standard master gel for each

time point separately. By manually removing artefacts (specks or debris that were

stained), only high quality spots which passed several strict criteria (e.g., showing the

same relationship for at least two replicates, at least 1.5 fold change and correlation of

more than 80% for replicate gels) were selected to determine spot density and permit

further analysis (Table 4.1). For example, 844 spots were found by built-in statistical

analysis to be differentially represented among three genotypes at 48 hr imbibition, but

through rigorous manual checking of spots only 312 were confirmed to be significantly

different in abundance or presence.

To compare high quality spots across all time points normalization was first achieved

using the density of constantly expressed spot proteins (Figures 4.4 and 4.7). Filtered

spot intensities were then extracted using normalized master gels (Figure 4.4) and

transferred to a data matrix consisting of 760 spots (Table 4.1) for further statistical

analysis by two-way ANOVA across all time points to show the interaction between

genotypes and time of imbibition (Table 4.3). This analysis found that a total of 630

responsive (significantly differentially abundant) protein spots had a more than 1.5-fold

difference in expression at least in one combination of genotype time across the nine

treatments.

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Figure 4.3 A representative sample of 2-DE gels obtained from three contrasting genotypes (cv

Rupali, KH850 and KJ 850) germinating under critical water supply (6.25% WHC) at various

times of imbibition (0, 48 and 72 hr). Identical 500 µg samples of soluble proteins from

imbibed seeds were resolved on the gels using 13 cm pH 4-7 IPG strips and stained with the

“Blue silver” technique (Candiano et al., 2004). Each 2-DE gel image above represents one out

of three replicate gels used for further image analysis. The distribution of molecular size

markers and pH gradient was as indicated in Figure 4.1.

KH85

72 hr

Rupali

48 hr

KJ850

0 hr

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4.3.1 Identification of differentially expressed proteins

MS/MS spectral analysis of the responsive proteins (RPs) identified a total of 442

proteins. The analysis was unable to significantly match to any peptides in the protein

database for some 29% of analyzed RPs (187 spots, Table 4.1). Around 75% of the

significant matches found in the protein database were identified as different isoforms

of seed storage proteins from a number of legume species including chickpea (Table

4.2). Legumin was the most abundant, being identified in more than 80% of the

peptides sequenced (Table 4.2). The other most abundant peptides were identified as

provicilin followed by convicilin which, like legumin, were found as a number of spots

across gels (Table 4.2). Spots identified as storage proteins were excluded from further

analysis.

Thus a total of 96 proteins, positively identified with significant hits in the databases

used, were further analysed for their expression profile across genotype and time of

imbibition. Based on statistical analysis of their abundance, 17 protein spots were

significantly different at 0 hr imbibition, 41 spots at 48 hr and the rest of the spots at 72

hr after initiating imbibition (Table 4.1).

Each set of spots has been shown separately on its corresponding advanced in silico

match set image for 0, 48 and 72 hr in Figure 4 A-B. It is noteworthy that a few of these

protein spots were found to be still expressed differentially in more than one time point,

for instance spot 131 but these spots were only shown once at the earliest time point

(Figure 4.4, A-C).

The number of proteins that appeared to be different at abundance was greater at 48 hr

among genotypes (Table 4.1, Figure 4.4, B), followed by 72 hr (Figure 4.4, C). These

time points coincided with radicle emergence in cv Rupali at 48 hr with further radicle

elongation by 72 hr. The radicle tip emerged in KH850 at 72 hr (Figure 4.2) while no

germination occurred in KH850 during the time course.

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Table 4.2 The representative seed storage proteins identified from LudwigNR and MSDB

databases across all time points and chickpea genotypes.

Accession No

Storage protein name Plant species Peptide sequence matched

Q0MUU5 Beta-conglycinin alpha'-subunit

Glycine max R.DPIYSNK.L R.SRDPIYSNK.L

Q647H1 Conarachin Arachis hypogaea K.QAGNEGFEYVAFK.T

Q41674 Convicilin Vicia narbonensis K.ELTFPGSAQEVNR.L

B5U8K3 Convicilin storage protein 1 Lotus japonicus R.AILTIVNPNDR.D R.SKLFENLQNYR.I

B5U8K4 Convicilin storage protein 2 Lotus japonicus K.AIITLVNPNDR.E R.SKLFENLQNYR.I

B0BCJ6 Cvc protein (Fragment) Lotus japonicus K.VLLEEQEKEPQQR.R

P04405 Glycinin G2 Glycine max R.RFYLAGNQEQEFLK.Y

Q43673 Legumin Vicia faba var. minor

R.FNKCQLDSINALEPDHR.V R.VESEAGLTETWNPNHPELQCAGVSLIR.R

O04219 Legumin (Fragment) Cicer arietinum K.FRDSHQK.V R.ATLQPNSLR.R R.ESEQGEGSKFR.D K.SEGGLIETWNPSNK.Q R.DQPEQNECQLEHLNALEPDNR.I

Q41676 Legumin A Vicia narbonensis R.CAGVALSR.V R.IESEGGLIETWNPNNR.Q

P15838 Legumin A2 Pisum sativum R.DSHQKVNR.F R.DFLEDALNVNR.H

Q99304 Legumin A2 primary translation product

Vicia faba var. minor

R.DSHQKVNR.F R.DFLEDALNVNR.H K.RDFLEDALNVNR.H R.IESEGGLIETWNPNNR.Q

P05692 Legumin J Pisum sativum R.VESEAGLTETWNPNHPELKCAGVSLIR.R

Q9SMJ4 Legumin, alpha and beta subunit

Cicer arietinum R.DFLEDALNVNR.R R.DFLEDALNVNRR.I K.SEGGLIETWNPSNK.Q R.FYLAGNHEQEFLR.Y R.RFYLAGNHEQEFLR.Y

Q03971 N-terminal incomplete legumin A1 pre-pro-polypeptide (Fragment)

Vicia faba var. minor

R.IESEGGLIETWNPNNR.Q

Q304D4 Provicilin Cicer arietinum R.QSHFANAQPQQK.D K.EVAFPGSAEEVDR.L K.EVAFPGSAEEVDRLLK.N R.NFLAGEEDNVISQIQRPVK.E

P02856 Vicilin, 14 kDa component Pisum sativum K.FFELTPEK.N

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Four protein spots (701, 702, 703 and 706) among 96 identified RPs were very small

but intensely stained (Table 4.4). These spots, with many small dot-sized spots close

together, formed a rather large diffuse spot. Although PDQuest software specified the

group of spots as highly significant it was not technically possible to excise them from

the 2-DE gels because of their small size and proximity to each other. Thus it was

decided to excise the whole diffuse spot for further MS/MS identification. The results

led to identification of three proteins in four spots including double- headed trypsin,

serine proteinase inhibitor and gamma-glutamylhydrolase (spots 701, 702, 703 and

706). However, assigning these protein identities specifically to any of the small spots

present was not possible and they are shown separately without presenting their kinetic

profile in Table 4.4.

Two-way AVOVA analysis on the normalized values of the expression profile for the

remaining 92 protein spots showed that these had significant changes in abundance

either across genotypes or time points with at least more than 1.5 fold differences (Table

4.3). This analysis also permitted the interaction between time and genotype to be

assessed. Interestingly, 11 spots (for example spots 201, 993, 830, 994, 630, 730 and

504) were absent or expressed at very low levels on gels for cv Rupali, compared with

six spots for KH850 and only one (spot 200) for KJ850; the later spot was also common

with KH850 (Table 4.3).

The Venn diagram analysis (Figure 4.5) of these differentially abundant protein spots

(Table 4.3) within genotypes during the whole imbibition time indicated significant

differences and overlaps among them. The number of more abundant spots in cv Rupali

was some 25% greater in comparison with KH850 and KJ850 (39 versus 30 or 31 spots,

Figure 4.5, A). Of these spots, ten were common across the three genotypes and five

increased in all genotypes. Also the number of unique spots showing increases in

abundance was 75% greater in cv Rupali than other genotypes (13 versus 8 spots) while

the total number of spots that showed a decrease in abundance across genotypes was

fewer than half this number (Figure 4.5, B).

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B

A

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Figure 4.4 Advanced match set images with protein spots detected on the basis of a set of

defined criteria for validation of each spot using PDQuest software from all genotypes for (A) 0,

(B) 48 and (C) 72 hr imbibition under limited water availability. These match sets were

developed in silico by analysis of nine 2-D gels for each time point as shown in Figure 3. Based

on this analysis all consistent spots on replicate gels for each genotype were computationally

merged to create this higher level match image after a set of defined criteria. The filtered

images for each genotype were compared with this image to reveal the difference between

genotypes. A considerable number of the spots (Tables 1 and 2) belonged to storage proteins

and these are not marked on the gel images to avoid complexity in tracking non-storage

proteins. The spot numbers with red arrows refer to the spot numbers listed in Tables 3 and 4.

The spots designated as i and ii and indicated with black arrows represents unchanged spots in

all three genotypes within all time points used to normalize the intensity of the spots for cross-

comparisons between time points.

C

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Figure 4.5 Venn diagram analysis of protein spots differentially expressed among the three

genotypes (cv Rupali shown as purple, KH850 shown as green and KJ850 shown as yellow) in

response to limited water availability 72 hr of imbibition. Protein spots showing increased

abundance (A), decreased abundance (B) or mixed changes in abundance (C). The number of

proteins in each category is shown in each section with the numbers that overlap across

genotypes indicated.

4.3.2 Functional cataloguing of proteins

All 96 proteins identified from the database comparisons were classified into nine major

categories (metabolism, respiration and energy, defense, stress and detoxification as

well as nutrient reservoir, protein processing, nucleic acid (DNA, RNA) processing,

signaling and transduction, nucleotide sugar metabolism and unknown proteins) on the

basis of their putative function (Tables 4.3, 4.4 and Figure 4.6). Functions of these

proteins were mostly annotated using standard database information in Interpro, Pfam

and UniProt with particular emphasis on the features related to the seed biology,

germination and water stress. Other published reports related to each family of proteins

were also considered in assigning putative function.

5

10

16 19

5 4

C

10 KJ850 KH850

Rupali

13

5

8 8

11 10

A

7 KJ850 KH850

Rupali

8

3

6 11

3 1

B

1 KJ850 KH850

Rupali

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Table 4.3 Differentially abundant proteins identified by MALDI TOF/TOF analysis with details

of protein identities and their relative expression density profile during germination over various

times of imbibition in three genotypes under limiting soil water content (6.25% WHC). A

detailed explanation of the data and analysis presented in the histograms in this table can be

found below in a footnote.

Spot No a

Accesion No b Protein name

Score

SC c (%

)

NPd Thr

Mw/pI (kDa/pI)e

Exp Mw/pI

(kDa/pI)f

Expression profile g Rupali KH850 KJ850

0 48 72 0 48 72 0 48 72

Metabolism

250 B9SI04 Anthranilate synthase component I, putative 52 1 1 68.93/6.08 28.3/5.0

380 B2ZAP3 Putative thioredoxin-like proten 72 2 1 55.91/4.98 51.62/5.04

860 B9RGF7

Nucleotide pyrophosphatase/ phosphodiesterase, putative 46 1 2 72.04/7.88 33.0/6.1

360 A9SQW5 Qb-SNARE, NPSN1-family 51 4 2 28.92/9.39 32.46/5.09

214 Q0R5Z6

Glucose-6-phosphate dehydrogenase-like protein 83 79 1 2.80/9.69 4.09/5

430 A8HNN4 Hydrolase 44 1 1 76.20/5.31 21.6/5.2

Respiration & energy

770 Q6EUD7 Phosphoenolpyruvate carboxykinase 50 1 1 73.4/5.84 39.83/5.81

736 Q6EUD7 Phosphoenolpyruvate carboxykinase 51 1 1 73.4/5.84 17.17/5.95

879 Q8L5C8

Putative mitochondrial NAD-dependent malate dehydrogenase 291 19 6 36.42/8.48 40.69/6.46

T*** G*** TXG**

T* TXG***

G*** TXG***

T*** G* TXG***

T*** G*** TxG***

TXG***

T*** G*** TXG***

T* G* TXG***

T*** G*** TXG**

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Table 4.3: Continued

370 B3IUE7 ATP synthase epsilon subunit 58 7 1 15.25/9.3 38.62/5.1

Defence, stress & detoxification

700 A0MWF1 Heat shock protein 17.8 (Hsp17.8) 102 5 2 17.88/8.83 8.02/5.85

931 Q2PXN2 17.5 kDa class I HSP 95 7 1 16.26/5.82 20.12/6.87

540 O82010 Small heat shock protein (sHSP) 106 16 2 14.89/6.2 22.04/5.48

351 B7FJ58 Hsp40/DnaJ_Rel 57 4 1 20.22/6.11 29.08/5.05

471 B7FJ58 Hsp40/DnaJ_Rel 45 4 1 20.22/6.11 35.29/5.25

530 Q8H1A6 Heat shock protein (Hsp20) 154 15 3 18.40/5.82 20.58/5.47

651 B7FJ58 Hsp40/DnaJ_Rel 50 4 1 20.22/6.11 30.275.52

922 O49816

Late embryogenesis abundant protein (Group 1 LEA) 89 25 3 19.09/8.63 16.6/7

920 O49817 Late embryogenesis abundant protein 2 283 32 2 16.73/9 13.2/7.0

930 O49817 Late embryogenesis abundant protein 2 294 31 4 16.73/9 17.87/7

T*** G*** TxG***

T*** G*

T*** G*** TXG***

T*** G** TXG***

T*** G*** TXG***

T* G***

T*** G*** TXG***

T*** G*** TXG***

G* TXG***

T** G** TXG***

T* G* TX G*

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Table 4.3: Continued

780 B2ZHC8

Late embryogenesis abundant protein (Group 3 LEA ) 149 4 3 47.19/5.37 46.35/5.93

511 Q0MRQ9

Late embryogenesis abundant protein (Group 5 LEA) 52 8 2 14.71/5.79 12.07/5.5

64 B9REU2

Late embryogenesis abundant protein D-34 (Group 6) 84 6 1 25.04/5.48 31.64/4.72

150 Q2HS98 Seed maturation protein 46 7 1 12.39/4.93 29.22/4.73

52 Q2HS98 Seed maturation protein 52 19 2 12.39/4.93 27.83/4.7

850 Q5UVI2 Dehydrin 1 48 5 1 19.63/5.65 27.33/6.03

610 Q0DRV6 Superoxide dismutase 1 (Cu-Zn) 96 13 1 15.36/5.71 9.44/5.76

435 A8CF89 Superoxide dismutase (Cu-Zn) 105 5 2 22.08/6.23 18.19/5.21

733 A8CF89 Superoxide dismutase (Cu-Zn) 48 5 1 22.078/6.23 18.5/5.79

635 A2X822 Glutathione peroxidase 81 8 1 19.52/5.24 18.98/5.69

13 Q8GYL9

Protein Brevis radix-like 2 49 2 1 42.12/5.71 14.7/4.53

821 Q6E2Z6 1-Cys peroxiredoxin 142 14 2 24.57/6.08 14.66/6.13

T*** TXG*

T* G*** TXG*

T*** G*** TXG***

T*** G* TxG**

T*** G*** TXG***

T** G*** TxG***

T*** G*** TxG***

T*** G** TXG**

T*** G*** TXG***

T* G* TXG***

T*** G*** TxG***

T*** G** TXG**

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Table 4.3: Continued

941 A2SZW8 1-Cys peroxiredoxin 1 89 5 2 24.57/6.08 25.06/6.7

528 Q39450 Pathogenesis related protein 146 18 2 16.93/5.48 15.67/5.33

Allergens/Nutrient reserves

200 Q84UI0 Allergen Len c 1.0102 88 2 1 47.44/5.34 6.71/4.9

221 Q84UI0 Allergen Len Allergen Len c 1.0102 155 9 3 47.44/5.34 16.02/4.85

520 Q84UI0 Allergen Len c 1.0102 51 2 1 47.44/5.34 16.95/5.53

561 Q84UI0 Allergen Len c 1.0102 51 2 1 47.44/5.34 33.45/5.38

867 Q84UI0 Allergen Len c 1.0102 203 5 3 47.44/5.34 36.65/6.26

410 Q84UI0 Allergen Len c 1.0102 (Fragment) ] 47 5 2 47.44/5.34 11.09/5.26

512 Q84UI0 Allergen Len c 1.0102 (Fragment) ] 195 7 2 47.44/5.34 12.91/5.36

515 Q84UI0 Allergen Len c 1.0102 106 7 3 47.44/5.34 13.06/5.45

350 Q84UI0 Allergen Len c 1.0102 90 5 2 47.44/5.34 27.28/5.14

504 Q84UI0 Allergen Len c 1.0102 (Fragment) 94 5 2 47.44/5.34 25.46/5.33

T*** G*** TXG***

T*** G*** TXG**

T** G** TXG**

TXG**

T***

T* G***

T** G** TXG**

T*** G*** TXG***

T**

T*** G*** TXG***

G*** TXG***

T*** G*** TXG***

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Table 4.3: Continued

910 Q647H1 Conarachin 55 1 1 76.34/5.23 9.18/6.55

Protein processing

921 Q1W3Z5 Serine proteinase inhibitor 51 66 1 13.41/5.44 15.76/6.87

381 P29828

Protein disulfide-isomerase 57 3 2 57.28/4.98 42.91/5.13

460 A2WPK2 Protein kinase-like 44 2 1 53.03/6.67 30.92/5.22

DNA, RNA processing

730 A2X5H4

Snase (Staphylococcal nuclease) 48 1 1 10.85/6.45 19.26/5.87

201 A8IFV1 SWI2/SNF2-like protein 55 1 1 13.49/6.49 3.75/4.88

155 A5C426

Putative RNA-directed DNA polymerase (reverse transcriptase), msDNA 50 1 1 92.91/9.86 28.5/4.75

993 Q70LR0 RNA polymerase II 51 2 2 61.80/6.84 71.98/6.72

720 A5BDV2 Reverse transcriptase 45 1 1 95.34/6.88 15.61/5.82

23 B9GX82

THO complex subunit 1 transcription elongation factor 44 3 1 70.06/5.49 17.62/4.68

440 B7P099 Intron_maturase 2 44 1 1 59.72/9.51 23.43/5.24

T* G* TxG**

T** G*** TxG**

T*** G*** TxG***

T*** G*** TXG***

T*** G*** TXG***

T*** TXG**

T*** G*** TXG***

T*** G***

T*** G*** TXG***

T* G*** TxG***

T*** G*** TXG***

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Table 4.3: Continued

Signaling and transduction

108 P30806 Calreticulin 63 35 1 2.64/5.15 42.64/4.74

34 P04464 Calmodulin 99 10 1 16.89/4.1 19.34/4.04

550 B8R8G7 Phytochrome D 42 1 2 70.61/5.94 30.53/.35

Nucleotide sugar metabolism

210 Q2HW16 Cupin, RmlC-type 95 6 2 53.23/5.42 8.06/5.03

510 Q2HW22 Cupin, RmlC-type 81 6 2 53.23/5.42 8.86/5.3

217 Q2HW22 Cupin, RmlC-type 134 6 2 53.23/5.42 6.45/5.06

470 Q2HW22 Cupin, RmlC-type 222 6 3 53.23/5.42 37.88/5.27

340 Q2HW16 Cupin, RmlC-type 90 2 1 53.72/5.65 22.95/5.13

940 Q2HW16 Cupin, RmlC-type 90 2 1 53.72/5.65 25.75/6.4

110 Q2HW16 Cupin, RmlC-type 69 2 1 53.72/5.65 6.85/4.85

518 Q2HW22 Cupin, RmlC-type 176 6 2 53.23/5.42 1.25/5.38

T*** TXG*

T*** G*** TXG*

G*** TxG***

T*** G*** TXG***

T*** TXG***

T*** TXG***

T*** G*** TXG***

G*** TXG***

G*** TXG*

T*** TXG*

G*** TXG***

T*** G*** TXG*

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Table 4.3: Continued

663 Q2HW16 Cupin, RmlC-type 70 2 1 53.73/5.65 31.67/5.75

830 Q2HW21 Cupin, RmlC-type 135 4 2 54.23/5.69 17.24/6.43

841 Q2HW19 Cupin, RmlC-type 168 4 2 54.23/5.69 22.49/6.24

994 Q2HW21 Cupin, RmlC-type 147 4 4 54.23/5.69 96.53/6.54

300 Q2HW22 Cupin, RmlC-type 214 6 3 53.23/5.42 8.08/5.09

822 Q2HW16 Cupin, RmlC-type 85 2 1 53.73/5.65 16.24/6.01

202 Q2HW22 Cupin, RmlC-type 285 6 3 53.23/5.42 8.25/4.99

204 Q2HW22 Cupin, RmlC-type 255 6 3 53.23/5.42 6.82/5.01

840 Q2HW21 Cupin, RmlC-type 118 4 2 54.23/5.69 24.46/6.32

942 Q2HW19 Cupin, RmlC-type 136 4 2 54.23/5.69 27.86/6.49

Unknown function

131 A2X8I7

Putative uncharacterized protein 48 9 1 9.5/9.5 20.2/4.9

661 A2YWR7

Putative uncharacterized protein 44 5 1 42.81/6.2 33.42/5.65

T*** G* TXG**

G*** TXG***

T*** G*** TXG***

T** G** TXG**

TXG*

T*** G*** TXG**

TXG*

T*** TxG***

G*** TxG*

T** G*** TxG***

T* G*** TXG**

T*** G*** TXG**

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Table 4.3: Continued

40 A5AJC6

Putative uncharacterized protein 46 2 2 35.72/7.82 25.4/4.6

630 A2YWR7

Putative uncharacterized protein 47 3 1 42.81/6.5 21.8/5.53

620 Q002L6

Putative uncharacterized protein 110 7 2 38.42/5.37 15.3/5.69

650 A2YWR7

Putative uncharacterized protein 54 3 1

42.81/5.45 31.38/5.53

634 A9PJ12

Putative uncharacterized protein 79 2 1 56.67/6.2 21.68/5.66

100 A2YVG0

Putative uncharacterized protein 44 4 1 24.98/7.94 6.15/4.69

781 A7R4L2

Chromosome undetermined scaffold_734, 62 3 2 54.59/5.76 43.15/5.82

418 Q0JAF2 Os04g0602300 protein) 45 2 1 24.24/5.8 12.07/5.29

451 B9TI68

Putative uncharacterized protein 43 4 1 27.39/11.3 29.54/5.2

516 A5ADR0

Putative uncharacterized protein 42 6 1 15.65/7.08 8.94/5.48

251 Q60D05

Putative uncharacterized protein 52 1 1 47.89/8.85 27.33/5.04

627 Q2QSF3

Putative uncharacterized protein 42 4 2 30.94/10.52 16.71/5.7

T*** G** TXG***

T*** G*** TXG***

T*** G*** TXG***

T*** TXG***

T** G*** TXG**

T** G*** TXG***

T*** TXG**

T** TXG*

T*** G*** TXG***

G*** TxG***

T*** G*** TxG***

G*** TXG*

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Table 4.3: Continued

851 A5AWQ4 Pentatricopeptide_repeat 45 1 1 17.52/6.42 29.97/6.31

a The numbers correspond to the numbers on the specific spots on the gels in Figure 4.4 A-C . b Accession numbers correspond to Uniprot entries, TrEMBL. c Sequence coverage percentage. d NP represents the number of peptides matched for identification of the proteins in the database

e Theoretical molecular weight (Mr) and pI, predicted from MS/MS analysis. f Experimental molecular weight (Mr) and pI were estimated using standard protein marker and

automatic assignment by image analyzer (PDQuest) software. g The average value of relative transformed density of each spot across nine treatments consist

of three genotypes (cv Rupali, KH850 and KJ850, as G on histograms) at three different

imbibition times (0, 48 and 72 hr after sowing, as T on histograms). The interaction is

designated as TxG. The small bars on each expression column represent the SE (N=3) while*

indicates p<0.05*, ** p<0.01 and *** p<0.001. The values (relative density %) for the

histogram was normalized by calculating the density ratio of each individual spot over the sum

of the spot density across genotype and time courses and their replicates. Accordingly, sum of

the values in each graph is 100%. An enlarged version of the histograms included in Table

4.3 is shown below:

Rel

ativ

e de

nsity

%

T*** G*** TXG***

0 48 72 0 48 72 0 48 72 Rupali KH850 KJ850

Time points: Genotypes:

SE Time

Significance level

Time X Genotype

Genotype

T*** G*** TxG***

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Defence, stress & detoxif ication (25.3%)

Metabolism (6.3%)

Signalling & transduction (3.2%)

DNA, RNA processing (7.4%)

Unknow n function (15.8%)

Nucleotide sugar metabolism (19.0%)

Allergens/Nutrient reserves (11.6%)

Respiration & energy (4.2%)

Protein processing (7.4%)

Table 4.4 Analysis of peptides recovered from a diffuse area that comprised a number of spots

too small to be sampled separately. The whole area was excised and MS/MS identified specific

proteins within the complex shown in the panels below.

Spot a No

Accession No Protein name S SC

(%) NP Thr

Mw/pI (kDa/pI)

Exp Mw/PI

(kDa/pI) Spot status

701 A0AQW6 Double-headed trypsin inhibitor

54 55 1 14.04/5.62 6.5/5.9

702 Q53UK2 Ser/Thr protein kinase 42 1 221.0/5.57 6.68/6.04

703 A0AQW6 Double-headed trypsin inhibitor

54 55 1 14.04/5.62 7.5/5.8

706 B2Z9Y5 Gamma-glutamylhydrolase

45 33 1 38.79/7.15 6.88/5.94

a All details regarding each column are similar to those mentioned in table 4.3.

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Figure 4.6 Distribution of 96 identified proteins into nine major functional classes. These

proteins were classified according to assignment of their putative function(s) by searching in

InterPro, Pfam or associated literature.

A significant group (25.3%) comprised proteins implicated in defense, stress response

and cellular detoxification which was markedly influenced by limited water availability

during germination within the three genotypes (Figure 4.1 and 4.6). A number of

important proteins found in this group were members of the heat shock proteins (HSPs)

and late embryogenesis abundant proteins (LEAs), superoxide dismutase (Cu-Zn),

glutathione peroxidase, 1-Cys peroxiredoxin, dehydrin-1 and a number of pathogenesis

related proteins (Table 4.3). Some of these proteins were over-represented in terms of

abundance level in cv Rupali at 0 and/or especially after 48 hr imbibition, compared

with the other two genotypes. The second largest group (19% designated as nucleotide

sugar metabolism, Table 4.3) corresponded to the cupin domain associated with the

enzyme RmlC (dTDP-4-dehydrorhamnose 3,5-epimerase). The protein spots containing

this domain were widely distributed on the gel. Four spots with three unique proteins

(4.2%) were involved in respiration and specifically the TCA Cycle metabolism. Two

of them, phosphoenolpyruvate carboxykinase (spot 736) and mitochondrial NAD-

dependent malate dehydrogenase (spot 879) increased very significantly in cv Rupali

but not in KH850 and KJ850. The other functional class, in which some proteins were

not detected from cv Rupali but were significantly over represented in the other two

genotypes, was the nucleic acid processing class (7.4%). This group included proteins

such Snase (Staphylococcal nuclease), SWI2/SNF2-like, RNA polymerase II and to

some extent maturase 2 (spots 730, 201, 993 and 440, respectively) which were

completely absent or had very low abundance at each time point in cv Rupali. In

contrast, spot 720 and 23 corresponding to “reverse transcriptase” and “THO complex

subunit1transcription elongation factor” were found to be significantly enriched in cv

Rupali compared with other genotypes (Table 4.3).

