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The Role of Hydrogen Peroxide (H2O2) in
Sensing and Signalling Abiotic Stimuli in Rice
Nyuk Ling Ma
A Dissertation Submitted for the Degree of
Doctor of Philosophy
Department of Life Sciences
Division of Cell and Molecular Biology
Imperial College London
2012
To my dear husband
“The path to our destination is not always a straight one.
We go down the wrong road, we get lost, we turn back.
Maybe it doesn’t matter which road we embark on.
Maybe what matters is that we embark”
- Barbara Hall
i
1 Acknowledgment
I owe a very special thanks to people that have provided help and support during my time in
Imperial College London.
My supervisor, Dr Radhika Desikan, has always been a great support and precious mentor to
me. Sometimes simple words can have great power. What she told and advised me has got
me through many of my hard times. Her advice and motivation has guided me to be a better
and successful person. I thank her for giving me the chance to explore my research, begin as a
research student and develop it into something I could be proud of. Also, I thank her for the
invaluable comments, constructive criticism, and all her effort in the review of this work. I
also would like to thank Dr Simon Archer for his constructive advice and support during my
project.
I would also like to acknowledge the SLAI Scholarship offered by the Ministry of Higher
Education Malaysia and the University Malaysia Terengganu for the financial assistance that
has supported this research.
Also, I would like to thank Prof. Dr. John Mansfield, Prof. Dr. Elaine Holmes, late Dr Judith
Nagy, Dr. Jia Li, Mark Bennett for the time, thoughts and technical support the proteomic and
NMR work. Here I would also like to acknowledge an amazing group of individuals for their
technical suggestions, compliments, ideas, and the friendships they bestowed upon me: Dr.
Nan Zhang, Dr. Roslinda, Dr. Zaidah, Dr. Liang Fang, Yvonne Stewart, Dr. Nicolas Joly, Dr. Miao
Guo, Huda abd Kadir, Mohd Naqi, Prof. Dr. Nor Aini Ab Shukor and Lei Wang.
Finally, but certainly not least, I would like to express my heartiest appreciation to my
beloved family. Thanks for all their endless love, care, moral and financial support, and
patience that sustained me in whatever I wanted to achieve. An eternal gratitude to my
lovely husband, Su Shiung Lam for his support and presence with me all the time.
Thanks God for always being with us.
ii
2 Statement of originality
This dissertation describes work performed in the Department of life sciences at the
Imperial college London between February 2008 and January 2012. All the work outlined
in this dissertation is my own except the where otherwise acknowledged and referenced.
The work contained in the dissertation has not been previously submitted, in whole or in
part, for a degree at any institution.
This dissertation is submitted in fulfilment of the requirements of the PhD. It does not
exceed 100,000 words and contains fewer than 150 figures.
iii
3 Abstract
This dissertation describes the integration of physiological, proteomic, and metabolomic
changes in conjunction with the synthesis of H2O2 under various stress conditions in
rice, Oryza sativa (Japonica var. Koshihikari). Dynamics of H2O2 production were observed
in the leaves following the treatment with drought, salinity, osmotic and cold stress. The
physiological parameters such as H2O2 content, photosynthetic activity, water content,
and lipid peroxidation were used as indicators to correlate the physiological status of the
plant with its metabolites and proteins profile/changes in response to stresses. A
targeted proteomic approach has been developed to identify oxidative modified thiols,
and a NMR-based analysis has been performed for metabolites profiling.
Activation of amino acids was observed in the treatment of short-term drought stress
while deficit of sugar and lipids in addition to accumulation of amino acids were observed
when treated with long-term drought stress. The high levels of lipid peroxidation and the
irreversible oxidation of protein thiols observed under long-term drought stress indicate
that the plants were likely to have suffered membrane damage. In osmotic stress
treatment, the plants showed rapid dehydration compared to that treated with drought
stress. The transient increase of both lipid peroxidation and reversible thiol oxidation
suggest that signalling of adaptation had been activated, resulting in fluctuation of carbon
and nitrogen metabolism in the plants. In salinity stress treatment, very low levels of lipid
peroxidation, decline in photosynthetic activities, and very few metabolites changes were
observed. This suggests that the dosage used in salinity test was insufficient to induce salt
stress to the plants. Another possibility is that the changes in thiol proteins following the
salt treatment were likely to have contributed to salt tolerance, hence keeping the
metabolites in the control level. Moreover, the metabolite changes observed in the
plants following exposure to exogenous H2O2 was found to overlap with those induced by
other stresses, indicating that H2O2 is likely to have effects on the carbohydrate, fatty acid,
and amino acids pathways. In summary, the results suggest that there were possible
overlap of H2O2 mediated-signalling pathway in coordinating cellular changes at both
proteomic and metabolomic level under abiotic stress conditions in rice plants. However,
further studies are needed to confirm the role and the underlying mechanism of H2O2 in
regulating the cellular changes.
iv
4 List of abbreviations
1D, 2D 1-dimensional, 2-dimensional Aconitate aconitic acid Ala alanine APX ascorbate peroxidase Arg arginine ASA ascorbic acid Asn asparagine BSA bovine serum albumin CAT catalase Chol choline Cit citrulline COSY correlation spectroscopy Cys cysteine DAB 3, 3-diaminobenzidine DHAR dehydroascorbate reductase ETC electron transfer chain GA glyceraldehyde GABA Gamma- amino-N-butyrate Gln glutamine Glu glutamate GPX glutathione peroxidase GR glutathione reductase GSH reduced glutathione GSSG glutathione disulphide HCA hierarchy cluster analysis His histamine Ile isoleucine Ino inosine IS indoxyl sulfate Lac lactate Leu leucine MAPK mitogen-activated protein kinase MDA malondialdehyde MDHA monodehydroascorbate radical MDHAR monodehydroascorbate reductase MS mass spectrometry NEM N-Ethylmaleimide NMR Nuclear magnetic resonance PCA principal components analysis PEG polyethylene glycol PEP phospho(enol)-pyruvate Phe phenylalanine PLSDA partial least squares linear discriminant analysis PMSF phenyl methyl sulfonyl fluroride PVP polyvinylpyrrolidone
v
SDS-PAGE Sodium dodecyl sulfate polyacrylamide gel electrophoresis SOD superoxide dismutase enzymes STOCSY statistical total correlation spectroscopy Suc sucrose TBA thiobarbituric acid Thr threonine TOCSY total correlation spectroscopy TRX thioredoxin Tyr tyrosine Val valine α-glc α-glucose β-glc β-glucose
vi
5 Table of content
1 Acknowledgment .......................................................................................................... i
2 Statement of originality ............................................................................................... ii
3 Abstract....................................................................................................................... iii
4 List of abbreviations .................................................................................................... iv
5 Table of content .......................................................................................................... vi
6 List of Figures .............................................................................................................. ix
7 List of Tables ............................................................................................................. xiii
1 Chapter One: INTRODUCTION ...................................................................................... 1
1.1 Rice species and family ...................................................................................... 1 1.1.1 Stresses that affect rice production ............................................................... 3 1.1.2 What make rice so susceptible to water stress .............................................. 4 1.1.3 Plant stress response ..................................................................................... 5
1.2 Chemistry of Reactive Oxygen Species (ROS) ..................................................... 6 1.2.1 Sources and regulation of H2O2 ...................................................................... 7 1.2.2 Removal of H2O2 .......................................................................................... 11 1.2.3 Hydrogen peroxide as a signalling molecule ................................................. 17 1.2.4 Hydrogen peroxide and interaction with hormones ..................................... 19
1.3 Deleterious Effects of ROS ............................................................................... 21 1.3.1 DNA damage ................................................................................................ 21 1.3.2 Lipid peroxidation ........................................................................................ 22 1.3.3 Oxidation of proteins ................................................................................... 22
1.4 Measurement of ROS ...................................................................................... 23 1.4.1 Electron paramagnetic resonance (EPR) ...................................................... 23 1.4.2 Staining and visualisation of H2O2 with chemicals ........................................ 24 1.4.3 Fingerprinting of oxidative stress ................................................................. 24
1.5 Rice and ROS in relation to abiotic stress ......................................................... 26 1.5.1 Genomics of rice .......................................................................................... 26 1.5.2 Rice proteomics ........................................................................................... 28 1.5.3 Rice Metabolomics ...................................................................................... 32
1.6 Aims of the study ............................................................................................. 33
2 Chapter Two: Methodology ....................................................................................... 34
2.1 Plant tissue culture .......................................................................................... 34 2.1.1 Media preparation and sowing of seeds ...................................................... 34 2.1.2 Surface sterilization of rice seeds ................................................................. 34 2.1.3 Micropropagation of rice ............................................................................. 34
2.2 Soil growth of plants ........................................................................................ 35 2.2.1 Growth conditions ....................................................................................... 35 2.2.2 Transferring in vitro grown plants to soil ...................................................... 35
vii
2.3 Stress treatments ............................................................................................ 37 2.3.1 Drought stress ............................................................................................. 37 2.3.2 Osmotic stress ............................................................................................. 37 2.3.3 Salinity treatment ........................................................................................ 37 2.3.4 Cold stress ................................................................................................... 38 2.3.5 Hydrogen peroxide treatment ..................................................................... 38
2.4 Physiological observations ............................................................................... 38 2.4.1 H2O2 content in plants ................................................................................. 38 2.4.2 DAB staining for H2O2 visualisation in tissues ............................................... 39 2.4.3 Chlorophyll content ..................................................................................... 39 2.4.4 Relative water content (RWC) ...................................................................... 40 2.4.5 TBARS assay ................................................................................................. 40 2.4.6 Data analysis ................................................................................................ 41
2.5 Metabolome analysis of response to stress ..................................................... 41 2.5.1 Plant extraction for NMR analysis ................................................................ 41 2.5.2 NMR spectra acquisition .............................................................................. 41 2.5.3 Pre-processing NMR spectral data ............................................................... 43 2.5.4 Data analysis ................................................................................................ 43 2.5.5 Identification of metabolites ........................................................................ 44
2.6 Proteomic analysis of rice responses to stress ................................................. 45 2.6.1 Establishment of protein extraction protocol ............................................... 45 2.6.2 Total protein quantification ......................................................................... 47 2.6.3 Electrophoresis of Proteins .......................................................................... 48 2.6.4 Visualize of protein in 2D gel ........................................................................ 50 2.6.5 Proteomic detection of oxidised and reduced thiol-containing proteins ...... 50 2.6.6 Protein identification techniques ................................................................. 53
3 Chapter Three: Physiological responses of rice (Oryza sativa) to various stress treatments ................................................................................................................. 55
3.1 Aims ................................................................................................................ 55 3.2 Introduction .................................................................................................... 55 3.3 Results ............................................................................................................. 57
3.3.1 DAB staining to establish the production of H2O2 ......................................... 57 3.3.2 Drought stress ............................................................................................. 59 3.3.3 Osmotic stress ............................................................................................. 62 3.3.4 Salinity stress ............................................................................................... 66 3.3.5 Cold stress ................................................................................................... 71 3.3.6 Physiological responses with ASA-pretreatment .......................................... 77 3.3.7 Cross tolerance ............................................................................................ 81
3.4 Discussion ........................................................................................................ 86 3.4.1 DAB staining ................................................................................................ 86 3.4.2 Physiological responses following the treatment of drought, osmotic and salinity stress ........................................................................................................... 86 3.4.3 Physiological responses with ASA- pretreatment ......................................... 90 3.4.4 Responses to cold stress .............................................................................. 92 3.4.5 Cross-tolerance –cold stress to salinity ........................................................ 94
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3.5 Conclusions ..................................................................................................... 96
4 Chapter Four: NMR profiling of rice (Oryza sativa) metabolites in response to abiotic stresses ...................................................................................................................... 97
4.1 Aim .................................................................................................................. 97 4.2 Introduction .................................................................................................... 97 4.3 Results ............................................................................................................. 99
4.3.1 Identification of compound by NMR spectroscopy ....................................... 99 4.4 Results ........................................................................................................... 108
4.4.1 Rice metabolome ....................................................................................... 108 4.5 Discussion ...................................................................................................... 140
4.5.1 Drought stress ........................................................................................... 140 4.5.2 Salinity stress ............................................................................................. 143 4.5.3 Osmotic stress ........................................................................................... 145 4.5.4 H2O2 and overall integration ...................................................................... 147
4.6 Conclusion ..................................................................................................... 152
5 Chapter Five: Targeted proteomics to identify oxidant-sensitive cysteine thiols following abiotic stress ............................................................................................ 153
5.1 Aims .............................................................................................................. 153 5.2 Introduction .................................................................................................. 153 5.3 Results ........................................................................................................... 154
5.3.1 Protein extraction methods ....................................................................... 154 5.3.2 Method development for identification of thiol modification proteins ...... 155 5.3.3 Labelling of reduced proteins by 5'IAF tag ................................................. 156 5.3.4 Selected 2D experiments ........................................................................... 157 5.3.5 Labelling oxidized proteins by blocking of reduced proteins with NEM ...... 165
5.4 Discussion ...................................................................................................... 172 5.4.1 Protein extraction ...................................................................................... 173 5.4.2 2 DE experiment ........................................................................................ 173
5.5 Conclusion ..................................................................................................... 177
6 Chapter six: Project overview and general discussion ............................................. 178
6.1 Background to the study ................................................................................ 178 6.2 General observations ..................................................................................... 178 6.3 Drought stress ............................................................................................... 179 6.4 Osmotic stress ............................................................................................... 182 6.5 Salinity stress ................................................................................................. 183 6.6 Crosstalk between stresses ............................................................................ 185 6.7 Identification of H2O2 induced responses and its correlation with other stresses 186 6.8 Summary of metabolic pathways affected ..................................................... 189 6.9 Future studies................................................................................................ 190
6
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List of Figures
Figure 1-1: Types of biotic and abiotic stresses that affect plant growth and development. .................................................................................................................................. 3
Figure 1-2: This diagram illustrates the generation of various ROS from reduction or oxidation of oxygen and its derivative. ...................................................................... 7
Figure 1-3: The sources of H2O2 production in plant cell and its cellular effects (picture adapted and modified from (Desikan et al., 1999). .................................................... 8
Figure 1-4: Sources and scavenging of reactive oxygen species (ROS) pathways in plant cells. ........................................................................................................................ 10
Figure 1-5: Transcription regulatory networks of abiotic stress signals and gene expression. ................................................................................................................................ 28
Figure 2-1: Micropropagation and growth of rice plants. ................................................ 36
Figure 2-2: Reduced or oxidized thiol proteins are labelled with a fluorescent iodoacetamide derivative 5-iodoacetamidofluorescein (IAF). .................................. 51
Figure 3-1: Visualization of hydrogen peroxide detected by DAB staining. ...................... 58
Figure 3-2: H2O2 content in leaves following 13 days of drought stress treatment. .......... 60
Figure 3-3: The relative water (RWC) in leaves over 13 days of drought stress was examined. ............................................................................................................... 60
Figure 3-4: The total chlorophyll a+b in leaves over 13 days of drought stress. ............... 61
Figure 3-5: Lipid peroxidation in leaves following 13 days of drought stress. ................... 62
Figure 3-6: H2O2 content in leaves after exposure to 15% and 30% PEG solution for up to 32 hours. ................................................................................................................. 64
Figure 3-7: The relative water (RWC) in leaves over 32 hours of PEG treatment. ............. 64
Figure 3-8: The chlorophyll content in leaves treated with 15% and 30% PEG. ................ 65
Figure 3-9: Lipid peroxidation in leaves after exposure to 15% and 30% PEG osmotic stress. ................................................................................................................................ 66
Figure 3-10: H2O2 content of rice leaves following high (150 mM) and low (118.54 mM) salinity stress. .......................................................................................................... 68
Figure 3-11: Relative water content in leaves after high dose (150 mM) NaCl and low dose (118.54 mM) NaCl treatment for 32 hours. ............................................................. 69
Figure 3-12: The total chlorophyll content in leaves following high dose (150 mM) NaCl and low dose (118.54 mM) NaCl for 32 hours. ......................................................... 70
Figure 3-13: Lipid peroxidation levels in leaves tissue following salinity treatment up to 32 hours. ...................................................................................................................... 70
Figure 3-14: Hydrogen peroxide production in leaves following cold stress at 10 °C and 20 °C. ........................................................................................................................... 71
Figure 3-15: Relative water content in leaves following cold stress, at 10 ⁰C and 20 ⁰C. .. 72
Figure 3-16: Chlorophyll a+b content in leaves following 10 ⁰C and 20 ⁰C cold stress. ..... 73
x
Figure 3-17: Lipid peroxidation in leaves treated with cold stress, 10 °C and 20 °C for 32 hours. ...................................................................................................................... 74
Figure 3-18: H2O2 production in the leaves of tissue culture plants treated at 10 °C for 32 hours. ...................................................................................................................... 75
Figure 3-19: RWC in the leaves of tissue culture plants treated at 10 °C for 32 hours. ..... 75
Figure 3-20: Total chlorophyll content in the leaves of tissue culture plants treated at 10 °C for 32 hours. ....................................................................................................... 76
Figure 3-21: Lipid peroxidation of the leaves of tissue cultured plants at 10 °C for 32 hours. ................................................................................................................................ 77
Figure 3-22: Evaluation of the effect of ASA pre-treatment on H2O2 production. ............ 78
Figure 3-23: Evaluation of the effect of ASA pre-treatment on water content. ................ 79
Figure 3-24: The evaluation of ASA pre-treatment on chlorophyll content in rice plants. 80
Figure 3-25: Effect of AsA pre-treatment on lipid peroxidation in rice plants. ................. 81
Figure 3-26: Hydrogen peroxide produced in leaves following combined cold and salinity stress. ...................................................................................................................... 82
Figure 3-27: Relative water content in leaves following the combined treatment of cold and salinity stress. ................................................................................................... 83
Figure 3-28: Chlorophyll content in leaves following the combined treatment of cold and salinity stress. .......................................................................................................... 84
Figure 3-29: Lipid peroxidation in leaves following 4 h pre-cold treatment and then 4 h of NaCl treatment........................................................................................................ 85
Figure 4-1: A typical spectrum of valine in aqueous solution. ........................................ 100
Figure 4-2: A typical 600MHZ COSY spectrum of leaf metabolites in aqueous phase obtained from control plants shown as contour plot. ............................................ 102
Figure 4-3: Two–dimensional COSY spectrum of rice leaf metabolites in chloroform solution. ................................................................................................................ 104
Figure 4-4: Two–dimensional TOCSY spectrum of rice leaf metabolites in aqueous phase. .............................................................................................................................. 105
Figure 4-5: Two–dimensional HSQC spectrum of rice leaf metabolites in aqueous solution. .............................................................................................................................. 106
Figure 4-6: Two–dimensional HMBC spectrum of rice leaf metabolites in aqueous solution. .............................................................................................................................. 107
Figure 4-7: Examples of typical 1D-NMR spectrum of leaf samples extracted in aqueous solution. ................................................................................................................ 111
Figure 4-8: Examples of typical 1D-NMR spectrum of root samples extracted in aqueous solution. ................................................................................................................ 112
Figure 4-9: Examples of typical 1D-NMR spectrum of leaf sample extracted in chloroform. .............................................................................................................................. 113
Figure 4-10: Examples of typical 1D-NMR spectrum of root samples extracted in chloroform. ........................................................................................................... 114
xi
Figure 4-11: PCA and PLS-DA of metabolite profiling data under different drought stress levels. .................................................................................................................... 116
Figure 4-12: Pearson correlation coefficient- based heat map coupled with clustering dendrogram showing a) leaf and b) root metabolites expression under 5, 7 and 9 days of drought stress. .......................................................................................... 118
Figure 4-13: Metabolites of leaf that changed at least 0.5 fold at 5, 7 and 9 days of drought treatment. ............................................................................................... 120
Figure 4-14: Metabolites of roots that changed at least 0.5 fold at 5, 7 and 9 days of drought treatment. ............................................................................................... 121
Figure 4-15: PCA and PLSDA of metabolite profiling data following salinity stress (118.54 mM NaCl). ............................................................................................................. 123
Figure 4-16: Metabolites of a) leaves and b) roots that changed at least 0.5 fold after 4 h of salinity (118.54 mM NaCl) treatment. ............................................................... 124
Figure 4-17: PCA and PLSDA of metabolite profiling data from osmotic stress treatment (15% PEG for 4 h). ................................................................................................. 126
Figure 4-18: Metabolites of a) leaf and b) root that changed at least 0.5 fold after 4h of osmotic (15% PEG) treatment. .............................................................................. 127
Figure 4-19: PLSDA score plots of leaf metabolite profiling data after treating with H2O2: low dose 10 mM (yellow), high dose 50 mM (red) and control (blue). ................... 129
Figure 4-20: Metabolites of leaf that changed at least 0.5 fold after high dose (50 mM) treatment. ............................................................................................................. 130
Figure 4-21: PLSDA score plots of root metabolite profiling data after treating with H2O2: low dose 10 mM (yellow), high dose 50 mM (red) and control (blue). ................... 132
Figure 4-22: Metabolites of roots that changes at least 0.5 fold after high dose (50 mM) treatment. ............................................................................................................. 133
Figure 4-23: Metabolites of leaf that changed at least 0.5 fold after low dose (10 mM) treatment. ............................................................................................................. 134
Figure 4-24: Metabolites of root that changed at least 0.5 fold after low dose (10 mM) treatment. ............................................................................................................. 136
Figure 4-25. Representation of the metabolic network of Oryza sativa under drought, osmotic and salinity stress corresponding with changes elicited by H2O2 treatment. .............................................................................................................................. 151
Figure 5-1: Coomassie-stained gel showing the different extraction methods to obtain pure rice total protein, with equal amounts of protein loaded. ............................. 155
Figure 5-2: Schematic illustration of 5-iodoacetamidofluorescein (5’IAF) tagging of thiol proteins. ................................................................................................................ 156
Figure 5-3: Gel show different fluorescent labelling under UV light after tagging with 5'IAF. .............................................................................................................................. 157
Figure 5-4: 2-DE analysis of proteins from plants treated with 10 mM H2O2 for 15 min. 159
Figure 5-5: : 2-DE analysis of proteins from plants treated with 50 mM H2O2 for 30 min. .............................................................................................................................. 162
xii
Figure 5-6: : 2-DE analysis of proteins from plants treated with 50 mM H2O2 for 1 h. .... 164
Figure 5-7: Schematic illustration of 5-iodoacetamidofluorescein (5’IAF) tagging of thiol proteins. ................................................................................................................ 166
Figure 5-8: Gels show the different amounts of DTT added to maximize the 5'IAF tagging. .............................................................................................................................. 167
Figure 5-9: Gel shows difference level of fluorescent intensity observed under UV light, following treatment with H2O2 and tagged with 5’IAF. .......................................... 168
Figure 5-10: Labelling of oxidase proteins from drought stress sample by blocking of reduced proteins with NEM before 5’IAF labelling. ................................................ 169
Figure 5-11: Labelling of oxidase proteins from 15% PEG treated sample by blocking of reduced proteins with NEM before 5’IAF labelling. ................................................ 170
Figure 5-12: Labelling of oxidase proteins from salinity treated (118.54 mM NaCl) sample by blocking of reduced proteins with NEM then labeled with 5’IAF. ...................... 171
Figure 5-13: 5’IAF labelling of oxidised proteins following NEM/DTT treatment, proteins were obtained from plants that were exposed to corsstalk experiment. ............... 172
7
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List of Tables
Table 1-1: Major ROS scavenging antioxidant enzymes, Table is adapted from (Gill and Tuteja, 2010; Mittler, 2002). Key Chl: chlorophyll, Cyt: cytochrome, Mit: mitochondria, Per: peroxisome, Apo: apoplast. ....................................................... 12
Table 1-2: ROS scavenging enzymatic antioxidants and their roles in transgenic rice for abiotic stress tolerance. .......................................................................................... 16
Table 4-1: Metabolites identified from aqueous and chloroform extract of rice leaves and roots. .................................................................................................................... 108
Table 4-2: Integration of metabolite responses in drought, osmotic and salinity compared to H2O2 treatment. ................................................................................................ 138
Table 4-3: Changes of metabolites in drought, osmotic and salinity stress independent of H2O2. The ↓ represents decrease of metabolites while ↑ represents increase of metabolites and Χ represents no changes in treated versus control plants. .......... 140
Table 5-1: Identities of H2O2 responsive proteins following 15 min of 10 mM H2O2 treatment by MALDI-TOF MS. ............................................................................... 160
Table 5-2: Identities of H2O2 responsive proteins following 30 min of 50 mM H2O2 treatment by MALDI-TOF MS. ............................................................................... 163
Table 5-3: Identities of H2O2 responsive proteins following 30 min of 50 mM H2O2 treatment by MALDI-TOF MS. ............................................................................... 165
Table 5-4: Identification and functional classification of the proteins modified following H2O2 treatment and its response under other stresses in rice plants. .................... 175
1
1 Chapter One: Introduction
Amongst all the crop plants, rice is a dominant staple food for more than half of the
world’s population. In addition to providing almost 23% of world’s calories intake, rice
has a greater yield per hectare compared to other staple crops (Khush, 2005). At present,
the rice production is still able to keep pace with world population growth with annual
production of 570 million tonnes achieved from cultivation across 155 million hectares of
rice fields worldwide. However, this trend is not expected to last long as the world
population is likely to achieve 9 billion in 2050 (http://www.worldometers.info/world-
population) and the demand for rice supply will steeply increase up to 800 million tonnes
by year 2030 (Van Nguyen and Ferrero, 2006). With the increment of human population,
there is a need to further increase the rice productivity by at least 40% in the next 25
years (Khush, 2005). Hence, to meet the hope of combating famine in the future, two
major issues have to be taken into account. Firstly, there is limited land available for
agricultural purposes, as it has to compete with urbanisation. Secondly, rice production is
underachieved in terms of yield potential due to various stresses from the environment.