Generally, the experimental Mr values assigned using the 2-DE gel position showed

variation from theoretical values for a number of the differentially expressed proteins in

Table 4.3. In many cases the experimental sizes were less than theoretical values. This

was most evident for cupin RmlC-type proteins and Allergen Len c which were found in

many spots on the gels. However the experimental Mr was more similar to the

theoretical values for most of the defence, stress and detoxification related proteins

(Table 4.3).

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Figure 4.7 Magnified genotype- and time-dependant display for some differentially expressed

protein spots derived from the corresponding 2-DE gels. The numbers on the top and left refer

to the time of imbibition (hr) and spot numbers, respectively. Spots I and II are non-normalized

views of un-changed spots shown in Figure 4.4, A-C.

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Figure 4.8 Self Organizing Tree Algorithm (SOTA) clustering analysis with heat map

representation of the expression profile for germination responsive proteins under limited water

availability with particular emphasis on sample clustering (genotype over imbibition time). All

92 proteins were also classified into 11 clusters based on their expression similarity; the details

can be found on Figure 4.9 and Appendix 3. The numbers in the column at the right hand side

are the spot numbers presented in Figure 4.4 and Table 4.3. The cluster analysis was conducted

on log 2 transformed data of -fold change ratio. Blue triangles cover the range of two main

clusters for in all genotypes. RU, cv Rupali; KJ, KJ850; and KH, KH850.

(0)

(0)

(0)

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Figure 4.9 Expression patterns for all proteins in each cluster (panels above) and corresponding

functional cataloguing for five selected clusters (pie diagrams above). Details of each cluster

and corresponding proteins are presented in Appendix 3. The grey lines show the specific trends

for each protein in the cluster with the average trend for the cluster presented in pink colour.

The numbers in black and N (in below) indicate number of proteins present in each specific

cluster and those numbers in red signify the cluster number (see also Appendix 3 for the spot

numbers and specific heat map display related to each cluster). P stands for proteins. Time of

imbibition (0, 48 and 72 hr) and genotypes (cv Rupali, KH850 and KJ850) have been defined

for cluster 9 for example.

Cluster 1 N=19 P

Cluster 2 N=21P

Cluster 7 N=18 P

Cluster 9 N=6 P

Cluster 11

Unknown function Miscellaneous MetabolismNutrient reservoir Defence, stress & detoxification Respiration & energyProtein processing DNA, RNA processing Signalling and transduction

0 48 72 0 48 72 0 48 72

1 2 3 4

5 6 7 8

10 11

19 21 2

2 4 3

6 4 9

18

9

cv Rupali KH850 KJ850

7 6 5 4 3 2 1 0

-1 -2 -3 -4 -5 -6 -7

Log

2 re

lativ

e fo

ld c

hang

es

0

0

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4.3.3 Clustering the abundance of differentially expressed proteins

Self organizing tree algorithm (SOTA) analysis which was achieved for 92 responsive

proteins, enabled the interaction of proteins to be classified into different clusters based

on their expression profile (Figure 4.8 and 4.9). To perform such an analysis, the

extracted expression data were changed to -fold expression against their corresponding

expression value in cv Rupali (0 hr) and then transformed to log2 to normalize the

expression and reduce the range of the data sets so that they could be more easily

presented. The analysis revealed 11 distinct clusters (see Appendix 3 for the spot

numbers and specific heat map display corresponding to each cluster) based on the

protein expression patterns for three genotypes over the time of imbibition (Figure 4.8).

A significant fraction of the proteins (just over 90%) appeared only in five clusters

(cluster 1, 2, 7, 9 and 11, Figure 4.9) with at least more than six proteins in each. In the

most abundant group, the overall average pattern increased from cv Rupali to KH850

and further to KJ850. Around 55% of these co-expressed proteins belonged to

unknown, miscellaneous and DNA/RNA processing groups. A pattern of increased

abundance was observed more or less in all genotypes with more than 40% of proteins

involved in “signalling and transduction” and “defence, stress and detoxification”. The

average protein expression values in clusters 8, 10 and 11 were considerably greater in

cv Rupali with respect to the other genotypes over time in which a large percentage of

DNA/RNA processing, protein processing and defence, stress and detoxification

functional classes were found in cluster 11 (Figure 4.9).

The other important outcome of SOTA was to cluster the overall performance of each

genotype according to their expression pattern for all 92 responsive proteins over time

(Figure 4.8, vertical dendrogram). Based on this view, two main clusters were

recognized; cv Rupali performance was far different and was separated in one cluster

while the data for KH850 and KJ850 in terms of overall expression patterns of proteins

at all time points were classified into the other main cluster. Despite the fact that

KH850 and KJ850 were both clustered together and behaved very similarly at 0 and 72

hr the branching position (average linkage) for KH850 at 48 hr also showed some

similarity to cv Rupali.

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4.4 Discussion

The physiological studies described above (Chapter 3) showed that the three genotypes

differed significantly in their rate of germination under conditions of severe water

limitation. In an attempt to find a biochemical and molecular basis for this genotypic

difference a proteomic approach was developed and used as a means of comparing the

genotypes following imbibition and subsequent development of the young seedling.

This type of approach has been applied successfully to mature plants in many species

exposed to abiotic stresses including drought, salinity and heat (Bhushan et al., 2007;

Alvarez et al., 2008; Wang et al., 2009a). In the present study germination under severe

water limitation resembles a stressful condition, and a rather similar molecular response

at the level of protein expression might be expected.

As seeds mature they become progressively dehydrated, with metabolic activity

including protein synthesis, exposed to severe water limitation. Under these conditions

proteins that protect essential enzymes from denaturation are expressed and these along

with the proteins they protect form the ‘metabolic’ complement that is suddenly

rehydrated following imbibition. As noted earlier (Chapter 2) metabolism is rapidly

initiated with stored reserves mobilized to provide amino acids for new protein

synthesis, sugar and other carbohydrates essential for structural components of new

cells and fatty acids released from the oil reserves or newly synthesized to form

lipoprotein membranes. The rate at which proteolysis and hydrolysis of the reserves is

activated is likely to limit overall metabolism and this alone may determine differences

in germination rate. Because of the significant hydraulic pressures that accompany the

ingress of water cell membranes and membrane bound organelles, like mitochondria,

are thought to be severely stressed requiring repair and reactivation so that respiration

and ATP synthesis can resume. The incoming water would contain oxygen and it is

equally likely that ROS would be generated from a series of rapidly activated reduction

reactions predicting that regulation and metabolism of ROS are features of the newly

activated metabolic pathways. These considerations provide a basis for predicting what

sorts of proteins might be reactivated or synthesized de novo in the period immediately

following imbibition.

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4.4.1 Gel analysis

Initial analyses that used a pH 3-11 IPG strip as the first dimension on the 2D PAGE

gels did not provide adequate resolution, with many protein spots superimposed,

limiting the ability to sample polypeptides for MS/MS analysis (Figure 4.1, left). Using

a more narrow IEF range (pH 4-7 IPG strips) provided much better resolution (Figure

4.1, right) revealing more than 700 differentially abundant, high quality, spots between

genotypes across the time course following imbibition (Table 4.1). Some recent

proteomic studies of mature plant tissues with chickpea have also used narrow pH 4-7

IPG strips to successfully resolve a greater proportion of proteins with fewer

overlapping spots (Bhushan et al., 2006; Pandey et al., 2006; Bhushan et al., 2007). In

oilseed rape, it has also been reported that pH 4-7 strips separated approximately 90%

of phosphoproteins during seed filling indicating the suitability of this pH range for seed

proteomic studies (Agrawal and Thelen, 2006).

4.4.2 Seed storage proteins

Seed storage proteins are a group of secretory proteins synthesized mostly within the of

the endoplasmic reticulum (ER) or protein storage vacules late in seed development and

during maturation (Shewry et al., 1995). These proteins account for as much as 40% of

seed weight in mature legume seeds serving as a source of amino acids and/or nitrogen

for embryo growth during germination (Shewry et al., 1995). Not surprisingly,

approximately 70% of the differentially abundant protein spots identified on gels across

the time course of the experiment were storage proteins (Table 4.1). In many seeds,

including those of legumes, globulins are the principal storage proteins with two groups

comprising legumins (11S fractions) and convicilin and/or vicilin (7S fractions)

(Shewry et al., 1995; Gallardo et al., 2008). These represent some 80% of total seed

proteins in pea with a similar proportion in Medicago truncatula (Djemel et al., 2005).

In germinating chickpea seeds the most frequently identified, differentially abundant

(nearly 80%), storage proteins on gels were legumin and vicilin and this was the case in

the three genotypes (Table 4.2). Spots identified as storage proteins were widely

distributed across the IEF and mass ranges of polypeptides on the gels and there are a

number of possible reasons for this distributed pattern. Each is likely to be encoded by

a multigene family resulting in considerable polymorphism and expression of a number

of isoforms (Magni et al., 2007; Gallardo et al., 2008; Sahnoun-Abid et al., 2009). For

example, legumin and vicilin content varied from 6-25% and 26-52% respectively of

total protein across 59 pea (Pisum sativum L.) lines and related taxa (Tzitzikas et al.,

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2006). Proteomic analysis of soybean (Glycine max L.) seed found from 34 to 44

storage protein spots on gels prepared from a range of cultivated genotypes (Natarajan

et al., 2006). Considerable variation in both mass and charge distribution can result

from post translational modifications such as proteolytic processing, phosphorylation or

glycosylation (Shewry et al., 1995). For example vicilin with a normal molecular mass

of 47-50 kDa can form a trimer of nearly 150 kDa (Gatehouse et al., 1981) or post-

translational cleavage at specific sites in the vicilin polypeptides creates various

molecular fragments with masses of 12-20 kDa (Gatehouse et al., 1981). Furthermore,

as germination proceeds and the storage proteins are mobilized and broken down, at any

time the products of proteolytic cleavage will be represented by a quantitatively and

qualitatively dynamic collection of peptides. While overall the storage proteins will be

decreasing as germination progresses at any one time some may be increasing

transiently while others will be decreasing. Because the storage proteins are so

prominent among the proteins resolved on gels a new fractionation technique to

precipitate storage globulins, glycinin and β conglycinin using 10 mM Ca2+ has been

suggested to remove a large proportion of the abundant storage proteins in soybean seed

proteomic analysis so that the response of other proteins to environmental stresses is

clearer (Krishnan et al., 2009). In the present study the many storage protein spots on

gels were excluded from further quantification in the expression profile analysis.

Excluding the storage proteins the expression profile of the remainder of differentially

abundant proteins (92 identified protein spots, Table 4.3) were analysed with particular

emphasis on their putative function. A number of enzyme proteins involved in

metabolism, respiration, stress response and cellular detoxification as well as protein

and nucleic acid processing were successfully identified through peptide sequence

analysis using MS/MS. Some of these proteins showed a consistent pattern of enhanced

expression following imbibition (Figure 4.5 and Table 4.3) suggesting they were

associated with the rapid germination trait shown by cv Rupali. Among the most

significant were groups of proteins classed as chaperone or chaperone-type and as

enzymes associated with the formation of Reactive Oxygen Species (ROS) and ROS

homeostasis (Bailly et al., 2008; Oracz et al., 2009).

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4.4.3 Chaperone-type proteins and the restoration of metabolism

One of the clear findings of the study was the identification of a number of chaperone-

type proteins, including late embryogenesis abundant (LEA) proteins and heat shock

proteins (HSP), as being differentially abundant with different patterns of expression

across the genotypes. Interestingly, almost all members of the LEA proteins from

different Groups 1-6 (Goyal et al., 2005; Tunnacliffe and Wise, 2007) plus some other

putative members of the family, except those of Group 4, were identified in this study.

They consisted of Group 1 LEA (spot 922), Group 2 LEA (spots 920 and 930), Group 3

LEA (spot 780), Group 5 LEA (spot 511), Group 6 LEA (spot 64), seed maturation

protein (spots 150 and 52) and dehyrin 1 (spot 850). A number of studies have shown

involvement of this hydrophilic protein family with responses to various abiotic

stresses, especially dehydration, salinity and low temperature whether constitutively

programmed as in seed maturation or as a consequence of environmental changes

(Boudet et al., 2006; Tunnacliffe and Wise, 2007; Dalal et al., 2009; Olvera-Carrillo et

al., 2010).

Group 1 LEA protein is essential for normal seed development in Arabidopsis but it is

not detectable in the mature seed (Manfre et al., 2006). This was also the case for cv

Rupali in the current study and expression gradually increased up to 72 hr following

imbibition, consistent with de novo production of this protein in response to water stress

(Table 4.3). In the other two genotypes Group 1 LEA protein was abundant in dry seed

but was not detectable 72 hr after imbibition. There are several reports from

experiments with crop plants suggesting that these proteins protect cell membrane

proteins from damage (Xu et al., 1996; Cheng et al., 2002; Chandra Babu et al., 2004)

or prevent protein aggregation during water stress (Goyal et al., 2005). Thus cv Rupali

may have an advantage in maintaining cell integrity and function during germination

that appears to be lacking in the slower germinating genotypes.

Functions suggested for Group 2 LEA proteins (spot 920 and 930) are diverse

depending on abiotic stresses and include chilling and freezing tolerance rather than as a

response to drought/ water stress (Hara et al., 2003; Lan et al., 2005; Yin et al., 2006;

Tunnacliffe and Wise, 2007). Like Group 1 LEA proteins, membrane protection and

reduced lipid peroxidation and even some cell growth inhibitory effects have been

proposed for Group 2 LEA proteins (Campos et al., 2006).

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Dehydrins were originally identified as members of Group 2 LEA proteins with

molecular mass ranging from 9 to 200 kDa in many tissues particularly in seeds at late

stages of embryogenesis under conditions of stress (Rorat, 2006; Xiao and Nassuth,

2006). In the present study, dehydrin 1 was found to be expressed in dry seed of all

genotypes. However, the abundance level was quite stable in cv Rupali over the course

of germination but was significantly lower than that of KJ850, the genotype not able to

germinate under severe water limitation. This might imply a non-significant role of

dehydrin 1 to complete germination under severe water limitation but the reason for

increased level in KJ850 is not known. In this regard a recent study has indicated no

direct correlation between expression of dehydrin proteins and changes in the water

relationships of leaf tissues (Wahid and Close, 2007) or in drought tolerance in

Arabidopsis (Puhakainen et al., 2004).

In spite of some fluctuation in regulation of the LEA protein of Group 3 (spot 780)

between genotypes, but with a slightly increasing trend in each, the expression level was

generally high in dry seeds and during 48 and 72 hr of imbibition in all genotypes.

Interestingly, a LEA protein of Group 3 (PsLEAm) was found to be induced in

mitochondria of pea seeds and is thought to protect mitochondrial enzymes from

damage due to dehydration as seeds mature (Grelet et al., 2005). LEA protein of Group

3 was also present in the three genotypes of mature dry chickpea seeds and it was also

relatively stable under severe water stress.

The prominent protein (spot 511) identified as Group 5 LEA protein increased

significantly, by many fold, in cv Rupali upon imbibition compared with much lower

increases in abundance in the other genotypes (Table 4.3 and Figure 4.7). As this

protein was not detected in dry seed of any genotype its abundance suggests de novo

synthesis during early stages of germination. A recent study has shown that a member

of Group 5 LEA proteins (MtPM25) prevents aggregation of proteins during drought

stress (Boucher et al., 2010) and is prominent in the radicle proteome under desiccation

(Boucher et al., 2010). Similarly, MtPM25 protein is found to be associated with

desiccation tolerance in radicles of M. truncatula during germination (Boudet et al.,

2006). In the present study a high level of expression for this LEA group 5 protein

coincided with radicle emergence in cv Rupali at 48 hr and, after further radicle

elongation, 72 hr after imbibition (Figure 4.2). Abundance in the other two chickpea

genotypes increased but remained much lower following imbibition. It seems likely that

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this category of LEA protein might play a specific role in protection and repair among

the LEA protein group and, in cv Rupali, contributes to the rapid germination trait.

The other large group of potentially protective polypeptides identified belonged to the

HSP family and included Hsp 17.8, Hsp 17.5, sHSP, Hsp20/DnaJ-Rel and Hsp40/DnaJ-

Rel (Table 4.3). These proteins were originally considered to be ATP-independent

chaperones, expressed ubiquitously under conditions of heat stress in both prokaryote

and eukaryote cells (Sun et al., 2002). However, diverse roles in cellular functions as

protective chaperones have been suggested to include transcription, translation, cell

signaling and even secondary metabolism (Basha et al., 2004) or facilitation of cell to

cell trafficking through plasmodesmata (Aoki et al., 2002). HSPs are very effective in

binding to denatured/unfolded proteins to re-establish functional conformation, in

particular under diverse stress conditions (Rajan and D’Silva, 2009; Sharma et al.,

2009). In developing seeds, it has been proposed that small HSPs have a general

protection role in seed desiccation tolerance (Wehmeyer and Vierling, 2000). Basically,

the specific member of HSPs, identified as 17.5 kDa class I HSP (spot 931) in the

present study, was not detected in dry seed in any of the genotypes but was expressed

later after imbibition at a high level, especially in KJ850 (Table 4.3). While this HSP

might be part of the response to water limitation its abundance was in fact lower in cv

Rupali. Similarly Hsp 17.6 (spot 700) showed an increasing expression profile in all

genotypes from dry seed to 72 hr after imbibition. While these two HSPs may function

in the stress response to limiting water supply their broad expression suggests a role in

providing normal cellular function (Wehmeyer and Vierling, 2000; Hegeman and

Grabau, 2001; Sun et al., 2002and rferences therein). Prominent preferentially

expressed HSP proteins, small heat shock protein (sHSP) (spot 540) and Hsp 20 (spot

530) were both induced and remained at higher levels in cv Rupali compared with

KH850 or KJ850. The increased expression in cv Rupali occurred either after radicle

emergence following imbibition (spot 540) or was high from the beginning (dry seed)

(Fig 4.2, Fig 4.7 and Table 4.3). The sharp increase in expression of Hsp20 (spot 530) in

KH850 (intermediate genotype) compared with KJ850 during imbibition might indicate

a role for this polypeptide in the rapid germination seen in cv Rupali and to a lesser

extent in KH850. The role is likely to be in a protective mechanism for a wide range of

cellular functions that are initiated or reinitiated during germination (Sun et al., 2002;

Basha et al., 2004). A particular member of the group, Hsp40/DnaJ-Rel, was

represented by three spots (spot 351, 471 and 651). Hsp40 (J-protein or DnaJ) is a co-

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chaperone of the Hsp70 complex and regulates the activity of Hsp70 ATPase. This

association assists a number of cellular processes such as proper polypeptide folding,

improvement of misfolded proteins, assembly of protein complexes or control of other

regulatory proteins (Han and Christen, 2004; Sharma et al., 2009). There was a

progressive increase in abundance of two corresponding protein spots (351 and 471)

related to Hsp40 that was more consistent in cv Rupali than that KJ850. Taken together,

almost all identified members of the HSP family showed relatively high abundance or a

more consistent expression pattern during the course of imbibition in cv Rupali. It seems

quite possible that as a group of proteins the HSPs provide conditions that permit the

genotype to cope better with the stress of germination under severe water limitation.

A number of proteins were identified from the analysis as enzymes involved in cellular

biosynthetic reactions. These included anthranilate synthase (AS), Qb-SNARE (soluble

N-ethylmaleimide-sensitive-factor attachment protein receptor), phosphoenolpyruvate

carboxykinase (PEPCK) and mitochondrial NAD-dependent malate dehydrogenase.

Anthranilate synthase (AS) was identified as spot 250 which in cv Rupali had an

increased abundance not only in dry seed but during germination. In contrast the two

other genotypes showed reduced abundance during germination. AS is a necessary

enzyme for the biosynthesis of the essential amino acid tryptophan (Trp) through the

shikimate pathway (Ishihara et al., 2006). This pathway also inviloves production of

some defensive secondary metabolites in plants including alkaloids and isoflavonoids as

part of the response to biotic and abiotic stresses (Frey et al., 1997; Dixon, 2001; Hu et

al., 2009). Perhaps greater capacity in cv Rupali contributes to the overall responce to

lower water availability.

The other significant component associated with metabolism was identified as Qb-

SNARE (spot 360). This polypeptide increased exponentially from a low abundance in

dry seed by nearly 20-fold after 48 hr and up to 50-fold after 72 hr imbibition in cv

Rupali. Although the expression profile showed a several fold enhancement at 48 hr in

other genotypes, expression subsequently decreased, particularly in KJ850. SNARE

proteins are part of a key complex responsible for trafficking between intracellular

compartments in eukaryotic cells (Ungar and Hughson, 2003; Yang et al., 2008; Schmitt

and Jahn, 2009). Trafficking is mostly mediated via vesicles within the cell and at cell

membranes. It is also proposed that SNARE complex-mediated trafficking plays a

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significant role in plant disease resistance by modulating vesicle fusion (Collins et al.,

2003). Molecular trafficking between cellular compartments and between cells are

likely to be important features of the restoration of metabolism and initiation of growth

during early germination and promotion of these events might be expected to enhance

germination rate.

Seed storage reserves in the form of carbohydrates, proteins and lipids (as

triacylglycerides [TAG]) fuel germination and early establishment of the initially totally

heterotrophic seedling. Glycolysis is a critical cytoplasmic process for sugar breakdown

and formation of precursors required to produce amino acids and organic acids for

diverse biosynthetic pathways (Siedow and Day, 2000; Fait et al., 2006) as well as ATP

through substrate phosphorylation. Sugars are the main and readily metabolized source

for energy release during respiration which is mostly provided in the seed by breakdown

of stored carbohydrates but also through gluconeogenesis. In gluconeogenesis lipids

(e.g. oils) are converted to sugars, particularly in seeds with high oil content such as

soybean and Arabidopsis (Siedow and Day, 2000; Rylott et al., 2003). In the present

study two differentially abundant protein spots (770 and 736) were identified as

phosphoenolpyruvate carboxykinase (PEPCK). Separation of two different spots is

consistent with the native protein being readily cleaved during isolation forming lower

molecular mass polypeptides (Walker et al., 1995). The abundance of PEPCK (spot

736) increased significantly at 48 hr imbibition in cv Rupali but there was no significant

increase in expression of the enzyme in other genotypes (Figure 4.7 and Table 4.3).

Differential abundance of PEPCK at spot 770 was less clear. PEPCK activity provides

an anaplerotic 4C input to the TCA cycle through carboxylation of PEP, and, in the

catabolic direction, provides intermediates for gluconeogenesis. During germination

TCA cycle activity would be the major and most effective source of reductant (NADH)

and, as a consequence, ATP in mitochondrial respiration. However, the TCA cycle

intermediates also provide C4 and C5 acids for the synthesis of a number of amino acids

and other metabolic intermediates requiring an anaplerotic C input to the cycle. PEPCK

is considered a necessary component in hypocotyl elongation and early post-

germinative growth in Arabidopsis due to its role in gluconeogensis providing

additional sugars via oil reserve breakdown (Rylott et al., 2003; Penfield et al., 2004;

Cernac et al., 2006; Malone et al., 2007). While the level of storage lipid in chickpea

seed is relatively low (<10%) (Zia-Ul-Haq et al., 2007) compared with Arabidopsis the

significant increase in expression of the enzyme in cv Rupali (spot 736, Figure 4.7)

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coincided with radicle elongation and it is reasonable to assume that some

gluconeogenesis as a result of fatty acid oxidation occurred. However, given the

relatively low levels of stored lipid the anaplerotic role for PEPCK seems the more

important and the most likely to differentially promote the synthetic and respiratory

metabolism associated with mitochondria in cv Rupali.

The only TCA Cycle respiratory enzyme that was identified as being differentially

expressed was mitochondrial NAD-dependent malate dehydrogenase (MDH; spot 879,

Figure 4.7). In cv Rupali after 72 hr imbibition there was a very high abundance of

MDH compared to the other genotypes implying increased respiration rate to meet the

high demand of energy for the rapid growing emergent radicle. In MDH antisense

transgenic tomato decreased levels of mitochondrial MDH activity results in significant

reduction of root dry matter and respiratory activity (Van der Merwe et al., 2009). This

indicates a particular importance of the enzyme for heterotrophic growing organs such

as root and this may well apply to the emerging radicle in the germinating chickpea

seed. Taken together the enhanced levels of MDH and PEPCK indicate the importance

of C4 carboxylic acid metabolism in promoting rapid germination.

4.4.4 ROS homeostasis and oxidative stress

It has been well documented that ‘reactive oxygen species’ (ROS) such as hydrogen

peroxide (H2O2), superoxide radical (O-2) and hydroxyl radical (OH-), play a key role in

oxidative signalling and in metabolism (Schopfer et al., 2001; Bailly, 2004; Bailly et al.,

2008; El-Maarouf-Bouteau and Bailly, 2008; Oracz et al., 2009). ROS are constantly

produced during the life of a seed from early in embryogenesis, during deposition of

storage reserves and desiccation to germination but increase considerably during

imbibition apparently as a consequence of environmental cues following water

absorption. The cues thought to be important include increased cytoplasmic viscosity

and molecular mobility, processes that facilitate metabolic reactions (Walters, 1998).

Importantly, the resumption of metabolism, particularly respiratory activity and

gluconeogenesis, becomes a major source of ROS (Møller, 2003; Oracz et al., 2007;

Graham, 2008). In seeds ROS play a dual role, functioning in cellular signalling

pathways and as toxic components formed as a consequence of stress. These roles

probably resulted from an interaction between ROS and hormonal signalling which

could lead to initiation of gene expression or changes in cellular redox during

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germination (Bailly et al., 2008 and references therein). In this way a narrow range of

ROS (homeostasis) would trigger the initiation of cellular processes involved in

germination. This ROS range which is defined between critical lower and upper levels

of required ROS has been suggested by Bailly et al (2008) as an “oxidative window for

germination”. Based on this hypothesis, there exist a number of proteins which function

as antioxidant (or scavenging enzymes) to control ROS accumulation and therefore

regulate the progression of germination. Some of these proteins were differentially

identified among genotypes in this study including different isoforms of superoxide

dismutase (Cu/Zn), 1-Cys peroxiredoxin, glutathione peroxidase, calreticulin,

calmodulin and a thioredoxin-like protein.