It has been estimated that abiotic stresses such as drought, saline soil, temperature
fluctuation, cause more than 50% of primary yield losses for major crop plants worldwide
(Boyer, 1982). Hence, the understanding of plant abiotic stress response mechanisms and
the generation of stress–tolerant crops gain increasing attention.
1.1 Rice species and family
Rice (Oryza sativa L.) is a member of the Poaceae family, as are barley (Hordeum vulgare
L.), wheat (Triticum aestivum L.) and corn (Zea mays L.). There are two species of
cultivated rice: (1) Oryza glaberrima from West Africa, which only grown near to its
centre of origin and (2) Oryza sativa that is originally from Asia and is grown on all
continents (Khush, 1997). Due to the low yield and difficult milling of Oryza glaberrima, it
is now slowly being replaced by Oryza sativa in present day Africa (Linares, 2002).
However, the desirable traits of Oryza glaberrima has also being introgressed into Oryza
sativa background, developing a new variety called “New rice for Africa” (NERICA)
2
(Semagn K et al., 2006). Rice is the first crop domesticated by humans dating as far back
as 13000 years ago at the Yangtze River, China. Since then, cultivated rice originated from
two independent domestication events, producing indica and japonica ecotypes (Cheng
et al., 2003).
Rice is grown in four types of ecosystems: irrigated, rainfed lowland, deep-water and
rainfed upland. Irrigated rice is the most common ecosystem as it provides well drained
and fertile soils that have low chances of being affected by drought or flooding, hence
contribute to almost 75% of global production. Rainfed lowland is the second most
important rice ecosystem, contributing to 25% of total rice production. This system
totally relies on the rainfall or drainage from higher lands in a watershed. Hence, the
hydrological conditions vary substantially depending on the location on the field. Deep-
water rice is planted in areas that are naturally flooded to depths greater than 50 cm
during the rainy (monsoon) season. The seeds are usually broadcast a few weeks before
the beginning of the rainy season, therefore suffer from drought at the early germination
stages and then submerge in water usually until the end of the growing season. This
cultivation type represents 8% of the total rice cultivation area and the examples are
Mekong river of Vietnam and Cambodia and Chao Phraya river of Thailand (Catling, 1992).
The last one, rainfed upland rice is usually grown in systems where little or no fertilizer is
applied on unbounded fields where good soil drainage or uneven land surface with low
water accumulation occurs. This system apparently represents the lowest yielding
ecosystem (Khush, 1997), and most of the farmers are among the poorest in the world
(Arraudeau, 1995). Therefore, water shortage can be a major problem limiting yields of
rice plants grown in rainfed upland and lowland cultivation systems (Wade et al., 1999).
3
1.1.1 Stresses that affect rice production
Stress is an external force that is applied to a system thereby causing change. In a
biological system, the resistance to stress is dependent on the strength and time period
of the stress applied and adaptation strength in the organism itself. Examples of the
exogenous environmental factors that limit plant production are shown in Figure 1-1,
comprising of biotic and abiotic factors.
Figure 1-1: Types of biotic and abiotic stresses that affect plant growth and development. Biotic stress includes stresses caused by interactions with other organisms in the environment, such as pathogen infection, herbivores and competition between individual plants. Abiotic stress consists of temperature fluctuation, supply of water and chemical/ nutrients, level of radiation, and mechanical stress applied onto the plants.
Amongst the abiotic stresses, water stress induced by drought, osmotic and salinity
contribute to the major cause of yield loss especially for upland rice that depends on rain
feed (Bernier et al., 2008) or saline soil grown rice (Munns and Tester, 2008). On the
other hand, oceans and the low temperature regions already occupy 80% of the total
earth’s surface. The rest of land area that is free from ice comprise 42% that regularly
experience below -20 °C (Juntilla and Robberecht, 1999); therefore, it is not surprising
that freezing stress affects rice grown in the temperate regions of the world. Many plant
species originating from tropical and sub-tropical areas suffer from chilling injuries when
Exogenous stress
Biotic stress
Infection
Herbivory
Competition
Abiotic stress
Temperature Cold-Chilling/Frost
Heat
Water Drought
Osmotic
Radiation UV
Light
Chemical stress
Salinity
Pollutions
Mechanical Wind
Submergence
4
temperatures fluctuate from optimum to 15 °C- 0 °C; this is called chilling injury to
distinguish it from freezing injury. It is a serious problem during germination and early
seedling growth in many plant species like maize and rice (Bedi and Basra, 1993). Soil is
classified as saline when the ECe is more than 4 dS/m, which is equivalent to
approximately 40 mM NaCl with osmotic pressure of 0.2 mpa. Rice is one of the cereals
that is most susceptible to salinity stress with a loss in yield (Munns and Tester, 2008).
1.1.2 What make rice so susceptible to water stress
Rice is very susceptible to water stress due to its root architecture; it has lower radial
water conductivity than most other herbaceous species due to the existence of an
extensive aerenchyma, apoplastic barriers and a restrictive endodermis (Miyamoto et al.,
2001; Ranathunge et al., 2004). Another experiment demonstrates that sorghum and rice
manage to extract same amount of water from the top of 60 cm soil but when extending
beyond that depth, rice showed little or no water uptake while sorghum maintains water
extraction to much greater depths (Fukai and Inthapan, 1988). A thin layer of epicuticular
wax is one of the important factors inhibiting rice to efficiently tolerate drought (Haque
et al., 1992). The thickness of the epicuticular wax in rice is about 20% of that of sorghum
(Nguyen et al., 1997). Hence, the ability of rice cuticle to retain water is low, even if its
stomata are closed (Fukai and Inthapan, 1988; Haque et al., 1992). A thick wax layer may
also protect the plant from direct sunlight by preventing leaf heating without needing
transpiration (Blum, 2005). Therefore, drought that is defined by reduction of rainfall or
irrigation (Fukai and Inthapan, 1988) can have a strong impact on the growth of rice. Due
to drought stress, the global reduction in rice production is approaching approximately 18
million tonnes annually (O'Toole, 2004). Moreover, in Asia alone, it is estimated that
almost 23 million ha of rice fields are drought-prone (Pandey et al., 2000).
5
1.1.3 Plant stress response
Plants as sessile organisms have evolved series of adaptation mechanisms in order to
survive the imposed stress. These responses include: stomatal closure under water stress
to maintain water content by controlling cell respiration (Saliendra et al., 1995),
alteration of carbohydrate and lipid composition in membranes to avoid water freezing
under sub-zero conditions (Morsy et al., 2007), and alteration of soluble carbohydrate
that can act as osmoprotectants under salt and osmotic stress to maintain the osmotic
pressure in plants (Kerepesi and Galiba, 2000). These are only few examples of plant
stress adaptation mechanisms. From the general perspective of plant stress responses,
one key common feature that outstands the others is the enhanced production of
reactive oxygen species (ROS) especially hydrogen peroxide (H2O2), within sub-cellular
compartments of the plant cell (de Azevedo Neto et al., 2005). Under redox balanced
conditions, perception of H2O2 can act as a second messenger (Schreck and Baeuerle,
1991) to control signals for useful biological functions, such as differentiation of cell walls,
somatic embryogenesis and callus tissue regeneration (Zang and Komatsu, 2007). A
microarray study showed that the expression of several hundred genes was altered upon
H2O2 treatment in Arabidopsis (Desikan et al., 2001). Under stress conditions,
accumulation of ROS regulates stomatal movement (Vilela et al., 2007), increases ABA
levels and membrane stability (Domonkos et al., 2008; Jordan et al., 1983), changes
photosynthetic activity (Abdalla, 2007), reprogrammes metabolites (Zulak et al., 2008)
and regulates the nitrogen cycle (Debouba et al., 2006; Mae and Ohira, 1981; Okazaki et
al., 2008). These processes activate defence mechanisms to allow the plant to cope with
the imposed stress. However, if the unfavourable condition exceeds the plant’s threshold
to cope with the stress, the accumulation of H2O2 (oxidative stress) will lead to DNA
damage and oxidation of lipids. At the worst case, prolonged and high accumulation of
H2O2 leads to membrane damage and cell death (Halliwell and Whiteman, 2004).
Since H2O2 acts as a core component mediating different stress adaptation mechanisms
(Desikan et al., 2004; Desikan et al., 2005; Libik et al., 2007), it is possible that some of the
biochemical pathways are correlated and counterbalance one another, now commonly
referred to as crosstalk between signalling pathways (Genoud and Metraux, 1999). Given
6
that H2O2 might be an essential node mediating stress responses to multiple stimuli, it is
essential to understand the chemistry and reactivity of this molecule in plant cells.
1.2 Chemistry of Reactive Oxygen Species (ROS)
Oxygen is essential for plant survival; it is a prerequisite for aerobic metabolism for
normal growth and development. The reduction of an oxygen molecule during
photosynthesis or respiration often results in the formation of reactive oxygen species
(ROS). Molecular oxygen is reduced through four steps, generating several ROS, including
superoxide anion radical (O2•-), hydrogen peroxide (H2O2), hydroxyl radical (HO•), and
perhydroxyl radical (HO2•) (Halliwell, 2006). The first step of reduction produces the
relatively short lived (≈2-4 µs) and indiffusable O2•- (Triantaphylides and Havaux, 2009).
The major site of O2•- production is thylakoid membrane-bound primary electron
acceptors of photosystem I (PSI). It can also occur in the electron transport chain (ETC) at
the level of PSI. In plant tissues, every 1-2% of O2 consumption leads to the generation of
O2•- (Puntarulo et al., 1988).
As O2•- is a charged molecule, the extra electron at the outer orbital can either be
protonated by the Fenton/ Haber-Weiss reactions to form a non-charged hydroxyl radical
(HO•), spontaneously dismutate, or catalysed by the enzyme superoxide dismutase (SOD)
to become hydrogen peroxide (H2O2) (Figure 1-2). Furthermore, O2•- can also donate an
electron to iron (Fe3+) to yield a reduced form of iron (Fe2+) and produce the first excited
electronic state of O2, singlet oxygen 1O2. 1O2 has a life time of approximately 3µs to 4µs
(Hatz et al., 2007) and is able to travel almost several hundred nanometers (nm).
However, it can last up to 100µs in a polar solvent. Since it can be scavenged efficiently
by β-carotene, tocopherol, and plastoquinone, it was suggested that 1O2 does not
primairly act as a toxin but rather as a signal that activates several stress-response
pathways (Op den Camp et al., 2003).
Amongst all the ROS, H2O2 is a much more stable molecule and has a longer half life of 1
ms. Hence, it is able to travel further via aquaporins that facilitate H2O2 transmembrane
7
movement together with water (Henzler and Steudle, 2000). O2•- and H2O2 produce OH•
by Fenton/Haber-Weiss reaction in the presence of metals, such as Fe. Due to the
absence of antioxidants for this ROS, excess production of OH• ultimately leads to cell
death (Vranova et al., 2002). OH• is highly reactive with almost all biological molecules
like DNA, protein, lipids and any cell constituent causing oxidative damage to plants
(Białkowski and Olinski, 1999; Triantaphylides et al., 2008).
Figure 1-2: This diagram illustrates the generation of various ROS from reduction or oxidation of oxygen and its derivative.
1.2.1 Sources and regulation of H2O2
Photosynthesizing plants are especially prone to oxidative damage because the main
source of ROS generation comes from energy transfer during photosynthesis. When
plants are exposed to light, H2O2 is produced via the Mehler reaction in chloroplasts and
electron transport in peroxisomes. Photorespiration in mitochondria appears to be the
main ROS producer for plants that were kept in darkness (Foyer, 2003; Neill et al., 2002).
It was estimated that every 1-5% of O2 consumed contributed to ROS production (Moller,
2001)(Figure 1-3).
1O2 is a natural by-product of photosynthesis in photosystem II (PSII) even under low light
conditions. The photosynthetic electron transfer chain (ETC) is responsible for production
of H2O2. ETC components include a number of enzymes on the reducing side of
photosystem I (PSI): Fe-S centre, reduced thioredoxin (TRX), and ferredoxin. Later studies
have also suggested that the acceptor side of the electron transfer chain (ETC) in PSII
8
Figure 1-3: The sources of H2O2 production in plant cell and its cellular effects (picture adapted and modified from (Desikan et al., 1999).
Chloroplast
Peroxisome
Mitochondrion
Direct effect on
protein
Reversible protein
phosphorylation
9
provides electron leakage, which changes O2 to O2•-. H2O2 is then produced either through
dismutation of O2•- by superoxide dismutase enzymes (SOD) or through the reduction of
superoxide using reductants such as ascorbate (AsA), thiols and ferredoxins (Asada, 2006).
The reduction of H2O2 to water by AsA is catalysed by ascorbate peroxidase (APX) and
oxidized to monodehydroascorbate radical (MDHA). This MDHA is subsequently reduced
back to AsA by either ferredoxin reduction or NAD(P)H catalyzed chloroplastic MDA
reductase (Figure 1-4) (Sano et al., 2005).
In peroxisomes, the main event is photorespiration, whereby O2•- radicals are being
produced as a consequence of their normal metabolism. MDHAR mediates the formation
of O2•- in the peroxisome ETC, which is comprised of flavoprotein NADH and cytochrome
b (del Rio et al., 2006). The main processes which generate H2O2 in peroxisomes are the
enzymatic reaction of flavin oxidases (del Rio et al., 2006; Mano and Nishimura, 2005).
The photorespiratory pathway appears to be a faster process compared to Mehler
reaction and electron transport in mitochondria for generating H2O2 (Foyer, 2003).
Plant mitochondria act as an energy factory which contributes to the major production
site of ROS (Noctor et al., 2007). The production of O2•- occurs mainly at two sites of the
electron transport chain: NAD(P)H dehydrogenases (Complex I) and the cytochrome bc1
complex (Complex III) (Moller, 2001). The mitochondria component in plants differs from
that in animals with specific ETC components that function in photorespiration. The ETC
harbours electrons with sufficient free energy to directly reduce O2 in aerobic respiration.
The superoxide anion will further form H2O2 via the reaction of mitochondrion–specific
manganese SOD (Mn-SOD) (Moller, 2001). The amount of H2O2 produced in plant
mitochondria is less than that of chloroplasts or peroxisomes when exposed to light
(Foyer, 2003), while it appear to be a major ROS production site in the dark (Rhoads et al.,
2006). The ROS production is dependent on reaction by various enzymes at different cell
compartments (Halliwell and Cross, 1994). H2O2 then reacts with reduced Fe2+ and Cu
that are produced from Fenton/Haber-Weiss reaction to form reactive HO• that can
penetrate cell membranes and travel in the cell (Rhoads et al., 2006).
10
Figure 1-4: Sources and scavenging of reactive oxygen species (ROS) pathways in plant cells. The production and detoxification of ROS by various enzymatic pathways are shown. The Fenton-Harber Weiss cycle detoxifies O2•
-and H2O2, and alternative oxidase (AOX) reduces the production rate of O2•- in
thylakoids. In some plants iron superoxide dismutase (FeSOD) might replace CuZnSOD in the chloroplast. H2O2 that escape this cycle undergo detoxification by SOD and the stromal ascorbate–glutathione cycle. Peroxiredoxin (PrxR) and glutathione peroxidase (GPX) are also involved in H2O2 removal in the stroma. In peroxisomes, ROS are scavenged by SOD, catalase (CAT) and ascorbate peroxidase (APX). In mitochondria, SOD and other components of the ascorbate–glutathione cycle are being induced following the production of H2O2. Although the pathways in the different compartments are mostly separated from each other, H2O2 can easily diffuse through membranes and antioxidants such as glutathione and ascorbic acid (reduced or oxidized) can be transported between the different compartments. Abbreviations: DHA, dehydroascrobate; DHAR, DHA reductase; FD, ferredoxin; FNR, ferredoxin NADPH reductase; GR, glutathione reductase; GSH, reduced glutathione; GSSG, oxidized glutathione; MDHA, monodehydroascorbate; MDHAR, MDHA reductase; PSI, photosystem I; PSII, photosystem II; Trx, thioredoxin.
11
ROS are also generated at the plasma membrane or in the apoplast. pH-dependent cell
wall peroxidases, germin like oxalate oxidases and amine oxidases have been proposed as
sources of H2O2 in the apoplast of plant cells (Bolwell and Wojtaszek, 1997). Upon
exposure to biotic stress, alkalinization of apoplast activated cell wall peroxidase occurs
and H2O2 is produced (Bolwell and Wojtaszek, 1997). In addition, external abiotic stresses
can also enhance the production of H2O2 for example, via plasma membrane localised
NADPH oxidase and cell wall peroxidase (Lherminier et al., 2009). The NADPH-dependent
oxidase system catalyzes the production of superoxide by reducing one oxygen electron
using NADPH as a donor. The superoxide that is generated by this enzyme will then be
converted to H2O2.
1.2.2 Removal of H2O2
Plants have developed mechanisms to precisely regulate the production of H2O2 by
means of many enzymatic and nonenzymatic methods so as to maintain the function of
each cell (Figure 1-4). Enzymatic antioxidants include superoxide dismutase (SOD),
catalase (CAT), ascorbate peroxidase (APX), glutathione peroxidase (GPX),
monodehydroascorbate reductase (MDHAR), dehydroascorbate reductase (DHAR), and
glutathione reductase (GR). The enzymatic antioxidants and their reactions are listed in
Table 1.1.
Three types of SOD are classified based on their metal cofactors: the copper/zinc (Cu/Zn-
SOD) form, the manganese (Mn-SOD) form and the iron (Fe-SOD) form, which are
localized in different cellular compartments (Mittler, 2002). The Mn-SOD is found in the
mitochondria and in peroxisomes (del Rio et al., 2003); some Cu/Zn-SOD isozymes are
found in the cytosolic fractions and also in chloroplasts of higher plants (Renato
Rodrigues et al., 2002). Fe-SOD isoenzymes have rarely been detected but are usually
associated with the chloroplast compartment (Alscher et al., 2002).
12
Table 1-1: Major ROS scavenging antioxidant enzymes, Table is adapted from (Gill and Tuteja, 2010; Mittler, 2002). Key Chl: chlorophyll, Cyt: cytochrome, Mit: mitochondria, Per: peroxisome, Apo: apoplast. Enzymatic antioxidants Location Reactions catalyzed
Superoxide dismutase Chl, Cyt, Mit, Per, Apo O2-. + O2
-.+ 2H+→2H2O2+O2
Catalase (CAT) Per H2O2→ H2O + 1/2O2
Ascorbate peroxidase (APX) Chl, Cyt, Mit, Per, Apo H2O2+ AA→2H2O+ DHA
Glutathione peroxidase (GPX) cyt H2O2+ GSH→H2O +GSSG
Glutathione reductase (GR) Chl, Cyt, Mit, Per, Apo GSSG+NAD(P)H→2GSH+NAD(P)+
In rice plants, up regulation of SOD was observed following various stress treatments, for
example under drought (Sharma, 2005), or chemical stress (Hsu and Kao, 2004; Sharma
and Shanker Dubey, 2005). Under salt stress elicited by NaCl, increment in SOD has been
observed in a salt-tolerant variety (Vaidyanathan et al., 2003). Using KCl to induce ionic
stress in another study showed that the induction of SOD activity conferred tolerance at a
mild dose but when treated with high dose of KCl, production of SOD was blocked leading
to cell damage (Santos et al., 2001). The increase in SOD activity was also observed when
rice plants were challenged with cold stress (Kuk et al., 2003). Kim and Lee (2005) showed
how ROS caused photo-oxidative damage at PSI and PSII when the function of SOD was
inhibited by using diethyldithiocarbamic acid (DDC), a SOD inhibitor. There have been
many reports of the production of abiotic stress tolerance in transgenic plants
overexpressing different SODs in rice (Table 1.2). In summary, high expression of SOD
conferred tolerance to drought, salinity, cold and KCl stress.
CAT has the highest turnover rates amongst all antioxidant enzymes: one molecule of CAT
can convert ≈6 million molecules of H2O2 to H2O and O2 per minute (Gill and Tuteja, 2010).
CAT is important in the removal of H2O2 in peroxisomes generated by oxidation of fatty
acids, photorespiration and purine catabolism (Gill and Tuteja, 2010). The number of CAT
isozyme varies in higher plants, as an example, Brassica has as many as 12 CAT isoforms
which are found on separate chromosomes, are differentially expressed and
independently regulated (Frugoli et al., 1996). Increment of CAT activity in rice plants
occurs following salinity stress (Vaidyanathan et al., 2003) and metal stress (Hsu and Kao,
2004). However, a decrease has been observed when rice plants were challenged with
13
drought (Sharma and Shanker Dubey, 2005). Hso and Kao (2007) pretreated rice seedling
with H2O2 under non-heat shock conditions and found that a significant amount of CAT
activity increased, which provided protection against Cd stress. There have also been
some reports on CAT producing abiotic stress tolerance in transgenic rice (Table 1.2).
Peroxidases (APX and GPX) catalysed by substrates such as ascorbate and glutathione
respectively, reduce H2O2 to water (Figure 1-4). Eight types of APX have been described
for Oryza sativa: two cytosolic (OsAPx1 and OsAPx2), two putative peroxisomal (OsAPx3
and OsAPx4), and four chloroplastic isoforms (OsAPx5, OsAPx6, OsAPx7 and OsAPx8)
(Hong and Kao, 2008). Pre-treatment of rice seedings with sodium nitroprusside (SNP), a
nitric oxide (NO) donor, showed an increase in APX activity and protection from
subsequent Cd stress (Hsu and Kao, 2004). RNA blot analyses of rice plants revealed that
under methyl viologen treatment (that induces superoxide formation), increased APX
activity was found compared to SOD, suggesting the importance of APX in defence
against oxidative stress (Shigeto et al., 2011). Sharma and Dubey (2005) found that mild
drought stressed rice plants had higher chloroplastic-APX activity than control grown
plants but the activity declined at the higher level of drought stress. The expression
patterns of APX have been analysed in roots of rice seedlings under NaCl stress; it was
noted that 150 mM and 200 mM NaCl increased the expression of OsAPx8 and the
activities of APX, but there was no effect on the expression of others APXs in roots (Hong
et al., 2007). When mutants in cytosolic APX (OsAPx1 and OsAPx2) in rice plants were
subjected to various abiotic stresses, a compensation in the activity of other peroxidases,
including GPX, were observed (Bonifacio et al., 2011). In another study, expression of
OsAPx4 rice gene into Arabidopsis exhibited multi-stress tolerance (Guan et al., 2010).
The animal GPX is a selenoprotein that detoxifies H2O2 at high rate, however, GPXs
production in stressed plants are slower compared to the H2O2 production. Moreover,
GPXs of plants do not contain selenium and only catalyse GSH-dependent reduction
(Noctor et al., 2002). In plants, APX and CAT are more important in the detoxification of
H2O2 while GPX is more important in oxidant metabolism, including removal of lipid and
alkyl peroxides (Noctor et al., 2002).
14
H2O2 reacts with cysteine side group as well as cleaves the polypeptide backbone and
results in the direct modification of thiols protein (Little and O'Brien, 1967). GSH acts as
substrate for glutathione peroxidase (GPX) in H2O2 removal to form glutathione
disulphide (GSSG) (Dickinson and Forman, 2002) and further protect proteins from being
oxidised (Noctor and Foyer, 1998). The GSH: GSSG ratios in cells are maintained by
glutathione reductase (GR). GR and GSH play a crucial role in determining the tolerance
of plants to various stresses (Rao and Reddy, 2008). Significant increment in GR activity
was observed in rice seedings exposed to drought (Sharma and Shanker Dubey, 2005) and
salinity stress (Hong et al., 2009). Pretreating rice seedlings with H2O2 also showed an
increment in GR activity, providing protection to subsequent exposure to Cd stress (Hsu
and Kao, 2007). Hong et al., (2009) further confirmed the increment of GR is mediated
and regulated by H2O2 production.
The plant glutathione transferases (GST), also known as glutathione S-transferase
catalyses the conjugation of electrophilic xenobiotic substrates with the tripeptide
glutathione (GSH:γ-glu-cys-gly). GST has the potential to remove cytotoxic or genotoxic
compounds that can cause damage to DNA, RNA and proteins (Noctor et al., 2002). An
increased GST activity was found in roots of Cd-exposed rice plants (Moons, 2003).
Synergy of GST and GSH reduced the peroxidase activity in GST overexpressing transgenic
rice lines (Hu et al., 2009a). The induction of Osgstu3 and Osgtu4, which encoded tau
class GSTs, was reported under various abiotic stress conditions in roots of rice seedling
(Moons, 2003).
Nonenzymatic antioxidants include ascorbate (vitamin C), tocopherol (vitamin E),
glutathione, flavonoids, alkaloids and carotenoids (Bray et al., 2000). Ascorbate has been
shown to react not only with H2O2, but also with O2-., HO., and lipid peroxides in order to
reduce the toxic side effects caused by these products (Noctor and Foyer, 1998). In a salt
tolerant rice cultivar, the level of ascorbate and GSH are significantly higher when
exposed to NaCl stress in the range of 100-300 mM. The combination of these two
antioxidants with CAT indicate that the ROS scavenging machineries is vital to overcome
salinity induced stress (Vaidyanathan et al., 2003).
15
Tocopherol, especially α-tocopherol, is a lipid soluble antioxidant that can inhibit lipid
peroxidation of free radical reactions (Hollander-Czytko et al., 2005). α-tocopherols have
been shown to prevent the propagation of lipid peroxidation by scavenging lipid peroxyl
radicals, which makes it an effective free radical trap. In addition, one molecule of
tocopherol enables scavenging of up to 120 1O2 molecules by resonance energy transfer
(Munne-Bosch, 2005). α-tocopherol that is synthesised in chloroplasts by regulation of
the enzyme tyrosine aminotransferase (TAT)(Munne-Bosch, 2005), has been shown to
increase upon challenge with high salt, H2O2, drought, and cold stress (Ouyang et al.,
2011).
16
Table 1-2: ROS scavenging enzymatic antioxidants and their roles in transgenic rice for abiotic stress tolerance. Gene Source Response Reference Superoxide dismutase (SOD) Cu/Zn SOD Avicennia
marina Transgenic plants were more tolerant to MV mediated oxidative stress, salinity and drought stress (Prashanth et al., 2008)
Mn SOD Pisum sativum
Leaf slices showed reduced electrolyte compared to (WT) leaf slices, less injury following treatment with PEG
(Wang et al., 2005)
Mn SOD Yeast SOD activities in chloroplast decreased upon exposure to salt stress.Transgenic plants maintained high level of SOD in chloroplast. The ascorbate peroxidase activity increased in transgenic plants.
(Tanaka et al., 1999)
Catalase CAT CAT KatE E.coli Transgenic rice plants showed increase of CAT 1.5 -2.5 fold when plants were subjected to salt stress. The
transgenic rice can survive the salt stress for prolonged 14 days compared to wild type plants. (Nagamiya et al., 2007)
CAT Triticum aestivum L.
In 5 °C of cold stress, CAT was 4-15 times higher in transgenic plants compared to wild types while H2O2 was kept at very low levels.
(Matsumura et al., 2002)
CAT Suaeda salsa Co-expression of CAT and GST resulted in the increment of SOD and CAT activities following salt and paraquat stresses, while GST activity increased only in paraquat stress. H2O2, malondialdehyde and electrolyte leakage decreased in transgenic rice compared to wild type.
(Zhao and Zhang, 2006)
Ascorbate peroxidase Knockdown OsAPx1 and OsAPx2
Rice Compensation by others peroxidases, including GPX, were observed. (Bonifacio et al., 2011)
OsAPXa Rice Transgenic plants showed very little changes in H2O2 and MDA content when subjected to cold stress. (Sato et al., 2011) Silencing APx1/2s
Rice Up-regulation of other peroxidases were observed under salinity, heat, high light and MV treatment. (Bonifacio et al., 2011)
Glutathione reductase GR Brassica
campestris High expression of GR improved the protection against photo-bleaching of chlorophyll and photo-oxidative action of MV in thylakoid membranes at 25 °C
(Kouril et al., 2003)
Glutathione S-transferases GST Rice Seedlings of the transgenic lines grown in submergence condition demonstrated enhanced germination and
growth rates at low temperature. (Takesawa et al., 2002)
OsGSTL1 Rice The over-expression lines showed increase of GST and GPX activities while decreased level of superoxide. (Hu et al., 2009a) GST Suaeda salsa Salt and paraquat stress tolerance due to GST, CAT, and SOD activities. (Zhao and Zhang, 2006)
17
1.2.3 Hydrogen peroxide as a signalling molecule
Contradictory to their animal counterparts, plants are able to tolerate much higher H2O2
concentrations. H2O2 is toxic for most animal cells at levels of 10-102 µM, whilst it can
extend from 102-105 µM in plants (Halliwell and Gutteridge, 1999). It is suggested that the
tolerance of plants to high H2O2 levels is due to the fact that plant antioxidant response
systems are designed to control the cellular redox state for signalling purposes rather
than for complete elimination of H2O2. The relatively stable and high concentrations of
H2O2 content imply that it plays a role as a signalling molecule in addition to acting as
product of primary photosynthesis metabolism. Moreover, rate of H2O2 production differs
in various cell compartments depending on the strength and duration of the imposed
stress. This in turn suggests that the plant has evolved its own unique strategy to use ROS
as biological stimuli in signal perception and activation of stress-response programmes.
A number of properties make H2O2 the ideal signalling molecule. Firstly it is produced in
the cell when the cell is in a stressful environment thus enabling the cell to adjust its
internal cellular response. Secondly it has a relatively longer half life, small size and ability
to travel across cell walls (Hancock et al., 2001). Furthermore, H2O2 can be rapidly
produced in a controlled manner. Effective removal mechanisms exist and H2O2 regulates
the expression of various genes, including those that encode antioxidant enzymes and
enzymes producing H2O2 (Geisler et al., 2006).
ROS generated in cellular compartments play a role as signalling molecules by either
activating signalling cascades resulting in the changes in nuclear transcription (Desikan et
al., 2001) or by directly oxidising components of signalling pathways. Alternatively, ROS
might change gene expression by targeting and modifying transcription factors (Apel and
Hirt, 2004). Plants contain a range of two-component histidine kinases (Seo et al., 2011)
that acts as ROS sensors and are involved in the transcriptional response regulated by
H2O2 (Desikan et al., 2001). A focused study on the histidine kinase receptor ETR1 from
Arabidopsis showed that it was essential for H2O2 perception leading to stomatal closure
(Desikan et al., 2006). In yeast and plants, these sensors are integrated into more
complex pathways involving mitogen-activated protein kinase (MAPK) cascade (Gustin et
18
al., 1998). MAPK signalling components in rice comprising more than 100 MAPK, MAPKK,
MAPKKK genes and involved in H2O2 mediated responses to various stresses, hormones,
and cytokinesis (Jonak et al., 2002).
Direct oxidation of proteins thiols by ROS leads to signal propagation and then
transduction. Apart from cysteine, ROS also reacts with tyrosine, tryptophan, histamine
and methionine (Moller et al., 2007). The oxidation of these molecules is usually caused
by singlet oxygen while H2O2 usually causes the oxidation only on cysteine groups (Moller
et al., 2007). The oxidation of two adjacent cysteine residues forms a disulphide bridge (S-
S) while oxidation of one cysteine can result in the formation of sulphenic acid (-SOH),
sulphinic acid (-SO2H) or sulphonic acid (-SO3H), with increasing concentrations of H2O2.
The oxidation of –SH and -SOH groups within cysteine can be reduced by thioredoxin and
glutaredoxins (Lemaire, 2004; Schurmann and Jacquot, 2000), whilst sulphinic acid groups
can be reduced back to the sulphenic acid group by sulphiredoxins. The oxidation of
sulphonic acid is irreversible (Biteau et al., 2003). This rapid and reversible modification of
a protein appears to be an ideal candidate for a signaling event. In plants, H2O2 oxidation
of thiol residues mediated signalling event of tyrosine dephosphorylation to avoid
programmed cell death caused by severe stress (Hung et al., 2007b). Oxidation
modification of ETR1 H2O2 signalling in stomatal guard cell was also reported (Desikan et
al., 2005). Pathogen triggred redox changes of S-nitrosylation of NPR1 residue by S-
nitrosoglutathione (GSNO) and TRX (Tada et al., 2008). Another nitrosylation of GAPDH
was reported in Arabidopsis when plants was treated with NO (Lindermayr et al., 2005).
ROS regulate the activity of transcription factors (TFs) through modification of cysteine
thiol groups. For example thiol modification of OXYR in E.coli (Georgiou, 2002) and Yap1
in yeast (Zheng et al., 1998) have long been reported. H2O2 reacts with different cysteinyl
residues on TFs and can give rise to differentially modified products and activate different
sets of genes. The H2O2 modified TFs might be activated by soluble thiol-containing low
molecular mass compounds such as GSH and GPX, or via the activation of protein kinases
that phosphorylate a TF, which later interact with H2O2-responsive elements to regulate
the expression of a gene (Apel and Hirt, 2004; Neill et al., 2002). Arabidopsis WRKY
proteins comprise of plant specific zinc-finger-type transcription factors (ZFT-TFs) and
19
have been found to be expressed during pathogen defence, wounding, and senescence.
The senescence-related WRKY53 factor is induced by H2O2 and can regulate its own
expression in a negative feed back loop (Miao et al., 2004). The promoter of the central
H2O2-scavenging enzyme cytosolic ascorbate peroxidase 1 (APX1) contains a heat shock
transcription factor (HSF) binding site and is very important in stress defence and H2O2
signalling (Mittler and Zilinskas, 1992). Accordingly, Arabidopsis mutants deficient in APX1
accumulate higher levels of H2O2 compared to wild-type plants under light stress (Mittler
et al., 2004). In rice, one of this HSFA4a is encoded by the Sp17 gene. Mutation of this
gene caused a disease lesion mimic phenotype, which suggests the role of HSFA4a as an
anti-apoptotic factor (Yamanouchi et al., 2002). Moreover, the observation of H2O2 and
O2.- overproduction associated with programmed cell death causing lesion formation
proves that HSFA4a is a redox-sensitive H2O2 sensor in plants (Mittler et al., 1996).
Examples of H2O2-dependent redox regulation are thiol group-containing thioredoxins
that regulate oxidation or reduction cycles of the ferredoxin/thioredoxin system in
chloroplasts, regulating many different enzymes, including those involved in the Calvin
cycle (Buchanan and Balmer, 2005). Analysis of the Arabidopsis transcriptome following
H2O2 treatment shows 175 genes being regulated. Of these, the function of these genes
have predicted importance for cell rescue and defence processes (Desikan et al., 2001).
Similarly, applying MV as a ROS agent also induces the expression of genes functioning in
detoxification and oxidative stress adaptation, including glutathione S-transferases (GSTs),
P450, and plant defence genes (Liu et al., 2010).
1.2.4 Hydrogen peroxide and interaction with hormones
It was well known that plants experience an ‘oxidative burst’, a rapid production of H2O2
in response to pathogen or environmental stresses (Cheng et al., 2007; Okuda et al., 1991;
Orozco-Cardenas and Ryan, 1999). This will then turn on other adaptation mechanisms
that protect the plants (Gualanduzzi et al., 2009; Yang and Poovaiah, 2002). H2O2 does
not work alone; salicylic acid (SA) is one of the molecules that work tightly with H2O2. The
accumulation of H2O2 interferes with the production of SA and vice versa (Alvarez et al.,
1998). The balance of SA and H2O2 is regulated by catalase, which when these catalase
20
binds to SA, could inhibit the production of H2O2 (Alvarez et al., 1998). This self-
amplification loop that involves both H2O2 and SA may generate a burst that is mentioned
above to establish an acquired signal for resistance against pathogens (Alvarez et al.,
1998; Park et al., 2008). SA-mediated H2O2 formation was also reported to be induced in
guard cells for stomatal closure signalling under stress (Park et al., 2008). In pathogen-
infected cells SA accumulation promotes reduction of the NPR1, a pathogen responsive
gene, that regulates plant defence responses against pathogens (Rivas-San Vicente and
Plasencia, 2011).
Abscisic acid (ABA) is another well-established signalling molecule that has long been
recognised as an important hormone involved in plant responses following drought,
osmotic and high salinity stress (Fricke et al., 2004; Rabbani et al., 2003; Zhang et al.,
2001). Water stress regulates transcription factor activity under ABA dependent and ABA
independent pathways and has been well described (Tuteja, 2007). Under water stress
conditions, ABA induces the production of H2O2 via either chloroplastic activity (Fricke et
al., 2004) or plasma membrane NADPH oxidase in guard cells (Zhang et al., 2001). The
elevation of H2O2 then activates calcium channels leading to stomatal closure (Pei et al.,
2000). ABA regulation of carbohydrate metabolism under salinity stress is also reported
(Kempa et al., 2008). Therefore, it is not surprising to observe influence of tissue
hydraulic and stomatal conductivity under the regulation of both H2O2 and ABA.
Interestingly, ABA also shows the potential of facilitating growth resumption following
releasing of stress (Fricke et al., 2004).
Ethylene (ET) has been long recognised as a plant hormone regulating stress responses.
Apart from regulating plant growth and development, ethylene also responds to biotic
stress and abiotic stress such as wounding, ozone and salt (Wang et al., 2010; Wang et al.,
2002). During pathogen defence, ethylene induces the production of camptothecin
mediated H2O2 burst causing plant cell death (PCD) on tissue that is being infected (de
Jong et al., 2002). While in abiotic stress regulation, the ethylene signalling components
ETR1 and EIN2 have been shown to regulate H2O2 responses in stomatal guard cells
(Desikan et al., 2006). The modulation of ethylene regulation correlating with H2O2
production also provided salt tolerance (Wang et al., 2010).
21
Jasmonic acid (JA) is synthesized from linoleic acid via the octadecanoid pathway. JA has
been reported to respond to wound and systemic resistance (Hu et al., 2009b). NADPH-
dependent respiratory burst mediated by JA is required for ROS generation (Liu et al.,
2005). There is an evidence of positive interaction between JA and ET signalling pathways
while antagonism occurs between SA and JA (Kunkel, 2002). The close interaction
between ROS and plant hormone signalling responses suggests that plants integrate
responses to both abiotic and biotic stimuli via these central molecules.
1.3 Deleterious Effects of ROS
Apart from acting as signalling molecules, excess or severe stress causing the
accumulation of excess ROS leads to uncontrolled oxidative stress on DNA (Białkowski
and Olinski, 1999), irreversible protein modification (Ghezzi and Bonetto, 2003) and the
breakdown of PUFA causing secondary damage (Moller et al., 2007). The details of these
processes, which are different from those involved in signalling, are described below.
1.3.1 DNA damage
Nucleic acids are targets of oxidative stress-induced damage. Reduction of H2O2 will
produce hydroxyl radicals that react rapidly with almost everything, including the
relatively stable nucleic acids. ROS can cause DNA strand breaks or modification at
deoxyribose sugar and at both purine and pyrimidine bases (Halliwell and Gutteridge,
1999). ROS are capable of inducing several changes on DNA, including base pair deletion,
pyrimidine dimers, cross-links, strand breaks and base modification such as alkylation and
oxidation (Britt, 1996). As an example: the addition of HO• to the position 8 of guanine in
DNA produces the 8-hydroxy-2’-deoxyguanosine radical. It can then easily undergo
mutation at the next cycle, to form 8-hydroxy-2’-deoxyguanosine (8OHdG) or it will attack
the deoxy-Rib sugar in DNA, causing massive damage to DNA (Evans et al., 2004). DNA
damage causes various physiological effects, such as reduced protein synthesis and
damage to photosynthetic proteins (Britt, 1996). Nevertheless, a number of mechanisms
are available for repairing DNA damage in the nucleus and mitochondria, which include
22
direct reversal of the damage, replacement of the base, and replacement of the whole
nucleotide (Britt, 1996).
1.3.2 Lipid peroxidation
The peroxidation of lipids is considered one of the most damaging processes known to
occur in every living organism. Lipid peroxidation is a phenomenon that can occur under
stress conditions, including salinity (Ayala-Astorga and Alcaraz-Melendez, 2010) and
water stress (Chai et al., 2005). Under this process, polyunsaturated precursors
breakdown into small hydrocarbon fragments such as ketones and malondialdehyde
(MDA). The process of lipid peroxidation can be divided into three distinct stages:
initiation, progression and termination. Initiation involves transition metal complexes,
such as Fe and Cu. HO˙ is also able to react with the H atom in any organic compound
with C-H bond. O2.- and H2O2 are capable of initiating lipid peroxidation by abstraction of
a hydrogen atom in an unsaturated fatty acyl chain of a polyunsaturated fatty acid (PUFA)
residue. Initiation of oxidation to fatty acids give rise to ROO.. Once initiated, ROO. further
propagates the peroxidation chain reaction by abstracting a hydrogen atom from
adjacent PUFA side chains. Resulting products are composed of reactive species including
lipid alkoxyl radicals, MDA, alkanes, lipid epoxides and alcohols (Moskovitz and Stadtman,
2003). These substances weaken the membrane fluidity, hence increase the leakiness of
membrane and lead to secondary damage to membrane proteins (Moller et al., 2007).
Moderate oxidative stress will usually stop the cell cycle and severe damage might trigger
death by apoptosis, necrosis or both (Halliwell, 2006).
1.3.3 Oxidation of proteins
Reactive oxygen has the potential to react with nearly all amino acid side groups as well
as cleaving the polypeptide backbone and result in the formation of reactive carbonyl
groups (ketones and aldehydes), and directly modify proteins on thiols (Little and O'Brien,
1967). Most types of protein oxidations are irreversible; those few that involve sulphur-
containing amino acids are reversible (Kukreja et al., 2005). In addition to oxidation of
sulfur-containing thiol to sulphonic acid (SO3H), carbonylation is the most commonly
23
occurring oxidative protein modification, which is irreversible. It has been found that OH•
induced oxidative stress leads to the carbonylation of proteins (Keller et al., 1993). The
oxidation of arginine, histamine, lysine, proline, threonine and tryptophan produces
carbonyl groups. An analysis of stressed plant proteins gave an estimation of 4 nmol
carbonyl groups per milligram of protein (Nguyen and Donaldson, 2005), and even higher
value with approximately 600-700 nmol/mg has also been reported in senescence leaf of
pea (Vanacker et al., 2006).