Two isoforms of superoxide dismutase (SOD) were detected at spots 610 (cytosolic

SOD), 435 (Figure 4.7) and 733 with a specific over-expression pattern of several fold

from dry seed to 48 hr imbibition then a sharp decrease to a level even below that of the

dry seed in cv Rupali (spots 610 and 435) compared with other genotypes. Expression

also increased in the intermediate genotype, KH850, up to 72hr coincident with radicle

protrusion and suggests that the scavenging role of this enzyme for controlling the

levels of ROS is induced and is transient. This idea is supported by the data for cv

Rupali where, during further radicle growth, expression of SOD declined. This is

consistent with the findings from seed germination for sunflower in that, as a result of

increased SOD activity and other ROS-scavenging enzymes (e.g. catalase), a balance

between ROS-producing and ROS-scavenging processes is maintained (Oracz et al.,

2009). In the practice of ‘seed priming’, germination is prevented by providing

insufficient water for full imbibition (water stress) and it has been reported that there is

induction of anti-oxidant enzymes such as SOD as a result (Bailly et al., 2000). The

pivotal role of SOD in conferring abiotic stress tolerance has recently been confirmed

by transferring the Sod1 gene from mangrove (a halophyte with high activity of SOD) to

rice, leading to a greater tolerance of salinity, drought and oxidative stress in the

transgenic (Prashanth et al., 2008).

The other potential ROS scavenging protein identified was glutathione peroxidase

(GPX, [spot 635, Figure 4.7]) where expression was enhanced more than three-fold or

more following imbibition in cv Rupali (Figure 4.2, 48 hr). In contrast GPX abundance

decreased at corresponding time points in the other genotypes (Table 4.3). In plants,

GPX is mainly involved in scavenging H2O2 and thus protecting cellular membranes

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from oxidative damage (Gueta-Dahan et al., 1997) or as a common oxidation and/or

redox signal in response to abiotic and biotic stresses (Wingate et al., 1988; Avsian-

Kretchmer et al., 2004; Miao et al., 2006; Navrot et al., 2006; Ramos et al., 2009). A

role for the reduced form of glutathione through protein carbonylation, which is an

irreversible oxidation process leading to a loss of function of the modified proteins, has

been proposed (Kranner and Grill, 1996; Job et al., 2005) in which ROS status elevates

the susceptibility of reserve proteins to proteolytic enzymes resulting in their more rapid

mobilization during germination.

Peroxiredoxins (Prx) are another ubiquitous family of thiol-dependent peroxidases with

H2O2 detoxification and/or signal transduction properties. They consist of four groups

based on their conserved cysteine residues and putative catalytic functions (Wood et al.,

2003; Tripathi et al., 2009). In the present study, two different isoforms of Group 1-Cys

peroxiredoxin (1-Cys Prx) showed different patterns of abundance especially in cv

Rupali depending on the time of imbibition (spots 821 and 941). This may reflect

variation in terms of function and there is evidence to support this idea. The expression

profile for 1-Cys Prx (spot 821) shows that the protein is in rather high abundance but at

different levels in dry seed of all genotypes with a general increase later during

imbibition. This may indicate a general antioxidant protection function for cellular

components against ROS damage during the last stages of seed development i.e.

desiccation (Stacy et al., 1996) and later during germination because by providing

insufficient water for full imbibition might be equivalent to environmental drought

stress (Dietz, 2003). On the other hand, 1-Cys Prx 1 (PER1- spot 941) was not detected

in dry seed of any genotype and the expression was significantly very low in cv Rupali

and KH850 compared with non-germinating genotype, KJ850. This implies firstly that

the protein is synthesized de novo upon water uptake and secondly that it might be

involved in inhibiting germination. Recent findings (Haslekas et al., 2003) in which

over expression of seed 1-Cys Prx 1 (PER1) contributed to inhibition of germination in

Arabidopsis under conditions of salt and mannitol stress support this idea. PER1(Stacy

et al., 1999) has also been shown to be maintained at high abundance in imbibed

dormant barley seeds, but not in imbibed non-dormant seeds (Stacy et al., 1999).

Calmodulin (CaM; spot 34; Table 4.3 and Figure 4.7) and calreticulin (spot 108) are

both calcium-regulated proteins with potential roles in ROS metabolism. Each was

differentially expressed among genotypes. CaM was not detected in dry seeds of any

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genotype but subsequently increased in all three. Interestingly the abundance was >2.5-

fold lower in cv Rupali compared to the other genotypes at corresponding time points

(Table 4.3) suggesting a role in slowing or inhibiting germination. CaM is known to be

involved in H2O2 signal transduction pathways (Desikan et al., 2001) and crosstalk

between these two components play a critical role in ABA signaling and relevant

antioxidant defence in the whole plant (Hu et al., 2007). In seeds, increased ABA levels

prolong dormancy and delay germination (Finch-Savage and Leubner-Metzger, 2006).

Therefore elevated expression of CaM in KH850 and KJ850 compared to cv Rupali may

indicate increased levels of H2O2 (beyond the appropriate level required for signalling)

and/or ABA which cause delay or inhibition of germination in these two genotypes. In

contrast, calreticulin (CRT; Table 4.3 and Figure 4.7) increased exponentially from an

insignificant level in dry seed to more than 20-fold in cv Rupali with a rather similar

pattern in KH850. CRT is a highly conserved protein found in endoplasmic reticulum

(ER) and having a multifunctional role (Michalak et al., 1999). The principal research

focus has been related to its function in calcium signaling (Nakamura et al., 2001)

and/or as a chaperonin-like component in protein folding, modification and assembly

under normal (Michalak et al., 1999) and stressful conditions (Persson et al., 2003; Jia et

al., 2008). These sorts of activity are clearly significant in germination and given the

pattern of CRT abundance in cv Rupali it suggests the protein is a key player in

germination under water stress. It is worth noting that calcium ion is a potent regulator

of the activity of a wide range of enzymes and its transport and compartmentalization

are likely to be important in determining metabolic potential in seeds (Trewavas, 2000).

It has been frequently proposed that changes in redox status of seed proteins from an

oxidized state in dry seed to reduced forms following water uptake better exposes

storage protein to proteolytic enzymes as well as facilitating the breakdown of stored

starch (Yano et al., 2001; Marx et al., 2003; Rhazi et al., 2003; Alkhalfioui et al., 2007).

One group of proteins that might mediate these reduction reactions and promote early

germination is the thioredoxins (Trx). A Trx-like protein (spot 380) was identified in

this study and was present as a pre-existing protein in dry seed of the germinating

genotypes (cv Rupali and KH850) but initially not in KJ850. While this pattern is

consistent with a role in events early in germination of the two genotypes that were able

to respond to the low level of water available the pattern of expression was very

variable (Table 4.3).

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4.4.5 Additional proteins associated with germination

A pathogenesis related (PR) protein (spot 528) was not detected in dry seed of any of

the genotypes but was significantly expressed in cv Rupali following imbibition with

much lower expression in the other two genotypes. A PR protein is reported to be

highly expressed in pea roots in response to salinity (Kav et al., 2004) or drought stress

(Dubos and Plomion, 2001). Interestingly, transgenic plants of Brassica napus over-

expressing pea PR genes have been shown to germinate and develop more effectively

under saline conditions (Srivastava et al., 2004). The exact PR function during

germination is yet to be defined but there is speculation that it is associated with RNase

activity (Park et al., 2004) or binding to cytokinins (Biesiadka et al., 2002).

The other very interesting protein induced significantly and differentially across

genotypes was a serine proteinase inhibitor (spot 921). This protein was not detected in

dry seeds but was expressed up to 20-fold more during imbibition in the non-

germinating genotype (KJ850) when compared with cv Rupali. Proteinase inhibitors

(PIs) regulate proteolytic function to avoid premature degradation or hydrolysis of

reserve proteins during the course of seed development (Welham and Domoney, 2000;

Clemente and Domoney, 2006) and germination (Cervantes et al., 1994; Hernández-

Nistal et al., 2009) and they have been implicated in response to water stress (Pinheiro

et al., 2005) or as a part of biotic defence against pests and pathogens (Ryan, 1990). It

seems likely that expression of this proteinase inhibitor was repressed in imbibed cv

Rupali seeds so that protein breakdown could proceed rapidly. Logically, because

imbibition resulted in different signals in the non-germinating genotype expression was

derepressed to prevent premature breakdown of storage protein.

Reverse transcriptase was identified as being highly differential on spots 155 and 720

(Table 4.3). The protein was significantly induced in cv Rupali at all time points of

imbibition in spot 720 and also after 72 hr imbibition in spot 155 in comparison to other

genotypes. Reverse transcriptase has been previously known to be implicated in

retroviral replication and retro-transposition but recently shown to be vital for normal

replication of chromosome telomeres in many eukaryotes (Lingner et al., 1997). This

may reflect the rapid onset of cell division activity in cv Rupali following water

absorption which would be less rapid in KH850 and not initiated in KJ850.

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THO complex (a multimeric nuclear factor required for transcription elongation)

consists of several protein components which is integrated into another larger multiple

protein complex called “transcription and export” (TREX) complex. The role of this

large complex is to process and package nascent mRNAs generated by transcription in

the nucleus and export them to the cytoplasm (Batisse et al., 2009; Hamada et al., 2009;

Kelly and Corbett, 2009; Yelina et al., 2010). Components of THO complex have been

specifically associated with transcriptional elongation (Chávez and Aguilera, 1997;

Rondón et al., 2003) and genomic stability (Chávez and Aguilera, 1997). In the current

study, subunit 1 of THO complex (spot 23) was exponentially expressed throughout

imbibition in the genotypes that germinated (cv Rupali and KH850) but with much

lower expression and quite a different pattern of abundance in KJ850. This clearly

suggests the importance of initiation of transcription and synthesis of new transcripts for

the progress of germination (Bewley, 1997) which in KJ850 may have been

compromised.

A large number of differentially abundant polypeptides (18 protein spots) were

identified as cupin RmlC with four different isoforms (Q2HW16 in 6, Q2HW19 in 2,

Q2HW21 in 3 and Q2HW22 in 7 spots) (Table 4.3). The corresponding expression

profiles for each isoform showed diverse patterns within and between genotypes. This

might be largely related to the degradation level of each isoform as observed molecular

masses for almost all were much smaller than the theoretical molecular masses of the

known proteins from the database analyses. It is very likely that ROS-linked (or

oxidative stress) pathways may account for such stress-induced degradation (Desimone

et al., 1996; Hajduch et al., 2001; Bhushan et al., 2007), or even the specific molecular

structure of the protein (e.g. conserved barrel sections present in the cupin molecule;

Dunwell et al., 2004) may be responsible for ready molecular cleavage. Cupin is a super

family with at least 18 diverse functional subclasses which is continuing to expand

(Dunwell et al., 2004). Some of the proposed functions for this family relate to plant

growth regulator responses, like gibberellin 20 oxidase (Spielmeyer et al., 2002) and

auxin binding protein, germin with SOD activity (Woo et al., 2000), germin like protein

(GLP) involved in enhanced germination under stress (De Los Reyes and McGrath,

2003) and seed storage proteins (Dunwell et al., 2000; Dunwell et al., 2004). Cupin

RmlC type (dTDP (deoxythimodone diphosphates)-4-dehydrorhamnose 3,5-epimerase)

found in this study, is an enzyme of the very large isomerase family and is implicated in

nucleotide sugar metabolism. Nucleotide sugars (activated monosaccharides) function

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as donors of sugar residues in glycosylation processes to produce polysaccharides in ER

and the Golgi apparatus (Ginsburg, 1978). However the nucleotide sugar

interconversion enzymes’s role is not well characterized in plants (Seifert, 2004). As

cupin RmlC (with highly significant identification scores) was identified from many

spots across all genotypes it is speculated that it may have an important role in

germination whether under water stress or not. Growth of the radicle requires synthesis

of new polysaccharides for the formation of new cell walls and consequently formation

of nucleotide sugar diphosphates will be a major metabolic activity in the germinating

seed (Mangat, 1979; Anich et al., 1990; Saxena and Brown, 2005; Pieslinger et al.,

2010).

A second large group consisting of multiple protein spots on gels was identified from

the databases as allergen Len c 1.0102. This protein is a member of the vicilin storage

protein family and has been associated with food allergic reactions in humans (López-

Torrejón et al., 2003; Vereda et al., 2010) and in pea a nuclear protein from the family

has been reported to be induced by dehydration (Castillo et al., 2000). However, the 10

spots found all represented a single isoform. Once again this protein was apparently

subjected to significant degradation with a wide divergence between observed and

theoretical molecular masses. Furthermore, posttranslational modifications through

proteolysis and glycosylation have been shown to be responsible for a variety of

variations in the subunit composition of vicilin leading to different molecular mass and

charge on 2-DE gels (Shewry et al., 1995; Magni et al., 2007). Taken together these

modifications may explain the presence of Len c 1.0102 in different spots and why it

proved difficult to relate its expression profile to reveal a possible role in germination

under severe water stress.

4.4.6 Clustering analysis

Cross-talk and interconnection of proteins in a specific biological sample using

expression profiling is of interest to integrated studies in systems biology (Urfer et al.,

2006; Faurobert et al., 2007; Pandey et al., 2008; Wang et al., 2009a). The hierarchical

clustering algorithm has been used to identify and imply interactions of proteins

responsive to the same biological mechanism. Using this analysis, proteins in the same

or close branches have similar abundance values and are assumed to be possibly related

or regulated by the same mechanism or be part of the same metabolic pathway (Urfer et

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al., 2006; Wang et al., 2009a). In the current study, SOTA clustering was used to

provide a more complete picture of synchronization of protein abundance presented in

Table 4.3. Under severely decreased water availability, which, in a sense, mimics

drought stress (on the whole plant), the same mechanism mentioned above (protecting

against oxidative stress) may be of dual significance. The induction of cell defence has

been shown also in a comparative proteomic analysis of chickpea during dehydration

(Bhshan et al., 2007, Pandey et al., 2008) or salinity stress (Wang et al., 2009) and it is

possible that gene expression for these proteins following imbibition is regulated by an

overriding mechanism that occurs in response to a cue that is one of the earliest events

after water enters the dry seed.

The expression data presented in cluster 1 (Figure 4.9; the second largest class)

demonstrated a very significant decrease (down regulation) in protein abundance for

nearly all proteins specifically in cv Rupali at 72 hr after imbibition. A similar but less

consistent pattern also occurred in cluster 2 (the largest class). As this stage coincided

with further and rapid growth of the radicle in cv Rupali, but which was not the case for

other two genotypes, it may indicate that these proteins are functionally no longer

involved in, or at least are not as important as they were initially, metabolic processes

related to early stages of germination. This temporal or developmental expression of

proteins has been reported in proteomic studies of Medicago Truncatula and barley seed

development and maturation (Gallardo et al., 2003; Finnie et al., 2006), germination and

radicle elongation of barley under normal conditions (Bønsager et al., 2007) and

soybean primary root growth in response to water stress (Yamaguchi et al., 2010).

Interestingly, SOTA analysis (Figure 4.8, 4.9 and Appendix 3) was able to show that the

overall expression pattern of proteins within the 92 analysed spots corresponding to cv

Rupali at all time points was significantly different to the other genotypes, clustering in

a separate class (Figure 4.8, transposed cluster tree). In other words, genotypic variation

found during germination under severely limiting water supply was related to the

proteome expression profile in which many proteins are co-ordinately involved in a

number of different metabolic pathways or specific metabolic events.

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4.5 Conclusions

The earlier physiological study (Chapter 3) established cv Rupali as the most rapidly

germinating among the three genotypes tested under severely limiting soil moisture

supply. Proteomic analysis has revealed a large number of proteins expressed earlier

and at higher levels of abundance in cv Rupali and it is tempting to associate these with

the ability of this cultivar to germinate effectively under the severe moisture limitation

applied. Among the proteins identified were those with chaperone-like functions,

including number of LEA and HSP proteins that are essential for the correct physical

properties of proteins formed during seed maturation and required for the earliest

metabolic events following imbibition. The analysis also revealed the early formation

of proteins involved in generating and metabolising ROS. ROS homeostasis is essential

to maintain the appropriate levels of these extremely reactive oxygen species but also

for very early signalling events following imbibition. While there is no doubt that

mitochondrial respiration is rapidly activated following the ingress of water, only NAD-

MDH was identified as an early differentially abundant enzyme of the TCA cycle.

However, in cv Rupali the level of PEPCK rose very rapidly to a high level of

expression indicating that C4 dicarboxylic acid supply through an anaplerotic C input to

the TCA cycle may have been a critical means to maintain energy supply at the same

time permitting biosynthetic functions of the cycle to occur. It is equally clear that a

range of other proteins that are more abundant in cv Rupali may have specific roles

following imbibition that are yet to be defined. Furthermore, some proteins, for

example proteinase inhibitors, were more highly expressed in the genotype that was

unable to germinate implying that in cv Rupali this inhibition was overcome as an early

event following the ingress of water and this too could well be a factor in the rapid

germination of this cultivar.

The proteomic analysis has identified a number of genes that must have been expressed

at elevated levels in cv Rupali to allow its more rapid germination phenotype. Further

transcriptional analyses are required to test this prediction and to reveal the regulatory

mechanisms that link the groups of genes involved. The clustering analysis provides a

link between groups of proteins and implies a common regulatory mechanism, possibly

at the transcriptional level. While the chaperone-like proteins and enzymes of ROS

homeostasis for example provide a useful starting point, further genetic analysis may

well identify other genes that are important to the early germination trait. This trait is an

important component of early vigour under field conditions of limited moisture supply

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and perhaps QTL or other molecular markers can be developed to exploit the

information obtained with the three chickpea cultivars in formulating new breeding

tools to improve the crop.

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CHAPTER 5

5 General Discussion

Despite the large body of research into seed germination and the number of models and

hypotheses put forward (Bewley, 1997; Finch-Savage and Leubner-Metzger, 2006) to

account for the significant changes in tissue development and function that follow the

ingress of water many questions remain to be answered about the very early events and

their relationship to the perception of environmental cues. Water uptake is the most

critical factor in the germination of non-dormant seeds and the internal cues that set

metabolism in train are likely to be a consequence of the physical features of re-

hydration. Put simply, the resumption of cellular metabolism depends on the

availability of sufficient water in each of the seed compartments so that essential

enzyme activities are rapidly re-activated. While this is likely to be readily achieved in

soil with an abundance of water, under conditions of low soil moisture a rapid

germination trait is likely to confer a considerable advantage. In this context, the results

presented in this study contribute to a better understanding of the mechanisms

associated with rapid germination under condition of limiting soil moisture in chickpea.

Importantly, the study developed an appropriate and highly reproducible assay system

that enabled a valid comparison among genotypes by carefully controlling variables

influencing imbibition. The assay system permitted a more rapidly germinating

genotype (cv Rupali) to be distinguished from a less rapidly germinating line (KH850)

and one that was unable to achieve radicle emergence (KJ850) under conditions of very

low available water. These three lines provided material to compare features of

germination at the protein level. The study exploited large scale proteomic analysis to

identify proteins that differed in abundance in each genotype at different time points

following imbibition, with particular emphasis on likely functional roles to indicate

potential genetic and physiological mechanisms for rapid germination in chickpea under

limited water supply.

Before comparing genotypes, a number of variables with potential impacts on the rate of

germination and further seedling growth, were assessed. They are reviewed in two main

categories; seed related features and the strategy used to assess possible variation in

germination.

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5.1 Seed related features

Seed quality and vigour is affected by the production environment especially the onset

of stresses during late embryogenesis and seed filling. This has been shown to have a

significant impact during early germination and pre-emergent seedling growth which is

totally heterotrophic and relies on seed reserves (Biere, 1991; Bettey et al., 2000; Weber

et al., 2005; Hanley, 2007). Even high temperature conditions before harvest (Egli,

2005) and temperature fluctuations after harvest (i.e. during seed storage) have a

considerable influence on germination performance (Huarte and Benech-Arnold, 2005).

Thus in the present study variation in seed quality resulting from differences in the

maternal environment were reduced by growing all genotypes at the same field site and

under the same agronomic and environmental conditions (Chapter 3). The sandy soil at

the field site was fertilized and prepared to be as homogeneous as possible even though

some subtle differences in the spatial distribution of nutrients in soil would have

occurred. The genotypes used showed slight differences in the onset flowering and as a

result the time to final seed harvest varied around 7-10 days.

Nevertheless, seed mineral analysis revealed some variation in macro- and micro-

element content (Chapter 3, Table 3.4). This was more evident for macro elements in cv

Rupali (later found to be the most competent genotype for germination) which had

significantly higher levels of N and to a lesser extent P and K than the others, especially

KJ850. A number of genes involved in nutrient efficiency have been recently

characterised that might result in subtle differences in nutrient uptake between

genotypes. In Arabidopsis, for instance, over expression of ATNRT2.7 (nitrate

transporter) or a mutation in the gene affects seed nitrate content (Chopin et al., 2007)

while over expression of the APase gene (AtPAP15; a catalyst for the breakdown of P

monoesters to release Pi from organic P compounds) has increased P efficiency in

soybean (Wang et al., 2009b). Rapid cell growth and division during germination or

seedling growth (Rengel and Graham, 1995; Hegeman and Grabau, 2001; Naegle et al.,

2005; Ozturk et al., 2006) requires adequate levels of nutrients from the seed reserves

and these variations seen could have influenced early events following imbibition.

Nutrient supply may also influence the level of stress tolerance (Bonilla et al., 2004).

The N content was approximately 30% higher in cv Rupali compared with the other

genotypes. In legume seeds, storage proteins account for up to 40% seed mass (Shewry

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et al., 1995) and 80% of total seed proteins (Djemel et al., 2005), providing the main

source of N and amino acids for the growing embryo during germination. Since around

70% of the total differentially abundant protein spots identified among contrasting

genotypes were storage proteins (Chapter 4) the extra N in cv Rupali was most likely in

this form. Therefore, in addition to the possible reasons indicated earlier in Chapter 4

for such substantial differences within storage proteins, another possible explanation

may be the higher N content in cv Rupali compared with the other genotypes. While the

largest difference in nutrient content among genotypes was found for N, more Fe and

Zn were also accumulated in cv Rupali compared to the genotype that did not

germinate. Among enzymes that were more abundant in cv Rupali compared to the

other genotypes was a Cu-Zn superoxide dismutase and no doubt expression of a

number of Fe-containing proteins associated with respiration might well have benefitted

from the extra available Fe.

While this study did not attempt to determine the impact of maternal environment on

seed quality, proteomic analysis identified a number of proteins that were formed prior

to maturation and which could have been influenced by seed production conditions.

Among these the LEAs and HSPs were two large groups of proteins with various

members identified as significantly different in abundance in dry seeds of the genotypes

(Chapter 4). Both these groups are known to be expressed during late embryogenesis

and seed filling and variations in dehydration or the imposition of other stresses such as

heat are likely to alter their abundance in the dry seed (Wehmeyer and Vierling, 2000;

Goyal et al., 2005; Tunnacliffe and Wise, 2007). Generally, LEA proteins act as

chaperones to prevent other proteins from aggregation during water stress (Goyal et al.,

2005) or to protect cell membrane proteins from damage (Cheng et al., 2002; Chandra

Babu et al., 2004). Like chaperone proteins, HSPs are also efficient in re-establishment

of functional conformation of denatured proteins under various stress conditions (Rajan

and D’Silva, 2009; Sharma et al., 2009) or in more general protection of diverse cellular

functions (Wehmeyer and Vierling, 2000; Basha et al., 2004). Thus their level of

expression is likely to affect germination associated events upon imbibition.

The possible role of the seed coat (testa) as a barrier to water flux into the seed was

another variable which was thought likely to affect the rate and extent of water

absorption during germination. During seed development the testa is physiologically

active and plays a key role in supplying translocated nutrients to the growing embryo,

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serving as a transient storage site and also a physical restriction (Biere, 1991; Weber et

al., 2005) to seed size. However, the data presented in this study (Table 3.3)

demonstrated that in spite of differences in colour and surface features between

contrasting genotypes (Rupali and KJ850) with the same sized seed, the rate of water

absorption was not significantly different at the lower levels of water supply employed.

Thus the seed coat in chickpea is apparently imparts little physical resistance to water

ingress. The similar rates of imbibition for the genotypes studied also support the idea

that physical properties of seed tissues (i.e. embryo and cotyledons) did not

differentially limit water absorption. This is a critical consideration because the specific

aim of the large scale proteomic analysis in this study was to reveal possible

mechanisms for rapid germination.

As discussed in detail in Chapter 3, the size of seed as a potential variable affecting

germination rate was another consideration for comparing genotypes in their rate of

germination. It was obvious from the data generated that variation in seed size within a

given genotype (e.g. cv Rupali) could lead to a significant variation in germination and

later on in early seedling growth by greater reserve mobilization to young seedling

tissues (radicle and epicotyl). This effect, however, was negatively related to the level of

available water; the more water supply was decreased, the impact of seed size was more

significant. In other words, smaller seeds were more successful in reaching the threshold

of imbibition to initiate germination related events and subsequent growth (Chapter 3,

Figure 3.4 and 3.6). The most plausible explanation for the effect of this physical factor

is that the surface area to volume ratio in large seeds is lower than that of small seeds

(Saxena et al., 1993; Kikuzawa and Koyama, 1999), resulting in reduced water influx

into the seeds due to a less favourable water gradient between seed and surrounding

environment, especially when water becomes extremely scarce.

Many studies have addressed the relationship between seed size, germination and

subsequent growth with a range of conclusions (Marshall, 1986; Evans and Etherington,

1990; Hanley, 2007; Daws et al., 2008; Kaya et al., 2008; Kaydan, 2008; Urbieta, 2008;

Loha et al., 2009). The reason is that the impact of seed size on germination may be

related to the context of each independent study. For instance, physiological and genetic

differences within and between species (Marshall, 1986; Daws et al., 2008),

environmental conditions during germination such as water availability (Leishman and

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Westoby, 1994; Tobe et al., 2007; Kaydan, 2008), and methods used for germination

assay like application of PEG (polyethylene glycol) (Etherington and Evans, 1986;

Kaydan, 2008) may mask or magnify the role of seed size (Etherington and Evans,

1986). In chickpea, indeterminate growth habit, continuous flowering during seed set

under different conditions (Upadhyaya, 2003) and the trade-off between seed size and

number contribute to considerable variation in seed size within a single genotype

(Eriksson, 1999). Given that the main aim of this study was to assess germination rate

under extremely decreased levels of water in the soil and to compare genotypes for this

trait, the data clearly indicated that it was mandatory to use the same sized seed to have

a valid comparison among genotypes. Particular care was taken to ensure this criterion

was met as far as possible in the study. The harvested seed was graded for size, colour

and shape and then samples were assembled with similar weights so that when the

comparisons for germination were made seeds with the same weight were used for each

genotype. In addition the seed samples were stored under uniform dry conditions at 4o

C before use.