1.4 Measurement of ROS
The production of H2O2 in plants function as a double-edged sword, depending on the
amount that has been produced and the stress level that has elicited them. The basal
level of production of H2O2 is an important mechanism produced under normal condition
to maintain routine metabolic activities. However, the overproduction of H2O2 leads to
oxidative damage and kill the cells at the end. Therefore, it is very important to
distinguish the levels of ROS in any tissue following stimulation over time and intensity of
stress applied, in order to further justify their function in signalling aspects or as
damaging factors. There are three major approaches that are normally used to qualify
and observe the occurrence of ROS: 1) trapping of ROS and then measuring its level, 2)
measuring the levels of damage done by ROS (Halliwell and Whiteman, 2004), and 3)
observing the absence of oxidative activity, known as oxidative stress fingerprinting, after
stress (Saleh and Plieth, 2009).
1.4.1 Electron paramagnetic resonance (EPR)
Electron paramagnetic resonance (EPR) has been widely used to detect ROS in plants
(Bacic and Mojovic, 2005). The technique however enables detection of fairly unreactive
radicals due to the nature of reactive species that are able to accumulate up to high
enough level to be measured and with very short haft life. Hence, a new technique is
invented; it involves the using of probe or trap that intercept reactive radicals and react
with them to form a stable radical before proceeding with EPR. Lee et al., (2009b) showed
a successful example of using EPR with suitable probe for •OH and H2O2 quantification.
24
Nevertheless, EPR technique is yet to be fully exploited due to the difficulties to infiltrate
spin trap molecules into the cells that can cause additional stress and affect the levels of
ROS in the cell (Shulaev and Oliver, 2006). Additionally, the probes used provide different
permeability preference and may accumulate in a particular cellular compartment
(Shulaev and Oliver, 2006).
1.4.2 Staining and visualisation of H2O2 with chemicals
Direct measurement of H2O2 can also be achieved by using horseradish (Armoracia
lapathifolia) peroxidase assays, which form a fluorescent product that can be easily
detected (Black and Brandt, 1974; Okuda et al., 1991; Zhou et al., 2006). Horseradish
peroxidase is a hemoprotein catalysing the oxidation by H2O2 of a number of substrates.
Histochemical staining methods provide an advantage over other assays because they
allow not only the detection of H2O2 but also the location that produces them. However,
the results are semiquantitative. Leaf infiltration with 3,3-diaminobenzidine (DAB) is a
common and popular technique used to localize H2O2 in plants (Hand, 1979; Park et al.,
2008). Other visualisation techniques include using fluorescent dyes such as
dichlorodihydrofluorescein (Desikan et al., 2004) or genetic probes, namely HyPer,
consisting of circularly permuted yellow fluorescent protein (cpYFP) inserted into the
regulatory domain of a H2O2-sensing protein, OxyR (Belousov et al., 2006).
1.4.3 Fingerprinting of oxidative stress
ROS when combined with biological molecules leave a unique chemical ‘fingerprint’ that
can be used to indicate that ROS have been formed (Halliwell and Gutteridge, 1999). This
fingerprint is an indication of the level of oxidative stress imposed upon the plant.
Measuring nucleic acid oxidation has been widely used in animals and humans, however,
application in plants is still very limited. One of the examples is measuring the 8-hydroxy-
2’-deoxyguanosine in the DNA of Cardamine pratensis plants to estimate the level of total
oxidative DNA damage (Białkowski and Olinski, 1999).
25
Carbonyl groups of proteins on the other hand have been used to study signatures of
protein oxidation by ROS because it can be readily determined spectrophotometrically by
reaction with 2,4-dinitrophenylhydrazine (DNPH). This technique has been applied to
determine the oxidative stress levels following UVB exposure in Arabidopsis (Landry et al.,
1995), bean leaves (Shi et al., 2005), chilling (Prasad, 1996) and aluminium exposure
(Boscolo et al., 2003) in maize (Zea mays). The sensitivity of DNP detection can also be
increased at least 10-fold by using an antibody-based ELISA method (Buss et al., 1997).
Other studies also use western blots, which, even though is semi-quantitative, can detect
as little as 1 pmol of DNP-modified carbonyl groups per milligram of protein (Keller et al.,
1993). Newly developed isotope-coded affinity tags have also been used to allow analysis
of different amount of proteins oxidized in different samples (Gygi et al., 1999;
Sethuraman et al., 2004).
The trace of ROS production can also be examined via determination of polyunsaturated
fatty acid peroxidation. In this process, unsaturated aldehydes such as 4-hydroxynonenal
(4-HNE) and malondialdehyde (MDA) are produced. These aldehydic secondary products
of lipid peroxidation are generally acceptable as oxidative stress indicators (Gavino et al.,
1981; Upchurch, 2008). MDA reacts with thiobarbituric acid (TBA) to form coloured
products called thiobarbituric acid reactive substances (TBARS), which can be measured
by a UV spectrophotometer (Gavino et al., 1981). The straightforward determination of
lipid peroxidation using TBA makes it a popular tool to observe plant oxidative damage by
ROS, also well used in rice stress signalling analysis (Kuk et al., 2003; Moradi and Ismail,
2007; Morsy et al., 2007; Vaidyanathan et al., 2003).
Changes of antioxidant activities have been reported in rice after mechanical stress
(Chandru et al., 2003), salinity stress (Fadzilla, 1997; Vaidyanathan et al., 2003), lead
treatment (Huang and Huang, 2008), chilling (Kuk et al., 2003), and drought (Sharma,
2005). However, the data is very difficult to interpret due to the lack of complete
antioxidant activity. The research of Saleh and Plieth (2009) provided a breakthrough by
using a new technology involving bioluminescence assays to comprehensively quantify
total antioxidative capacity, the peroxidase activity and total superoxide scavenging
activity under salt, drought, cold and heat stress. The results proved the plethora shifting
26
of antioxidant activies following stresses and these quantitative traits may be promising
for implementation in high-throughput screening for robustness of novel mutants,
transgenics or breeds.
1.5 Rice and ROS in relation to abiotic stress
1.5.1 Genomics of rice
In 2002, the full sequence of two major rice cultivars (Oryza sativa L.), japonica-type (cv.)
Nipponbare (Goff et al., 2002) and indica-type cv. 93-11 (Yu et al., 2002), were completed.
Since then, extensive studies have been carried out with this model plant due to the
richest set of resources available for plant genomic studies (Goff et al., 2002; Sasaki et al.,
2002; Yu et al., 2002). Rice has been chosen as the model cereal for functional genomics
because of its commercial value, relatively small genome size (~430 Mb) (Upadhyaya and
Dennis, 2007), diploid origin (2X24) and more importantly, it possesses almost 98%
similarity in sequence, structure, order and function of genes among all the cereals and
grasses (Delseny, 2003).
Global analysis of transcription level in rice plants under different stresses and in different
organs have been intensively studied; a few examples include high salinity (Kawasaki et
al., 2001), drought (Gorantla et al., 2007), high temperature (Yamakawa et al., 2007), cold
and H2O2 (Cheng et al., 2007), light (Jung et al., 2008), pathogen attack (Sana, 2010) and
soil chromium contamination (Dubey et al., 2010). Rabbani et al.,(2003) tried to integrate
the transcription level under cold, drought, salinity and ABA treatment using microarray
and RNA gel blot analyses. Important to this finding was the revelation of 73 specific
genes some of which were shared between these four stresses. Comparative analysis of
Arabidopsis and rice showed that 51 out of the 73 genes had been reported in
Arabidopsis with similar annotation. Moreover, the expression of approximately 40% of
drought/ salinity inducible genes was also induced by cold stress, while more than 98% of
high salinity and 100% of ABA-inducible genes were induced by drought stress in both
rice and Arabidopsis. These results imply the existence of significant overlap between
stress signalling pathways.
27
Numerous stress-induced genes have been identified and their characterised function is
illustrated in Figure 1.5. Dehydration-responsive element binding protein (DREB2) and
NAC are examples of ABA independent TFs (Nakashima et al., 2007; Quan et al., 2010),
whereas the ABA-responsive element (ABRE) binding protein factor (ABF) TF functions in
ABA-dependent gene expression (Mukherjee et al., 2006). Those transcription factors are
usually found in osmotic, water deficiency and salinity stress signal perception (Dubouzet
et al., 2003). In rice, five DREB cDNAs were identified: OsDREB1A, OsDREB1B, OsDREB1C,
OsDREB1D, and OsDREB2A (Dubouzet et al., 2003). OsDREB1A and OsDREB1B were
induced by cold stress while OsDREB2A regulates salt and drought stress (Dubouzet et al.,
2003). Overexpression of OsDREB1A in rice induced expression of DRE/CRT genes and
OsDREB1A overexpression lines showed phenotypes similar to AtDREB1A (Arabidopsis)
overexpression line, with improved stress tolerance (Dubouzet et al., 2003).
The NAC gene family encodes the largest families of plant-specific transcription factors.
There are 75 putative NAC genes in rice, which are mainly involved in regulating plant
development and stress tolerance (Olsen et al., 2005). Salt and drought induces ERD1
expression via ABA–independent pathway, however, no DRE/CRT element was found in
its promoter region, suggesting a novel regulatory pathway for salt and drought adaption
via NACR transcription factors (Nakashima et al., 2007). Instead, the promoter region was
replaced by the MYC-like site, which can be recognized by transcription factors of the
NAC family. Hu et al., (2006) reported a NAC transcription factor significantly enhanced
drought and salinity tolerance in rice. Ohnishi et al.,(2005) showed a member of NAC
family, OsNAC6 was induced under salinity, drought, cold and ABA.
Three MYB transcription factors, namely MYBS1, MYBS2, and MYBS3, each with a single
DNA-binding domain (1R MYB), were identified in rice and shown to bind specifically to
the TATA box (TATCCA) in the sugar response complex (SRC) of the a-amylase gene
(aAmy3) promoter (Lu et al., 2002). The rice MYBS3 is activated by abscisic acid (ABA),
CdCl2, and NaCl (Yanhui et al., 2006) and also cold stress (Su et al., 2010). In addition to
these major pathways, MYB/MYC regulons are reported to involve gene expression in
wounding and biotic stress.
28
Figure 1-5: Transcription regulatory networks of abiotic stress signals and gene expression. The figure is adapted and modified from (Saibo et al., 2009). Abbreviation: AREB/ABF: ABA-responsive element-binding protein/ABA-binding factor, MYC: myelocytomatosis oncogene, MYB: myeloblastosis oncogene, DREB: Dehydration-responsive element binding protein; NAC: NAM, ATAF, CUC, Gly: glyoxalase family proteins. Despite the identification of key transcription factors and the stress that they are
regulating, more progression in EST sequence is needed in order to keep pace with the
information gap as one of the stress transcriptomic analysis shows that over two-thirds of
the possible rice coding sequence is unidentified with the current public database
(Rabbani et al., 2003). More importantly, it is not clear how the ROS-regulated gene
expression (as described in section 1.4), are reflected at the translational level. Changes
in the transcriptome are not always closely related to the changes in proteome (Komatsu,
2007; Schroeder et al., 2001). Therefore, multidisciplinary approaches, including
proteomics and metabolomics, are needed to uncover the complexity of plant stress
signalling mechanisms.
1.5.2 Rice proteomics
The analysis of a protein is the most direct approach to defining the function of its
associated genes. Proteomic and metabolomics studies linked to genome-sequence
information play an indispensable role in the functional genomics era. However, we have
to bear in mind that the genome and proteome of an organism do not always correspond
29
to each other on a one to one basis. A single gene can initiate mRNAs that produce
multiple transcripts, which produce different proteins.
The function of stress-induced proteins has been expanded by proteomic analysis in
specific rice organs, such as embryos (Fukuda et al., 2003), mitochondria (Chen et al.,
2009), roots (Lee et al., 2009a; Yan et al., 2005), shoots, leaves (Salekdeh et al., 2002),
anthers (Liu and Bennett, 2011) and cell-suspension cultures (Rao et al., 2010). Various
stress conditions have been applied to rice in order to find the protein markers that are
sensitive to stress, including heat (Dong-Gi Lee et al., 2007), drought (Ali and Komatsu,
2006; Salekdeh et al., 2002), cold (Cui et al., 2005; Makoto and Setsuko, 2007), salt
(Abbasi and Komatsu, 2004; Parker et al., 2006; Yan et al., 2005), osmotic (Xiong et al.,
2010; Zang and Komatsu, 2007), UV (Du et al., 2011), cadmium (Lee et al., 2010), copper
(Zhang et al., 2009), nitrogen stress (Chen Song, 2011) and exposure to the rice blast
fungus (Magnaporthe grisea) (Sun Tae Kim et al., 2003). In all of these studies, a
quantitative approach was used to assess the change in abundance of proteins following
stress application.
In a drought stress experiment, Ali and Komatsu (2006) found that the levels of actin
depolymerising factor, light-harvesting complex chain II, superoxidase dismutase (SOD),
and salt-induced protein changed upon exposure to drought or osmotic stresses. When
comparing proteins of drought tolerant cultivars, out of 1000 proteins in leaves, only S-
like RNase homolog, actin-depolymerizing factor, and ribulose-1, 5-bisphosphate
carboxylase/oxygenase (RuBisCo) activase showed increase in abundance and isoflavone
reductase-like protein was found reduced in abundance (Salekdeh et al., 2002). The up-
regulation of actin depolymerising factor in both (Ali and Komatsu, 2006; Salekdeh et al.,
2002) studies coincide with its up-regulation in salt stress treatment (Yan et al., 2005),
which suggests the role of this protein in dynamic reorganization of cytoskeleton during
osmotic stress induced by drought and salinity conditions.
In two week old leaf sheaths exposed to NaCl, eight proteins consistently showed
significant changes. Of these eight proteins, only five were able to be identified as
photosystem II oxygen-evolving complex protein, two fructose bisphosphate aldolases,
30
oxygen-evolving enhancer protein (OEE) 2, and SOD (Abbasi and Komatsu, 2004). In three
weeks old rice seedling post-salinity treatment, 34 proteins were up regulated and 20
were down regulated. In which, three of them are from enolase, the other six novel
proteins are salt-responsive: UDP-glucose pyrophosphorylase, cytochrome c oxidase
subunit 6b-1, glutamine synthetase root isozyme, putative nascent polypeptide
associated complex alpha chain, putative splicing factor-like protein and putative actin-
binding proteins (Yan et al., 2005). Parker (2006) studied proteomics of rice leaf laminae
after salinity stress and found that 32 out of 2,500 proteins shows changes in abundance
following salinity. However, only 11 of these proteins were identified by tandem MS.
RuBisco activase, ferritin, putative phosphoglycerate kinase and SOD were found to be
increased whereas S- adenosyl-L-methionine synthetase were found to be decreased. The
level of SOD isoforms were found up regulated in drought (Salekdeh et al., 2002) and salt
stress (Parker et al., 2006),which might imply its highly effective function as an
antioxidant.
In osmotic treatment that was induced by 20% PEG for 8 days, 28 proteins were found to
be altered. However, only 12 of them were identified and categorised according to their
metabolism function: redox metabolism (Prx and putative thioredoxin peroxidase),
photosynthesis (rbcS and rbcL), cytoskeleton stability (putative actin-binding protein,
ABP), defence (putative chitinase), protein metabolism (ribonuclease) and signal
transduction (voltage-dependent anion selective channel protein and osmotin-like
protein) (Xiong et al., 2010). Zang and Komatsu (2007) used a different chemical,
mannitol to test the proteomic response to osmotic stress and found 12 proteins were up
regulated and 3 proteins were down regulated. Amongst these, the level of heat shock
protein and dnaK-type molecular chaperone were reduced, level of 26 S proteasome
regulatory subunit and endosperm luminal binding protein, and glyoxalase I were
increased.
Changing temperature from optimized to chilling temperature causes a phase transition
in membrane lipids. Cui et al.,(2005) explained that cold treatment induces those of
protein-biosynthesis factors, molecular chaperones, proteases, biosynthesis of cell wall
components, oxidative and detoxifying enzymes and those that linked to energy and
31
signal transduction. Whilst (Lee et al., 2009a) examined the effect of cold stress on rice
roots and identified a group of proteins that were responsive to it: acetyl transferase,
phosphogluconate dehydrogenase, NADP- specific isocitrate dehydrogenase, fructokinase,
PrMC3, putative alpha-soluble N-ethylmaleimide-sensitive factor (NSF) attachment
protein, and glyoxalase1.
The lists of proteomic studies in rice that have been published to date are more toward
identification of those proteins that change in abundance upon different stresses. On the
other hand, post-translational modification (PTM) such as phosphorylation and thiol
modification caused by stress factors are equally important in stress signalling functions.
PTMs modify the primary structure of proteins in a sequence-specific way that includes
the reversible addition and removal of functional groups, alters and modifies the protein
function, increasing the complexity and dynamics of proteins (Kwon et al., 2006).
Several strategies are in development to aid in the progress of identifying PTM in plants.
Two-dimensional polyacrylamide gel-electrophoresis (2D-PAGE) is now a routine
approach to identify proteins that are expressed in different organs, tissues, and cellular
compartments. Many gel-free proteomics systems have also been developed for the
purpose of differential proteome analysis. Examples are multidimensional protein
identification methods (MudPIT) (Koller et al., 2002), isotope-coded affinity tags (ICAT)
(Griffin et al., 2001), and isobaric tags for relative and absolute quantification (iTRAQ).
Recently, 2D-PAGE coupled to MS such as 2D-fluorescence difference gel electrophoresis
(DIGE) is also applied to proteomic studies (Fu et al., 2008). With the development of this
technology, stress-related oxidised and reduced proteins have easily been identified with
applying 5-iodoacetamidofluorescein to tag oxidized proteins prior to 2D-PAGE. This tag
reacts with free thiol and hence showed increases or decreases in fluorescence intensity
following stress treatments (Baty et al., 2002; Cuddihy et al., 2008). Despite the pivotal
roles of PTMs in cellular functions, studies on PTMs have not really been feasible due to
large amount of proteins needed and insufficiencies in method for their detection
especially in rice. Therefore, this study put the primary interest in applying above
techniques in rice to identify those H2O2 sensitive proteins to decipher the H2O2 signalling
mechanisms under stress.
32
1.5.3 Rice Metabolomics
Metabolome is the quantitative complement of all the low-molecular weight molecules
(<3000 m/z) present in cells in a particular physiological or developmental state. Changes
in the levels of individual enzymes have a significant effect on metabolic fluxes. In
addition, as the metabolome is the ‘downstream’ reaction of transcriptome and
proteome, it is believed that the metabolome is the amplified reaction for both genomics
and proteomics; therefore it can be considered to reflect the phenotype.
In metabolomic experiments, the main purpose is to define the cell or tissue in a given
state at a particular time point as an effort to quantify all the metabolites in a cellular
system (Hollywood et al., 2006). Studies on rice metabolomics have so far focussed on
improving the crop quality (Shu et al., 2008), which include the studies on the
metabolites produced in GM rice (Wakasa et al., 2006), the metabolites changes at
different developmental stages (Tarpley et al., 2005), and the natural metabolite
variation between rice plants (Kusano et al., 2007).
So far, limited information is available on the influence of environmental stresses on
plant metabolome, except with few studies that have been performed on salt stress in
tomato (Johnson et al., 2003), nutrient deficiency in Arabidopsis (Hirai et al., 2004; Kaplan
et al., 2004; Kim et al., 2007), drought stress in pea (Charlton et al., 2008), biotic stress in
opium poppy (Zulak et al., 2008), drought in Thellungiella salsuginea (Lugan et al., 2010),
drought and salt stress in rice (Fumagalli et al., 2009; Ishikawa et al., 2010). Therefore, it
is envisaged that future studies could be performed to observe how metabolites are
influenced under mild and severe stress, and also to investigate if the metabolites are
related to any physiological and morphological responses.
33
1.6 Aims of the study
Integration of ‘omic’ science, including oxidative proteomics, and metabolomics changes
of rice plants under each stress condition correlated with their physiological response
provide a new insight into stress tolerance mechanisms of plants. Moreover, by knowing
that H2O2 is a central component in sensing and responding to environment stresses,
identifying the levels of H2O2 required for normal plant growth and development, in
addition to the levels which may cause plant tissue damage or cell death is important.
The aims of this project were to investigate:
1. The concentration of H2O2 produces in normal rice plants compared to rice plants
exposed to different stresses such as drought, cold, salinity and osmotic stress and
study its effects on physiology of plants.
2. To test the relationship between a combination of stresses and H2O2 changes in
rice.
3. To identify specific proteins that are sensitive to H2O2 produced in response to
these stresses.
4. To clarify the metabolome status under various stresses and to identify specific
responsive pathways under different stresses.
34
2 Chapter Two: Methodology
2.1 Plant tissue culture
2.1.1 Media preparation and sowing of seeds
Normal MS liquid media were prepared by adding 3% sucrose to MS medium (Murashige
and Skoog basal medium with vitamins, Duchefa). For MS solid medium, 0.25% phytagel
(Sigma) was added. Medium pH were adjusted to 5.8 with acid hydroxide before
autoclaving. For rooting purpose, only liquid MS media were used with 8% of sucrose
instead of 3%. For micropropagation, 3% sucrose was added to MS liquid medium before
autoclaving and 3 mg/L 6-benzylaminopurine (BAP) (Sigma, USA) was added to MS
medium after autoclaving.
2.1.2 Surface sterilization of rice seeds
Rice seeds Oryza sativa (Japonica Koshihikari) were obtained from Syngenta. Healthy
seeds were dehusked carefully without injuring the embryo. The seeds were then soaked
in 70% ethanol for 15 minutes with gentle agitation and then rinsed 3 times with sterile
distilled water. These seeds were then surface sterilized in 50% of commercial bleach for
45 minutes with agitation. Following this, the seeds were rinsed 5 times in sterile distilled
water. The seeds were then placed on top of the sterile solid MS medium (Murashige and
skoog basal medium with vitamins, Duchefa) prepared in sterile tissue culture disposal
vials (Greiner Bio-one, UK) (Yongdong et al., 2009). All the above procedures were carried
out in a laminar flow hood.