5.2 Assay system

Water fluctuation in the soil profile is one of the most important variables affecting

germination rate (Phil, 2003). In fact, creation of a defined and homogenous distribution

of water in field soil in situ, is extremely difficult especially when a constant and

extremely limited supply of water is required. Due to this technical restriction, PEG has

been used in many germination studies to provide defined conditions of water deficit.

As discussed earlier in detail in Chapter 3 there are a number of disadvantages with this

method that may affect the validity of results (Etherington and Evans, 1986; Finch-

Savage et al., 2005) especially if germination under such condition is targeted at

examining events at the molecular level. For these reasons the assay method developed

and used in this study was thought to provide a simple and reproducible alternative.

The assay system was able to create a diverse range of water contents in pure sand from

an extremely low level of 5% to 100% WHC (Chapter 3, Figure 3.2 and 3.3) similar to

the situation in the soil in situ. More importantly, the average water loss due to

evaporation at a given WHC was statistically very low (<4%/day) and also resulted in a

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constant trend over the period of study (up to 7 days). This semi-soil condition

guaranteed relatively normal germination conditions with reproducible outcomes.

As noted in details in Chapter 3, using diverse combinations of seed number WHC it

was possible to understand the interesting correlation between critical imbibition and

germination rate which is consistent with the ‘hydro-time model’ presented in previous

studies (Bradford, 2002; Finch-Savage et al., 2005; Urbieta et al., 2008). This means

that at constant temperature there is a specific physiological threshold for seed water

potential (Ψb), below which germination will not occur. This estimated Ψb was between

75 and 80% of initial seed mass for cv Rupali which was 20-25% less than that reported

previously for chickpea (Saxena et al., 1993). A lower Ψb may be associated with a

number of biochemical processes which requires investigation at the molecular level

(Finch-Savage and Leubner-Metzger, 2006); this was, in fact, the main aim of the

current study. Similarity of the assay system to soil conditions in terms of physical

properties also resulted in normal architecture of emergent organs (radicle and epicotyl).

This allowed a comparison between the heterotrophic pre-emergent seedling growth

under different regimes of water availability among different sized seeds. While

partitioning of reserves to emergent organs was significantly reduced with decreasing

water, the rate of partitioning of reserves in seedlings raised from small seed was

substantially higher than that of larger seeds (Chapter 3) indicting a better early

establishment of small-seeded seedlings at extremely limited water supply (Soltani et

al., 2006). As noted above this may reflect significant differences in the surface to

volume relationship and the ingress of water but in practical terms it may also indicate

an agronomic advantage for small seeds in the field at limiting water supplies.

Taken together the considerations and precautions taken ensured that significant

variation among chickpea genotypes to germinate and to grow a radicle subsequently

under extremely decreased water supply was apparent (Figure 3.7). This was most

evident when the water level was decreased to 6.25% and 5% WHC in which cv Rupali

and KJ850 were most different in their response. Therefore, cv Rupali with lower water

threshold (Ψb) can initiate the germination earlier and continue subsequent seedling

growth more rapidly than the other genotypes.

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5.3 Proteins associated with the rapid germination trait in cv Rupali

The proteomic analysis and functional cataloguing of differentially identified proteins

(Table 4.3) showed that two large groups of proteins, namely LEA proteins and

especially HSPs play an essential role in defining seed Ψb (threshold water potential).

These chaperone-like proteins are involved in responses to various abiotic stresses,

especially dehydration, salinity and low or high temperature whether constitutively

programmed, as occurred in late seed maturation, and/or as a result of environmental

change (Aoki et al., 2002; Sun et al., 2002; Basha et al., 2004; Puhakainen et al., 2004;

Boudet et al., 2006; Tunnacliffe and Wise, 2007; Dalal et al., 2009; Rajan and D’Silva,

2009; Sharma et al., 2009; Olvera-Carrillo et al., 2010). As noted earlier, a diverse

group of LEA proteins identified in this study are believed to functionally protect cell

membrane proteins from damage (Cheng et al., 2002; Chandra Babu et al., 2004) or

prevent protein aggregation during water stress (Goyal et al., 2005). In both cases, they

are specifically important for the proper function of various metabolic or signalling

enzymes. Some of these proteins such as Group 3 and Group 6 LEA proteins pre-

existed in dry seeds but others like Group 5 LEA were apparently expressed de novo in

cv Rupali during early germination (Table 4.3). The latter protein may give an

advantage to cv Rupali by maintaining cell integrity and function during germination

compared with the slower germinating genotypes.

The role of various members of the HSPs in adjustment of seed Ψb seems to be more

prominent due to their diverse roles in cellular functions over the course of germination.

HSPs, as protective chaperones, are shown to be involved in processes like

transcription, translation, cell signalling (Basha et al., 2004) or cell trafficking (Aoki et

al., 2002) and re-establishment of functional conformation in denatured/unfolded

proteins (Rajan and D’Silva, 2009). Generally, almost all identified members of the

HSP family showed preferentially high abundance or a more consistent expression trend

either in dry seed or during the course of imbibition in cv Rupali; again providing

conditions that allowed the genotype to initiate and to cope better with germination

under severe water limitation. Therefore, if low seed Ψb is assumed to be a more

stressful condition for cellular activities, then the abundance or newly established

expression of the above mentioned proteins in the cell may play a critical role for

initiation of germination events.

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Comparative analysis also identified a number of proteins such as Anthranilate synthase

(AS), Qb-SNARE (soluble N-ethylmaleimide-sensitive-factor attachment protein

receptor), phosphoenolpyruvate carboxykinase (PEPCK) and mitochondrial NAD-

dependent malate dehydrogenase (MDH) with enzymatic functions in cellular

metabolism (Table 4.3). Increased abundance or further expression of these enzymes

during imbibition in cv Rupali may contribute to its overall response to lower water

availability. For instance, Anthranilate synthase (SA) is an essential enzyme in the

shikimate pathway (Ishihara et al., 2006) that is important for plant defensive reactions

to biotic and abiotic stresses through production of secondary metabolites (Frey et al.,

1997; Dixon, 2001; Hu et al., 2009). This pathway also leads to the synthesis of

tryptophan. The SNARE proteins complex are also key players in cellular and

intercellular trafficking via vesicles (Ungar and Hughson, 2003; Yang et al., 2008;

Schmitt and Jahn, 2009). Molecular trafficking of the carbohydrate building blocks for

new cell walls and the lipoproteins for new cell membranes is likely to be an important

feature of the restoration of metabolism and initiation of growth during early

germination. While the proteomic analysis did not identify differential abundance of

expansin proteins there has been some speculation (Chen and Bradford, 2000) about

their involvement in endosperm weakening during tomato seed germination or in

loosening cell walls in the expanding embryo axis tissues (Hernandez-Nistal et al.,

2006). Progressive hydration and consequent loosening of cell walls in dehydrated cells

may be one of the important physical events that was not reflected in the proteomic data

but which might well be more effective at the higher cellular viscosity, during

imbibition in cv Rupali.

The level of PEPCK increased exponentially to a high level of expression in cv Rupali

suggesting that C4 dicarboxylic acid supply through an anaplerotic C input to the TCA

cycle may have been a critical means to maintain energy supply at the same time

permitting biosynthetic functions of the cycle to occur. A very high level of MDH

expression in cv Rupali also implies increased respiration rate to meet the increasing

demand of energy for the rapid growing emergent radicle. Consistent with this, in

tomato decreased levels of mitochondrial MDH activity has led to significant reduction

of root dry matter and respiratory activity (Van der Merwe et al., 2009) indicating the

importance of the enzyme for heterotrophic growing organs such as roots. Overall the

enhanced levels of MDH and PEPCK indicate the importance of C4 carboxylic acid

metabolism in promoting rapid germination. The idea needs further testing using

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transcript analysis, enzyme assay and in experiments designed to measure the fluxes of

C through the C4 acids and the TCA cycle.

One of the interesting proteins which was only expressed following imbibition but at

relatively high levels in cv Rupali was a pathogenesis related protein (PR) (Table 4.3).

Although the function of this protein in germination is yet to be well defined a potential

role in rapid germination under limiting water was possibly indicated. It has been

reported that over-expression of PR in B. napus results in better germination and further

development under saline condition (Srivastava et al., 2004). However, complete

characterization of PR in the future would provide more information on its possible role

in germination. In contrast, a proteinase inhibitor (PI) was expressed at very

substantially higher (>20 fold) levels in imbibed seeds of the non-germinating genotype

KJ850 compared with cv Rupali. Due to a specific function of PIs in preventing

premature hydrolysis or degradation of storage proteins throughout seed development

and germination (Cervantes et al., 1994; Welham and Domoney, 2000; Clemente and

Domoney, 2006; Hernández-Nistal et al., 2009), it is speculated that the expression of

this protein might be a secondary mechanism in response to slower or incomplete

signalling events in the non-germinating genotype under limiting water.

While the stored enzyme proteins and transcripts in seeds might be used temporarily in

early germination (Bewley, 1997; Holdsworth et al., 2008) germination cannot progress

without new transcription (Rajjou et al., 2004) to sustain the cellular metabolism

required and for the mobilisation of storage reserves (Bewley and Black, 1994; Bewley,

1997). In this study several proteins were identified, such as subunit 1 of THO complex,

which might be involved in transcription elongation and genomic stability (Chávez and

Aguilera, 1997; Rondón et al., 2003). Similarly, reverse transcriptase, which was also

found to be more abundant in the rapidly germinating genotype, would be essential for

normal replication of chromosome telomeres in cell division (Lingner et al., 1997).

As proposed earlier, pre-existing or newly expressed chaperone proteins encoded by

stored RNA in seeds (LEAs and particularly HSPs) may be necessary components to

protect or repair a wide range of proteins and re-establish normal biosynthesis or

metabolic processes in the cell upon imbibition. This may have been particularly

important for the rapid restoration of mitochondrial respiration and related enzymes.

Reactivation of metabolism, especially respiration and gluconeogenesis due to increased

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cytoplasmic viscosity and molecular mobility after water absorption (Walters, 1998) is a

significant source of increased levels of ROS (Møller, 2003; Oracz et al., 2007;

Graham, 2008). Given that ROS play a dual role in seeds, functioning in cellular

signalling pathways and as toxic components formed as a consequence of stresses,

mainly water stress (Bailly et al., 2008 and references therein), ROS levels should be

balanced between a defined range (ROS homeostasis) to initiate cellular processes

involved in germination. Interestingly, identification and the expression profiles of a

number of ROS scavenging proteins detected in this study was in agreement with the

idea of ROS homeostasis proposed by Bailly et al., (2008). Accordingly, these proteins

include various isoforms of superoxide dismutase (Cu/Zn), 1-Cys peroxiredoxin,

glutathione peroxidase, calreticulin, calmodulin and thioredoxin-like protein which act

mainly as antioxidants to control ROS accumulation and thereby regulate the

progression of germination.

ROS has been demonstrated to be a key player in transducing signals that sense

environmental cues, such as moisture, initiating a series of events as germination

proceeds. The ROS signalling cascade causes: 1) degradation and mobilization of

storage proteins through protein hydrolysis and oxidation (Bailly, 2004; Bailly et al.,

2008; Oracz et al., 2009); 2) activation of germination-responsive gene expression (El-

Maarouf-Bouteau and Bailly, 2008); 3) regulation of redox status for better functioning

of proteolytic enzymes and facilitation of starch breakdown during imbibition (Rhazi et

al., 2003; Alkhalfioui et al., 2007). Reactivation of metabolism, especially respiration,

upon water absorption by seeds leads to production of ROS the level of which is

modulated by antioxidant enzymes to an appropriate level for signalling. This is

consistent with the expression pattern of most of the antioxidant enzymes identified in

the present study (Figure 5.1) in which there was a sharp increase in expression

coincident with radicle emergence in cv Rupali at 48 hr imbibition as opposed to KJ850.

The increase may relate to an elevated level of ROS resulting from increased activity of

ROS-producing processes such as respiration or other metabolic reactions. Interestingly,

the subsequent decrease in abundance of these enzymes to levels similar to those seen in

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ROS

Chaperone proteins

Metabolism restoration

Respiration

Antioxidant machinery

Protein oxidationProtein oxidation

Enzyme function lossDegradation of storage

proteins

Activation of transcriptionfactors Redox regulation

Ca2+ signaling

922 920 930 780 511 064 850

LEAs

700 931 540

1 2 3 4 5 6 7 8 9 10 111 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

530 351

HSPs471

1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 1 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

922 920 930 780 511 064 850

LEAs

700 931 540

1 2 3 4 5 6 7 8 9 10 111 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

530 351

HSPs471

1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 1 11 1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

250

1 2 3 4 5 6 7 8 9 10 11

AS

360

1 2 3 4 5 6 7 8 9 10 11

SNARE

250

1 2 3 4 5 6 7 8 9 10 11

AS

360

1 2 3 4 5 6 7 8 9 10 11

SNARE

1 2 3 4 5 6 7 8 9 10 11

770

PEPCK

736

1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

879

PEPCK MD

770

PEPCK

736

1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

879

PEPCK MD

610

SOD

435 635

1 2 3 4 5 6 7 8 9 10 11

SOD11 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

GPX 1-Cys Prx

821 941

1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

PER1

610

SOD

435 635

1 2 3 4 5 6 7 8 9 10 11

SOD11 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

GPX 1-Cys Prx

821 941

1 2 3 4 5 6 7 8 9 10 11 1 2 3 4 5 6 7 8 9 10 11

PER1

Trx-like

380

1 2 3 4 5 6 7 8 9 10 11

Trx-like

380

1 2 3 4 5 6 7 8 9 10 11

THO11 2 3 4 5 6 7 8 9 10 11

023 720

1 2 3 4 5 6 7 8 9 10 11

Rev TrTHO11 2 3 4 5 6 7 8 9 10 11

023 720

1 2 3 4 5 6 7 8 9 10 11

Rev Tr

921

1 2 3 4 5 6 7 8 9 10 11

PI

921

1 2 3 4 5 6 7 8 9 10 11

PI

034

1 2 3 4 5 6 7 8 9 10 11

108

1 2 3 4 5 6 7 8 9 10 11

CaM CRT

034

1 2 3 4 5 6 7 8 9 10 11

108

1 2 3 4 5 6 7 8 9 10 11

CaM CRT

Gene expression

Germination

Figure 5.1 A proposed scheme for the essential role of ROS and chaperone proteins in rapid

germination of chickpea under severe limitation of water availability. Only some of the

chaperone proteins identified and those with possible role in ROS and downstream pathways are

shown using their abundance profile. The level corresponding to dry seed, 48 and 72 hr

imbibition are shown in red, light blue and dark blue for cv Rupali, KH850 and KJ850,

respectively. The numbered spots relate to proteins listed in Table 4.3. The arrows in pink

represent signalling functions; those in blue show regulating functions and dotted arrow

indicates indirect signalling. LEAs, late embryogenesis abundant proteins; HSPs, heat shock

proteins; AS, Anthranilate synathase; SNARE, soluble N-ethylmaleimide-sensitive-factor

attachment protein receptor proteins; PEPCK, phosphoenolpyruvate carboxykinase; MD,

mitochondrial NAD-dependent malate dehydrogenase; SOD1, superoxide dismutase 1 (Cu/Zn);

SOD, superoxide dismutase (Cu/Zn); GPX, glutathione peroxidase; 1-Cys Prx, 1-Cys

peroxiredoxin; PER1, 1-Cys peroxiredoxin 1; CRT, calreticulin; CaM, Calmodulin; Trx-like,

thyroxin-like protein; Rev Tr, Reverse transcriptase; THO1, THO complex unit1; PI, Serine

proteinase inhibitor.

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dry seeds, may well reflect a changing role for ROS in germination and the need for

homeostasis (Bailly et al., 2008).

A central role for ROS and the associated regulation of ROS levels at the earliest stages

following imbibition and later as storage reserves are mobilised to provide new

materials for radicle emergence is depicted in the scheme of Figure 5.1. The scheme

provides a possible link between initial signalling events and the reactivation of

metabolism and concurrent gene expression. Thus the generation of ROS at appropriate

levels in response to the various cues that accompany or follow the ingress of water

might have been more rapid in cv Rupali compared to the other genotypes. While the

proteomic data do not provide direct evidence for this role as an explanation for the

more rapid germination trait they do offer a possible testable hypothesis.

5.4 Conclusions

This study established clear differences in the rate of germination among a small

number of chickpea genotypes and identified cv Rupali as the most able to germinate

rapidly under conditions of severely limited water supply. The proteomic analysis

employed showed that there were significant differences in abundance among the non

storage protein complement present in cv Rupali seeds following imbibition compared

to a genotype that germinated more slowly and one which imbibed but did not

germinate effectively. Among the apparently important proteins were members of the

LEA and HSP families, a group of enzymes involved in ROS metabolism and

homeostasis, proteins linked to the TCA cycle and the supply of C4 acids to the

synthetic functions of the cycle. While some proteins were expressed during late seed

development and stored until required for germination others were newly expressed

following imbibition. Thus the ‘rapid germination’ trait shown by cv Rupali under

severe water limitation could be accounted for by a number of integrated metabolic

events and pathways that together permit more rapid radicle emergence and elongation.

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REFERENCES

110

REFERENCES

Abbasi FM, Komatsu S (2004) A proteomic approach to analyze salt-responsive

proteins in rice leaf sheath. Proteomics 4: 2072-2081

Agrawal GK, Thelen JJ (2006) Large scale identification and quantitative profiling of

phosphoproteins expressed during seed filling in oilseed rape. Molecular and

Cellular Proteomics 5: 2044-2059

Ahsan N, Lee D, Lee S, Kang K, Lee J, Kim P, Yoon H, Kim J, Lee B (2007) Excess

copper induced physiological and proteomic changes in germinating rice seeds.

Chemosphere 67: 1182-1193

Ali GM, Komatsu S (2006) Proteomic analysis of rice leaf sheath during drought

stress. Journal of Proteome Research 5: 396-403

Alkhalfioui F, Renard M, Vensel WH, Wong JH, Tanaka CK, Hurkman WJ,

Buchanan BB, Montrichard F (2007) Thioredoxin-linked proteins are reduced

during germination of Medicago truncatula seeds. Plant Physiology 144: 1559-

1579

Almoguera C, Jordano J (1992) Developmental and environmental concurrent

expression of sunflower dry-seed-stored low-molecular-weight heat-shock

protein and Lea mRNAs. Plant Molecular Biology 19: 781-792

Alvarado V, Bradford KJ (2002) A hydrothermal time model explains the cardinal

temperatures for seed germination. Plant, Cell and Environment 25: 1061-1069

Alvarado V, Bradford KJ (2005) Hydrothermal time analysis of seed dormancy in

true (botanical) potato seeds. Seed Science Research 15: 77-88

Alvarez S, Marsh EL, Schroeder SG, Schachtman DP (2008) Metabolomic and

proteomic changes in the xylem sap of maize under drought. Plant, Cell and

Environment 31: 325-340

Page 131: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

111

Anich M, Fanta N, Mancilla M, Kettlun AM, Valenzuela MA, Traverso-Cori A

(1990) Apyrase activity and changes in metabolites during germination and

tuberization of Solanum tuberosum. Phytochemistry 29: 1411-1415

Aoki K, Kragler F, Xoconostle-Cázares B, Lucas WJ (2002) A subclass of plant heat

shock cognate 70 chaperones carries a motif that facilitates trafficking through

plasmodesmata. Proceedings of the National Academy of Sciences of the United

States of America 99: 16342-16347

Ashikari M, Matsuoka M (2006) Identification, isolation and pyramiding of

quantitative trait loci for rice breeding. Trends in Plant Science 11: 344-350

Avsian-Kretchmer O, Gueta-Dahan Y, Lev-Yadun S, Gollop R, Ben-Hayyim G

(2004) The salt-stress signal transduction pathway that activates the gpx1

promoter is mediated by intracellular H2O2, different from the pathway induced

by extracellular H2O2. Plant Physiology 135: 1685-1696

Bailly C (2004) Active oxygen species and antioxidants in seed biology. Seed Science

Research 14: 93-107

Bailly C, Benamar A, Corbineau F, Côme D (2000) Antioxidant systems in sunflower

(Helianthus annuus L.) seeds as affected by priming. Seed Science Research 10:

35-42

Bailly C, El-Maarouf-Bouteau H, Corbineau F (2008) From intracellular signaling

networks to cell death: the dual role of reactive oxygen species in seed

physiology. Comptes Rendus Biologies 331: 806-814

Basha E, Lee GJ, Breci LA, Hausrath AC, Buan NR, Giese KC, Vierling E (2004)

The identity of proteins associated with a small heat shock protein during heat

stress in vivo indicates that these chaperones protect a wide range of cellular

functions. Journal of Biological Chemistry 279: 7566-7575

Batisse J, Batisse C, Budd A, Böttcher B, Hurt E (2009) Purification of nuclear poly

(A)-binding protein Nab2 reveals association with the yeast transcriptome and a

messenger ribonucleoprotein core structure. Journal of Biological Chemistry

284: 34911-34917

Page 132: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

112

Bettey M, Finch-Savage WE (1998) Stress protein content of mature Brassica seeds

and their germination performance. Seed Science Research 8: 347-355

Bettey M, Finch-Savage WE, King GJ, Lynn JR (2000) Quantitative Genetic

Analysis of Seed Vigour and Pre-Emergence Seedling Growth Traits in Brassica

oleracea. New Phytologist 148: 277-286

Bewley JD (1997) Seed germination and dormancy. The Plant Cell 9: 1055-1066

Bewley JD, Black M (1994) Seeds: Physiology of Development and Germination.

Plenum Press, New York

Bhushan D, Pandey A, Chattopadhyay A, Choudhary MK, Chakraborty S, Datta

A, Chakraborty N (2006) Extracellular matrix proteome of chickpea (Cicer

arietinum L.) illustrates pathway abundance, novel protein functions and

evolutionary perspect. Journal of Proteome Research 5: 1711-1720

Bhushan D, Pandey A, Choudhary MK, Datta A, Chakraborty S, Chakraborty N

(2007) Comparative proteomics analysis of differentially expressed proteins in

chickpea extracellular matrix during dehydration stress. Molecular Cellular

Proteomics 6: 1868-1884

Biere A (1991) Parental effects in Lychnis flos-cuculi. I: Seed size, germination and

seedling performance in a controlled environment. Journal of Evolutionary

Biology 4: 447-465

Biesiadka J, Bujacz G, Sikorski MM, Jaskolski M (2002) Crystal structures of two

homologous pathogenesis-related proteins from yellow lupine. Journal of

Molecular Biology 319: 1223-1234

Bonilla I, El-Hamdaoui A, Bolaños L (2004) Boron and calcium increase Pisum

sativum seed germination and seedling development under salt stress. Plant and

Soil 267: 97-107

Bønsager BC, Finnie C, Roepstorff P, Svensson B (2007) Spatio-temporal changes in

germination and radical elongation of barley seeds tracked by proteome analysis

of dissected embryo, aleurone layer, and endosperm tissues. Proteomics 7:

4528-4540

Page 133: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

113

Borisjuk L, Walenta S, Weber H, Mueller Klieser W, Wobus U (1998) High

resolution histographical mapping of glucose concentrations in developing

cotyledons of Vicia faba in relation to mitotic activity and storage processes:

glucose as a possible developmental trigger. The Plant Journal 15: 583-591

Borisjuk LL, Rolletschek HH, Radchuk RR, Weschke WW, Wobus UU, Weber

HH (2004) Seed development and differentiation: a role for metabolic

regulation. Plant Biology 6: 375-386

Boucher V, Buitink J, Lin X, Boudet J, Hoekstra FA, Hundertmark M, Renard D,

Leprince O (2010) MtPM25 is an atypical hydrophobic late embryogenesis-

abundant protein that dissociates cold and desiccation-aggregated proteins.

Plant, Cell and Environment 33: 418-430

Boudet J, Buitink J, Hoekstra FA, Rogniaux H, Larre C, Satour P, Leprince O

(2006) Comparative analysis of the heat stable proteome of radicles of Medicago

truncatula seeds during germination identifies late embryogenesis abundant

proteins associated with desiccation tolerance. Plant Physiology 140: 1418-1436

Bradford KJ (1990) A water relations analysis of seed germination rates. Plant

Physiology 94: 840-849

Bradford KJ (2002) Applications of hydrothermal time to quantifying and modeling

seed germination and dormancy. Weed Science 50: 248-260

Bradford KJ (2005) Threshold models applied to seed germination ecology. New

Phytologist 165: 338-341

Buitink J, Leger JJ, Guisle I, Vu BL, Wuilleme S, Lamirault G, Bars AL, Meur

NL, Becker A, Kuster H, Leprince O (2006) Transcriptome profiling uncovers

metabolic and regulatory processes occurring during the transition from

desiccation-sensitive to desiccation-tolerant stages in Medicago truncatula

seeds. The Plant Journal 47: 735-750

Burstin J, Marget Pl, Huart M, Moessner A, Mangin B, Duchene C, Desprez B,

Munier-Jolain N, Duc G (2007) Developmental genes have pleiotropic effects

Page 134: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

114

on plant morphology and source capacity, eventually impacting on seed protein

content and productivity in pea. Plant Physiology 144: 768-781

Calcagno F, Gallo G (1993) Physiological and morphological basis of abiotic stress

resistance in chickpea. In KB Singh, MC Saxena, eds, Breeding For Stress

Tolerance In Cool-season Food Legumes. ICARDA and Sayce Publishing,

Chichester, UK, pp 293-309

Campos F, Zamudio F, Covarrubias AA (2006) Two different late embryogenesis

abundant proteins from Arabidopsis thaliana contain specific domains that

inhibit Escherichia coli growth. Biochemical and Biophysical Research

Communications 342: 406-413

Candiano G, Bruschi M, Musante L, Santucci L, Ghiggeri GM, Carnemolla B,

Orecchia P, Zardi L, Righetti PG (2004) Blue silver: a very sensitive colloidal

Coomassie G-250 staining for proteome analysis. Electrophoresis 25: 1327-1333

Cánovas FM, Dumas-Gaudot E, Recorbet G, Jorrin J, Mock HP, Rossignol M

(2004) Plant proteome analysis. Proteomics 4: 285–298

Castillo J, Rodrigo MI, Márquez JA, Zúñiga Á, Franco L (2000) A pea nuclear

protein that is induced by dehydration belongs to the vicilin superfamily.

European Journal of Biochemistry 267: 2156-2165

Catusse J, Job C, Job D (2008a) Transcriptome- and proteome-wide analyses of seed

germination. Comptes Rendus Biologies 331: 815-822

Catusse J, Rajjou L, Job C, Job D (2008b) Proteome of seed development and

germination. In GK Agrawal, R Rakwal, eds, Plant Proteomics: Technologies,

Strategies, and Applications, John Wiley and Sons, Hoboken, New Jersey. John

Wiley and sons, New Jersey, USA pp 191–205

Catusse J, Strub JM, Job C, Van Dorsselaer A, Job D (2008c) Proteome-wide

characterization of sugarbeet seed vigor and its tissue specific expression.

Proceedings of the National Academy of Sciences 105: 10262

Cernac A, Andre C, Hoffmann-Benning S, Benning C (2006) WRI1 is required for

seed germination and seedling establishment. Plant Physiology 141: 745-757

Page 135: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

115

Cervantes E, Rodríguez A, Nicolás G (1994) Ethylene regulates the expression of a

cysteine proteinase gene during germination of chickpea (Cicer arietinum L.).

Plant Molecular Biology 25: 207-215

Chandra Babu R, Zhang J, Blum A, David Ho TH, Wu R, Nguyen HT (2004)

HVA1, a LEA gene from barley confers dehydration tolerance in transgenic rice

(Oryza sativa L.) via cell membrane protection. Plant Science 166: 855-862

Charles MT, Dominique R, Kumar J, Dangi OP (2002) A preliminary study of the

functional properties of chickpea leaves. In Annual Meeting of the Canadian

Society of Food and Nutrition,May 2002, Edmonton,, Alberta, Canada

Chávez S, Aguilera A (1997) The yeast HPR1 gene has a functional role in

transcriptional elongation that uncovers a novel source of genome instability.

Genes and Development 11: 3459-3470

Chen F, Bradford KJ (2000) Expression of an expansin is associated with endosperm

weakening during tomato seed germination. Plant Physiology 124: 1265-1274

Cheng Z, Targolli J, Huang X, Wu R (2002) Wheat LEA genes, PMA80 and

PMA1959, enhance dehydration tolerance of transgenic rice (Oryza sativa L.).

Molecular Breeding 10: 71-82

Chibani K, Ali-Rachedi S, Job C, Job D, Jullien M, Grappin P (2006) Proteomic

analysis of seed dormancy in Arabidopsis. Plant Physiology 142: 1493-1510

Chopin F, Orsel M, Dorbe M-F, Chardon F, Truong H-N, Miller AJ, Krapp A,

Daniel-Vedele F (2007) The Arabidopsis ATNRT2.7 nitrate transporter controls

nitrate content in seeds. The Plant Cell 19: 1590-1602

Clarke HJ, Khan TN, Siddique KHM (2004) Pollen selection for chilling tolerance at

hybridisation leads to improved chickpea cultivars. Euphytica 139: 65-74

Clemente A, Domoney C (2006) Biological significance of polymorphism in legume

protease inhibitors from the Bowman-Birk family. Current Protein and Peptide

Science 7: 201-216

Page 136: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

116

Clerkx EJM, El-Lithy ME, Vierling E, Ruys GJ, Blankestijin-de Vries H (2004)

Analysis of natural allelic variation of Arabidopsis seed germination and seed

longevity traits between the accessions Landsberg erecta and Shakdara, using a

new recombinant inbred line population. Plant Physiology 135: 432-443

Collins NC, Thordal-Christensen H, Lipka V, Bau S, Kombrink E, Qiu J,

Huckelhoven R, Stein M, Freialdenhoven A, Somerville SC, Schulze-Lefert

P (2003) SNARE-protein-mediated disease resistance at the plant cell wall.

Nature 425: 973-977

Comai L, Harada JJ (1990) Transcriptional activities in dry seed nuclei indicate the

timing of the transition from embryogeny to germination. Proceedings of the

National Academy of Sciences of the United States of America 87: 2671-2674

Crowe JH, Crowe LM (1992) Membrane integrity in anhydrobiotic organisms: toward

a mechanism for stabilizing dry cells. In GN Somero, CB Osmond, CL Bolis,

eds, Water and life: comparative analysis of water relationships at the

organismic, cellular, and molecular levels. Springer-Verlag Berlin, pp 87-103

Dahiya OS, Tomer RPS, Kumar A (1997) Evaluation of viability and vigour

parameters with respect to field emergence in chickpea. Seed Research 25:

19-24

Dalal M, Tayal D, Chinnusamy V, Bansal KC (2009) Abiotic stress and ABA-

inducible group 4 LEA from Brassica napus plays a key role in salt and drought

tolerance. Journal of Biotechnology 139: 137-145

Dani V, Simon WJ, Duranti M, Croy RRD (2005) Changes in the tobacco leaf

apoplast proteome in response to salt stress. Proteomics 5: 737-745

Daws MI, Crabtree LM, Dalling JW, Mullins CE, Burslem DFRP (2008)

Germination responses to water potential in neotropical pioneers suggest large-

seeded species take more risks. Annals of Botany 102: 945-951

De Los Reyes BG, McGrath JM (2003) Cultivar-specific seedling vigor and

expression of a putative oxalate oxidase germin-like protein in sugar beet (Beta

vulgaris L.). Theoretical and Applied Genetics 107: 54-61

Page 137: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

117

Desikan R, A-H-Mackerness S, Hancock JT, Neill SJ (2001) Regulation of the

Arabidopsis transcriptome by oxidative stress. Plant Physiology 127: 159-172

Desimone M, Henke A, Wagner E (1996) Oxidative stress induces partial degradation

of the large subunit of ribulose-1, 5-bisphosphate carboxylase/oxygenase in

isolated chloroplasts of barley. Plant Physiology 111: 789-796

Dietz KJ (2003) Plant peroxiredoxins. Annual Review of Plant Biology 54: 93-107

Dixon RA (2001) Natural products and plant disease resistance. Nature 411: 843-847

Djemel N, Guedon D, Lechevalier A, Salon C, Miquel M, Prosperi JM, Rochat C,

Boutin JP (2005) Development and composition of the seeds of nine genotypes

of the Medicago truncatula species complex. Plant Physiology and

Biochemistry 43: 557-566

Dooki AD, Mayer-Posner FJ, Askari H, Zaiee A, Salekdeh GH (2006) Proteomic

responses of rice young panicles to salinity. Proteomics 6: 6498-6507

Dubos C, Plomion C (2001) Drought differentially affects expression of a PR-10

protein, in needles of maritime pine (Pinus pinaster Ait.) seedlings. Journal of

Experimental Botany 52: 1143-1144

Dunwell JM, Khuri S, Gane PJ (2000) Microbial relatives of the seed storage proteins

of higher plants: conservation of structure and diversification of function during

evolution of the cupin superfamily. Microbiology and Molecular Biology

Reviews 64: 153-179

Dunwell JM, Purvis A, Khuri S (2004) Cupins: the most functionally diverse protein

superfamily? Phytochemistry 65: 7-17

Dure L, Waters L (1965) Long-lived messenger RNA: evidence from cotton seed

germination. Science 147: 410-412

Egli DB (2005) Air temperature during seed filling and soybean seed germination and

vigor. Crop Science 45: 1329

Page 138: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

118

El-Maarouf-Bouteau H, Bailly C (2008) Oxidative signalling in seed germination and

dormancy. Plant Signaling and Behavior 3: 175-182

Elkoca E, Haliloglu K, Esitken A, Ercisli S (2007) Hydro- and osmopriming improve

chickpea germination. Acta Agriculturae Scandinavica: Section B, Soil and

Plant Science 57: 193-200

Eriksson O (1999) Seed size variation and its effect on germination and seedling

performance in the clonal herb Convallaria majalis. Acta Oecologica 20: 61-66

Etherington J, Evans C (1986) Technique for ecological studies of seed germination in

relation to soil water potential. Plant and Soil 95: 285-288

Evans CE, Etherington JR (1990) The effect of soil water potential on seed

germination of some British plants. New Phytologist 115: 539-548

Fait A, Angelovici R, Less H, Ohad I, Urbanczyk-Wochniak E, Fernie AR, Galili G

(2006) Arabidopsis seed development and germination is associated with

temporally distinct metabolic switches. Plant Physiology 142: 839-854

FAO (2010) FAOSTAT. http://www.fao.org/statistics/countrystat/

Faurobert M, Mihr C, Bertin N, Pawlowski T, Negroni L, Sommerer N, Causse M

(2007) Major proteome variations associated with cherry tomato pericarp

development and ripening. Plant Physiology 143: 1327-1346

Fieuw S, Patrick JW (1993) Mechanism of photosynthate efflux from Vicia faba L.

seed coats. Journal of Experimental Botany 44: 65-74

Finch-Savage WE, Côme D, Lynn JR, Corbineau F (2005) Sensitivity of Brassica

oleracea seed germination to hypoxia: A QTL analysis. Plant Science 169:

753-759

Finch-Savage WE, Leubner-Metzger G (2006) Seed dormancy and the control of

germination. New Phytologist 171: 501-523

Page 139: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

119

Finch-Savage WE, Phelps K (1993) Onion (Allium cepa L.) seedling emergence

patterns can be explained by the influence of soil temperature and water

potential on seed germination. Journal Experimental Botany 44: 407-414

Finch-Savage WE, Rowse HR, Dent KC (2005) Development of combined imbibition

and hydrothermal threshold models to simulate maize (Zea mays) and chickpea

(Cicer arietinum) seed germination in variable environments. New Phytologist

165: 825-837

Finnie C, Bak-Jensen KS, Laugesen S, Roepstorff P, Svensson B (2006) Differential

appearance of isoforms and cultivar variation in protein temporal profiles

revealed in the maturing barley grain proteome. Plant Science 170: 808-821

Frey M, Chomet P, Glawischnig E, Stettner C, Grun S, Winklmair A, Eisenreich

W, Bacher A, Meeley RB, Briggs SP (1997) Analysis of a chemical plant

defense mechanism in grasses. Science 277: 696-699

Gallardo K, Job C, Groot S, Puype M, Demol H, Vandekerckhove J, Job D (2002a)

Proteomics of Arabidopsis seed germination. A comparative study of wild-type

and gibberellin-deficient seeds. Plant Physiology 129: 823–837

Gallardo K, Job C, Groot SPC, Puype M, Demol H, Vandekerckhove J, Job D

(2001) Proteomic analysis of Arabidopsis seed germination and priming. Plant

Physiology 126: 835-848

Gallardo K, Job C, Groot SPC, Puype M, Demol H, Vandekerckhove Jl, Job D

(2002b) Importance of methionine biosynthesis for Arabidopsis seed

germination and seedling growth. Physiologia Plantarum 116: 238-247

Gallardo K, Le Signor C, Vandekerckhove J, Thompson RD, Burstin J (2003)

Proteomics of Medicago truncatula seed development establishes the time frame

of diverse metabolic processes related to reserve accumulation. Plant Physiology

133: 664-682

Gallardo K, Thompson R, Burstin J (2008) Reserve accumulation in legume seeds.

Comptes Rendus Biologies 331: 755-762

Page 140: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

120

Gan YT, Miller PR, McDonald CL (2003) Response of kabuli chickpea to seed size

and planting depth. Canadian Journal of Plant Science 83: 39-46

Gatehouse JA, Croy RRD, Boulter D, Shewry PR (1984) The synthesis and structure

of pea storage proteins. Critical Reviews in Plant Sciences 1: 287-314

Gatehouse JA, Croy RRD, Morton H, Tyler M, Boulter D (1981) Characterisation

and subunit structures of the vicilin storage proteins of pea (Pisum sativum L.).

European Journal of Biochemistry 118: 627-633

Gazanchian A, Khosh Kholgh Sima NA, Malboobi MA, Majidi Heravan E (2006)

Relationships between emergence and soil water content for perennial cool-

season grasses native to Iran. Crop Science 46: 544-553

Gil J, Nadal S, Luna D, Moreno MT, De Haro A (1996) Variability of some physico-

chemical characters in Desi and Kabuli chickpea types. Journal of the Science of

Food and Agriculture 71: 179-184

Ginsburg V (1978) Comparative biochemistry of nucleotide-linked sugars. Progress in

Clinical and Biological Research 23: 595-600

Goggin DE, Powles SB, Steadman KJ (2010) Selection for low or high primary

dormancy in Lolium rigidum Gaud seeds results in constitutive differences in

stress protein expression and peroxidase activity. Journal of Experimental

Botany: Online published

Goldberg RB, DePaiva G, Yadegari R (1994) Plant embryogenesis: zygote to seed.

Science 266: 605-614

Golombek S, Heim U, Horstmann C, Wobus U, Weber H (1999)

Phosphoenolpyruvate carboxylase in developing seeds of Vicia faba L.: gene

expression and metabolic regulation. Planta 208: 66-72

Golombek S, Rolletschek H, Wobus U, Weber H (2001) Control of storage protein

accumulation during legume seed development. Journal of Plant Physiology

158: 457-464

Page 141: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

121

Goyal K, Walton LJ, Tunnacliffe A (2005) LEA proteins prevent protein aggregation

due to water stress. Biochemical Journal 388: 151-157

Graham IA (2008) Seed storage oil mobilization. Annual Review of Plant Biology 59:

115-142

Grelet J, Benamar A, Teyssier E, Avelange-Macherel MH, Grunwald D, Macherel

D (2005) Identification in pea seed mitochondria of a late-embryogenesis

abundant protein able to protect enzymes from drying. Plant Physiology 137:

157-167

Gueta-Dahan Y, Yaniv Z, Zilinskas BA, Ben-Hayyim G (1997) Salt and oxidative

stress: similar and specific responses and their relation to salt tolerance in Citrus.

Planta 203: 460-469

Gummerson RJ (1986) The Effect of Constant Temperatures and Osmotic Potentials

on the Germination of Sugar Beet. Journal of Experimental Botany 37: 729-741

Gygi SP, Rochon Y, Franza BR, Aebersold R (1999) Correlation between protein and

mRNA abundance in yeast. Molecular and Cellular Biology 19: 1720-1730

Hajduch MM, Rakwal RR, Agrawal GGK, Yonekura MM, Pretova AA (2001)

High-resolution two-dimensional electrophoresis separation of proteins from

metal-stressed rice (Oryza sativa L.) leaves: drastic reductions/fragmentation of

ribulose-1,5-bisphosphate carboxylase/oxygenase and induction of stress-related

proteins. Electrophoresis 22: 2824-2831

Hajheidari M, Abdollahian-Noghabi M, Askari H, Heidari M, Sadeghian SY, Ober

ES, Salekdeh GH (2005) Proteome analysis of sugar beet leaves under drought

stress. Proteomics 5: 950-960

Hajheidari M, Eivazi A, Buchanan BB, Wong JH, Majidi I, Salekdeh GH (2007)

Proteomics uncovers a role for redox in drought tolerance in wheat. Journal of

Proteome Research 6: 1451-1460

Hamada T, Igarashi H, Taguchi R, Fujiwara M, Fukao Y, Shimmen T, Yokota E,

Sonobe S (2009) The putative RNA-processing protein, THO2, is a

Page 142: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

122

microtubule-associated protein in tobacco. Plant and Cell Physiology 50:

801-811

Han W, Christen P (2004) cis-Effect of DnaJ on DnaK in ternary complexes with

chimeric DnaK/DnaJ-binding peptides. FEBS Letters 563: 146-150

Hanley ME (2007) Seed size and seedling growth: differential response of Australian

and British Fabaceae to nutrient limitation. The New phytologist 174: 381

Hanley ME, Cordier PK, May O, Kelly CK (2007) Seed size and seedling growth:

differential response of Australian and British Fabaceae to nutrient limitation.

New Phytologist 174: 381-388

Hanley ME, Fegan EL (2007) Timing of cotyledon damage affects growth and

flowering in mature plants. Plant, Cell and Environment 30: 812-819

Hanley ME, May OC (2006) Cotyledon damage at the seedling stage affects growth

and flowering potential in mature plants. New Phytologist 169: 243-250

Hara M, Terashima S, Fukaya T, Kuboi T (2003) Enhancement of cold tolerance and

inhibition of lipid peroxidation by citrus dehydrin in transgenic tobacco. Planta

217: 290-298

Haslekas C, Viken MK, Grini PE, Nygaard V, Nordgard SH (2003) Seed 1-cysteine

peroxiredoxin antioxidants are not involved in dormancy, but contribute to

inhibition of germination during stress. Plant Physiology 133: 1148-1157

Hedley CL, Harvey DM, Keely RJ (1975) Role of PEP carboxylase during seed

development in Pisum sativum. Nature 258: 352-354

Hegeman CE, Grabau EA (2001) A novel phytase with sequence similarity to purple

acid phosphatases is expressed in cotyledons of germinating soybean seedlings.

Plant Physiology 126: 1598-1608

Hendrix SD, Nielsen E, Nielsen T, Schutt M (1991) Are seedlings from small seeds

always inferior to seedlings from large seeds? Effects of seed biomass on

seedling growth in Pastinaca sativa L. New Phytologist: 299-305

Page 143: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

123

Hernandez-Nistal J, Labrador E, Martin I, Jimenez T, Dopico B (2006)

Transcriptional profiling of cell wall protein genes in chickpea embryonic axes

during germination and growth. Plant Physiology and Biochemistry 44: 684-692

Hernández-Nistal J, Martín I, Jiménez T, Dopico B, Labrador E (2009) Two cell

wall Kunitz trypsin inhibitors in chickpea during seed germination and seedling

growth. Plant Physiology and Biochemistry 47: 181-187

Hoekstra FA, Golovina EA, Buitink J (2001) Mechanisms of plant desiccation

tolerance. Trends in Plant Science 6: 431-438

Holdsworth MJ, Finch-Savage WE, Grappin P, Job D (2008) Post-genomics

dissection of seed dormancy and germination. Trends in Plant Science 13: 7-13

Hosseini NM, Palta JA, Berger JD, Siddique KHM (2009) Sowing soil water content

effects on chickpea (Cicer arietinum L.): seedling emergence and early growth

interaction with genotype and seed size Agricultural Water Management 96:

1732–1736

Hu P, Meng Y, Wise RP (2009) Functional contribution of chorismate synthase,

anthranilate synthase, and chorismate mutase to penetration resistance in barley-

powdery mildew interactions. Molecular Plant-Microbe Interactions 22: 311-320

Hu X, Jiang M, Zhang J, Zhang A, Lin F, Tan M (2007) Calcium–calmodulin is

required for abscisic acid-induced antioxidant defense and functions both

upstream and downstream of H2O2 production in leaves of maize (Zea mays)

plants. New Phytologist 173: 27-38

Huarte R, Benech-Arnold RL (2005) Incubation under fluctuating temperatures

reduces mean base water potential for seed germination in several non-cultivated

species. Seed Science Research 15: 89-97

Hynek R, Svensson B, Jensen ON, Barkholt V, Finnie C (2009) The plasma

membrane proteome of germinating barley embryos. Proteomics 9: 3787-3794

Ibrikci H, Knewtson SJB, Grusak MA (2003) Chickpea leaves as a vegetable green

for humans: evaluation of mineral composition. Journal of the Science of Food

and Agriculture 83: 945-950

Page 144: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

124

Ishibashi N, Yamauchi D, Minamikawa T (1990) Stored mRNA in cotyledons of

Vigna unguiculata seeds: nucleotide sequence of cloned cDNA for a stored

mRNA and induction of its synthesis by precocious germination. Plant

Molecular Biology 15: 59-64

Ishihara A, Asada Y, Takahashi Y, Yabe N, Komeda Y, Nishioka T, Miyagawa H,

Wakasa K (2006) Metabolic changes in Arabidopsis thaliana expressing the

feedback-resistant anthranilate synthase [alpha] subunit gene OASA1D.

Phytochemistry 67: 2349-2362

Jia XY, Xu CY, Jing RL, Li RZ, Mao XG, Wang JP, Chang XP (2008) Molecular

cloning and characterization of wheat calreticulin (CRT) gene involved in

drought-stressed responses. Journal of Experimental Botany 59: 739-751

Job C, Rajjou L, Lovigny Y, Belghazi M, Job D (2005) Patterns of protein oxidation

in Arabidopsis seeds and during germination. Plant Physiology 138: 790-802

Kav NNV, Srivastava S, Goonewardene L, Blade SF (2004) Proteome-level changes

in the roots of Pisum sativum in response to salinity. Annals of Applied Biology

145: 217-230

Kaya M, Kaya G, Kaya M, Atak M, Saglam S, Khawar K, Ciftci C (2008)

Interaction between seed size and NaCl on germination and early seedling

growth of some Turkish cultivars of chickpea ( Cicer arietinum L.). Journal of

Zhejiang University - Science 9: 371-377

Kaydan D (2008) Germination, seedling growth and relative water content of shoot in

different seed sizes of triticale under osmotic stress of water and NaCl. African

Journal of Biotechnology 7: 2862

Kaydan D, Yagmur M (2008) Germination, seedling growth and relative water content

of shoot in different seed sizes of triticale under osmotic stress of water and

NaCl. African Journal of Biotechnology 7: 2862-2868

Kelly SM, Corbett AH (2009) Messenger RNA export from the nucleus: a series of

molecular wardrobe changes. Traffic 10: 1199-1208

Page 145: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

125

Kikuzawa K, Koyama H (1999) Scaling of soil water absorption by seeds: an

experiment using seed analogues. Seed Science Research 9: 171-178

Kitano H (2002) Systems biology: a brief overview. Science 295: 1662-1664

Kranner I, Grill D (1996) Significance of thiol-disulfide exchange in resting stages of

plant development. Botanica Acta 109: 8-14

Krishnan HB, Oehrle NW, Natarajan SS, Usda ARS (2009) A rapid and simple

procedure for the depletion of abundant storage proteins from legume seeds to

advance proteome analysis: A case study using Glycine max. Proteomics 9:

3174–3188

Kuiper PJC (1998) Adaptation mechanisms of green plants to environmental stress:

The role of plant sterols and the phosphatidyl linolenoyl cascade in the

functioning of plants and the response of plants to global climate change. In P

Csermely, ed, Stress of Life: From Molecules to Man, Vol 851. Annals of the

New York Academy of SciencesNew York, pp 209-215

Laksanalamai P, Robb FT (2004) Small heat shock proteins from extremophiles: a

review. Extremophiles 8: 1-11

Lan Y, Cai D, Zheng YZ (2005) Expression in Escherichia coli of three different

soybean late embryogenesis abundant (LEA) genes to investigate enhanced

stress tolerance. Journal of Integrative Plant Biology 47: 613-621

Lanfermeijer FC, van Oene MA, Borstlap AC (1992) Compartmental analysis of

amino-acid release from attached and detached pea seed coats. Planta 187: 75-82

Laux T, Wurschum T, Breuninger H (2004) Genetic regulation of embryonic pattern

formation. The Plant Cell Online 16: S190-S202

Le BH, Wagmaister JA, Kawashima T, Bui AQ, Harada JJ, Goldberg RB (2007)

Using genomics to study legume seed development. Plant Physiology. 144:

562-574

Page 146: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

126

Leishman MR, Westoby M (1994) The Role of Seed Size in Seedling Establishment in

Dry Soil Conditions -- Experimental Evidence from Semi-Arid Species. Journal

of Ecology 82: 249-258

Leport L, Turner NC, French RJ, Barr MD, Duda R, Davies SL, Tennant D,

Siddique KHM (1999) Physiological responses of chickpea genotypes to

terminal drought in a Mediterranean-type environment European Journal of

Agronomy 11: 279-291

Lingner J, Hughes TR, Shevchenko A, Mann M, Lundblad V, Cech TR (1997)

Reverse transcriptase motifs in the catalytic subunit of telomerase. Science 276:

561-567

Loha A, Tigabu M, Fries A (2009) Genetic variation among and within populations of

Cordia africana in seed size and germination responses to constant temperatures.

Euphytica 165: 189-196

López-Torrejón G, Salcedo G, Martín-Esteban M, Díaz-Perales A, Pascual CY,

Sánchez-Monge R (2003) Len c 1, a major allergen and vicilin from lentil

seeds: protein isolation and cDNA cloning. Journal of Allergy and Clinical

Immunology 112: 1208-1215

Magni C, Scarafoni A, Herndl A, Sessa F, Prinsi B, Espen L, Duranti M (2007)

Combined 2D electrophoretic approaches for the study of white lupin mature

seed storage proteome. Phytochemistry 68: 997-1007

Malone S, Chen ZH, Bahrami AR, Walker RP, Gray JE, Leegood RC (2007)

Phosphoenolpyruvate carboxykinase in Arabidopsis: changes in gene

expression, protein and activity during vegetative and reproductive development.

Plant and Cell Physiology 48: 441-450

Manfre AJ, Lanni LM, Marcotte Jr WR (2006) The Arabidopsis group 1 late

embryogenesis abundant protein ATEM6 is required for normal seed

development. Plant Physiology 140: 140-149

Mangat BS (1979) The effect of Diuron on the soluble nucleotide pool and growth of

bean and corn seedlings. Pesticide Biochemistry and Physiology 10: 251-258

Page 147: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

127

Marshall DL (1986) Effect of seed size on seedling success in three species of Sesbania

(Fabaceae). American Journal of Botany 73: 457-464

Marx C, Wong JH, Buchanan BB (2003) Thioredoxin and germinating barley: targets

and protein redox changes. Planta 216: 454-460

McIntosh GH, Topping DL ( 2000) Food legumes in human nutrition. In R Knight, ed,

Linking Research and Marketing Opportunities for Pulses in the 21st Century.