2.1.3 Micropropagation of rice
All the plants were incubated in a Fitotron (Sanyo) preset to 27±1 °C, humidity 85%, and
light intensity of 120 µE m-2s-1 on a 16 h/8 h day/night cycle. Seedlings that had
germinated well were used for micropropagation (Figure 2.1). The seedlings were cut
below the first node and above the coleoptiles; 2 cm long explants were obtained and
35
sub-cultured onto solid MS medium supplemented with 3 mg/L BAP for one week. The
one week old seedlings were then transferred into glass culture flasks (FisherScience)
containing rooting liquid medium and grown for another two weeks for experimental
purposes. The tissue culture plants were used for liquid based experiments such as H2O2,
salinity, osmotic and cold stress.
2.2 Soil growth of plants
2.2.1 Growth conditions
The incubator conditions were set identical to tissue culture condition, 85% humidity,
27±1 °C, and 120 µE m-2s-1 1 6h/8 h day/night. Seeds were soaked in water overnight
before sowing in 24 cell trays containing John Innes No.2 loam-based compost, purchased
from Monro Horticulture, UK. The germinated seedlings after almost 7 days were then
fed with 2 mg/L of nutrient solution (Solufeed Super Monro Horticulture, UK) once a
week, enough to dampen the compost. The 3 week old plants were ready for experiment
usage or for transfer into a bigger pot for regeneration of rice seeds. The soil grown
plants were mainly used in drought stress and cold stress experiments.
2.2.2 Transferring in vitro grown plants to soil
Two week old well-rooted green plants in tissue culture rooting media, approximately 10-
15 cm in height were washed under tap water and left overnight with the roots in cool
tap water. The next day, explants were transferred into 24 cell trays containing John
Innes No. 2 loam-based compost. The explants were held carefully in upright position
while pressing gently into the compost around plant roots. Again, explants were fed with
nutrient Solufeed Super once every week.
36
Figure 2-1: Micropropagation and growth of rice plants. a. Seeds germinating in basal MS medium after 1-2 weeks of culture. b.Germinated seedling were subcultured into MS medium supplemented with 3 mg/L BAP. c. Explants were cut below the first node and at upper part of coleoptile. These 2 cm long explants were then subcultured to MS medium with 3 mg/L BAP. d. Microshoots were produced covering the cut edge after 1 month. e. The explants were left to grow longer in the same medium before transfer to rooting medium. f. All of the explants that produced shoots also produced roots after 3-4 weeks subculture in 8% sucrose liquid rooting media. g. Transfer of tissue culture explants (on) to soil. h. Rice seeds were sown directly onto John Innes loam no2 compost. The 3 week old rice plants (approx 15 cm tall) were ready for experimental use. i. Direct sowing rice plants produced flowers and seeds after 4-5 months. j. Seeds ready to harvest after 6 months potting on soil. k. Rice plants from tissue culture. l. Rice plants from direct seed sowing.
c b a
f e d
l k j
i h g
37
2.3 Stress treatments
2.3.1 Drought stress
Plants at almost 12-15 cm in height grown from direct sowing of seeds onto soil were
watered until the soil was saturated. The exposed soil surface was completely covered
with masking tape to avoid water loss from evaporation. Drought stress was applied by
withholding water for 15 days and plant samples were harvested at two day intervals,
whilst control plants were well watered at two day intervals. Plant samples were
harvested at day 1, 3, 5, 7, 9, 11 and 13 at 11 am; they were then frozen in liquid nitrogen
and kept at -80 °C prior to analysis.
2.3.2 Osmotic stress
Polyethylene glycol (PEG) 6000 at 15% and30 % was used to mimic the osmotic stress in
an in vitro environment. Three week old explants from in vitro liquid culture were
transferred into 15% and 30% of sterile PEG 6000 solution. The control plants were
incubated in tap water without PEG 6000 solution. Plants were harvested at 0.5, 1, 2, 4, 8,
16 and 32 hours, frozen into liquid nitrogen and kept at -80 °C prior to analysis.
2.3.3 Salinity treatment
In-vitro liquid cultured rice plants were used for the salinity test. NaCl at two different
concentrations were used: 150 mM and 118.54 mM. Beside the purpose of comparing
the effect of different salinity dosage on plants, 118.54 mM NaCl is equivalent to
osmolarity of 15% PEG (determined by using osmometers, Loser Messtechnik). Hence,
this concentration also can be used to compare the differences of osmolarity effect from
15% PEG treated plants. Three week old explants from in vitro liquid culture were
transferred into solution with or without NaCl. Both samples of control and NaCl treated
plants were harvested at 0.5, 1, 2, 4, 8, 16 and 32 hours. Plant samples were then frozen
into liquid nitrogen and kept at -80 °C prior to analysis.
38
2.3.4 Cold stress
Cold stress was applied by transferring plants into a pre-set Fitotron at 10 °C and 20 °C
respectively. Apart from temperature, the other settings were set exactly similar with the
control plants chamber, with 85% humidity and 120 µE m-2s-1 16 h/ 8 h day/ night. For
tissue culture based cold stress, two week old plants in liquid culture were used whilst in
soil based cold treatment, three week old seedlings were used. Plant samples from cold
treatment and control treatments were harvested at 0.5, 1, 2, 4, 8, 16 and 32 hours. Plant
samples were then frozen into liquid nitrogen and kept at -80 °C prior to analysis.
2.3.5 Hydrogen peroxide treatment
Two different concentrations of hydrogen peroxide (H2O2), 10 mM and 50 mM, was
applied to three week old in-vitro rice seedlings, whilst control plants were kept in tap
water. Plant samples from H2O2 treatment and control treatments were harvested at 0.5,
1, 2, 4, 8, 16 and 32 hours. Plant samples were then frozen into liquid nitrogen and kept
at -80 °C prior to analysis.
2.4 Physiological observations
2.4.1 H2O2 content in plants
The H2O2 content in all stress experiments were examined according to the method of
Okuda et al., (1991). Leaves were ground with a mortar and pestle in liquid nitrogen into
fine powder and approximately 1 ml of 0.2 M perchloric acid was added to 200 mg
sample powder. The slurry was then centrifuged at 12,000 g at 4 °C for 15 min. The
supernatant was neutralized to pH 7.5 with 4 M sodium hydroxide and the solution was
centrifuged at 3,000 g at 4 °C for 5 min. An aliquot of supernatant was applied to 0.03 g of
anion exchange resin pre-packed into a 1 ml syringe. The column was washed with 800 µl
of distilled water. The elution was then performed with 400 µl of 12.5 mM 3-
dimethylaminobenzoic acid (DMAB) in 0.375 M phosphate buffer (pH 6.5), 80 µl of 3-
methyl-2-benzothiazoline hydrazone (MBTH) and 2.5 unit of horseradish peroxidase
(Sigma Ltd) was added to water to make a total volume of 1.5 ml. The reaction mixture
39
was activated at 25 °C for 5 min and stopped at 4 °C for 15 min. The absorbance was
monitored at 590 nm using a spectrophotometer and the actual amount of H2O2
produced was determined using a standard curve produced with known amounts of H2O2
(Okuda et al., 1991).
2.4.2 DAB staining for H2O2 visualisation in tissues
Leaves were collected after each stress experiment and vacuum infiltrated for few
minutes with 1 mg/ml 3, 3-diaminobenzidine solution (DAB)-HCl, pH 3.8. Leaves were
then left overnight in DAB in dark conditions. The leaves were cleared in 100% ethanol in
test tubes placed in boiling water and then fixed in a solution of 3:1:1 ethanol: lactic acid:
glycerol. Images of representative leaves were taken using a Leica digital camera that was
attached to a microscope. The images were all taken under magnification of 100X. The
figures were then analyzed using Image J software to quantify the area of brown colour
representing DAB staining. Images were first changed to black and white background to
enable the selection of saturated brown color. The total area of highlighted black
saturation were then quantified.
2.4.3 Chlorophyll content
Three leaf discs, approximately 3 cm each, were cut after each experiment and analysed
immediately. Chlorophyll pigments were extracted in 1.5 ml 100% methanol at 50 °C for 1
h. Absorbance at 665.2 nm and 652 nm was measured, and chlorophyll content was
determined by the method of Porra et al.,(1989) by using the equation below:
Chl a (nmol/mg): (16.29* OD665)-(8.54*OD652) Chl b (nmol/mg): ( 30.66*OD652)-(13.58*OD665)
40
2.4.4 Relative water content (RWC)
Rice seedlings were harvested randomly after each treatment and the fresh weight of
whole seedlings above the root was measured immediately. The leaf was then soaked in
distilled water over night at 4 °C to let the leaf absorb water. The following day, the leaf
was taken out and blotted dry in tissue paper before measuring its turgid weight. The leaf
was subsequently oven-dried at 80 °C over-night to obtain its dry weight. The relative
water content (RWC) was determined by the formula below (Turner, 1981):
2.4.5 TBARS assay
The TBARS assay was used to quantify oxidative stress by measuring the peroxidative
damage to lipids that occurs with free radical generation. The process of damaging lipids
involves the production of MDA, which reacts with thiobarbituric acid (TBA) under
conditions of high temperature and acidity generating a chromogen that can be
measured spectrophotometrically (Janero, 1990).
Leaves were ground with mortar and pestle in liquid nitrogen into fine powder. A total of
1 g of plant tissue were homogenised with 5 ml of cold acetone and filtered through
Whatman no.4 paper. Then 200 μl of filtered plant sample was added to 200 μl of 20%
TCA aqueous solution and 100 μl 0.67% TBA aqueous solution. The mixture was then
heated in a boiling water bath for 15 min and cooled quickly under running tap water.
The mixture was then centrifuged at 15,000 rpm for 15 min. The clear supernatant was
made up to 1 ml with distilled water and absorbance was recorded at 532 nm and 600 nm.
For the blank, 200 μl of distilled water instead of plant extract treated with the same way
was used. To prepare a standard curve, MDA was freshly diluted with 0.01 N HCl into
different ratios 1:75, 1:100, 1:150, 1:200, 1: 300. The molar absorbance (MA, 1.67 X 106)
was close to the reported molar extinction coefficient of 1.57 X 106 at 532 nm. The final
concentration of MDA was determined by using the equation below:
MDA (nmol/ml)=[(A532-A600)/157 000] 106 (Zhou et al., 2006)
Fresh weight – Dry weight Turgid weight –Dry weight
X100
41
2.4.6 Data analysis
Data were collected randomly from three biological and experimental replicates for each
treatment. Student t-test was used when only two sample groups were involved while
ANOVA was used to calculate the variation when involved more than two groups of
sample.
2.5 Metabolome analysis of response to stress
2.5.1 Plant extraction for NMR analysis
Plant sample was harvested in liquid nitrogen and freeze dried prior to the extraction
process. Approximately 100 mg of each sample was homogenized using tissue lyser
(QIAGEN) at 30 Hz for two mins. The resulting homogenate was mixed with 1 ml of
mixture (methanol:water=1:1, v:v) and 1 ml of chloroform, vortexed for 1.5 mins and
centrifuged at 13,000 g for 10 mins, yielding an aqueous phase containing water-soluble
metabolites, sample residual substance and an organic phase containing chloroform-
solvable metabolites. The aqueous supernatant was decanted into a 2 ml Eppendorf tube
and the chloroform phase was carefully transferred into a new glass vial without
disturbing the plant material layer. All fractions were dried using a speed vacuum and the
dry extracts were stored at -80 ⁰C. The aqueous phase of each leaf and root extract was
resuspended in 600 μl of phosphate buffer (0.1 M, pH 7) containing 10% of D2O and 0.01%
of sodium 3-(trimethylsilyl) propionate-2,2,3,3-d4 (TSP), centrifuged at 10,000 g for 10
mins and transferred into 5 mm NMR tube for spectroscopic analysis. The chloroform
phase extracts were resuspended in 600 μl of chloroform-d 99.8% (Sigma Aldrich) and
transferred into same type of NMR tube.
2.5.2 NMR spectra acquisition
In 1H- NMR, an external electron magnetic field is applied to align the nuclear spin of
corresponding proton nuclei. The nuclei are irradiated with different frequencies that
enable them to promote from a lower energy spin state to a higher energy spin state.
This process is called resonance, which can be detected via absorption frequencies. In
42
short, the signal is recorded as function of one time variable. A spectrum displaying
intensities versus frequencies. These absorption frequencies depend on the number of
electrons that the nucleus possesses, one peak corresponds to one frequency. If two or
more spins are scalar coupled, multiplets will be observed.
1H NMR spectra were acquired on a 600.13 MHz NMR Bruker spectrometer (Rheinstetten,
Germany) at 300 K. These experiments were performed in collaboration with Department
of Medicine, Imperial College London, which was carried out by Dr Jia Li. A one
dimensional 1H NMR spectrum was obtained for each sample using an excitation
sculpting pulse sequence (ZGESGP) for sufficient water signal suppression. A total of 256
scans were collected into 64 k data points. For spectra assignment purposes, a series of 2-
dimensional NMR spectra such as 1H-1H correlation spectroscopy (COSY), 1H-1H total
correlation spectroscopy (TOCSY), 1H J-resolved, 1H–13C heteronuclear single quantum
coherence (HSQC) and 1H–13C heteronuclear multiple bond coherence (HMBC) was
performed on selected samples. In COSY and TOCSY experiments, 80 transients for each
of 128 increments were collected into 2 k data points. MLEV-17 was employed as spin-
lock scheme in TOCSY experiment with the mixing time of 80 ms. For J-resolved spectra,
32 transients for each of 50 increments were collected into 2 k data points with spectral
width of 6313 Hz in F2 (chemical shift dimension) and 50 Hz in F1 (J coupling constant
dimension). HSQC and HMBC NMR spectra were recorded using the gradient selected
pulse sequences. In HSQC experiments, composite pulse broadband 13C decoupling
(globally alternating optimized rectangular pulses, GARP) was employed during the
acquisition period; 240 transients for each of 128 increments were collected into 2k data
points with spectral width of 6313 Hz in 1H dimension and 26410 Hz in the 13C dimension.
In HMBC experiments, a total of 400 transients were collected into 2k data points for
each of 128 increments with spectral width of 6313 Hz in 1H dimension and 33201 Hz in
the 13C dimension with the long-range coupling constant of 6 Hz. These data were zero-
filled to 4k data points in the evolution dimension prior to appropriate apodisation
function respectively prior to Fourier transformation with forward linear prediction.
43
2.5.3 Pre-processing NMR spectral data
1H NMR spectra obtained were manually phased, baseline-corrected and referenced to
TSP resonance at δ 0.00 for aqueous and δ 7.26 for chloroform extracted sample using
Topspin software (Bruker, Rheinstetten Germany). The complete spectra (δ 0.0-10.0)
were then imported into MATLAB and digitized into 20 000 data points for each spectrum
with a resolution of 0.005 ppm with the in-house developed MATLAB scripts (Dr Cloarec,
Imperial College London). Region belonged tp water signal and noise were removed
before the data were transferred to SIMCA +P software for clustering analysis and R
software package for Hierarchical cluster analysis (HCA).
The heat map was generated based on expression level of each metabolite in a given
sample with red colour indicative of higher expression and blue colour representing lower
expression to show the global view of metabolites expression in each time points and
treatment. Each metabolite that showed changes was then analyzed by plotting log2
transformation ratio of treated samples versus control samples into bar charts.
2.5.4 Data analysis
2.5.4.1 Software
Statistical analysis, binning, quantification and visualisation of the results were performed
using several commercial software programme which included in-house Matlab toolbox
(Imperial College London), Simca+P (Umetrics, Sweden), SPSS (Chicago, USA), and R –
programme (New Zealand).
2.5.4.2 Binning
Prior the statistical analysis, data were binned over the range of peak positions that
contributes to vary within the 1D NMR dataset. This method identifies the start and end
point of each peak, therefore subsequent statistical analysis can be performed on the
NMR resonance peaks. Peaks that contributed to water distortion and noise were cut out
at this point to reduce the risk of misinterpreting statistical results.
44
2.5.4.3 Principal components analysis (PCA)
PCA is a very useful tool providing the overview of variance in the data matrix. Before PCA
and any multivariate data analysis, raw data set was scaled. In this study, unit variance
scaling was used. PCA explored class differences and highlighted explanatory Metabolites
group (Trygg et al., 2006).
2.5.4.4 Partial least squares linear discriminant analysis (PLSDA)
PLSDA is a supervised method used to determine the presence of data trends in NMR
spectra that correlate with class variables. Linear discriminant analysis was performed
and models were validated with cross-validation, in which 80% of the data was used to
built the model and 20% of the data was omitted and used for predicting the dataset to
assess the data, and therefore scored the quality of the PLSDA (Trygg et al., 2006).
2.5.4.5 Student t-test
Two tailed Student’s t-test was carried out on statistical differences between input
variables (control and treated plants at different stress levels and time point). The p value
in this study was all set to p<0.05, in which p value less than 0.05 contribute to the most
likely metabolites that were altered after stress. Metabolites that significantly changed
versus control in each experiment were chosen and plotted into a bar chart. The ratio of
treated metabolite versus control was log2 transformed and 0.5 cutoff point was used to
select the metabolite before plotting into the bar chart.
2.5.5 Identification of metabolites
2-D spectra such as 1H-1H COSY and 1H-1H TOCSY were used in metabolite identification
with the help and advice from Dr Jia Li. Additionally, STOCSY, as a computing and
statistical analysis aid were used to seek the signals contributing from the same molecule
in a series of complex NMR spectra, especially for 1D experiment acquisition. The specific
patterns of cross peaks and characteristic chemical shifts in 1D and 2D make it a powerful
45
and reliable method to directly identify a large number of metabolites in crude extracts.
To confirm the identification, spiking with an authentic standard is a necessary step.
Automated structure and metabolite assignment was done with comparison of the 1D
and 2D information obtained from a searchable library. The libraries offer information of
the standard compounds with their scalar connectivity and chemical shifts of the couple
peaks. Examples can be found at:
http://www.chemspider.com,
http://www.emolecules.com,http://www.bmrb.wisc.edu/search,
http://www.ebi.ac.uk/nmrshiftdb,http://riodb01.ibase.aist.go.jp/sdbs/cgi-
bin/direct_frame_top.cgi.
2.6 Proteomic analysis of rice responses to stress
2.6.1 Establishment of protein extraction protocol
Protein extraction methods can vary depending on the reproducibility and representation
of the total proteome, especially for rice plants that contain huge amount of non-protein
contaminants. These contaminants included organic acids, lipids, polyphenols, pigments
and terpenes (Wang et al., 2003). This implies that a reliable protein extraction method is
one of the first critical steps for proteomic studies. To address this, several protein
extraction protocols were compared for best isolation, purification and solubilization of
rice proteins.
2.6.1.1 Phenol extraction
The protein extraction method was adapted and modified from (Isaacson et al., 2006).
Leaf samples were harvested and ground into powder with a pre-chilled mortar and
pestle in liquid nitrogen. The powder was then transferred into a 20 ml tube and filled
with 10% of TCA/Acetone. Samples were mixed well with vortexing or inversion before
centrifugation at 16, 000 x g for 3 min at 4 ⁰C. The supernatant was discarded and the
tube was filled with 0.1 M ammonium acetate in 80% methanol. The mixture was mixed
well by vortexing and centrifuged as above. The leaf material was subsequently washed
with 80% acetone and centrifuged. The supernatant after centrifugation was discarded
46
and the pellet was air dried at room temperature for at least 10 min to remove the
residual acetone. When dried, 1:1 phenol: SDS buffer (30% sucrose, 2% SDS, 0.1M Tris-
HCl pH8, 5% 2-mercaptoethanol) was added with ratio of 4:4 ml/g of the starting material.
The plant material in phenol: SDS buffer was then centrifuged at 16,000 x g for 5 min. The
well-separated upper phenol phase was then transferred into a new 2 ml tube and the
tube filled with 0.1 M ammonium acetate in 80% methanol before storing at -20 ⁰C for 10
min to overnight. To collect the white protein pellet, the phenol phase of 0.1 M
ammonium acetate in 80% methanol was centrifuged as above and the supernatant was
carefully discarded. The pellet was subsequently washed with 100% methanol and then
with 80% acetone. Finally, the protein pellet was dissolved in SDS sample buffer (100 mM
Tris HCl pH6.8, 10% glycerol, 25% SDS,) and stored at -20 °C for subsequent quantification.
2.6.1.2 Tris HCl extraction
Leaves tissue were harvested and ground into powder with a pre-chilled mortar and
pestle in liquid nitrogen. To the powdered plant sample, extraction buffer (50 mM Tris-
HCl, complete protease inhibitor cocktail tablets (Roche Diagnostics, Germany) with pH
7.5 was added with a ratio of 1:2 ml/g buffer: fresh weight tissue. The homogenate was
centrifuged at 12,000 x g for 10 min and the process was repeated twice, as described
earlier (Hancock et al., 2005). The supernatant was carefully discarded and the protein
pellet was dissolved in SDS sample buffer (100 mM Tris HCl pH 6.8, 10% glycerol, 25%
SDS,) and stored at -20 °C for subsequent quantification.
2.6.1.3 Mg/NP-40 extraction
Plant samples were harvested and ground into powder with a pre-chilled mortar and
pestle in liquid nitrogen. Proteins were extracted using Mg/NP-40 buffer (0.5 M Tris-HCl
(pH 8.3), 2% V/V NP-40, 20 mM MgCl2, 1 mM phenyl methyl sulfonyl fluroride (PMSF), 2%
mercaptoethanol and 1% w/v polyvinylpyrrolidone (PVP). The slurry was centrifuged at
12,000 g for 15 min at 4 ⁰C.
47
To remove the non-protein interfering compounds, the supernatant obtained from above
was first precipitated with TCA/ acetone. Then, 0.2 volumes of 50% w/v PEG 6000 stock
solution was added to the protein pellet and stirred for 30 min to mix up the protein. At
this point, the protein obtained was labelled as ‘PEG fraction’. To avoid the PEG
interfering with other subsequent experiments, e.g. for 2D purposes, the PEG fraction
protein was first washed with TCA/ acetone to remove the PEG before dissolving into SDS
sample solution. The protein was then labelled as ‘PEG in SDS’.
2.6.2 Total protein quantification
2.6.2.1 Bradford assay for 1D
Bradford reagent (Sigma Ltd) was diluted 2 fold with distilled H2O. BSA was used at
various concentrations to prepare a standard curve (0.1-10 mg/ml). An aliquot of protein,
10 µl, was added to 1 ml of the diluted Bradford reagent and mixed. The solutions were
then left for 15 min at room temperature. The change in coloration of the reagent was
measured with disposable plastic cuvettes with the aid of a UV spectrophotometer
(Eppendorf) at the wavelength of 595 nm. The BSA standard curve was generated by
plotting the absorbance of the standards against the quantity of the protein. The
concentration of proteins was determined by referring to the BSA standard curve.
2.6.2.2 Quantification using 2D Quant kit
To determine the total protein content more precisely without interference from sample
reagents such as SDS, DTT, urea and thiourea, 2-D quantification kit (GE HealthCare) was
used. The procedure was carefully followed according to the protocol provided from the
kit. The BSA standard was prepared in the ranges of 0-50 µg in not more than 25 µl
sample solution whilst tested protein sample was prepared in series of dilutions to ensure
that the assay falls within the BSA sample range. A total of 500 µl precipitation solution
(GE HealthCare) was added to each tube that contained protein sample, vortexed briefly
and incubated at room temperature for 2-3 min. The tube was then filled with 500 µl of
co-precipitant (GE HealthCare) and mixed by inversion. The tubes were then spun at a
48
minimum of 10,000 g for 5 min and the supernatants were discarded to obtain the
protein pellet. Copper solution in total amount of 100 µl and 400 µl distilled water were
added to each tube and briefly vortexed to dissolve the protein. One ml of working color
reagent (GE HealthCare) was then added to each tube and incubated at room
temperature for 15-20 min. The change in coloration of the reagent was measured with
disposable plastic cuvettes with the aid of a UV spectrophotometer (Eppendorf, UK) at
the wavelength of 480 nm. The BSA standard curve was generated by plotting the
absorbance of the standards against the quantity of the protein. The concentration of
proteins was determined by referring to the BSA standard curve.
2.6.3 Electrophoresis of Proteins
2.6.3.1 1D gel electrophoresis
The proteins obtained from the above extraction methods were quantified and analysed
by one dimensional SDS-PAGE electrophoresis. Pre-cast gradient gels (4-12%, Bio-Rad)
were used for this purpose. The gel chamber was filled with SDS running buffer (25 mM
Tris-HCl, 200 mM glycine and 0.1% w/v SDS). Before loading the sample, protein was
reduced by adding equal volume of sample loading buffer (10% w/v SDS, 10 mM DTT, 20%
v/v glycerol, 0.2 M Tris-HCl and 0.05% w/v bromophenol blue at pH 6.8) and heated for 3
to 5 min on a heating block at 100 °C in a sealed screw-cap microcentrifuge tube. A total
of 30 µg proteins were then applied to each gel lane in addition to a pre-stained standard
molecular weight marker (Biorad). The samples were then electrophoresed at 200 V for 1
h.
Following electrophoresis, the gel was removed from the gel case and rinsed several
times with distilled water. The gel was fixed in fixing solution (40% methanol and 10%
acetic acid) for 30 mins before visualized by staining with Coomassie Brilliant Blue (Sigma)
for at least 1 h at room temperature. The gel was then detained in destaining solution (20%
methanol, 10% acetic acid) for 30 mins and repeated to remove all trace of Coomassie
Blue stain.
49
2.6.3.2 2D electrophoresis
2.6.3.2.1 Preparation of IPG strips and sample loading
Immobilized pH gradient (IPG) strips (GE Healthcare Life science), pH3-10 (11 cm) were
used to analyze rice proteins. IPG strips were rehydrated overnight in 200 µl Destreak
rehydration solution (GE Healthcare). The destreak rehydration solution was prepared by
adding 15 mg Destreak solution to 1ml rehydration buffer (7 M Urea, 2 M thiourea, 2%
CHAPS, 0.5% IPG buffer, 0.002% bromophenol blue). For 2D analysis, proteins were
extracted with phenol extraction (as described in section 2.6.1.1). The protein pellets
were resuspended with vortexing in sample buffer containing 7 M urea, 2 M thiourea, 4%
CHAPS, 2% IPG buffer, and 40 mM DTT. Proteins were quantified using 2D quantification
kit (GE HealthCare) and approximately 250 μg of proteins were loaded carefully onto an
IPG strip and focused using an Ettan IPGphor 3 System (GE Healthcare life science) with a
ceramic manifold as follows: step and hold at 200 V for 30 min; gradient to 1000 V for 66
min; gradient to 3000 V for 2 hours; gradient to 5000 V for 6 hours; gradient to 8000 V to
6 hours; step and hold at 600 V for 1h.
2.6.3.2.2 Equilibration of IPG strip
After isoelectric focusing, the IPG strips were placed in individual tubes and equilibrated 2
times in 10 ml equilibration buffer (6 M Urea, 75 mM Tris-HCl pH8.8, 29.3% glycerol, 2%
SDS, 0.002% bromophenol blue). Alternatively, they were stored at -80 °C until
processing. For the first equilibration step, 100 mg DTT was added to the 10 ml
equilibration buffer and equilibrated for 15 min. After the first equilibration, the solution
was then rinsed off with SDS running buffer and changed to 10 ml equilibration buffer
containing 250 mg iodoacetamide and treated for 15 min. When finished, strips were
washed again with SDS running buffer (25 mM Tris-base, pH 7.8, 192 mM glycine, 0.1%
SDS) and ready to apply to the SDS gel.
50
2.6.3.2.3 Applying of IPG strip to SDS gel
IPG strips were positioned horizontally atop 10-20% precast polyacrylamide gradient gels
(10X18 cm, Biorad). Gels were sealed in place with agarose solution (25 mM Tris base,
192 mM glycine, 0.1% SDS, 0.5% agarose, 0.002% bromophenol blue). Gels were routinely
run at 100 V for 4 hours (PowerPac Basic 300V Power Supply, Biorad). After
electrophoresis, the gel case was carefully cracked open and the gel was transferred into
a plastic box with SDS running buffer.
2.6.4 Visualize of protein in 2D gel
Fixing and staining of 2D protein gel for visualization were the same as for 1D gel,
described in section 2.6.3.1.
2.6.5 Proteomic detection of oxidised and reduced thiol-containing proteins
Reactive oxygen species (ROS) can induce and modulate several biological responses. The
most likely mechanism of redox regulation is the post-translational modification of
protein thiols (Ghezzi and Bonetto, 2003). Hence, there is a need to develop a technique
to detect the oxidized thiols during stress conditions. The development of this method is
clearly shown in the diagram below and is based on the method used by Baty et al., (2005)
(Figure 2-2). The –SH group of thiol can react either with H2O2 or 5’-iodoacetamide
fluorescein (5’IAF) (Sigma Ltd). Hence, the protein extracted from control and treated
plants were both treated with 5’IAF before applying to 2D electrophoresis and their
fluorescence intensities were compared following 2D electrophoresis separation. Since
5’IAF tags only to reduced protein and most of the free thiols are in reduced form, higher
flourescence intensity in control compared to oxidised thiols were observed. The direct
labelling of proteins with 5’IAF picks up the free thiols proteins, thereby fluorescing and
overshadow the small amount of oxidized thiol groups of the same protein that shows
less fluorescence. Therefore, a modified approach was established.
51
Figure 2-2: Reduced or oxidized thiol proteins are labelled with a fluorescent iodoacetamide derivative 5-iodoacetamidofluorescein (IAF). To detect oxidized thiols the reduced thiols are blocked with NEM, the sample is treated with DTT and then the newly reduced thiols labelled with IAF. In comparison, for reduced thiols all sites are immediately labelled with 5’IAF. Figure modified and adapted from (Cuddihy et al., 2008).
2.6.5.1 Labelling of reduced thiols with 5’IAF
Procedure followed was as described in (Hancock et al., 2005). To each protein pallet
extracted with phenol (30 μg), 100 μM of 5’IAF (Sigma Ltd) (prepared in 10 mM DMSO
stock solution) was added and incubated in the dark at room temperature for 10 min. The
insoluble material from the extract was removed by centrifugation at 16, 000 x g for 5
min. The excess 5’-IAF was removed by passing 75 μl of labelled protein extract through
desalting spin columns (Biorad) that were equilibrated with 3x 400 μl sample buffer. The
eluate was stored at -20 °C before running on the 1D gel. The treatment condition that
resulted in most visible changes in term of fluorescent emission to proteins was chosen
for 2D experiments. The samples were labelled as above and applied to IPG strips for 2D
experiments.
Proteomic approaches to identify
redox protein
Labelling of reduced thiol
Proteins were labelled
with 5'IAF
Labelling of oxidized thiol
proteins blocked by
NEM
Reduced with DTT
Tag with 5'IAF
52
2.6.5.2 Labelling of oxidized thiols
Proteins extracted (100 µg) from control and stress-treated tissues were resuspended in
200 mM of N-Ethylmaleimide (NEM, Sigma Ltd) and incubated at room temperature for
15 min. The excess NEM solution was removed by passing 75 μl of extract through a
desalting spin column (Biorad) equilibrated with 3 X 400 μl of SDS sample buffer (100 mM
Tris HCl pH 6.8, 10% glycerol, 25% SDS). The oxidized thiols in the filtrate were
subsequently reduced by incubating with 1 mM DTT for 10 min at room temperature. To
label the oxidized thiols, 5’IAF was added to a final concentration of 100 μM and
incubated in the dark at room temperature for 15 min. The excess 5’-IAF was then
removed by passing 75 μl of labelled protein extract through a new desalting spin column
(Biorad) treated as before. The eluted protein was then keep at -20 °C. Protocol was
adapted and modified from (Cuddihy et al., 2008).
2.6.5.3 Image acquisition and analysis of protein spots
After tagging with 5’IAF, the proteins were electrophoresed on either 1D or 2D gels with
the gel case totally covered in foil, to avoid photo bleaching caused by light. After
electrophoresis, the gels were removed from the gel tanks and placed in SDS
electrophoresis Buffer. The fluorescence on gels resulting from 5’IAF tagging was
immediately visualised on Safe imager blue UV light box (Invitrogen) and the picture was
captured with a Gel Doc 2000 (Biorad). The gel was then stained with colloidal coomassie
brilliant blue and destained as in section 2.6.3.1. The images with 3 replicates were
analysed with Progenesis SameSpots (Nonlinear Dynamics), spot detection, spot
measurement, background subtraction and spot matching were performed. Prior to
performing spot matching between gel images, one gel image was selected as the
reference gel. Protein spots that showed changes were selected and processed for
protein in-gel digestion.
53
2.6.6 Protein identification techniques
2.6.6.1 In-gel trypsin digestion
The gels were washed with clean double distilled H2O a few times. The desired protein
bands or protein spots were cut out from the gels using a very fine needle, subsequently
cut into small pieces (1 mm2) and placed inside 0.65 ml siliconized tubes (Sigma Ltd). To
destain the proteins present in the gel, enough volume of 25 mM ammonium bicarbonate
in 5% acetonitrile was added and occasionally vortexed, depending on the staining
intensity. The supernatant was discarded by using gel loading pipette tips. The process
was repeated until clear white protein gels were observed.
The next step was in-gel reduction and alkylation of low-level protein. For reduction of
proteins, gel pieces were covered with 10 mM DTT in 25 mM NH4HCO3 and centrifuged
briefly before placing in a 56 ⁰C pre-set heating block for 1 h to allow the reaction to
proceed. Subsequently, the supernatant was discarded and changed to 55 mM
iodoacetamide, vortexed and centrifuged briefly before leaving at room temperature for
45 min. The gel pieces were finally washed with NH4HCO3 and subsequently spun down
to discard the supernatant. Now, the gel pieces were covered with 25 mM NH4HCO3 in 50%
ACN, vortexed, centrifuged and the process repeated. The gel pieces were then dried
completely with speed vac for approximately 20 min.
For trypsin digestion, the trypsin solution was freshly prepared by adding 12.5 ng/μL
trypsin (Promega sequencing grade modified) in 25 mM NH4HCO3. Trypsin was added
approximately at 3 x more volume of the gel size to cover each protein spot and
rehydrated on ice for 10 min. A solution of 25 mM NH4HCO3 was added to the gel to
completely cover the gel pieces and incubated at 37 ⁰C for 4 h to overnight. The digested
solution was then transferred into a clean siliconized tube and labelled as ‘tube solution’.
To the gel pieces, enough 50% ACN in 5% formic acid was added and vortexed for 30 min.
After briefly spun, the supernatant was collected into ‘tube solution’. The total volume
was then reduced to 10 μl by using a speed vac (Eppendorf) and the peptide solution
were cleaned with C18 Zip Tip (Millipore) to clear the remaining trypsin in the digested
peptide solution. The protocol was adapted and modified from Shevchenko et al., (2007).
54
2.6.6.2 Mass spectral data processing
The samples were analysed on an Applied Biosystems QTrap MS coupled to an Agilent
1100 LC stack. The Agilent stack consisted of a Binary pump, Capillary pump, Well Plate
autosampler and a column oven with integrated 6 port valve. The system was configured
to load samples onto a trap column (Agilent Zorbax SB 5 um x 0.3 mm x 35 mm) using the
binary pump; the trap column was washed and then switched into the capillary flow;
peptides were separated on a capillary column (Agilent SB 5 um 0.5 mm x 150 mm
column). The LC was interfaced to the MS with a Turbo Ion Spray Source (Applied
Biosystems). The loading and data acquisition was kindly performed by our mass
spectrometry technician, Mr Mark Bennet. MS spectra analysis was conducted with
Proteomics Analyzer (Applied Biosystems, Framingham, MA, USA). Data were analyzed
using GPS Explorer software (Applied Biosystem) and MASCOT software (Matrix Science,
London, UK). Parameters were set to Variable Modification - Oxidation, 1 Allowed Missed
Cleavage and Oryza sativa (rice) was selected as the taxonomy.
55
3 Chapter Three: Physiological responses of rice (Oryza sativa) to various
stress treatments
3.1 Aims
To observe the changes in plant physiological traits and the similarity and differences in
adaptation to different stresses, rice plants were treated with different stresses ranging
from mild to severe strength. The choice of stresses were those that were the most
susceptible to rice plants, including drought, salinity, cold and osmotic stress (Bernier et
al., 2008; Kazemitabar et al., 2003; Munns and Tester, 2008). At the same time,
physiological changes that are predominantly affected such as water and chlorophyll
content, relative to the accumulation of H2O2 and lipid peroxidation were all taken into
account to assess the effects caused by stress.
3.2 Introduction
Plants as sessile organisms are highly susceptible to exogenous environmental variables
compared to humans and animals. Providing optimal environment quality is an important
factor to achieve maximum physiological performance of any crop. However, in the real
natural environment, optimal environment is difficult to achieve as it fluctuates from
time to time, varying from sub-optimal to supra-optimal temperature, based on climatic
changes. This environmental limitation affects plants in many ways including distortion of
their physiology, morphology and development, which contribute to considerable losses
in productivity of major crops.
Amongst all environmental stresses, changing temperatures, the water status of soil and
salinity are the most crucial signals affecting plant growth (Trewavas and Malho, 1997)
(Boyer, 1982; Munns, 2002). This particularly affects crops like rice, a staple food for
more than 60% of the world’s population (Shanmugam et al., 2006). It has been predicted
that the stagnating yield of rice per annum is unable to keep pace with world’s
population growth rate of 1.93% per annum in the future (Shanmugam et al., 2006). Most
56
importantly, there is almost no hope for increasing the area of rice plantation due to
environmental constraints; the only way for improving rice production is therefore by
increasing the productivity of rice.
The key element in improving rice productivity is to first understand how environmental
changes could affect physiological and molecular responses. Fundamental processes such
as photosynthesis, respiration, circadian rhythms, seed germination, stomatal function,
transpiration and plant water content are associated with chemical and physical
processes of rice plants. The normal environmental constraints that affect plant growth
and yield product are explained in section 1.1.1. Depending on the strength of the
environmental stress, plants adjust their internal chemical and physical activity in
adaptation to environmental changes. For example, water (Bouchabke et al., 2008) and
chlorophyll content (Chaves et al., 2009) decrease following drought are the fundamental
indicators of plant adaptation to stress. Chlorophyll is an extremely important
biomolecule which absorbs light for photosynthesis to provide energy for plants. On the
other hand, water provides a transport medium for soluble sugars and essential ions
while keeping the plant cell at a certain turgid pressure. More importantly, in
photosynthesis, water and carbon dioxide are used to produce sugar and oxygen.
Hydrogen peroxide is a by-product of normal photorespiration process but over-
produced in response to multiple stresses; it is an important signal which provides
protection from harmful effects of stress via stimulation of, for example, stomatal closure
and regulation of cell proliferation (Shevchenko et al., 2007). However, increased
accumulation of H2O2 is unavoidable when stress proceeds rapidly causing plant cell
damage. Therefore when optimal levels of H2O2 are exceeded in the cell and the plant
undergoes oxidative stress, an important consequence is lipid peroxidation, a widely used
stress indicator of plant membrane damage. Lipid peroxidation is a process of
polyunsaturated fatty acid oxidation, producing the by-product, malondialdehyde (MDA).
The content of MDA can be measured by its reaction with thiobarbituric acid (TBA), which
yields a reddish colour that can be measured with a spectrophotometer (Heath and
Packer, 1968). Hence, it has been widely used as a convenient assay to quickly and easily
measure the oxidation of fatty acids.
57
Nevertheless there is little information on H2O2 production in relation to the other
physiological processes in rice plants. Hence, investigating how this signalling molecule
associates in physiological processes to combat stress provides an insight into
development of stress tolerant plants.
3.3 Results
3.3.1 DAB staining to establish the production of H2O2
As a first step to show that rice plants generate H2O2 following exposure to multiple
abiotic stresses, H2O2 accumulation in vascular tissues of rice leaves was detected by 3,3’-
Diaminobenzidine (DAB) staining (Figure 3.1). DAB has been used in
immunohistochemical staining of nucleic acids and proteins; following penetration into
plant cells, it is oxidized by H2O2 to give a dark-brown colour (Graham and Karnovsky,
1966). With the same magnified image (10X), DAB-stained area was clearly observed to
be distributed around vein areas. The DAB stain in control leaves were less obvious
compared to treated plants. Obvious increase in intensity of DAB stain following drought
stress was observed, mainly after 5 days treatment (Figure 3.1 a). Semi-quantification of
the stained area showed a significant increase at 11 days following drought stress (Figure
3.1b). While in osmotic and salinity treatments, accumulation of brown staining was
observed as early as 30 minute after treatment, which persisted up to 24 and 12 h for
osmotic and salinity stress respectively (Figure 3.1 c, e). Semi-quantitative analysis of DAB
staining showed the accumulation of brown staining following 1 h of NaCl treatment
(Figure 3.1 e,f) and a broad peak was observed starting from 2 h to 16 h following post
osmotic stress treatment with 30% PEG (Figure 3.1 d).
After observing that H2O2 was induced under different stresses, establishment of a
quantitative method to measure its content under those stresses became the primary
important task.
58
Figure 3-1: Visualization of hydrogen peroxide detected by DAB staining. Staining was done on 3-week-old rice plants subjected to subsequent a) drought treatment, c) PEG 30%, and e) 150 mM NaCl. Each image represents three biological replicates. Quantification of H2O2 is based on the brown stained area presented; b) Drought treatment, d) PEG30% and f) 150 mM NaCl.
59
3.3.2 Drought stress
Rice plants, at the three weeks old stage, were grown in soil under well-watered
conditions for control treatments and drought stress was applied by withholding water
for 13 days in the same incubator chamber. The effect of drought stress was studied by
observing physiological and biochemical changes in terms of H2O2 content, relative water
content (RWC), chlorophyll content and lipid peroxidation.
Following preliminary DAB quantification, H2O2 content in treated rice leaves was
quantified as described in the Methods chapter (2.4.2). Amongst the drought-treated
plants, significant changes in H2O2 content were observed at day 9, 11 and 13 compared
to control treatments (Figure 3-2). H2O2 content appeared to be at the highest level
following drought stress after 11 days, whereas there was a significant decrease versus
controls at day 9 (p<0.05) (Figure 3-2). H2O2 increase therefore appeared to be transient
at day 11, given that at day 9 and day 13 a significant decrease was observed. This could
indicate increased antioxidant activities at day 9 and day 13 following drought stress.
The relative water content in treated plants were maintained at almost 100% initially but
decreased to 30% at the end of the experiment. From day 5 drought treatment, plants
started to sense the drought by showing a 30% decrease in water content, and a further
decrease in water content (i.e. 70%) was observed on day 13 (Figure 3-3). Prolonged
drought stress is thought to have disrupted the water uptake in plants, causing a linear
decrease in water content as indicated by a strong negative statistical correlation across
the drought period, r=-0.926 (p<0.05).
60
Figure 3-2: H2O2 content in leaves following 13 days of drought stress treatment. Letters indicate the significant difference in H2O2 content between treatment day (ANOVA, p<0.05) and the asterisks show significant difference compared to control treatments (t-test, p<0.05). Values are means±SEM of three repeated experiments (N=3), and each experiment was performed with 3 biological replicates.
Figure 3-3: The relative water (RWC) in leaves over 13 days of drought stress was examined. Letters indicate the significant difference in RWC between treatment times (ANOVA, p<0.05) and the asterisks show significant difference compared to control treatments (t-test, p<0.05). Values are means±SEM of three repeat experiments (N=3), and each experiment was performed with 3 biological replicates.
61
Chlorophyll content fluctuated over two weeks of treatment in both drought treated and
control plants. There was a noticeable decrease versus controls especially at day 9, 11
and 13 (Figure 3-4). Prolonged drought stress therefore appeared to be reducing
photosynthetic activity, which was seen as a negative statistical correlation between
chlorophyll content and an increase in drought period, r= -0.633 (p<0.05).
Figure 3-4: The total chlorophyll a+b in leaves over 13 days of drought stress. Letters indicate the significant difference in chl between treatment times (ANOVA, p<0.05) and the asterisks show significant difference compared to control treatments (t-test, p<0.05). Values are means±SEM of three repeat experiments (N=3), and each experiment was performed with 3 biological replicates.
Lipid peroxidation in drought-treated and control plants showed no difference at the
beginning of the experiment at days 1, 3, and 5 but treated plants showed a notable
increase of lipid peroxidation from days 7 to 11, followed by a decrease at day 13 (Figure
3-5). In addition, the lipid peroxidation at day 9 and 11 was very rapid and significantly
higher than that at day 7 and day 13.
62
In summary, drought stress led to water loss from day 3 and it correlated with an
increase in lipid peroxidation, H2O2 accumulation and decrease in chlorophyll content
from day 7 post-treatment.