Kluwer Academic Publishers, Dordrecht, The Netherlands, pp 655–666

Miao Y, Lv D, Wang P, Wang XC, Chen J (2006) An Arabidopsis glutathione

peroxidase functions as both a redox transducer and a scavenger in abscisic acid

and drought stress responses. The Plant Cell 18: 2749-2766

Michalak M, Corbett EF, Mesaeli N, Nakamura K, Opas M (1999) Calreticulin: one

protein, one gene, many functions. Biochemical Journal 344: 281-292

Millan T, Clarke HJ, Siddique KHM, Buhariwalla HK, Gaur PM, Kumar J, Gil J,

Kahl G, Winter P (2006) Chickpea molecular breeding: New tools and

concepts. Euphytica 147: 81-103

Miller SS, Bowman LA, Gijzen M, Miki BLA (1999) Early development of the seed

coat of soybean (Glycine max). Annals of Botany 84: 297-304

Miranda M, Borisjuk L, Tewes A, Dietrich D, Rentsch D, Weber H, Wobus U

(2003) Peptide and amino acid transporters are differentially regulated during

seed development and germination in faba bean. Plant Physiology 132: 1950-

1960

Miranda M, Borisjuk L, Tewes A, Heim U, Sauer N, Wobus U, Weber H (2001)

Amino acid permeases in developing seeds of Vicia faba L.: expression precedes

storage protein synthesis and is regulated by amino acid supply. The Plant

Journal 28: 61-71

Mittler R (2002) Oxidative stress, antioxidants and stress tolerance. Trends in Plant

Science 7: 405-410

Page 148: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

128

Moïse JA, Han S, Gudynait -Savitch L, Johnson DA, Miki BLA (2005) Seed coats:

structure, development, composition, and biotechnology. In Vitro Cellular and

Developmental Biology-Plant 41: 620-644

Møller IM (2003) Plant mitochondria and oxidative stress: electron transport, NADPH

turnover, and metabolism of reactive oxygen species. Annual Review of Plant

Physiology and Plant Molecular Biology 52: 561-591

Morgante M, Salamini F (2003) From plant genomics to breeding practice. Current

Opinion in Biotechnology 14: 214-219

Morohashi Y, Shimokoriyama M (1972) Physiological studies on germination of

Phaseolus mungo seeds: II. Glucose and organic-acid matabolisms in the early

phases of germination. Journal of Experimental Botany 23: 54-61

Muller K, Job C, Belghazi M, Job D, Leubner-Metzger G (2010) Proteomics reveal

tissue-specific features of the cress (Lepidium sativum L.) endosperm cap

proteome and its hormone-induced changes during seed germination. Proteomics

10: 1-11

Naegle ER, Burton JW, Carter TE, Rufty TW (2005) Influence of seed nitrogen

content on seedling growth and recovery from nitrogen stress. Plant and Soil

271: 329-340

Nakabayashi K, Okamoto M, Koshiba T, Kamiya Y, Nambara E (2005) Genome

wide profiling of stored mRNA in Arabidopsis thaliana seed germination:

epigenetic and genetic regulation of transcription in seed. The Plant Journal 41:

697-709

Nakamura H (2005) Thioredoxin and its related molecules: update 2005. Antioxidants

and Redox Signalling 7: 823-828

Nakamura K, Zuppini A, Arnaudeau S, Lynch J, Ahsan I, Krause R, Papp S, De

Smedt H, Parys JB, Müller-Esterl W (2001) Functional specialization of

calreticulin domains. The Journal of Cell Biology 154: 961-972

Natarajan SS, Xu C, Bae H, Caperna TJ, Garrett WM (2006) Characterization of

storage proteins in wild (Glycine soja) and cultivated (Glycine max) soybean

Page 149: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

129

seeds using proteomic analysis. Journal of Agricultural and Food Chemistry 54:

3114-3120

Navrot N, Collin V, Gualberto J, Gelhaye E, Hirasawa M, Rey P, Knaff DB,

Issakidis E, Jacquot J, Rouhier N (2006) Plant glutathione peroxidases are

functional peroxiredoxins distributed in several subcellular compartments and

regulated during biotic and abiotic stresses. Plant Physiology 142: 1364-1379

Ni B, Bradford KJ (1992) Quantitative models characterizing seed germination

responses to Abscisic acid and osmoticum. Plant Physiology. 98: 1057-1068

Offler CE, Patrick JW (1993) Pathway of photosynthate transfer in the developing

seed of Vicia faba L: a structural assessment of the role of transfer cells in

unloading from the seed coat. Journal of Experimental Botany 44: 711-724

Olvera-Carrillo Y, Campos F, Reyes JL, Garciarrubio A, Covarrubias AA (2010)

Functional analysis of the group 4 late embryogenesis abundant proteins reveals

their relevance in the adaptive response during water deficit in Arabidopsis.

Plant Physiology 154: 373-390

Oracz K, Bouteau HEM, Farrant JM, Cooper K, Belghazi M, Job C, Job D,

Corbineau F, Bailly C (2007) ROS production and protein oxidation as a novel

mechanism for seed dormancy alleviation. The Plant Journal 50: 452-465

Oracz K, El-Maarouf-Bouteau H, Kranner I, Bogatek R, Corbineau F, Bailly C

(2009) The mechanisms involved in seed dormancy alleviation by hydrogen

cyanide unravel the role of reactive oxygen species as key factors of cellular

signaling during germination. Plant Physiology 150: 494-505

Ozturk L, Yazici MA, Yucel C, Torun A, Cekic C, Bagci A, Ozkan H, Braun HJ,

Sayers Z, Cakmak I (2006) Concentration and localization of zinc during seed

development and germination in wheat. Physiologia Plantarum 128: 144-152

Pandey A, Chakraborty S, Datta A, Chakraborty N (2008) Proteomics approach to

identify dehydration responsive nuclear proteins from chickpea (Cicer arietinum

L.). Molecular and Cellular Proteomics 7: 88-107

Page 150: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

130

Pandey A, Choudhary MK, Bhushan D, Chattopadhyay A, Chakraborty S, Datta

A, Chakraborty N (2006) The nuclear proteome of chickpea (Cicer arietinum

L.) reveals predicted and unexpected proteins. Journal of Proteome Research 5:

3301-3311

Pandey A, Mann M (2000) Proteomics to study genes and genomes. Nature 405:

837-846

Panjabi-Sabharwal V, Karan R, Khan T, Pareek A (2009) Abiotic Stress Responses:

Complexities in Gene Expression. In A Pareek, SK Sopory, HJ Bohnert, eds,

Abiotic Stress Adaptation in Plants. Springer Netherlands, pp 177-198

Park CJ, Kim KJ, Shin R, Park JM, Shin YC, Paek KH (2004) Pathogenesis-related

protein 10 isolated from hot pepper functions as a ribonuclease in an antiviral

pathway. The Plant Journal 37: 186-198

Penfield S, Rylott EL, Gilday AD, Graham S, Larson TR, Graham IA (2004)

Reserve mobilization in the Arabidopsis endosperm fuels hypocotyl elongation

in the dark, is independent of abscisic acid, and requires phosphoenolpyruvate

carboxykinase 1. The Plant Cell Online 16: 2705-2718

Persson S, Rosenquist M, Svensson K, Galvao R, Boss WF, Sommarin M (2003)

Phylogenetic analyses and expression studies reveal two distinct groups of

calreticulin isoforms in higher plants. Plant Physiology 133: 1385-1396

Peterson GL (1983) Determination of total protein. Methods in Enzymology 91:

95-115

Peumans WJ, Caers LI, Carlier AR (1979) Some aspects of the synthesis of long-

lived messenger ribonucleoproteins in developing rye embryos. Planta 144:

485-490

Phil A (2003) When and how many? Hydrothermal models and the prediction of seed

germination. New Phytologist 158: 1-3

Pieslinger AM, Hoepflinger MC, Tenhaken R (2010) Cloning of Glucuronokinase

from Arabidopsis thaliana, the Last Missing Enzyme of the myo-Inositol

Page 151: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

131

Oxygenase Pathway to Nucleotide Sugars. Journal of Biological Chemistry 285:

2902-2910

Pinheiro C, Kehr J, Ricardo CP (2005) Effect of water stress on lupin stem protein

analysed by two-dimensional gel electrophoresis. Planta 221: 716-728

Prashanth SR, Sadhasivam V, Parida A (2008) Over expression of cytosolic

copper/zinc superoxide dismutase from a mangrove plant Avicennia marina in

indica rice var Pusa Basmati-1 confers abiotic stress tolerance. Transgenic

Research 17: 281-291

Pritchard SL (2002) Germination and storage reserve mobilization are regulated

independently in Arabidopsis. The Plant Journal 31: 639

Puhakainen T, Hess MW, Mäkelä P, Svensson J, Heino P, Palva ET (2004)

Overexpression of multiple dehydrin genes enhances tolerance to freezing stress

in Arabidopsis. Plant Molecular Biology 54: 743-753

Radchuk R, Radchuk V, Götz KP, Weichert H, Richter A, Emery RJ, Weschke W,

Weber H (2007) Ectopic expression of phosphoenolpyruvate carboxylase in

Vicia narbonensis seeds: effects of improved nutrient status on seed maturation

and transcriptional regulatory networks. The Plant Journal 51: 819-839

Rajan VB, D’Silva P (2009) Arabidopsis thaliana J-class heat shock proteins: cellular

stress sensors. Functional and Integrative Genomics 9: 433-446

Rajjou L, Belghazi M, Huguet R, Robin C, Moreau A, Job C, Job D (2006)

Proteomic investigation of the effect of salicylic acid on Arabidopsis seed

germination and establishment of early defense mechanisms. Plant Physiology.

141: 910-923

Rajjou L, Gallardo K, Debeaujon I, Vandekerckhove J, Job C, Job D (2004) The

effect of alpha-Amanitin on the Arabidopsis seed proteome highlights the

distinct roles of stored and neosynthesized mRNAs during germination. Plant

Physiology 134: 1598-1613

Ramos J, Matamoros MA, Naya L, James EK, Rouhier N, Sato S, Tabata S,

Becana M (2009) The glutathione peroxidase gene family of Lotus japonicus:

Page 152: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

132

characterization of genomic clones, expression analyses and immunolocalization

in legumes. New Phytologist 181: 103-114

Ren ZH, Gao JP, Li LG, Cai XL, Huang W, Chao DY, Zhu MZ, Wang ZY, Luan

S, Lin HX (2005) A rice quantitative trait locus for salt tolerance encodes a

sodium transporter. Nature Genetics 37: 1141-1146

Rengel Z, Graham RD (1995) Importance of seed Zn content for wheat growth on Zn-

deficient soil. Plant and Soil 173: 259-266

Rhazi L, Cazalis R, Lemelin E, Aussenac T (2003) Changes in the glutathione thiol-

disulfide status during wheat grain development. Plant Physiology and

Biochemistry 41: 895-902

Riccardi F (1998) Protein changes in response to progressive water deficit in maize

quantitative variation and polypeptide identification. Plant Physiology 117:

1253-1263

Rochat C, Wuillème S, Boutin JP, Hedley CL (1995) A mutation at the rb gene,

lowering ADPGPPase activity, affects storage product metabolism of pea seed

coats. Journal of Experimental Botany 46: 415-412

Rolletschek H, Borisjuk L, Radchuk R, Miranda M, Heim U, Wobus U, Weber H

(2004) Seed-specific expression of a bacterial phosphoenolpyruvate carboxylase

in Vicia narbonensis increases protein content and improves carbon economy.

Plant Biotechnology Journal 2: 211-219

Romijn EP, Christis C, Wieffer M, Gouw JW, Fullaondo A, van der Sluijs P,

Braakman I, Heck AJR (2005) Expression clustering reveals detailed co-

expression patterns of functionally related proteins during B cell differentiation.

Molecular and Cellular Proteomics 4: 1297

Rondón AG, Jimeno S, García-Rubio M, Aguilera A (2003) Molecular evidence that

the eukaryotic THO/TREX complex is required for efficient transcription

elongation. Journal of Biological Chemistry 278: 39037-39043

Rorat T (2006) Plant dehydrins—tissue location, structure and function. Cellular and

Molecular Biology Letters 11: 536-556

Page 153: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

133

Rowse HR, McKee JMT, Higgs EC (1999) A Model of the Effects of Water Stress on

Seed Advancement and Germination. New Phytologist 143: 273-279

Ryan CA (1990) Protease inhibitors in plants: genes for improving defenses against

insects and pathogens. Annual Review of Phytopathology 28: 425-449

Rylott EL, Gilday AD, Graham IA (2003) The gluconeogenic enzyme

phosphoenolpyruvate carboxykinase in Arabidopsis is essential for seedling

establishment. Plant Physiology 131: 1834-1842

Sabaghpour SH, Kumar J, Rao TN (2003) Inheritance of growth vigour and its

association with other characters in chickpea. Plant Breeding 122: 542-544

Sahnoun-Abid I, Recorbet G, Zuber H, Sommerer N, Centeno D, Dumas-Gaudot

E, Smiti-Aschi S (2009) Proteomic characterisation of subclover seed storage

proteins during germination. Journal of the Science of Food and Agriculture 89:

1787-1801

Salekdeh GH, Komatsu S (2007) Crop proteomics: aim at sustainable agriculture of

tomorrow. Proteomics 7: 2976-2996

Salekdeh GH, Siopongco J, Wade LJ, Ghareyazie B, Bennett J (2002) Proteomic

analysis of rice leaves during drought stress and recovery. Proteomics 2:

1131-1145

Salon C, Munier-Jolain NG, Duc G, Voisin AS, Grandgirard D (2001) Grain

legume seed filling in relation to nitrogen acquisition: a review and prospects

with particular reference to pea. Agronomie 21: 539-552

Salvi S, Tuberosa R (2005) To clone or not to clone plant QTLs: present and future

challenges. Trends in Plant Science 10: 297-304

Samarah NH, Abu-Yahya A (2008) Effect of maturity stages of winter- and spring-

sown chickpea (Cicer arietinum L.) on germination and vigour of the harvested

seeds. Seed Science and Technology 36: 177-190

Page 154: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

134

Sandoval JA, Huang ZH, Garrett DC, Gage DA, Chapman KD (1995) N-

acylphosphatidylethanolamine in dry and imbibing cottonseeds (amounts,

molecular species, and enzymatic synthesis). Plant Physiology 109: 269-275

Saqib M, Zörb C, Schubert S, J. (2006) Salt-resistant and salt sensitive wheat

genotypes show similar biochemical reaction at protein level in the first phase of

salt stress. Journal of Plant Nutrition and Soil Science 169: 542–548

Saxena IM, Brown R (2005) Cellulose biosynthesis: current views and evolving

concepts. Annals of Botany 96: 9-21

Saxena NP, Johansen C, Saxena MC, Silim SN (1993) Selection for drought and

salinity tolerance in cool-season food legumes. In KB Singh, MC Saxena, eds,

Breeding for Tolerance in Cool Season Food Legumes. Wiley, Chichester, UK,

pp 245-270

Schmitt HD, Jahn R (2009) A tethering complex recruits SNAREs and grabs vesicles.

Cell 139: 1053-1055

Schopfer P, Plachy C, Frahry G (2001) Release of reactive oxygen intermediates

(superoxide radicals, hydrogen peroxide, and hydroxyl radicals) and peroxidase

in germinating radish seeds controlled by light, gibberellin, and abscisic acid.

Plant Physiology 125: 1591-1602

Scott B, Soren E, Tulene K, Gopalakrishnakone P, Andreja L, Robert L, Richard

L (2008) Proteomic analysis of the venom of Heterometrus longimanus (Asian

black scorpion). Proteomics 8: 1081-1096

Seifert GJ (2004) Nucleotide sugar interconversions and cell wall biosynthesis: how to

bring the inside to the outside. Current Opinion in Plant Biology 7: 277-284

Seki M, Narusaka M, Ishida J, Nanjo T, Fujita M, Oono Y, Kamiya A, Nakajima

M, Enju A, Sakurai T (2002) Monitoring the expression profiles of 7000

Arabidopsis genes under drought, cold and high-salinity stresses using a full-

length cDNA microarray. The Plant Journal 31: 279-292

Page 155: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

135

Selbach M, Schwanhäusser B, Thierfelder N, Fang Z, Khanin R, Rajewsky N

(2008) Widespread changes in protein synthesis induced by microRNAs. Nature

455: 58-63

Sharma N, Hotte N, Rahman MH, Mohammadi M, Deyholos MK, Kav NNV

(2008) Towards identifying Brassica proteins involved in mediating resistance to

Leptosphaeria maculans: A proteomics-based approach. Proteomics 8:

3516-3535

Sharma RA (1985) Influence of drought stress on the emergence and growth of

chickpea seedlings. International Chickpea Newsletter 12: 15-16

Sharma SK, Christen P, Goloubinoff P (2009) Disaggregating chaperones: an

unfolding story. Current Protein and Peptide Science 10: 432-446

Shewry PR, Napier JA, Tatham AS (1995) Seed storage proteins: structures and

biosynthesis. The Plant Cell 7: 945-956

Siddique KHM, Brinsmead RB, Knight R, Knights EJ, Paull JG, Rose IA (2000)

Adaptation of chickpea (Cicer arietinum L.) and faba bean (Vicia faba L.) to

Australia. . In R Knight, ed, Linking Research and Marketing Opportunities for

Pulses in the 21st Century. Kluwer Academic Publishers Dordrecht, The

Netherlands pp 289–303

Siddique KHM, Regan KL, Tennant D, Thomson BD (2001) Water use and water

use efficiency of cool season grain legumes in low rainfall Mediterranean-type

environments. European Journal of Agronomy 15: 267-280

Siddique KHM, Sykes J (1997) Pulse production in Australia past, present and future.

Australian Journal of Experimental Agriculture 37: 103 - 111

Siedow JN, Day DA (2000) Respiration and photorespiration. In BB Buchanan, W

Gruissem, RL Jones, eds, Bochemistry and Molecular Biology of Plants.

Courrier Companies, Inc. , USA, pp 676-728

Singh KB, Omar M, Saxena MC, Johnsen C (1997) Screening for drought resistance

in spring chickpea in the Mediterranean region. Journal of Agronomy and Crop

Science 178: 227-235

Page 156: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

136

Skylas DJ, Cordwell SJ, Hains PG, Larsen MR, Basseal DJ, Walsh BJ, Blumenthal

C, Rathmell W, Copeland L, Wrigley CW (2002) Heat shock of wheat during

grain filling: proteins associated with heat-tolerance. Journal of Cereal Science

35: 175-188

Smith AJ, Rinne RW, Seif RD (1989) Phosphoenolpyruvate carboxylase and pyruvate

kinase involvement in protein and oil biosynthesis during soybean seed

development. Crop Science 29: 349-353

Soltani A, Gholipoor M, Zeinali E (2006) Seed reserve utilization and seedling growth

of wheat as affected by drought and salinity. Environmental and Experimental

Botany 55: 195-200

Spielmeyer W, Ellis MH, Chandler PM (2002) Semidwarf (sd-1),“green revolution”

rice, contains a defective gibberellin 20-oxidase gene. Proceedings of the

National Academy of Sciences of the United States of America 99: 9043-9048

Srivastava S, Fristensky B, Kav NNV (2004) Constitutive expression of a PR10

protein enhances the germination of Brassica napus under saline conditions.

Plant Cell Physiology. 45: 1320-1324

Stacy RAP, Munthe E, Steinum T, Sharma B, Aalen RB (1996) A peroxiredoxin

antioxidant is encoded by a dormancy-related gene, Per1, expressed during late

development in the aleurone and embryo of barley grains. Plant Molecular

Biology 31: 1205-1216

Stacy RAP, Nordeng TW, Culiáñez Macià FA, Aalen RB (1999) The dormancy

related peroxiredoxin anti oxidant, PER1, is localized to the nucleus of barley

embryo and aleurone cells. The Plant Journal 19: 1-8

Sun W, Van Montagu M, Verbruggen N (2002) Small heat shock proteins and stress

tolerance in plants. Biochimica et Biophysica Acta -Gene Structure and

Expression 1577: 1-9

Sun W, Van Montagu M, Verbruggen N (2002) Small heat shock proteins and stress

tolerance in plants. Biochimica et Biophysica Acta (BBA)-Gene Structure and

Expression 1577: 1-9

Page 157: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

137

Tabe LM, Droux M (2001) Sulfur assimilation in developing lupin cotyledons could

contribute significantly to the accumulation of organic sulfur reserves in the

seed. Plant Physiology 126: 176-187

Templeman TS, DeMaggio AE, Stetler DA (1987) Biochemistry of Fern Spore

Germination: Globulin Storage Proteins in Matteuccia struthiopteris L. Plant

Physiology 85: 343-349

Thomson BD, Siddique KHM (1997) Grain legume species in low rainfall

Mediterranean-type environments. II. Canopy development, radiation

interception, and dry-matter production. Crops Research 54: 189–199

Tobe K (2009) Comparison of initial growth responses to water availability of two

Chinese desert dune species and nine cultivated species. Arid Land Research and

Management 23: 14

Tobe K, Zhang L, Omasa K (2007) Seed size effects on seedling emergence of desert

psammophytes in China. Arid Land Research and Management 21: 181-192

Toker C, Luch C, Tejera NA, Serraj R, Siddique KHM (2007) Abiotic stresses. In S

Yadav, ed, Chickpea Breeding and Management. CAB International,

Wallingford, UK, pp 478-500

Trewavas A (2000) Signal perception and transduction. In BB Buchanan, W Gruissem,

RL Jones, eds, Biochemistry and Molecular Biology of Plants, Courrier

Companies, Inc., USA, pp 930-987

Tripathi BN, Bhatt I, Dietz KJ (2009) Peroxiredoxins: a less studied component of

hydrogen peroxide detoxification in photosynthetic organisms. Protoplasma

235: 3-15

Tunnacliffe A, Wise M (2007) The continuing conundrum of the LEA proteins.

Naturwissenschaften 94: 791-812

Turner NC, Wright GC, Siddique KHM (2001) Adaptation of grain legumes (pulses)

to water-limited environments. Advances in Agronomy 71: 193–231

Tyers M, Mann M (2003) From genomics to proteomics. Nature 422: 193-197

Page 158: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

138

Tzitzikas EN, Vincken JP, de Groot J, Gruppen H, Visser RGF (2006) Genetic

variation in pea seed globulin composition. Journal of Agricultural and Food

Chemistry 54: 425-433

Ungar D, Hughson FM (2003) Snare protein structure and function. Annual Review of

Cell and Developmental Biology 19: 493-517

Upadhyaya H (2003) Geographical patterns of variation for morphological and

agronomic characteristics in the chickpea germplasm collection. Euphytica 132:

343-352

Urbieta IR (2008) Soil water content and emergence time control seedling

establishment in three co-occurring Mediterranean oak species. Canadian

Journal of Forest Research 38: 2382

Urbieta IR, Perez-Ramos IM, Zavala MA, Maranon T, Kobe RK (2008) Soil water

content and emergence time control seedling establishment in three co-occurring

Mediterranean oak species. Canadian Journal of Forest Research 38: 2382-2393

Urfer W, Grzegorczyk M, Jung K (2006) Statistics for proteomics: a review of tools

for analyzing experimental data. Practical Proteomics 6: 48-55

Van der Merwe MJ, Osorio S, Moritz T, Nunes-Nesi A, Fernie AR (2009)

Decreased mitochondrial activities of malate dehydrogenase and fumarase in

tomato lead to altered root growth and architecture via diverse mechanisms.

Plant Physiology 149: 653-669

Varshney RK, Hiremath PJ, Lekha P, Kashiwagi J, Balaji J, Deokar AA, Vadez V,

Xiao Y, Srinivasan R, Gaur PM (2009) A comprehensive resource of drought-

and salinity- responsive ESTs for gene discovery and marker development in

chickpea(Cicer arietinum L.). BMC Genomics 10: 523

Vereda A, Andreae DA, Lin J, Shreffler WG, Iba ez MD, Cuesta-Herranz J,

Bardina L, Sampson HA (2010) Identification of IgE sequential epitopes of

lentil (Len c 1) by means of peptide microarray immunoassay. Journal of

Allergy and Clinical Immunology 126: 596-601

Page 159: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

139

Vierling E (1991) The roles of heat shock proteins in plants. Annual Review of Plant

Biology 42: 579-620

Wahid A, Close TJ (2007) Expression of dehydrins under heat stress and their

relationship with water relations of sugarcane leaves. Biologia Plantarum 51:

104-109

Walker RP, Trevanion SJ, Leegood RC (1995) Phosphoenolpyruvate carboxykinase

from higher plants: purification from cucumber and evidence of rapid proteolytic

cleavage in extracts from a range of plant tissues. Planta 196: 58-63

Walters C (1998) Understanding the mechanisms and kinetics of seed aging. Seed

Science Research 8: 223-244

Wang X, Fan P, Song H, Chen X, Li X, Li Y (2009a) Comparative proteomic analysis

of differentially expressed proteins in shoots of Salicornia europaea under

different salinity. Journal of Proteome Research 8: 3331-3345

Wang X, Wang Y, Tian J, Lim BL, Yan X, Liao H (2009b) Overexpressing AtPAP15

enhances phosphorus efficiency in soybean. Plant Physiology 151: 233-240

Watson BS, Asirvatham VS, Wang L, Sumner LW (2003) Mapping the proteome of

barrel medic (Medicago truncatula). Plant physiology 131: 1104-1123

Weber H, Borisjuk L, Heim U, Buchner P, Wobus U (1995) Seed coat-associated

invertases of fava bean control both unloading and storage functions: cloning of

cDNAs and cell type-specific expression. The Plant Cell Online 7: 1835-1846

Weber H, Borisjuk L, Heim U, Buchner P, Wobus U (1995) Seed Coat-Associated

Invertases of Fava Bean Control Both Unloading and Storage Functions:

Cloning of cDNAs and Cell Type-Specific Expression. Plant Cell 7: 1835-1846

Weber H, Borisjuk L, Wobus U (2005) Molecular development of legume seed

development. Annual Review of Plant Biology 56: 253-279

Weckwerth W (2008) Integration of metabolomics and proteomics in molecular plant

physiology – coping with the complexity by data-dimensionality reduction.

Physiologia Plantarum 132: 176-189

Page 160: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

140

Wehmeyer N, Vierling E (2000) The expression of small heat shock proteins in seeds

responds to discrete developmental signals and suggests a general protective role

in desiccation tolerance. Plant Physiology 122: 1099-1108

Welham T, Domoney C (2000) Temporal and spatial activity of a promoter from a pea

enzyme inhibitor gene and its exploitation for seed quality improvement. Plant

Science 159: 289-299

Willenborg CJ, Wildeman JC, Miller AK, Rossnagel BG, Shirtliffe SJ (2005) Oat

germination characteristics differ among genotypes, seed sizes, and osmotic

ootentials. Crop Science 45: 2023-2029

Williams PC, Singh U (1987) The Chickpea–Nutritional quality and the evaluation of

quality. In MC Saxena, KB Singh, eds. CAB International, Wallingford, UK, pp

329–356

Williams PC, Singh U (1987) Nutritional quality and the evaluation of quality. In MC

Saxena, KB Singh, eds, The Chickpea. CAB International, Wallingford, UK, pp

329–356

Wingate VPM, Lawton MA, Lamb CJ (1988) Glutathione causes a massive and

selective induction of plant defense genes. Plant Physiology 87: 206-210

Witzel K, Weidner A, Surabhi GK, Varshney RK, Kunze G, Buck-Sorlin GH,

BÖRner A, Mock HP (2010) Comparative analysis of the grain proteome

fraction in barley genotypes with contrasting salinity tolerance during

germination. Plant, Cell and Environment 33: 211-222

Woo EJ, Dunwell JM, Goodenough PW, Marvier AC, Pickersgill RW (2000)

Germin is a manganese containing homohexamer with oxalate oxidase and

superoxide dismutase activities. Nature Structural and Molecular Biology 7:

1036-1040

Wood ZA, Poole LB, Karplus PA (2003) Peroxiredoxin evolution and the regulation

of hydrogen peroxide signalling. Science 300: 650

Wood ZA, Schröder E, Robin Harris J, Poole LB (2003) Structure, mechanism and

regulation of peroxiredoxins. Trends in Biochemical Sciences 28: 32-40

Page 161: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

141

Xiao H, Nassuth A (2006) Stress-and development-induced expression of spliced and

unspliced transcripts from two highly similar dehydrin 1 genes in V. riparia and

V. vinifera. Plant Cell Reports 25: 968-977

Xu D, Duan X, Wang B, Hong B, Ho THD, Wu R (1996) Expression of a late

embryogenesis abundant protein gene, HVA1, from barley confers tolerance to

water deficit and salt stress in transgenic rice. Plant Physiology 110: 249-257

Yadav SS, Redden RJ, Chen W, Sharma B (2007) Chickpea Breeding and

Management. CAB International, Wallingford, Oxfordshire, UK

Yamaguchi M, Valliyodan B, Zhang J, Lenoble ME, Yu O, Rogers EE, Nguyen

HT, Sharp RE (2010) Regulation of growth response to water stress in the

soybean primary root. I. Proteomic analysis reveals region-specific regulation of

phenylpropanoid metabolism and control of free iron in the elongation zone.