Figure 3-5: Lipid peroxidation in leaves following 13 days of drought stress. Letters indicate the significant difference in lipid peroxidation between treatment times (ANOVA, p<0.05) and the asterisks show significant difference compared to control treatments (t-test, p<0.05). Values are means±SEM of three repeatexperiments (N=3), and each experiment was performed with 3 biological replicates. 3.3.3 Osmotic stress
Drought stress is closely related to osmotic stress response; in many studies the latter is
used as an indicator of the plant’s response to water deficit. Nevertheless in this study it
was aimed to observe what correlation, if any, existed between the application of
drought stress to soil grown plants and osmotic stress applied via PEG treatment to rice
seedlings grown in vitro. PEG has been used to modify the osmotic potential in solution
cultures for the purposes of mimicking drought environment (Lagerwerff et al., 1961). In
this study, PEG 6000 was used to prevent its uptake by roots at this high molecular
63
weight (Kaufmann and Eckard, 1971). Two weeks old rice plants that were grown in tissue
culture were used in all osmotic stress experiments. Plants were transferred into culture
flasks containing either 15% PEG or 30% PEG for osmotic stress and water for control
treatments for up to 32 hours. The leaves from treated and non-treated plants were
harvested at 30 min, 1 h, 2 h, 4 h, 8 h, 16 h and 32 h, and these samples were then frozen
in liquid nitrogen for further analyses.
There were significant differences in H2O2 concentrations in PEG treated and non-treated
plants (Figure 3-6). In the 15% PEG treatment, there was a H2O2 burst at 2 h, which
peaked at 4 h but returned gradually to control levels after 16 h. In 30% PEG treatment,
the initial H2O2 production was maintained at normal levels for the first 2 h but there was
a notable increase at 4 h of treatment, which persisted up to 16 h of treatment before it
returned to control values after 32 h treatment. PEG treatment, high dose and low dose,
have both significantly induced the production of H2O2 at 4 h and 8 h compared to control
(p<0.05). However, the H2O2 peaks were transient in both treatments and it returned
back to the same level as in control plants after 32 h stress. Both treatments therefore
exhibited a single H2O2 burst over the treated period, although the timing of maximal
production shifted with higher osmotic stress.
There were significant differences in the relative water content of the PEG treated plants
compared to the non-treated plants (p<0.05) (Figure 3-7). Prolonged osmotic stress
resulted in the decrease of water content in plants during the osmotic treatment period,
as indicated by a strong negative correlation observed in both PEG 15% (r=-0.864, p<0.01)
and PEG 30% (r=-0.867, p<0.01) over the 32 hours of treatment. The effects of water loss
due to the imposed osmotic stress were observed from 8 h onward in both PEG
treatments. The plants in 15% PEG withheld only 50% of its initial water content at the
end of 32 h treatment, whilst 30% PEG treated plants experienced more rapid water loss
(up to almost 70%) at the end of the experiment.
64
Figure 3-6: H2O2 content in leaves after exposure to 15% and 30% PEG solution for up to 32 hours. Lowercase letters show the significant difference between 15% PEG treatment, uppercase letters show the significant difference between 30% PEG and the asterisks show significant difference of treated plants at specific time points compared to control treatments. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeat experiments (N=3), and each experiment was performed with 3 biological replicates.
Figure 3-7: The relative water (RWC) in leaves over 32 hours of PEG treatment. Lowercase letters show the significant difference between 15% PEG treatment, uppercase letters show the significant difference between 30% PEG and the asterisks show significant difference of treated plants at specific time points compared to control treatment. The statistical analyses are performed with ANOVA (p<0.05). Values are means±SEM of three repeat experiments(N=3), and each experiment was performed with 3 biological replicates.
65
Chlorophyll content in control plants was significantly higher compared to those in PEG
treatments; it was higher in 15% PEG compared to 30% PEG up to 4 h stress (Figure 3-8).
At the initial time point of 30 min, plants in 30% PEG treatment showed a faster response
to a decrease in chlorophyll levels compared to 15% PEG. Moreover, plants in 30% PEG
treatment showed only half of the chlorophyll content compared to 15% PEG at the first
4 h of treatment but showed a recovery to same level as 15% PEG treatment from 8 h
onward. Overall, there was considerable variation in the timings where chlorophyll
changes were noticed.
Figure 3-8: The chlorophyll content in leaves treated with 15% and 30% PEG. Lowercase letters show the significant difference between 15% PEG treatment and the asterisks show significant difference of treated plants at specific time points compared to control treatment. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
66
The peak of H2O2 production following 2 h of 15% PEG treatment was observed to be as
high as those observed on day 11 of drought stress. Such a rapid H2O2 production may
have an influence on the membrane lipids. To analyse this, the lipid peroxidation in rice
plants at each time point following PEG treatment was examined. ANOVA test showed a
significant difference in lipid peroxidation of 15% PEG treated plants compared to control
plants (p<0.05) but surprisingly, no significant difference in 30% PEG versus control plants
(Figure 3-9). It is important to note that there was a significant increase compared to
control in lipid peroxidation from 2-8 h post-15% PEG treatment and it took 16 h to
recover back to control levels .
Figure 3-9: Lipid peroxidation in leaves after exposure to 15% and 30% PEG osmotic stress. Lowercase letters show the significant difference between 15% PEG treatment and the asterisks show significant difference of treated plant in specific time point compared to control treatment. The statistic analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
3.3.4 Salinity stress
Salinity stress contributes to ionic stress in addition to osmotic stress. Hence, plants may
undergo different adaptation and response pathways under osmotic and salinity
conditions. To examine the differences that account for the physiological changes, plants
67
were treated at salinity levels with the osmolarity level adjusted to the same level as in
previous osmotic stress experiments. The physiological changes in plants following
salinity stress and osmotic stress under the same water potential level can then be
concluded to be due to the effects of ionic response. Two doses of salinity stresses,
namely 150 mM and 118.54 mM NaCl were used; in which 118.54 mM NaCl contributes
to equivalence of osmolarity under PEG 15% treatment. The osmolarity of 30% PEG was
too high to be detected with the osmometer (Loser Messtechnik), however, it is
equivalent to a NaCl concentration that would have a damaging effect on plants,
therefore a maximum concentration of 150 mM NaCl was used based on previous
research (Fumagalli et al., 2009).
As in osmotic stress treatment above, three week old plants were transferred from MS
culture media to culture flasks containing water for control treatments or to culture flasks
containing either 118.54 mM or 150 mM NaCl for salinity treatment. Control and salinity-
treated plants produced significant amount of H2O2 (Figure 3-10). Rice seedlings treated
with the higher dose (150 mM) of NaCl showed an increase in H2O2 production following
0.5 h treatment, but a maximal amount of H2O2 was observed at 1 h, following which the
synthesis of H2O2 decreased slowly, but only attaining control levels after 16 h treatment.
For the lower dose of NaCl (118.5 mM), significant increment of H2O2 production
compared to control was observed at 1, 2 and 4 h post treatment, returning rapidly to
control levels by 8 h post treatment. Surprisingly the lower dose of salt induced a
maximal H2O2 production at a time (2 h) later than that induced by higher dose. Plants in
both salinity treatments showed recovery of H2O2 production from 8 h post-treatment to
the control level until the end of 32 h treatment.
68
Figure 3-10: H2O2 content of rice leaves following high (150 mM) and low (118.54 mM) salinity stress. The letters indicate significant difference between 150 mM NaCl treatment and asterisks show significant difference compared to control treatment. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
The RWC in salinity treated plants showed a significant difference from those of control
plants (p<0.05) (Figure 3-11). Effects of salinity stress on RWC were observed as early as
30 min post-treatment in both NaCl concentrations with almost 20% reduction in RWC.
Both NaCl treated plants experience almost 50% water loss following 8 h of salinity stress.
In high salt treatment (150 mM), extending the treated period resulted in more water
loss, giving a strong negative correlation regression versus prolonged treatment (r=-0.962,
p<0.01). Contrary to this, a water content recovery was observed in low dose salinity
(118.54 mM NaCl) at 16 and 32 h. Therefore, lower NaCl treatment resulted in steady
reduction of water content up to 8 h, followed by a recovery, whilst higher NaCl
treatment exhibited a profound water loss from the beginning of the experiment.
69
Figure 3-11. Relative water content in leaves after high dose (150 mM) NaCl and low dose (118.54 mM) NaCl treatment for 32 hours. Lowercase letters show the significant difference between 150 mM NaCl treated plants, uppercase letters show the significant difference between 118.54 mM NaCl treated plants and the asterisks show significant difference of stress treated plants compared to control plants. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications. In terms of chlorophyll content, the salinity–treated plants showed a significant reduction
in chlorophyll content compared to controls (p<0.05) (Figure 3-12). The chlorophyll
content was reduced to half of control values as early as 30 min after treatment and it
was maintained at low levels for 32 h in both NaCl treatments. Interestingly the lower salt
stress levels caused more rapid decline in chlorophyll content at the first 2 h treatment
compared to higher salinity treatment. Overall, both low and high salinity stress were
found to have an influence on the chlorophyll content in leaves.
Lipid peroxidation in 150 mM NaCl treated plants was found to increase significantly
compared to the control and the plants treated with a lower dose of NaCl (i.e. 118.54 mM)
(p<0.05) (Figure 3-13). Two peaks of lipid peroxidation were significant higher compared
to control, observed at 2 h and 8 h post treatment respectively. Surprisingly, the low
dose salinity treatment (118.54 mM) conferred to little or no change in lipid peroxidation.
70
Figure 3-12: The total chlorophyll content in leaves following high dose (150 mM) NaCl and low dose (118.54 mM) NaCl for 32 hours. Lowercase letters show the significant difference between treatments of 150 mM NaCl and the asterisks show significant difference of treated plants compared to control plants. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
Figure 3-13: Lipid peroxidation levels in leaves tissue following salinity treatment up to 32 hours. Lowercase letters show the significant difference between the treatment of 150 mM NaCl and the asterisks show significant difference of treated plants compared to control plants. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
71
3.3.5 Cold stress
3.3.5.1 (a) Soil grown plants.
As rice plants grow at temperatures around 30 °C, the cold stress in this study was set at
two temperatures, 10 °C and 20 °C, for up to 32 h treatment. The control plants were
grown in an incubator pre-set to 28 °C and all the physiological changes were observed.
Plants treated at both cold temperatures, 10 °C and 20 °C, produced significantly higher
H2O2 than control plants (p<0.05) (Figure 3-14). There were clearly two peaks of H2O2
produced in both 10 °C and 20 °C treatments. The first H2O2 peak was found to occur
after 1 h of the cold treatments. The second H2O2 peak was observed at 8 h for the
treatment at 20 °C, whilst a slower response was observed for the treatment at 10 °C in
which the peak only appeared at 32 h. After the first peak, plants in 20 °C treatment
showed a recovery of H2O2 back to control at 4 h post-treatment. Whilst plants in 10 °C
treatment showed recovery of H2O2 production from 2 h to 16 h before it peaked again at
32 h. The concentration of H2O2 produced after 20 °C was almost four fold over that seen
in the control treatments at 1 h, 2 h, 8 h, 16 h and 32 h treatment.
Figure 3-14: Hydrogen peroxide production in leaves following cold stress at 10 °C and 20 °C. The lowercase letters show the difference between 10 ⁰C treatments, the uppercase letters show the significant difference between 20 ⁰C treatments and the asterisks show the significant difference between control and stress treatments. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
72
RWC was assessed in soil grown rice plants subjected to either 10 °C or 20 °C for 32 hours.
The student t-test showed that cold stress led to water loss in plants treated with either
10 °C or 20 °C compared to control (p<0.05). Nevertheless, plants at 20 °C showed faster
response by losing water rapidly following 2 h of treatment compared to control.
However, plants at 10 °C showed a slower response in which significant decrease in RWC
was seen only after 4 h of cold treatment. Subsequently, plants lost water approximately
5% to 10% every hour in both cold temperatures. These resulted in a significant
difference in RWC observed at almost half of the control values, at the end time point
(p<0.05) (Figure 3-15). The 10 °C and 20 °C treated plants showed same levels of RWC
from 4 h post-treatment, suggesting that the plants responsed and managed water loss in
a similar manner under the cold stress treatment.
Figure 3-15: Relative water content in leaves following cold stress, at 10 ⁰C and 20 ⁰C. Letters show the significant difference between 10 ⁰C treatment and asterisks show the significant difference between control and stress treatment. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
73
Chlorophyll content of rice plants following 10 ⁰C, 20 ⁰C and control treatment is
presented in Figure 3-16. Generally, treating rice plants in 10 ⁰C cold stress yielded a
significant difference in chlorophyll content compared to control plants at 1 h, 2 h and 4
h treatment (p<0.05) and the chlorophyll content remained at a low level for the first 4 h
until a recovery phase was observed from 8 h onwards. Whilst in 20 ⁰C treatment, the
chlorophyll contetn appeared to be a significant difference only at the first 30 min
treatment then there was no significant difference versus controls.
Figure 3-16: Chlorophyll a+b content in leaves following 10 ⁰C and 20 ⁰C cold stress. The lowercase letters show the significant difference between 10 ⁰C treatment, the uppercase letters show the significant difference between 20 ⁰C treatment and the asterisks show the difference between control and stress treatment. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
The oxidation of lipid membranes in plant treated at 20 ⁰C was found to increase
significantly compared to both the control and the plants treated at 10 ⁰C (P<0.05)
(Figure 3-17). Surprisingly, there were no significant differences between the control and
the plants treated at 10 ⁰C. The plants in the 20 ⁰C treatment showed almost twice the
levels of lipid oxidation than that observed in the control treated plants for the first 2 h.
However, it then showed a decrease from 8 h to 16 h before increasing to reach a peak at
16 h. The increment following 16 h treatment at 20 ⁰C was transient as it reduced again
at 32 h (p<0.05).
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Figure 3-17: Lipid peroxidation in leaves treated with cold stress, 10 °C and 20 °C for 32 hours. The asterisks show the significant difference between 20 °C treatment and control. The statistical analyses are performed by ANOVA (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications. 3.3.5.2 (b) Cold stress in tissue culture
Both temperatures used for cold treatments in soil-grown plants showed a significant
increase in H2O2 production. Three-week-old tissue cultured plants were treated at 10 ⁰C
and subsequent H2O2, RWC, chlorophyll and lipid peroxidation were quantified. The H2O2
produced after 10 ⁰C post-treatment was significantly different from that in control
plants (p<0.05) (Figure 3-18). There was no significant difference in H2O2 production in
cold treated and control plants for the first 2 h of cold treatment. Interestingly after 2 h
there was a significant decrease in H2O2 levels compared to control treatments before a
significant burst at 4 h. Even then the burst was seen to be transiently declining at 8 h.
Unlike cold treatment in soil gown plants, cold treated tissue culture plants surprisingly
showed no significant difference in water content compared to control plants
(p>0.05)(Figure 3-19).
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Figure 3-18: H2O2 production in the leaves of tissue culture plants treated at 10 °C for 32 hours. The letters show the significant different between cold treatment at various times (ANOVA, p<0.05) and the asterisks show the difference between treated plants and control plants (t-test, p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
Figure 3-19: RWC in the leaves of tissue culture plants treated at 10 °C for 32 hours. The statistical analyses are performed by ANOVA and t-test (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications. No significant differences of RWC were observed following cold stress.
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In terms of chlorophyll content, significant reduction of chlorophyll was observed after 30
min of treatment with almost 40% reduction from the control (p<0.05) (Figure 3-20).
Thereafter a recovery of chlorophyll content relative to controls was observed with no
significant difference between control and treated plants, up to 32 h, when it reduced
again versus control. Interestingly, there was a significant decrease of chlorophyll content
at 8h in both treated and control plants.
Figure 3-20: Total chlorophyll content in the leaves of tissue culture plants treated at 10 °C for 32 hours. Letters show significant difference between treatments at various time points (ANOVA, p<0.05) and the asterisks show significant difference of cold treated and control plants (t-test, p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
When plants were subjected to cold stress, significant increase in lipid peroxidation was
observed (p<0.05) (Figure 3.21). Lipid peroxidation increased in cold treated rice plants as
early as 30 min post–treatment; moreover it was almost double than in control plants up
to 8 hours post-treatment. At 16 h post-treatment, lipid peroxidation significantly
decreased in cold treated plants to less then control values but it was then back to the
same level as in the control at 32 h post-treatment (Figure 3.21).
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Figure 3-21: Lipid peroxidation of the leaves of tissue cultured plants at 10 °C for 32 hours. Letters show significant difference between cold treatment (ANOVA, p<0.05) and the asterisks show significant difference between treated plants and control plants (t-test, p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications. 3.3.6 Physiological responses with ASA-pretreatment
Ascorbic acid (ASA) is vitamin c with antioxidant properties. The exogenous application of
ASA has been reported to induce salinity tolerance in crops with the ability to mitigate
the effect of salinity stress (Al-Hakimi and Hamada, 2001). There are also reports of using
ASA to protect plant membranes from the damage induced by reactive oxygen species
(Rajasekaran, 1999) and to improve xylem nutrient transport in plants (Gadallah, 1996).
In order to establish whether H2O2 mediates the physiological responses observed
following the abiotic stresses tested in this study, ASA was used to first block the
production of H2O2 and the subsequent physiological changes were observed. Plants
were pre-treated with 100 mM ASA for 30 min as recommended by Elmore et al., (1990)
and followed by a further treatment with 118.54 mM NaCl or 15% PEG for 4 h. This time
point was chosen as both PEG and NaCl at these concentrations showed significant
increase in H2O2 levels compared to controls (Figure 3-6 and Figure 3-10).
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Figure 3.22 clearly shows that pre-treatment of ASA inhibited the increase in H2O2 in
response to NaCl or PEG, and maintained it at control levels, while plants treated with
salt or PEG without ASA pre-treatment showed a significant amount of H2O2
accumulation (Figure 3.22).
Figure 3-22: Evaluation of the effect of ASA pre-treatment on H2O2 production. a) Control rice plants were treated with water for 4 h and 30 min, ASA pre-treated plants (100 mM ASA for 30 min) were then either followed by water treatment as control or followed with salinity treatment (118.54 mM NaCl for 4 h), and the NaCl treated plants were treated with water for 30 min before being subjected to NaCl for 4 h; b) Control rice plants were treated with water for 4 h and 30 min, ASA pre-treated plants (100 mM ASA for 30 min) were either followed by water treatment as control or 15% PEG for 4 h, and the PEG treated plants were treated with water for 30 min before being subjected to 15% PEG for 4 h. Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications. The apparent physiological impact of NaCl and 15% PEG treatment on rice plants were
observed as a significant decrease in relative water content (Figure 3.23). With 30 min of
ASA pre-treatment however, salt treated plants showed water content at levels similar to
that seen in control plants (Figure 3.23a). However, pretreat of ASA before 15% PEG
treatment conferred to no difference in term of water content in comparison to 15% PEG
without ASA pre-treatment (Figure 3.23 b).
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Figure 3-23: Evaluation of the effect of ASA pre-treatment on water content. a) Control rice plants were treated with water for 4 h and 30 min, ASA pre-treated plants (100 mM ASA for 30 min) were either followed by water treatment as control or followed by 118.54 mM NaCl treatment for 4 h, and the NaCl treated plants were treated with water for 30 min before being subjected to NaCl for 4 h; b) Control rice plants were treated with water for 4 h and 30 min, ASA pre-treated plants (100 mM ASA for 30 min) were either followed by water treatment as control or followed by 15% PEG treatment for 4 h, and the PEG treated plants were treated with water for 30 min before being subjected to 15% PEG for 4 h. The letters indicate statistical difference between treatments (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
Similar to RWC, plants treated with either NaCl or 15% PEG alone without ASA pre-
treatment showed significant decrease in chlorophyll content (Figure 3.24) (p<0.05). The
chlorophyll content was significantly increased (p<0.05) when plants were pre-treated
with ASA prior to NaCl treatment. However, no changes was abserved in 15% PEG
treatment, either with or without ASA pre-treatment. This indicates that H2O2 contributes
towards low salt-induced physiological responses, whereas for the time and dose of PEG
used, H2O2 does not appear to contribute towards the physiological responses.
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Figure 3-24: The evaluation of ASA pre-treatment on chlorophyll content in rice plants. a) Control rice plants were treated with water for 4 h and 30 min, ASA pre-treated plants (100 mM ASA for 30 min) were either followed by water treatment as control or followed by 118.54 mM NaCl treatment for 4 h, and the NaCl treated plants were treated with water for 30 min before being subjected to NaCl for 4 h; b) Control rice plants were treated with water for 4 h and 30 min, ASA pre-treated plants (100 mM ASA for 30 min) were either followed by water treatment as control or followed by 15% PEG treament for 4 h, and the PEG treated plants were treated with water for 30 min before being subjected to 15% PEG for 4 h. The letters indicate statistical difference between treatments (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
Lipid peroxidation in rice plants treated with 118.54 mM NaCl is shown in Figure 3.25 (a),
in which 30 min ASA pre-treatment reduced the at least 4 fold of lipid peroxidation
activities compared to NaCl treatment without prior to ASA treatment. Moreover, pre-
treatment of ASA could even reduce the lipid peroxidation rate of both NaCl treated and
non-NaCl treated plants to lower than control treatments. On the other hand, ASA pre-
treatment had only little effect on reducing the damage caused by lipid oxidation in 15%
PEG treatment (Figure 3.25 b). This fits in with the rest of the observations of a lack of
effect of ASA pre-treatment on PEG-induced changes in RWC and chlorophyll content.
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Figure 3-25: Effect of AsA pre-treatment on lipid peroxidation in rice plants. a) Control rice plants were treated with water for 4 h and 30 min, ASA pre-treated plants (100 mM ASA for 30 min) were either followed by water treatment as control or followed by 118.54 mM NaCl treatment for 4 h, and the NaCl treated plants were treated with water for 30 min before being subjected to NaCl for 4 h; b) Control rice plants were treated with water for 4 h and 30 min, ASA pre-treated plants (100 mM ASA for 30 min) were either followed by water treatment as control or followed by 15% PEG treatment for 4 h, and the PEG treated plants were treated with water for 30 min before being subjected to 15% PEG for 4 h. The letters indicate statistical difference between treatments (p<0.05). Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications. 3.3.7 Cross tolerance
Pre-treatment of plants with a milder stress was proven to induce cross tolerance stress
adaptation towards another severe stress (Rizhsky et al., 2004). This might possibly be
due to the milder stress induces the production of H2O2 in plants conferring tolerance
towards lethal stress levels applied later. This phenomenon has been reported by H2O2
pre-treatment of maize that renders it more tolerant to a lethal level of salinity stress (de
Azevedo Neto et al., 2005). Hence, similar experiments were set up to examine the
possibility that H2O2 produced via cold stress might protect the rice plants from a later
exposure to salinity treatment (Kazemitabar et al., 2003). Tissue cultured rice plants were
first treated with cold stress at 10 °C for 4 h before transferring into 118.54 mM NaCl for
another 4 h at 28 ⁰C; parameters were chosen based on the results from previous
experiments.