Plant, Cell and Environment 33: 223-243

Yan S, Tang Z, Su W, Sun W (2005) Proteomic analysis of salt stress-responsive

proteins in rice root. Proteomics 5: 235-244

Yang HJ, Nakanishi H, Liu S, McNew JA, Neiman AM (2008) Binding interactions

control SNARE specificity in vivo. The Journal of Cell Biology 183: 1089-1100

Yano H, Wong JH, Cho MJ, Buchanan BB (2001) Redox changes accompanying the

degradation of seed storage proteins in germinating rice. Plant and Cell

Physiology 42: 879-883

Yelina NE, Smith LM, Jones AME, Patel K, Kelly KA, Baulcombe DC (2010)

Putative Arabidopsis THO/TREX mRNA export complex is involved in

transgene and endogenous siRNA biosynthesis. Proceedings of the National

Academy of Sciences 107: 13948-13953

Yeung EC, Meinke DW (1993) Embryogenesis in angiosperms: Development of the

suspensor. The Plant Cell 5: 1371-1381

Yin Z, Rorat T, Szabala BM, Ziólkowska A, Malepszy S (2006) Expression of a

Solanum sogarandinum SK3-type dehydrin enhances cold tolerance in

transgenic cucumber seedlings. Plant Science 170: 1164-1172

Page 162: Large scale comparative proteomics analysis of genotypic … · genotypes (cv Rupali, cv Norwin, KH850, KL850, KJ850) in response to critical water supply (10, 7.5, 6.25 and 5% WHC)

REFERENCES

142

Zhang H, Pala M, Oweis T, Harris H (2000) Water use and water-use efficiency of

chickpea and lentil in a Mediterranean environment. Australian Journal of

Agricultural Research 51: 295-304

Zhu J, Alvarez S, Marsh EL, LeNoble ME, Cho IJ, Sivaguru M, Chen S, Nguyen

HT, Wu Y, Schachtman DP (2007) Cell wall proteome in the maize primary

root elongation zone. II. Region-specific changes in water soluble and lightly

ionically bound proteins under water deficit. Plant Physiology 145: 1533

Zia-Ul-Haq M, Ahmad M, Iqbal S, Ahmad S, Ali H (2007) Characterization and

Compositional Studies of Oil from Seeds of Desi Chickpea (Cicer arietinum L.)

Cultivars Grown in Pakistan. Journal of the American Oil Chemists' Society 84:

1143-1148

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APPENDICES

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Appendix 1 Details for analysis of spectra for identification of proteins using a single peptide. Spot No Protein identification Observed Mr(expt) Mr(calc) Delta Miss Score Expect Rank Peptide

250 Anthranilate synthase component I, putative 816.3489 815.3416 815.4865 -0.1449 0 52 0.012 5 R.SLAAVISR.Q

380 Putative thioredoxin-like proten 1637.6002 1636.5929 1636.7845 -0.1916 1 70 8.1e-005 1 K.LRSDYDFGHTLDAK.H

430 Hydrolase 816.2947 815.2875 815.4865 -0.1990 0 44 0.078 2 K.ALVSALSR.Y

736 Phosphoenolpyruvate carboxykynase 1305.6709 1304.6636 1304.6976 -0.0340 0 46 0.025 2 K.LVEYDALLVDR.F

370 ATP synthase epsilon subunit 1196.4943 1195.4870 1195.5292 -0.0421 0 58 0.0019 1 R.EAQDTFQMAR.D

931 17.5 kDa class I HSP 1195.7344 1194.7271 1194.6972 0.0299 0 76 2.2e-005 1 K.KPDVKPIQITG.- 150 Seed maturation protein 996.7230 995.7157 995.5400 0.1757 0 44 0.035 1 R.RPEQEPIK.Y

610 Superoxide dismutase (Cu-Zn) 2044.1571 2043.1498 2043.0385 0.1113 1 96 1.5e-007 1 R.AVVVHADPDDLGKGGHELSK.T

635 Glutathione peroxidase 1582.8864 1581.8791 1581.7940 0.0851 1 76 1.8e-005 1 K.GGLFGDNIKWNFSK.F

13 Protein Brevis radix-like 2 1435.5992 1434.5919 1434.5746 0.0173 1 45 0.034 1 K.KQEEDEEEEDR.V 351 Hsp40/DnaJ_Rel 927.5616 926.5544 926.5913 -0.0369 1 47 0.021 2 R.LVEILKGR.L 471 Hsp40/DnaJ_Rel 927.5263 926.5190 926.5913 -0.0723 1 45 0.035 2 R.LVEILKGR.L 651 Hsp40/DnaJ_Rel 927.3242 926.3169 926.5913 -0.2744 1 44 0.039 2 R.LVEILKGR.L 850 Dehydrin 1 1041.2737 1040.2664 1040.5152 -0.2487 1 45 0.037 1 K.EKLPGHHSH.-

64 Late embryogenesis abundant protein D-34 1686.6265 1685.6192 1685.8373 -0.2180 0 84 3.4e-006 1 K.AVEWSDAAAIQAAEVR.A

733 Superoxide dismutase (Cu-Zn) 1365.5984 1364.5911 1364.7048 -0.1137 1 48 0.017 1 K.KLTHGAPEDEIR.H

200 Allergen Len 1351.5415 1350.5342 1350.6415 -0.1073 0 75 2.9e-005 1 K.SVSSESEPFNLR.S 520 Allergen Len c 1.0102 1078.4711 1077.4638 1077.5567 -0.0928 1 51 0.015 1 R.SRNPIYSNK.F 561 Allergen Len c 1.0102 1078.4829 1077.4756 1077.5567 -0.0810 1 45 0.057 1 R.SRNPIYSNK.F 910 Conarachin 1459.3530 1458.3457 1458.6779 -0.3322 0 55 0.0049 1 K.QAGNEGFEYVAFK.T 921 Serin proteinase ihibitor 979.4193 978.4120 978.4883 -0.0762 1 51 0.057 1 R.FGEDLSRR.Y 460 Protein kinase-like 1305.4987 1304.4914 1304.5779 -0.0865 0 44 0.045 2 K.NMNEVAGADAAAR.F

730 Snase (Staphylococcal nuclease) 1305.7074 1304.7001 1304.6989 0.0012 1 44 0.039 2 R.DFLPFLQRNR.R

201 SWI2/SNF2-like protein 1218.4382 1217.4309 1217.5935 -0.1626 1 55 0.0037 1 R.TQVCAAGGGGGKR.R

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Table Continued

155

Putative RNA-directed DNA polymerase (reverse transcriptase), msDNA 1106.6372 1105.6299 1105.5458 0.0842 0 50 0.011 1 R.VGFWWGAQR.L

720

Reverse_transcriptase or Putative uncharacterized protein 1348.7310 1347.7237 1347.7412 -0.0174 1 45 0.031 1 R.RAISGGFLSVWR.A

23

THO complex subunit 1 transcription elongation factor 2561.8120 2560.8047 2561.1852 -0.3805 0 44 0.037 1 R.DFVEGGMIFQLLEDLTEMSTMR.N

440 Intron_maturase 2 999.4314 998.4241 998.5509 -0.1267 1 44 0.04 2 K.GDLKSPNLR.S 108 Calreticulin 954.4378 953.4305 953.4971 -0.0665 1 51 0.0065 1 -.KVFFEER.F 34 Calmodulin 1810.7552 1809.7479 1809.8897 -0.1418 1 94 4.3e-007 1 R.VFDKDQDGFISAAELR.H

340 Cupin, RmlC-type 1369.7644 1368.7571 1368.7150 0.0421 0 78 1.4e-005 1 R.EGSLLLPHFNSR.A 940 Cupin, RmlC-type 1411.7567 1410.7494 1410.7255 0.0239 1 89 8.7e-007 1 R.SKIFENLQNYR.L 110 Cupin, RmlC-type 1369.7987 1368.7914 1368.7150 0.0764 0 69 0.0001 1 R.EGSLLLPHFNSR.A 663 Cupin, RmlC-type 1411.7603 1410.7530 1410.7255 0.0275 1 67 0.00017 1 R.SKIFENLQNYR.L 822 Cupin, RmlC-type 1411.6511 1410.6438 1410.7255 -0.0817 1 74 3.4e-005 1 R.SKIFENLQNYR.L

131 Putative uncharacterized protein 816.4608 815.4535 815.4865 -0.0329 0 44 0.058 1 K.ALSVASLR.R

634 Putative uncharacterized protein 1716.9240 1715.9167 1715.8042 0.1125 0 75 2.2e-005 1 K.FSGEEFENFLAVAEK.L

100 Putative uncharacterized protein 985.5828 984.5755 984.5604 0.0151 0 44 0.087 1 R.LADEIVGLR.Q

418 Os04g0602300 protein) 802.3994 801.3921 801.4205 -0.0285 1 45 0.056 1 K.RQQSQR.M

451 Putative uncharacterized protein 1065.6021 1064.5948 1064.5073 0.0875 0 43 0.055 1 -.MAFDAGAGIGR.I

516 Putative uncharacterized protein 945.4978 944.4905 944.4862 0.0043 0 42 0.092 1 R.GCLAGVVGGR.X

251 Putative uncharacterized protein 816.3676 815.3603 815.4865 -0.1262 0 52 0.012 1 K.SLAAVLSR.F

851 Pentatricopeptide_repeat 866.3759 865.3686 865.5021 -0.1336 0 48 0.017 1 K.QHLIEVK.G

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Appendix 2 Peptide view with MS/MS fragmentation and its corresponding table of matched fragment ions.

Immon. a a0 b b0 Seq. v w w' y y* y0 #

1 60.0444 60.0444 42.0338 88.0393 70.0287 S 8

2 86.0964 173.1285 155.1179 201.1234 183.1128 L 671.3835 670.3883 729.4618 712.4352 711.4512 7

3 44.0495 244.1656 226.1550 272.1605 254.1499 A 600.3464 616.3777 599.3511 598.3671 6

4 44.0495 315.2027 297.1921 343.1976 325.1870 A 529.3093 545.3406 528.3140 527.3300 5

5 72.0808 414.2711 396.2605 442.2660 424.2554 V 430.2409 443.2613 474.3035 457.2769 456.2929 4

6 86.0964 527.3552 509.3446 555.3501 537.3395 I 317.1568 330.1772 344.1928 375.2350 358.2085 357.2245 3

7 60.0444 614.3872 596.3766 642.3821 624.3715 S 230.1248 229.1295 262.1510 245.1244 244.1404 2

8 129.1135 R 74.0237 73.0284 175.1190 158.0924 1

a a* b b* Seq. y y* #

1 86.0964 114.0913 L 14

2 242.1975 225.1710 270.1925 253.1659 R 1524.7077 1507.6812 13

3 329.2296 312.2030 357.2245 340.1979 S 1368.6066 1351.5801 12

4 444.2565 427.2300 472.2514 455.2249 D 1281.5746 1264.5481 11

5 607.3198 590.2933 635.3148 618.2882 Y 1166.5477 1149.5211 10

6 722.3468 705.3202 750.3417 733.3151 D 1003.4843 986.4578 9

7 869.4152 852.3886 897.4101 880.3836 F 888.4574 871.4308 8

8 926.4367 909.4101 954.4316 937.4050 G 741.3890 724.3624 7

9 1063.4956 1046.4690 1091.4905 1074.4639 H 684.3675 667.3410 6

10 1164.5432 1147.5167 1192.5382 1175.5116 T 547.3086 530.2821 5

11 1277.6273 1260.6008 1305.6222 1288.5957 L 446.2609 429.2344 4

12 1392.6543 1375.6277 1420.6492 1403.6226 D 333.1769 316.1503 3

13 1463.6914 1446.6648 1491.6863 1474.6597 A 218.1499 201.1234 2

14 K 147.1128 130.0863 1

R.SLAAVISR.Q 250

K.LRSDYDFGHTLDAK.H 380

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Immon. a a0 b b0 Seq. v w y y* y0 #

1 44.0495 44.0495 72.0444 A 8

2 86.0964 157.1335 185.1285 L 687.3784 686.3832 745.4567 728.4301 727.4461 7

3 72.0808 256.2020 284.1969 V 588.3100 601.3304 632.3726 615.3461 614.3620 6

4 60.0444 343.2340 325.2234 371.2289 353.2183 S 501.2780 500.2827 533.3042 516.2776 515.2936 5

5 44.0495 414.2711 396.2605 442.2660 424.2554 A 430.2409 446.2722 429.2456 428.2616 4

6 86.0964 527.3552 509.3446 555.3501 537.3395 L 317.1568 316.1615 375.2350 358.2085 357.2245 3

7 60.0444 614.3872 596.3766 642.3821 624.3715 S 230.1248 229.1295 262.1510 245.1244 244.1404 2

8 129.1135 R 74.0237 73.0284 175.1190 158.0924 1

a b Seq. y y* #

1 86.0964 114.0913 L 11

2 185.1648 213.1598 V 1192.6208 1175.5943 10

3 314.2074 342.2023 E 1093.5524 1076.5259 9

4 477.2708 505.2657 Y 964.5098 947.4833 8

5 592.2977 620.2926 D 801.4465 784.4199 7

6 663.3348 691.3297 A 686.4196 669.3930 6

7 776.4189 804.4138 L 615.3824 598.3559 5

8 889.5029 917.4979 L 502.2984 485.2718 4

9 988.5714 1016.5663 V 389.2143 372.1878 3

10 1103.5983 1131.5932 D 290.1459 273.1193 2

11 R 175.1190 158.0924

K.ALVSALSR.Y 430

K.LVEYDALLVDR.F

770

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# Immon. a a* a0 b b* b0 Seq. v w w' y y* y0 #

1 102.0550 102.0550 84.0444 130.0499 112.0393 E 10

2 44.0495 173.0921 155.0815 201.0870 183.0764 A 1051.4626 1067.4939 1050.4673 1049.4833 9

3 101.0709 301.1506 284.1241 283.1401 329.1456 312.1190 311.1350 Q 923.4040 922.4087 996.4567 979.4302 978.4462 8

4 88.0393 416.1776 399.1510 398.1670 444.1725 427.1460 426.1619 D 808.3770 807.3818 868.3982 851.3716 850.3876 7

5 74.0600 517.2253 500.1987 499.2147 545.2202 528.1936 527.2096 T 707.3294 720.3498 722.3290 753.3712 736.3447 735.3607 6

6 120.0808 664.2937 647.2671 646.2831 692.2886 675.2620 674.2780 F 560.2609 652.3235 635.2970 5

7 101.0709 792.3523 775.3257 774.3417 820.3472 803.3206 802.3366 Q 432.2024 431.2071 505.2551 488.2286 4

8 104.0528 923.3927 906.3662 905.3822 951.3877 934.3611 933.3771 M 301.1619 300.1666 377.1966 360.1700 3

9 44.0495 994.4299 977.4033 976.4193 1022.4248 1005.3982 1004.4142 A 230.1248 246.1561 229.1295 2

10 129.1135 R 74.0237 73.0284 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 101.1073 84.0808 129.1022 112.0757 K 11

2 198.1601 181.1335 226.1550 209.1285 P 1067.6095 1050.5830 10

3 313.1870 296.1605 341.1819 324.1554 D 970.5568 953.5302 9

4 412.2554 395.2289 440.2504 423.2238 V 855.5298 838.5033 8

5 540.3504 523.3239 568.3453 551.3188 K 756.4614 739.4349 7

6 637.4032 620.3766 665.3981 648.3715 P 628.3665 611.3399 6

7 750.4872 733.4607 778.4822 761.4556 I 531.3137 514.2871 5

8 878.5458 861.5193 906.5407 889.5142 Q 418.2296 401.2031 4

9 991.6299 974.6033 1019.6248 1002.5982 I 290.1710 3

10 1092.6776 1075.6510 1120.6725 1103.6459 T 177.0870 2

11 G 76.0393 1

R.EAQDTFQMAR.D

370

K.KPDVKPIQITG.-

931

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a a* b b* Seq. y y* #

1 129.1135 112.0869 157.1084 140.0818 R 8

2 226.1662 209.1397 254.1612 237.1346 P 840.4462 823.4196 7

3 355.2088 338.1823 383.2037 366.1772 E 743.3934 726.3668 6

4 483.2674 466.2409 511.2623 494.2358 Q 614.3508 597.3243 5

5 612.3100 595.2835 640.3049 623.2784 E 486.2922 469.2657 4

6 709.3628 692.3362 737.3577 720.3311 P 357.2496 340.2231 3

7 822.4468 805.4203 850.4417 833.4152 I 260.1969 243.1703 2

8 K 147.1128 130.0863

# a a* b b* Seq. y y* #

1 44.0495 72.0444 A 20

2 143.1179 171.1128 V 1973.0087 1955.9821 19

3 242.1863 270.1812 V 1873.9403 1856.9137 18

4 341.2547 369.2496 V 1774.8719 1757.8453 17

5 478.3136 506.3085 H 1675.8034 1658.7769 16

6 549.3507 577.3457 A 1538.7445 1521.7180 15

7 664.3777 692.3726 D 1467.7074 1450.6809 14

8 761.4305 789.4254 P 1352.6805 1335.6539 13

9 876.4574 904.4523 D 1255.6277 1238.6012 12

10 991.4843 1019.4793 D 1140.6008 1123.5742 11

11 1104.5684 1132.5633 L 1025.5738 1008.5473 10

12 1161.5899 1189.5848 G 912.4898 895.4632 9

13 1289.6848 1272.6583 1317.6797 1300.6532 K 855.4683 838.4417 8

14 1346.7063 1329.6797 1374.7012 1357.6747 G 727.3733 710.3468 7

15 1403.7278 1386.7012 1431.7227 1414.6961 G 670.3519 653.3253 6

16 1540.7867 1523.7601 1568.7816 1551.7550 H 613.3304 596.3039 5

17 1669.8293 1652.8027 1697.8242 1680.7976 E 476.2715 459.2449 4

18 1782.9133 1765.8868 1810.9082 1793.8817 L 347.2289 330.2023 3

19 1869.9454 1852.9188 1897.9403 1880.9137 S 234.1448 217.1183 2

20 K 147.1128 130.0863 1

R.RPEQEPIK.Y 150

R.AVVVHADPDDLGKGGHELSK.T 610

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# a a* b b* Seq. y y* #

1 30.0338 58.0287 G 14

2 87.0553 115.0502 G 1525.7798 1508.7532 13

3 200.1394 228.1343 L 1468.7583 1451.7318 12

4 347.2078 375.2027 F 1355.6743 1338.6477 11

5 404.2292 432.2241 G 1208.6058 1191.5793 10

6 519.2562 547.2511 D 1151.5844 1134.5578 9

7 633.2991 616.2726 661.2940 644.2675 N 1036.5574 1019.5309 8

8 746.3832 729.3566 774.3781 757.3515 I 922.5145 905.4880 7

9 874.4781 857.4516 902.4730 885.4465 K 809.4304 792.4039 6

10 1060.5574 1043.5309 1088.5524 1071.5258 W 681.3355 664.3089 5

11 1174.6004 1157.5738 1202.5953 1185.5687 N 495.2562 478.2296 4

12 1321.6688 1304.6422 1349.6637 1332.6371 F 381.2132 364.1867 3

13 1408.7008 1391.6743 1436.6957 1419.6692 S 234.1448 217.1183 2

14 K 147.1128 130.0863 1

# a a* b b* Seq. y y* #

1 101.1073 84.0808 129.1022 112.0757 K 11

2 229.1659 212.1394 257.1608 240.1343 Q 1307.4870 1290.4604 10

3 358.2085 341.1819 386.2034 369.1769 E 1179.4284 1162.4018 9

4 487.2511 470.2245 515.2460 498.2195 E 1050.3858 1033.3593 8

5 602.2780 585.2515 630.2729 613.2464 D 921.3432 904.3167 7

6 731.3206 714.2941 759.3155 742.2890 E 806.3163 789.2897 6

7 860.3632 843.3367 888.3581 871.3316 E 677.2737 660.2471 5

8 989.4058 972.3793 1017.4007 1000.3742 E 548.2311 531.2045 4

9 1118.4484 1101.4219 1146.4433 1129.4168 E 419.1885 402.1619 3

10 1233.4753 1216.4488 1261.4703 1244.4437 D 290.1459 273.1193 2

11 R 175.1190 158.0924 1

K.GGLFGDNIKWNFSK.F 635

K.KQEEDEEEEDR.V 13

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APPENDICES

151

# a a* b b* Seq. y y* #

1 86.0964 114.0913 L 8

2 185.1648 213.1598 V 814.5145 797.4880 7

3 314.2074 342.2023 E 715.4461 698.4196 6

4 427.2915 455.2864 I 586.4035 569.3770 5

5 540.3756 568.3705 L 473.3194 456.2929 4

6 668.4705 651.4440 696.4654 679.4389 K 360.2354 343.2088 3

7 725.4920 708.4654 753.4869 736.4604 G 232.1404 215.1139 2

8 R 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 86.0964 114.0913 L 8

2 185.1648 213.1598 V 814.5145 797.4880 7

3 314.2074 342.2023 E 715.4461 698.4196 6

4 427.2915 455.2864 I 586.4035 569.3770 5

5 540.3756 568.3705 L 473.3194 456.2929 4

6 668.4705 651.4440 696.4654 679.4389 K 360.2354 343.2088 3

7 725.4920 708.4654 753.4869 736.4604 G 232.1404 215.1139 2

8 R 175.1190 158.0924 1

R.LVEILKGR.L 351

R.LVEILKGR.L 471

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APPENDICES

152

# a a* b b* Seq. y y* #

1 86.0964 114.0913 L 8

2 185.1648 213.1598 V 814.5145 797.4880 7

3 314.2074 342.2023 E 715.4461 698.4196 6

4 427.2915 455.2864 I 586.4035 569.3770 5

5 540.3756 568.3705 L 473.3194 456.2929 4

6 668.4705 651.4440 696.4654 679.4389 K 360.2354 343.2088 3

7 725.4920 708.4654 753.4869 736.4604 G 232.1404 215.1139 2

8 R 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 102.0550 130.0499 E 9

2 230.1499 213.1234 258.1448 241.1183 K 912.4799 895.4533 8

3 343.2340 326.2074 371.2289 354.2023 L 784.3849 7

4 440.2867 423.2602 468.2817 451.2551 P 671.3008 6

5 497.3082 480.2817 525.3031 508.2766 G 574.2481 5

6 634.3671 617.3406 662.3620 645.3355 H 517.2266 4

7 771.4260 754.3995 799.4209 782.3944 H 380.1677 3

8 858.4581 841.4315 886.4530 869.4264 S 243.1088 2

9 H 156.0768 1

K.EKLPGHHSH.- 850

R.LVEILKGR.L 651

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APPENDICES

153

# Immon. a a* a0 b b* b0 Seq. v w w' y y* y0 #

1 44.0495 44.0495 72.0444 A 16

2 72.0808 143.1179 171.1128 V 1571.7449 1584.7653 1615.8075 1598.7809 1597.7969 15

3 102.0550 272.1605 254.1499 300.1554 282.1448 E 1442.7023 1441.7070 1516.7390 1499.7125 1498.7285 14

4 159.0917 458.2398 440.2292 486.2347 468.2241 W 1256.6230 1387.6965 1370.6699 1369.6859 13

5 60.0444 545.2718 527.2613 573.2667 555.2562 S 1169.5909 1168.5957 1201.6171 1184.5906 1183.6066 12

6 88.0393 660.2988 642.2882 688.2937 670.2831 D 1054.5640 1053.5687 1114.5851 1097.5586 1096.5745 11

7 44.0495 731.3359 713.3253 759.3308 741.3202 A 983.5269 999.5582 982.5316 981.5476 10

8 44.0495 802.3730 784.3624 830.3679 812.3573 A 912.4898 928.5211 911.4945 910.5105 9

9 44.0495 873.4101 855.3995 901.4050 883.3945 A 841.4526 857.4839 840.4574 839.4734 8

10 86.0964 986.4942 968.4836 1014.4891 996.4785 I 728.3686 741.3890 755.4046 786.4468 769.4203 768.4363 7

11 101.0709 1114.5527 1097.5262 1096.5422 1142.5477 1125.5211 1124.5371 Q 600.3100 599.3148 673.3628 656.3362 655.3522 6

12 44.0495 1185.5899 1168.5633 1167.5793 1213.5848 1196.5582 1195.5742 A 529.2729 545.3042 528.2776 527.2936 5

13 44.0495 1256.6270 1239.6004 1238.6164 1284.6219 1267.5953 1266.6113 A 458.2358 474.2671 457.2405 456.2565 4

14 102.0550 1385.6696 1368.6430 1367.6590 1413.6645 1396.6379 1395.6539 E 329.1932 328.1979 403.2300 386.2034 385.2194 3

15 72.0808 1484.7380 1467.7114 1466.7274 1512.7329 1495.7064 1494.7223 V 230.1248 243.1452 274.1874 257.1608 2

16 129.1135 R 74.0237 73.0284 175.1190 158.0924 1

# Immon. a a* a0 b b* b0 Seq. v w w' y y* y0 #

1 101.1073 101.1073 84.0808 129.1022 112.0757 K 12

2 86.0964 214.1914 197.1648 242.1863 225.1598 L 1179.5389 1178.5436 1237.6171 1220.5906 1219.6066 11

3 74.0600 315.2391 298.2125 297.2285 343.2340 326.2074 325.2234 T 1078.4912 1091.5116 1093.4909 1124.5331 1107.5065 1106.5225 10

4 110.0713 452.2980 435.2714 434.2874 480.2929 463.2663 462.2823 H 941.4323 1023.4854 1006.4589 1005.4748 9

5 30.0338 509.3194 492.2929 491.3089 537.3144 520.2878 519.3038 G 886.4265 869.3999 868.4159 8

6 44.0495 580.3566 563.3300 562.3460 608.3515 591.3249 590.3409 A 813.3737 829.4050 812.3785 811.3945 7

7 70.0651 677.4093 660.3828 659.3988 705.4042 688.3777 687.3937 P 716.3210 715.3257 758.3679 741.3414 740.3573 6

8 102.0550 806.4519 789.4254 788.4413 834.4468 817.4203 816.4363 E 587.2784 586.2831 661.3151 644.2886 643.3046 5

9 88.0393 921.4789 904.4523 903.4683 949.4738 932.4472 931.4632 D 472.2514 471.2562 532.2726 515.2460 514.2620 4

10 102.0550 1050.5215 1033.4949 1032.5109 1078.5164 1061.4898 1060.5058 E 343.2088 342.2136 417.2456 400.2191 399.2350 3

11 86.0964 1163.6055 1146.5790 1145.5949 1191.6004 1174.5739 1173.5899 I 230.1248 243.1452 257.1608 288.2030 271.1765 2

12 129.1135 R 74.0237 73.0284 175.1190 158.0924 1

K.AVEWSDAAAIQAAEVR.A 64

K.KLTHGAPEDEIR.H

733

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APPENDICES

154

# a a* b b* Seq. y y* #

1 60.0444 88.0393 S 12

2 159.1128 187.1077 V 1264.6168 1247.5903 11

3 246.1448 274.1397 S 1165.5484 1148.5218 10

4 333.1769 361.1718 S 1078.5164 1061.4898 9

5 462.2195 490.2144 E 991.4843 974.4578 8

6 549.2515 577.2464 S 862.4417 845.4152 7

7 678.2941 706.2890 E 775.4097 758.3832 6

8 775.3468 803.3418 P 646.3671 629.3406 5

9 922.4153 950.4102 F 549.3144 532.2878 4

10 1036.4582 1019.4316 1064.4531 1047.4265 N 402.2459 385.2194 3

11 1149.5422 1132.5157 1177.5372 1160.5106 L 288.2030 271.1765 2

12 R 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 60.0444 88.0393 S 9

2 216.1455 199.1190 244.1404 227.1139 R 991.5320 974.5054 8

3 330.1884 313.1619 358.1833 341.1568 N 835.4308 818.4043 7

4 427.2412 410.2146 455.2361 438.2096 P 721.3879 704.3614 6

5 540.3253 523.2987 568.3202 551.2936 I 624.3352 607.3086 5

6 703.3886 686.3620 731.3835 714.3569 Y 511.2511 494.2245 4

7 790.4206 773.3941 818.4155 801.3890 S 348.1878 331.1612 3

8 904.4635 887.4370 932.4585 915.4319 N 261.1557 244.1292 2

9 K 147.1128 130.0863 1

K.SVSSESEPFNLR.S

200

R.SRNPIYSNK.F

520

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APPENDICES

155

# a a* b b* Seq. y y* #

1 60.0444 88.0393 S 9

2 216.1455 199.1190 244.1404 227.1139 R 991.5320 974.5054 8

3 330.1884 313.1619 358.1833 341.1568 N 835.4308 818.4043 7

4 427.2412 410.2146 455.2361 438.2096 P 721.3879 704.3614 6

5 540.3253 523.2987 568.3202 551.2936 I 624.3352 607.3086 5

6 703.3886 686.3620 731.3835 714.3569 Y 511.2511 494.2245 4

7 790.4206 773.3941 818.4155 801.3890 S 348.1878 331.1612 3

8 904.4635 887.4370 932.4585 915.4319 N 261.1557 244.1292 2

9 K 147.1128 130.0863 1

# a a* b b* Seq. y y* #

1 101.0709 84.0444 129.0659 112.0393 Q 13

2 172.1081 155.0815 200.1030 183.0764 A 1331.6266 1314.6001 12

3 229.1295 212.1030 257.1244 240.0979 G 1260.5895 1243.5630 11

4 343.1724 326.1459 371.1674 354.1408 N 1203.5681 1186.5415 10

5 472.2150 455.1885 500.2100 483.1834 E 1089.5251 1072.4986 9

6 529.2365 512.2100 557.2314 540.2049 G 960.4825 943.4560 8

7 676.3049 659.2784 704.2998 687.2733 F 903.4611 886.4345 7

8 805.3475 788.3210 833.3424 816.3159 E 756.3927 739.3661 6

9 968.4108 951.3843 996.4058 979.3792 Y 627.3501 610.3235 5

10 1067.4793 1050.4527 1095.4742 1078.4476 V 464.2867 447.2602 4

11 1138.5164 1121.4898 1166.5113 1149.4847 A 365.2183 348.1918 3

12 1285.5848 1268.5582 1313.5797 1296.5531 F 294.1812 277.1547 2

13 K 147.1128 130.0863 1

R.SRNPIYSNK.F

561

K.QAGNEGFEYVAFK.T

910

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APPENDICES

156

# a a* b b* Seq. y y* #

1 120.0808 148.0757 F 8

2 177.1022 205.0972 G 832.4272 815.4006 7

3 306.1448 334.1397 E 775.4057 758.3791 6

4 421.1718 449.1667 D 646.3631 629.3365 5

5 534.2558 562.2508 L 531.3362 514.3096 4

6 621.2879 649.2828 S 418.2521 401.2255 3

7 777.3890 760.3624 805.3839 788.3573 R 331.2201 314.1935 2

8 R 175.1190 158.0924 1

# Immon. a a* a0 b b* b0 Seq. v w y y* y0 #

1 87.0553 87.0553 70.0287 115.0502 98.0237 N 13

2 120.0478 234.0907 217.0641 262.0856 245.0591 M 1099.5127 1098.5174 1191.5423 1174.5157 1173.5317 12

3 87.0553 348.1336 331.1071 376.1285 359.1020 N 985.4697 984.4745 1044.5069 1027.4803 1026.4963 11

4 102.0550 477.1762 460.1497 459.1656 505.1711 488.1446 487.1606 E 856.4272 855.4319 930.4639 913.4374 912.4534 10

5 72.0808 576.2446 559.2181 558.2341 604.2395 587.2130 586.2290 V 757.3587 770.3791 801.4213 784.3948 783.4108 9

6 44.0495 647.2817 630.2552 629.2712 675.2767 658.2501 657.2661 A 686.3216 702.3529 685.3264 684.3424 8

7 30.0338 704.3032 687.2767 686.2926 732.2981 715.2716 714.2876 G 631.3158 614.2893 613.3053 7

8 44.0495 775.3403 758.3138 757.3298 803.3352 786.3087 785.3247 A 558.2631 574.2944 557.2678 556.2838 6

9 88.0393 890.3673 873.3407 872.3567 918.3622 901.3356 900.3516 D 443.2361 442.2409 503.2572 486.2307 485.2467 5

10 44.0495 961.4044 944.3778 943.3938 989.3993 972.3727 971.3887 A 372.1990 388.2303 371.2037 4

11 44.0495 1032.4415 1015.4149 1014.4309 1060.4364 1043.4099 1042.4258 A 301.1619 317.1932 300.1666 3

12 44.0495 1103.4786 1086.4521 1085.4680 1131.4735 1114.4470 1113.4630 A 230.1248 246.1561 229.1295 2

13 129.1135 R 74.0237 73.0284 175.1190 158.0924 1

R.FGEDLSRR.Y

921

K.NMNEVAGADAAAR.F 960

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APPENDICES

157

# a a* b b* Seq. y y* #

1 88.0393 116.0342 D 10

2 235.1077 263.1026 F 1190.6793 1173.6527 9

3 348.1918 376.1867 L 1043.6109 1026.5843 8

4 445.2445 473.2395 P 930.5268 913.5003 7

5 592.3130 620.3079 F 833.4740 816.4475 6

6 705.3970 733.3919 L 686.4056 669.3791 5

7 833.4556 816.4291 861.4505 844.4240 Q 573.3216 556.2950 4

8 989.5567 972.5302 1017.5516 1000.5251 R 445.2630 428.2364 3

9 1103.5996 1086.5731 1131.5946 1114.5680 N 289.1619 272.1353 2

10 R 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 74.0600 102.0550 T 13

2 202.1186 185.0921 230.1135 213.0870 Q 1117.5531 1100.5266 12

3 301.1870 284.1605 329.1819 312.1554 V 989.4945 972.4680 11

4 461.2177 444.1911 489.2126 472.1860 C 890.4261 873.3996 10

5 532.2548 515.2282 560.2497 543.2232 A 730.3955 713.3689 9

6 603.2919 586.2654 631.2868 614.2603 A 659.3583 642.3318 8

7 660.3134 643.2868 688.3083 671.2817 G 588.3212 571.2947 7

8 717.3348 700.3083 745.3298 728.3032 G 531.2998 514.2732 6

9 774.3563 757.3298 802.3512 785.3247 G 474.2783 457.2518 5

10 831.3778 814.3512 859.3727 842.3461 G 417.2568 400.2303 4

11 888.3992 871.3727 916.3941 899.3676 G 360.2354 343.2088 3

12 1016.4942 999.4676 1044.4891 1027.4626 K 303.2139 286.1874 2

13 R 175.1190 158.0924 1

R.TQVCAAGGGGGKR.R 201

730 R.DFLPFLQRNR.R

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APPENDICES

158

# a a* b b* Seq. y y* #

1 72.0808 100.0757 V 9

2 129.1022 157.0972 G 1007.4846 990.4581 8

3 276.1707 304.1656 F 950.4631 933.4366 7

4 462.2500 490.2449 W 803.3947 786.3682 6

5 648.3293 676.3242 W 617.3154 600.2889 5

6 705.3507 733.3457 G 431.2361 414.2096 4

7 776.3879 804.3828 A 374.2146 357.1881 3

8 904.4464 887.4199 932.4414 915.4148 Q 303.1775 286.1510 2

9 R 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 129.1135 112.0869 157.1084 140.0818 R 12

2 200.1506 183.1240 228.1455 211.1190 A 1192.6473 1175.6208 11

3 313.2346 296.2081 341.2296 324.2030 I 1121.6102 1104.5837 10

4 400.2667 383.2401 428.2616 411.2350 S 1008.5261 991.4996 9

5 457.2881 440.2616 485.2831 468.2565 G 921.4941 904.4676 8

6 514.3096 497.2831 542.3045 525.2780 G 864.4726 847.4461 7

7 661.3780 644.3515 689.3729 672.3464 F 807.4512 790.4246 6

8 774.4621 757.4355 802.4570 785.4304 L 660.3828 643.3562 5

9 861.4941 844.4676 889.4890 872.4625 S 547.2987 530.2722 4

10 960.5625 943.5360 988.5574 971.5309 V 460.2667 443.2401 3

11 1146.6418 1129.6153 1174.6368 1157.6102 W 361.1983 344.1717 2

12 R 175.1190 158.0924 1

R.RAISGGFLSVWR.A 720

R.VGFWWGAQR.L 155

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APPENDICES

159

# a a* b b* Seq. y y* #

1 88.0393 116.0342 D 22

2 235.1077 263.1026 F 2447.1655 2430.1390 21

3 334.1761 362.1710 V 2300.0971 2283.0706 20

4 463.2187 491.2136 E 2201.0287 2184.0021 19

5 520.2402 548.2351 G 2071.9861 2054.9595 18

6 577.2617 605.2566 G 2014.9646 1997.9381 17

7 708.3021 736.2971 M 1957.9432 1940.9166 16

8 821.3862 849.3811 I 1826.9027 1809.8761 15

9 968.4546 996.4495 F 1713.8186 1696.7921 14

10 1096.5132 1079.4866 1124.5081 1107.4816 Q 1566.7502 1549.7237 13

11 1209.5973 1192.5707 1237.5922 1220.5656 L 1438.6916 1421.6651 12

12 1322.6813 1305.6548 1350.6762 1333.6497 L 1325.6076 1308.5810 11

13 1451.7239 1434.6974 1479.7188 1462.6923 E 1212.5235 1195.4970 10

14 1566.7509 1549.7243 1594.7458 1577.7192 D 1083.4809 1066.4544 9

15 1679.8349 1662.8084 1707.8298 1690.8033 L 968.4540 951.4274 8

16 1780.8826 1763.8561 1808.8775 1791.8510 T 855.3699 838.3434 7

17 1909.9252 1892.8986 1937.9201 1920.8936 E 754.3222 737.2957 6

18 2040.9657 2023.9391 2068.9606 2051.9340 M 625.2796 608.2531 5

19 2127.9977 2110.9712 2155.9926 2138.9661 S 494.2391 477.2126 4

20 2229.0454 2212.0188 2257.0403 2240.0138 T 407.2071 390.1806 3

21 2360.0859 2343.0593 2388.0808 2371.0542 M 306.1594 289.1329 2

22 R 175.1190 158.0924 1

# Immon. a a* a0 b b* b0 Seq. v w y y* y0 #

1 30.0338 30.0338 58.0287 G 9

2 88.0393 145.0608 127.0502 173.0557 155.0451 D 882.5156 881.5203 942.5367 925.5102 924.5261 8

3 86.0964 258.1448 240.1343 286.1397 268.1292 L 769.4315 768.4363 827.5098 810.4832 809.4992 7

4 101.1073 386.2398 369.2132 368.2292 414.2347 397.2082 396.2241 K 641.3365 640.3413 714.4257 697.3991 696.4151 6

5 60.0444 473.2718 456.2453 455.2613 501.2667 484.2402 483.2562 S 554.3045 553.3093 586.3307 569.3042 568.3202 5

6 70.0651 570.3246 553.2980 552.3140 598.3195 581.2930 580.3089 P 457.2518 456.2565 499.2987 482.2722 4

7 87.0553 684.3675 667.3410 666.3569 712.3624 695.3359 694.3519 N 343.2088 342.2136 402.2459 385.2194 3

8 86.0964 797.4516 780.4250 779.4410 825.4465 808.4199 807.4359 L 230.1248 229.1295 288.2030 271.1765 2

9 129.1135 R 74.0237 73.0284 175.1190 158.0924 1

R.DFVEGGMIFQLLEDLTEMSTMR.N 23

K.GDLKSPNLR.S 440

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APPENDICES

160

# a a* b b* Seq. y y* #

1 101.1073 84.0808 129.1022 112.0757 K 7

2 200.1757 183.1492 228.1707 211.1441 V 826.4094 809.3828 6

3 347.2442 330.2176 375.2391 358.2125 F 727.3410 710.3144 5

4 494.3126 477.2860 522.3075 505.2809 F 580.2726 563.2460 4

5 623.3552 606.3286 651.3501 634.3235 E 433.2041 416.1776 3

6 752.3978 735.3712 780.3927 763.3661 E 304.1615 287.1350 2

7 R 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 72.0808 100.0757 V 16

2 219.1492 247.1441 F 1711.8286 1694.8020 15

3 334.1761 362.1710 D 1564.7602 1547.7336 14

4 462.2711 445.2445 490.2660 473.2395 K 1449.7332 1432.7067 13

5 577.2980 560.2715 605.2930 588.2664 D 1321.6383 1304.6117 12

6 705.3566 688.3301 733.3515 716.3250 Q 1206.6113 1189.5848 11

7 820.3836 803.3570 848.3785 831.3519 D 1078.5527 1061.5262 10

8 877.4050 860.3785 905.3999 888.3734 G 963.5258 946.4993 9

9 1024.4734 1007.4469 1052.4684 1035.4418 F 906.5043 889.4778 8

10 1137.5575 1120.5310 1165.5524 1148.5259 I 759.4359 742.4094 7

11 1224.5895 1207.5630 1252.5844 1235.5579 S 646.3519 629.3253 6

12 1295.6266 1278.6001 1323.6216 1306.5950 A 559.3198 542.2933 5

13 1366.6638 1349.6372 1394.6587 1377.6321 A 488.2827 471.2562 4

14 1495.7063 1478.6798 1523.7013 1506.6747 E 417.2456 400.2191 3

15 1608.7904 1591.7639 1636.7853 1619.7588 L 288.2030 271.1765 2

16 R 175.1190 158.0924 1

-.KVFFEER.F 108

R.VFDKDQDGFISAAELR.H 34

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# a a* b b* Seq. y y* #

1 102.0550 130.0499 E 12

2 159.0764 187.0713 G 1240.6797 1223.6531 11

3 246.1084 274.1034 S 1183.6582 1166.6317 10

4 359.1925 387.1874 L 1096.6262 1079.5996 9

5 472.2766 500.2715 L 983.5421 966.5156 8

6 585.3606 613.3556 L 870.4581 853.4315 7

7 682.4134 710.4083 P 757.3740 740.3474 6

8 819.4723 847.4672 H 660.3212 643.2947 5

9 966.5407 994.5356 F 523.2623 506.2358 4

10 1080.5837 1063.5571 1108.5786 1091.5520 N 376.1939 359.1674 3

11 1167.6157 1150.5891 1195.6106 1178.5840 S 262.1510 245.1244 2

12 R 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 60.0444 88.0393 S 11

2 188.1394 171.1128 216.1343 199.1077 K 1324.7008 1307.6743 10

3 301.2234 284.1969 329.2183 312.1918 I 1196.6058 1179.5793 9

4 448.2918 431.2653 476.2867 459.2602 F 1083.5218 1066.4952 8

5 577.3344 560.3079 605.3293 588.3028 E 936.4534 919.4268 7

6 691.3773 674.3508 719.3723 702.3457 N 807.4108 790.3842 6

7 804.4614 787.4349 832.4563 815.4298 L 693.3679 676.3413 5

8 932.5200 915.4934 960.5149 943.4884 Q 580.2838 563.2572 4

9 1046.5629 1029.5364 1074.5578 1057.5313 N 452.2252 435.1987 3

10 1209.6262 1192.5997 1237.6212 1220.5946 Y 338.1823 321.1557 2

11 R 175.1190 158.0924 1

R.EGSLLLPHFNSR.A 340

R.SKIFENLQNYR.L 940

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# a a* b b* Seq. y y* #

1 102.0550 130.0499 E 12

2 159.0764 187.0713 G 1240.6797 1223.6531 11

3 246.1084 274.1034 S 1183.6582 1166.6317 10

4 359.1925 387.1874 L 1096.6262 1079.5996 9

5 472.2766 500.2715 L 983.5421 966.5156 8

6 585.3606 613.3556 L 870.4581 853.4315 7

7 682.4134 710.4083 P 757.3740 740.3474 6

8 819.4723 847.4672 H 660.3212 643.2947 5

9 966.5407 994.5356 F 523.2623 506.2358 4

10 1080.5837 1063.5571 1108.5786 1091.5520 N 376.1939 359.1674 3

11 1167.6157 1150.5891 1195.6106 1178.5840 S 262.1510 245.1244 2

12 R 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 60.0444 88.0393 S 11

2 188.1394 171.1128 216.1343 199.1077 K 1324.7008 1307.6743 10

3 301.2234 284.1969 329.2183 312.1918 I 1196.6058 1179.5793 9

4 448.2918 431.2653 476.2867 459.2602 F 1083.5218 1066.4952 8

5 577.3344 560.3079 605.3293 588.3028 E 936.4534 919.4268 7

6 691.3773 674.3508 719.3723 702.3457 N 807.4108 790.3842 6

7 804.4614 787.4349 832.4563 815.4298 L 693.3679 676.3413 5

8 932.5200 915.4934 960.5149 943.4884 Q 580.2838 563.2572 4

9 1046.5629 1029.5364 1074.5578 1057.5313 N 452.2252 435.1987 3

10 1209.6262 1192.5997 1237.6212 1220.5946 Y 338.1823 321.1557 2

11 R 175.1190 158.0924 1

110 R.EGSLLLPHFNSR.A

663 R.SKIFENLQNYR.L

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# a b Seq. y y* #

1 44.0495 72.0444 A 8

2 157.1335 185.1285 L 745.4567 728.4301 7

3 244.1656 272.1605 S 632.3726 615.3461 6

4 343.2340 371.2289 V 545.3406 528.3140 5

5 414.2711 442.2660 A 446.2722 429.2456 4

6 501.3031 529.2980 S 375.2350 358.2085 3

7 614.3872 642.3821 L 288.2030 271.1765 2

8 R 175.1190 158.0924 1

# a a* b b* Seq. y y* #

1 60.0444 88.0393 S 11

2 188.1394 171.1128 216.1343 199.1077 K 1324.7008 1307.6743 10

3 301.2234 284.1969 329.2183 312.1918 I 1196.6058 1179.5793 9

4 448.2918 431.2653 476.2867 459.2602 F 1083.5218 1066.4952 8

5 577.3344 560.3079 605.3293 588.3028 E 936.4534 919.4268 7

6 691.3773 674.3508 719.3723 702.3457 N 807.4108 790.3842 6

7 804.4614 787.4349 832.4563 815.4298 L 693.3679 676.3413 5

8 932.5200 915.4934 960.5149 943.4884 Q 580.2838 563.2572 4

9 1046.5629 1029.5364 1074.5578 1057.5313 N 452.2252 435.1987 3

10 1209.6262 1192.5997 1237.6212 1220.5946 Y 338.1823 321.1557 2

11 R 175.1190 158.0924 1

822 R.SKIFENLQNYR.L

K.ALSVASLR.R 131

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# a a* b b* Seq. y y* #

1 120.0808 148.0757 F 15

2 207.1128 235.1077 S 1569.7431 1552.7166 14

3 264.1343 292.1292 G 1482.7111 1465.6846 13

4 393.1769 421.1718 E 1425.6896 1408.6631 12

5 522.2195 550.2144 E 1296.6470 1279.6205 11

6 669.2879 697.2828 F 1167.6045 1150.5779 10

7 798.3305 826.3254 E 1020.5360 1003.5095 9

8 912.3734 895.3468 940.3683 923.3418 N 891.4934 874.4669 8

9 1059.4418 1042.4153 1087.4367 1070.4102 F 777.4505 760.4240 7

10 1172.5259 1155.4993 1200.5208 1183.4942 L 630.3821 613.3556 6

11 1243.5630 1226.5364 1271.5579 1254.5313 A 517.2980 500.2715 5

12 1342.6314 1325.6048 1370.6263 1353.5998 V 446.2609 429.2344 4

13 1413.6685 1396.6420 1441.6634 1424.6369 A 347.1925 330.1660 3

14 1542.7111 1525.6846 1570.7060 1553.6795 E 276.1554 259.1288 2

15 K 147.1128 130.0863 1

# a b Seq. y y* #

1 86.0964 114.0913 L 9

2 157.1335 185.1285 A 872.4836 855.4571 8

3 272.1605 300.1554 D 801.4465 784.4199 7

4 401.2031 429.1980 E 686.4196 669.3930 6

5 514.2871 542.2821 I 557.3770 540.3504 5

6 613.3556 641.3505 V 444.2929 427.2663 4

7 670.3770 698.3719 G 345.2245 328.1979 3

8 783.4611 811.4560 L 288.2030 271.1765 2

9 R 175.1190 158.0924 1

K.FSGEEFENFLAVAEK.L 634

R.LADEIVGLR.Q 100

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# a a* b b* Seq. y y* #

1 129.1135 112.0869 157.1084 140.0818 R 6

2 257.1721 240.1455 285.1670 268.1404 Q 646.3267 629.3002 5

3 385.2306 368.2041 413.2255 396.1990 Q 518.2681 501.2416 4

4 472.2627 455.2361 500.2576 483.2310 S 390.2096 373.1830 3

5 600.3212 583.2947 628.3161 611.2896 Q 303.1775 286.1510 2

6 R 175.1190 158.0924 1

# a b Seq. y y* #

1 104.0528 132.0478 M 11

2 175.0900 203.0849 A 934.4741 917.4476 10

3 322.1584 350.1533 F 863.4370 846.4104 9

4 437.1853 465.1802 D 716.3686 699.3420 8

5 508.2224 536.2173 A 601.3416 584.3151 7

6 565.2439 593.2388 G 530.3045 513.2780 6

7 636.2810 664.2759 A 473.2831 456.2565 5

8 693.3025 721.2974 G 402.2459 385.2194 4

9 806.3865 834.3815 I 345.2245 328.1979 3

10 863.4080 891.4029 G 232.1404 215.1139 2

11 R 175.1190 158.0924 1

K.RQQSQR.M 418

-.MAFDAGAGIGR.I 451

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# a b Seq. y y* #

1 30.0338 58.0287 G 10

2 190.0645 218.0594 C 888.4720 871.4455 9

3 303.1485 331.1435 L 728.4414 711.4148 8

4 374.1857 402.1806 A 615.3573 598.3307 7

5 431.2071 459.2020 G 544.3202 527.2936 6

530.2755 558.2704 V 487.2987 470.2722 5

7 629.3439 657.3389 V 388.2303 371.2037 4

8 686.3654 714.3603 G 289.1619 272.1353 3

9 743.3869 771.3818 G 232.1404 215.1139 2

10 R 175.1190 158.0924 1

# Immon. a a0 b b0 Seq. v w y y* y0 #

1 60.0444 60.0444 42.0338 88.0393 70.0287 S 8

2 86.0964 173.1285 155.1179 201.1234 183.1128 L 671.3835 670.3883 729.4618 712.4352 711.4512 7

3 44.0495 244.1656 226.1550 272.1605 254.1499 A 600.3464 616.3777 599.3511 598.3671 6

4 44.0495 315.2027 297.1921 343.1976 325.1870 A 529.3093 545.3406 528.3140 527.3300 5

5 72.0808 414.2711 396.2605 442.2660 424.2554 V 430.2409 443.2613 474.3035 457.2769 456.2929 4

6 86.0964 527.3552 509.3446 555.3501 537.3395 L 317.1568 316.1615 375.2350 358.2085 357.2245 3

7 60.0444 614.3872 596.3766 642.3821 624.3715 S 230.1248 229.1295 262.1510 245.1244 244.1404 2

8 129.1135 R 74.0237 73.0284 175.1190 158.0924 1

R.GCLAGVVGGR.X 516

K.SLAAVLSR.F 251

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Appendix 2 Peptide view with MS/MS fragmentation and its corresponding table of

matched fragment ions (Bold Red) using the most intense peaks for identification

proteins with single peptides

# Immon. a a* a0 b b* b0 Seq. y y* y0 #

1 101.0709 101.0709 84.0444 129.0659 112.0393 Q 7

2 110.0713 238.1299 221.1033 266.1248 249.0982 H 738.4509 721.4243 720.4403 6

3 86.0964 351.2139 334.1874 379.2088 362.1823 L 601.3919 584.3654 583.3814 5

4 86.0964 464.2980 447.2714 492.2929 475.2663 I 488.3079 471.2813 470.2973 4

5 102.0550 593.3406 576.3140 575.3300 621.3355 604.3089 603.3249 E 375.2238 358.1973 357.2132 3

6 72.0808 692.4090 675.3824 674.3984 720.4039 703.3774 702.3933 V 246.1812 229.1547 2

7 101.1073 K 147.1128 130.0863 1

K.QHLIEVK.G 851

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Appendix 3 Clusterograms displaying expression profile of germination responsive proteins.

Clu

ster

Div

ersit

y

No

of p

rote

ins

Clu

ster

No

(0)

(0)

(0)

(0)

(0)

(0)

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(0)

(0)

(0)

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Appendix 3 Clusterograms displaying expression profiles of germination responsive proteins.

The numbers in the column on the right hand side are the spot numbers presented in Figure 4.4

and Table 4.3

(0)

(0)

(0)