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In terms of H2O2 production, plants that were subjected to control treatments for 8 h
(ctrl+ctrl) showed a basal level of H2O2 production (Figure 3-26). In addition, plants
treated under control conditions for 4 h followed by 4 h NaCl stress (ctrl + NaCl) showed a
significant increase in H2O2. Interestingly, 4 h cold stress pre-treatment followed by 4 h
NaCl stress (cold + NaCl) significantly reduced the levels of H2O2 compared to control +
NaCl. Interestingly, it was also noted that cold treatment followed by a recovery under
control conditions for 4 h (cold + ctrl) showed lower H2O2 compared to cold treatment
alone (see Figure 3-26).
Figure 3-26: Hydrogen peroxide produced in leaves following combined cold and salinity stress. The first bar represents combined cold and salinity treatment, in which rice plants were pre-treated at 10 °C for 4 h before treating with 118.54 mM NaCl solution of for another 4 h. Second bar represents rice plants that under went 4 h 10 °C cold stress and then being transferred back to control environment that was set to 28 °C. Third bar represents plants that went through 4 h control treatment before transferring into 118.54 mM NaCl solution, while the last bar represents plants that were left for 8 h in controlled environment at 28 °C. Letter labels show the significant difference (ANOVA, p<0.05) between samples. Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
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Plants showed totally the opposite pattern for RWC, in which, plants treated in any
salinity combination that showed higher H2O2 content had lower water content while
plants in any treatment without salinity combination showed lower H2O2 content and
higher water content (p<0.05) (Figure 3-26 and Figure 3-27). On the other hand, no
changes of RWC were observed in plants with 4 h of cold pre-treatment (p<0.05). This
might probably be due to the recovery period for 4 h in control treatment before the
reading of RWC were taken.
Figure 3-27: Relative water content in leaves following the combined treatment of cold and salinity stress. The first bar represents combined cold and salinity treatment, in which rice plants pre-treated in 10 °C for 4 h before placing in 118.54 mM NaCl solution for another 4 h. Second bar represents rice plants that under went 4 h 10 °C cold stress and then transferred back to control environment that was set to 28 °C. Third bar represents to the plants that were first left for 4 h in control environment before transferred into 118.54 mM NaCl solution, while the last bar represent plants that were left in 8 h of controlled environment (28 °C). Letter labels shows the significant difference (ANOVA, p<0.05) between samples. Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
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Plants in all stress treatments, namely cold followed by salinity treatment, cold followed
by control, and control followed by salinity resulted in the decrease of chlorophyll
content compared to non-treated control plants (p<0.05) (Figure 3-28). Whereas, these
combined treatments showed a significant increase of lipid peroxidation in rice plants
(Figure 3-29). Moreover, the lipid peroxidation in treated plants was more than half of
the control treatments. The cold treatment or NaCl treatment alone resulted in high level
of lipid peroxidation, whilst combination of these two treatments showed a significant
reduction in lipid peroxidation (p<0.05) (Figure 3-29).
Figure 3-28: Chlorophyll content in leaves following the combined treatment of cold and salinity stress. The first bar represents combined cold and salinity treatment, in which rice plants were pre-treated at 10 °C for 4 h before placing in 118.54 mM NaCl solution for another 4 h. Second bar represents rice plants that under went 4 h 10 °C cold stress and then transferred back to controlled environment that was set to 28 °C. Third bar represents plants that were first left for 4 h in control environment before transferring into 118.54 mM NaCl solution, while the last bar represents plants that were left for 8 h in controlled environment (28 °C). Letter labels show the significant difference (ANOVA, p<0.05) between samples. Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
85
Figure 3-29: Lipid peroxidation in leaves following 4 h pre-cold treatment and then 4 h of NaCl treatment. The first bar represents combined cold and salinity treatment, in which rice plants were pre-treated in 10 °C for 4 h before placing in 118.54 mM NaCl solution for another 4 h. Second bar represents rice plants that under went 4 h 10 °C cold stress and then transferred back to controlled environment that was set to 28 °C. Third bar represents plants that were first left for 4 h in control environment before transferring into 118.54 mM NaCl solution, while the last bar represents plants that were left for 8 h in controlled environment (28 °C). Letter labels show the significant difference (ANOVA, p<0.05) between samples. Values are means±SEM of three repeated runs (N=3), and each run was performed with 3 biological replications.
Interestingly, there were significant differences between chlorophyll and lipid
peroxidation in 4 h cold treatment followed by recovery phase compared to 8 h-control
plants. These might be due to the recovery stage after cold stress that allow plants to
reduce the production of H2O2 (Figure 3-26) and recover the water content (Figure 3-27)
but still affecting chlorophyll and lipid peroxidation.
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3.4 Discussion
This study was designed to examine the physiological changes in rice plants when
subjected to single stress treatment or successive treatment with two stresses. The effect
of mild to severe stress and short term to long-term stress and their correlation between
H2O2 concentration, relative water content, chlorophyll activity, and lipid peroxidation in
leaves were examined.
3.4.1 DAB staining
DAB staining has been used extensively as a qualitative technique in plant physiology to
identify and locate the site of H2O2 production (Chandru et al., 2003). In this study, DAB
staining was performed following the application of drought, salinity, and osmotic stress.
The results from DAB staining shows more intensive staining in all stress-treated samples
compared to the controls. This implied that the production of H2O2 or other ROS was
induced following stresses; however, a better quantitative method of H2O2 production is
needed to conclude that the level is statistically higher than in control treatment. The site
of H2O2 production was not clear, although it appeared to be along the veins of the leaves.
Following extensive preliminary experiments, a protocol was developed according to
(Zhou et al., 2006) to extract and quantify H2O2 using a peroxidase-based colorimetric
method following stress treatments.
3.4.2 Physiological responses following the treatment of drought, osmotic and salinity
stress
Drought, osmotic and salinity-induced water stress in rice plants was observed with a
significant decrease in relative water content. Rice is naturally a drought–susceptible crop
due in part to its root system (
Drought stress
Miyamoto et al., 2001) and its thin wax layer on leaves
(Haque et al., 1992) that do not allow them to conserve water under a stressful
environment. Hence, it adapts a quick basic mechanism for reducing the impact of
drought by rapid stomatal closure at the beginning of the water deficit period (Saliendra
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et al., 1995). Early stomatal closure may be desirable at the initial period of drought and
increase the chances of plants to survive drought, but not where drought is prolonged
and relatively severe. This was shown by a strong negative correlation of RWC with
prolonged drought treatment (Figure 3-3).
The reduction in RWC following drought stress could occur as a result of lack of stomatal
closure or enhanced transpiration via the cuticle (Boyer et al., 1997). Stomatal closure is
usually only a rapid response to short-term drought stress; therefore it is possible that in
this study, the stomatal closure is rapidly overridden by enhanced transpiration and lack
of water uptake from roots (Brix, 1962). This needs to be investigated. Water stress is
known to inhibit photosynthetic activities in rice plants (Abdalla, 2007). Interestingly,
while the production of H2O2 on day 9 was found to reduce to a level that is less than the
level shown by the control, a significant negative correlation between the H2O2
production with total chlorophyll content (-0.997, p<0.05) was observed. The low
chlorophyll content observed from day 5 to day 13 was found proportional to the low
water content observed in leaf tissues at the same treated period. These results show
that drought stress affecting H2O2 production, RWC and chlorophyll content in plants for
optimum growth and development.
Different varieties of rice exhibit different degrees of dehydration tolerance and the
factor that contributes to such differences is the capacity of the cell membrane to
prevent electrolyte leakage by decreasing water content, also known as cell membrane
stability (CMS) (Tripathy et al., 2000). Hence, it is thought that the maintenance of
membrane function, which function to maintain the cell activity has been reported to
have correlation with yields under drought (Tripathy et al., 2000). It was found in this
study that there was a strong negative correlation between RWC and lipid peroxidation
(r=-0.989; p<0.05) following 13 days of drought treatment, indicating that the application
of drought stress had led to the decrease of CMS. Similar findings have been observed by
other studies in rice (Sharma, 2005) and soybean (Anjum et al., 2011), where an increase
in lipid peroxidation under prolonged drought stress treatment was observed. The
overproduction of H2O2 during dehydration period attacks the polyunsaturated fatty
acids (PUFAs) and affects the cell membrane stability (Montillet et al., 2005). The
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decrease in cell membrane stability could lead to cell membrane leakage, which in the
case of thylakoid membrane would lead to ionic influx and damage of thylakoid (Jordan
et al., 1983). It is possible that in this present study drought stress led to increased
membrane damage of chloroplasts as reflected by reduced chlorophyll content.
Osmotic stress
Osmotic stress is likely to exhibit a similar response to that shown in the drought
treatment, however, the effects shown by osmotic stress were observed to occur more
rapidly in a few hours rather than in a few days as shown by the drought treatment.
Interestingly, the amount of H2O2 produced following 4-16 h of 30% PEG treatment was
almost the same as those produced following 11 days of drought treatment. It was
observed that the plant treated with osmotic stress was able to maintain its water
content up to 8 h post-treatment with only a small reduction in cell volume. The reason
for the very small reduction in cell volume is likely due to turgor loss, causing relatively
small changes in the volume that may have an impact on the larger changes of water
potential (Sharkey and Seemann, 1989). These observations suggest that an osmotic
adjustment was likely to occur in roots (Ogawa and Yamauchi, 2006). Osmotic stress
seemed to have activated the production of H2O2 while at the same time a reduction of
chlorophyll content was observed. In combination with the water deficit condition
created in both drought and osmotic stress, this indicates that osmotic stress is likely to
have an effect on the photosynthetic ability of the plants (Chaves et al., 2002). It has been
reported that the introduction of osmotic stress may lead to the occurrence of oxidative
stress (Kocheva et al., 2009). Nevertheless, low lipid peroxidation in 30 % PEG treatment
and recovery of lipid peroxidation in 15% PEG in this study suggests that the oxidative
stress might be reversible with the release of the stress. The amount of lipid peroxidation
observed was also much lower compared to drought due to several factors: (1) The
capacity of H2O2 caused by osmotic stress was unable to cause that much lipid
peroxidation compared to drought. (2) The growth of plants under tissue culture provides
better condition for plant growth in addition to creating different membrane composition
under different cultivation methods. (3) The plants used for drought stress experiment
were more mature compared to the seedlings used in osmotic stress.
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Salinity stress
The application of NaCl in liquid culture was found to have caused the leaf cells to lose
water as early as 30 min post-salinity treatment in both high and low NaCl doses (Figure
3-11). In low dose treatment, the loss of cell volume and turgor is a transient effect as the
cells were found to regain their original volume and turgor within a few hours after the
treatment (Figure 3-11). It was also found that H2O2 production showed positive
correlation with the water and chlorophyll content during the 4-16 h post-treatment with
118.54 mM NaCl. According to Moradi and Ismail (2007), plants could detoxify the H2O2
via various antioxidant activities in order to prevent the potential damage to the
photosystems. Salinity is known to have an influence in inhibiting photosynthesis in rice
plants (Moradi and Ismail, 2007; Soussi et al., 1998). However, the inhibition of
photosynthesis may occur over a transient or a short period due to perturbed water
relations and the local synthesis of ABA (Fricke et al., 2004). These processes are also key
mechanisms in maintaining low lipid peroxidation in plants (Moradi and Ismail, 2007;
Vaidyanathan et al., 2003). In this study, the chlorophyll content and lipid peroxidation
were found to remain at low levels throughout the 118.54 mM NaCl treatment, probably
the indication of stress adaptation mechanism in plants via various antioxidants activities
that were likely to occur during the salinity treatment. However, the mechanism
underlying the recovery process remains to be investigated.
In high-dose NaCl (150 mM) treatment, two phases of H2O2 production were observed:
the increment of H2O2 production from 30 min to 2 h post-treatment, followed by a
recovery phase from 4 h onward in which the H2O2 content returns to a level similar to
that shown by the control. Chlorophyll and water content were also found to reduce
following these two phases. According to Munns and Tester (2008), two phases of H2O2
production are typically observed following salt stress, in which the first phase is due to
rapid response to the increase in external osmotic pressure while the second phase is due
to accumulation of Na+ in leaves. Interestingly, in the second phase, a low level of lipid
peroxidation was observed in this study. This was probably due to the salt accumulating
up to a toxic level and limiting cell division, thereby limiting availability of lipid
membranes for oxidation (Munns and Tester, 2008).
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It is interesting to observe that plants responded differently when subjected to different
water stress conditions. It is very likely that the plant underwent different responsive
pathways when subjected to different stress conditions, and also when subjected to
different intensities of a same stress condition. Reduction or blocking of H2O2 production
with ASA pretreatment were then performed to examine physiological changes in rice
plants under condition without H2O2 production.
3.4.3 Physiological responses with ASA- pretreatment
In other studies, H2O2 formation is reported to be involved in damaging proteins and
nucleic acids (Baty et al., 2005; Little and O'Brien, 1967; Vranova et al., 2002). The levels
of H2O2 in plant cells are normally controlled by antioxidant activities (Halliwell, 2000).
However, H2O2 can be overproduced under stress and protective activity by antioxidants
may then become inadequate (Gualanduzzi et al., 2009; Jiao and Wang, 2000; Sharma,
2005; Vaidyanathan et al., 2003). Tsugane et al.,(1999) suggested that under stress
conditions, full potential for anti-oxidative activity and associated resistance might be
blocked. If so, artificial induction of anti-oxidative activity might increase resistance to
stress. A straightforward way to test this is to increase the cellular level of enzyme
substrates such as ascorbic acid (ASA) (Shalata and Neumann, 2001). ASA detoxifies H2O2
and acts directly to neutralize superoxide radicals, singlet oxygen or superoxide and as a
secondary anti-oxidant during reductive recycling of the oxidized form of α-tocopherol,
another lipophilic anti-oxidant molecule (Noctor and Foyer, 1998).
Exogenous application of ASA significantly reduced the H2O2 production in both salinity
and osmotic stress treated plants. However, it is interesting to observe that the additional
of ASA inhibited the accumulation of TBARS, recovery of chlorophyll level and water
content in the leaves of salinity-treated plants but not in osmotic stress-treated plants.
The inhibitory effect of ASA on lipid peroxidation was reported to increase the survival of
tomato seedlings to salt stress (Shalata and Neumann, 2001). Adding ASA inhibited the
leakage of essential electrolytes following peroxidative damage to plasma membranes in
salinity treated plants (McKersie et al., 1999) while others showed that the protective
effect of ASA is more related to reduced ROS damage to essential proteins and DNA
91
(Becana et al., 1998; Noctor and Foyer, 1998). Shalata and Neumann(2001) also
suggested that additional ASA may have affected meristematic cells in the root and shoot
tissues, building up new cells that are damaged by salinity. This suggests that the
physiological effects, particularly those that are damaging to plants, could be avoided if
the H2O2 production is appropriately regulated by these treatments.
Contradictory to our results in osmotic stress treatment, the application of ASA was
shown to ameliorate negative effects of osmotic stress in potato (Daneshmand et al.,
2010). It is possible that complete removal of H2O2 may be replaced by another signalling
molecule that causes the physiological changes (Athar et al., 2009). Such a molecule could
be nitric oxide (Aihong et al., 2011). This would imply that H2O2 is not the only causal
factor to regulate the physiological processes studied here in response to osmotic stress.
However, H2O2 is definitely produced following osmotic stress, and therefore the changes
caused thereof remain to be identified, perhaps via proteomic or metabolomic studies.
Use of transgenic plants altered in H2O2 production would no doubt be the ideal way to
test the exact role of H2O2 in physiological responses.
Variations in lipid oxidation were observed under drought, salinity, cold and osmotic
stress. The variations could be attributed to several factors: (1) The variation observed
between soil grown plants and tissue culture plants is likely to be influenced by the
difference in polyunsaturated fatty acids component, which is the major bulding block for
plant membrane (Gavino et al., 1981). (2) Plants with different ages were used in tissue
culture study compared to soil based study, hence it is thought that the protective
mechanism may have changed accordingly when these plant grow. (3) Soil grown plants
of same size and same age, which were used as controls in drought and cold stress
studies, also showed variation in lipid peroxidation and it is thought this could be
attributed to the seed quality. Seed domancy is always a common problem in old seed
stock and it has been reported that this phenomenon is linked to the ROS production and
protein oxidation (Oracz et al., 2007). According to El-Maarouf-Bouteau and Bailey (2008),
this could induce oxidative signalling in plants. It was observed that the plant germinated
from old seed stock showed much lower lipid peroxidation values compared to the plant
92
germinated from fresh seed stock (data not shown) while the same level of H2O2 was
observed.
3.4.4 Responses to cold stress
An interesting pattern of biphasic H2O2 production was observed in plants treated at 10
°C and 20 °C. It has been previously suggested that the first H2O2 peak in chilling sensitive
plants may be a key signal that switches physical transition of cell membranes from a
flexible liquid-crystalline state to a solid gel phase (Uemura and Steponkus, 1999). This
change in physical state of the membrane affects the cellular function in a number of
ways. The most immediate effect is the increment of permeability leading to cellular
leakage and ion imbalance. As a consequence of abnormal metabolism, cells accumulate
toxic metabolites and reactive oxygen species, hence produce another peak of H2O2 when
the stress condition proceeds (Gualanduzzi et al., 2009; Okuda et al., 1991).
The first peak occurs 1 h after cold stress and was found in plants treated at both 10 °C
and 20 °C. The second peak came much earlier in 20 °C treatment following 8 h of
treatment and was maintained at high levels thereafter, whilst in 10 °C, H2O2 was kept
low after the first peak and the second peak appeared at the end of the treatment at 32 h.
Lower but distinct biphasic peaks of H2O2 in 10 °C treatment were correlated with little or
no lipid peroxidation observed at the same time and hence might be the indicator that
the tolerance activity has been swithed on to protect the plants at the later cold stress.
This finding is in agreement with Hung et al., (2007a) in the sense that 10 °C pre-
treatment increased the chilling tolerance to more severe cold stress at 4 °C in Mung
Bean. When replacing the 10 °C cold stress with spraying the plants with H2O2, the
treatment provided exactly the same tolerance ability towards later 4 °C cold treatment
(Hung et al., 2007a). It was therefore proposed that it is likely to be H2O2 and not other
molecules that conferred the chilling tolerance (Hung et al., 2007a). Therefore, it is
possible that biphasic H2O2 production seen in rice in this study induces production of
various antioxidants which in turn offer protection from chilling damage in rice at later
time points. Kuk et al., (2003) shows that various antioxidant activities occur under cold
93
stress that conferred chilling tolerance to plants. There is still no clear explanation of the
production of biphasic H2O2 observed, but it could be due to the multiple primary sensors
that are involved in perceiving specific aspects of the cold stress (Xiong et al., 2002).
These potential sensors include Ca2+ influx channels, two component histidine kinases
and G-proteins receptors (Xiong et al., 2002).
In this study, cold stress conferred a slow dehydration in soil grown plants but not in
water-rich liquid cultured plants. Limited water uptake by roots is the possible reason in
the decrease of RWC observed, since chilling conditions result in a decline in root
hydraulic conductance and causes chilling sensitive plants to dry out and die (Aroca et al.,
2005). In addition, an initial reduction followed by a quick recovery of net chlorophyll
content was observed in all cold treatments. It has been reported that the chlorophyll a
and b content in cucumber showed a decrease when subjected to cold treatment (Wise
and Naylor, 1987). Furthermore, Allen and Ort (2001) explained that the phenomenon is
mostly due to the chilling temperature around the leaves that may have disrupted
thylakoid electron transport, carbon assimilation and stomatal conductance.
In relation to what has been discussed above, cold stress is likely to lead to the induction
of dehydration in plants, and appears to be common to drought, salt and cold treatment.
Therefore, it is thought that the decrease in turgor pressure induced biosynthesis of ROS
that in turn activated signalling mechanisms such as Ca2+ influx and signal transducers
(CDPKs and MAPKs) to transcription factors (Knight and Knight, 2001). The whole
mechanism is likely to be activated under other stresses such as drought and salt,
therefore crosstalk is likely to occur (Knight and Knight, 2001). It has been reported that a
very brief exposure to low temperature resulted in the onset of the above-mentioned
signalling pathways (Nordin Henriksson and Trewavas, 2003). Hence, a 10 °C treatment
was chosen for the next experiment due to its lower H2O2 and lipid peroxidation
production compared to 20 °C. The lower H2O2 and lipid peroxidation observed at 10 °C
treatment might be an indicator of possible activation of protective mechanisms rather
than an indicator of oxidative stress, therefore their effect on subsequent salinity stress
was examined.
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3.4.5 Cross-tolerance –cold stress to salinity
Cross-tolerance that is possibly mediated by H2O2 production has been illustrated in the
protective mechanism induced by 10 °C cold stress pre-treatment that allowed survival of
plants against damage induced by freezing stress (Hung et al., 2007a). H2O2 pre-treatment
also protected maize from lethal doses of salinity stress (de Azevedo Neto et al., 2005).
Hence, in this study, plants were pre-treated with 4 h of 10 °C cold stress before
transferring into salinity solution. At this time point and temperature, H2O2 production
occurs (Figure 3-26) and therefore it was hypothesised that the production of H2O2 could
offer cross-tolerance towards salinity stress. Similar experiments have been adopted to
show how plants confer the ability to tolerate lethal dose of same stress if pre-treated
with a milder level of the stress beforehand. The protection mechanism from one stress
to another (cross-tolerance), or from the mild stress to stronger stress (acclimation) is a
new area of plant stress signalling, with only few reports been published recently (Hsu
and Kao, 2007; Rizhsky et al., 2002; Uchida et al., 2002; Wahid et al., 2007). Since most of
the published works have shown the success of cold stress in inducing cross tolerance to
other stresses, and it is widely accepted that low dose of NaCl (118.54 mM) may lead to
reversible effects on plant cells, these two stresses were chosen for cross-tolerance
experiments.
In this study, cold pre-treatment appeared to reduce the H2O2 production and lipid
peroxidation caused by salinity stress but the treatment was found to have little influence
on reducing or improving the negative effects on the chlorophyll and water content in
plants. Upon cold stress treatment, the plants showed similar H2O2 production level
compared to that shown in the control. This is different from that observed in the
previous tissue culture experiments, as the plants were allowed for a 4 h stress release
period (i.e. back to control treatment) before their H2O2 levels were measured. However,
the level of lipid peroxidation was not able to reduce back to control level in NaCl treated
plants with cold pre-treatment. This might be due to the remaining H2O2 that was not
fully be reduced by antioxidant causing oxidation to these lipids. This according to Hsu
and Kao (2007) is highly related with the importance of the timing of H2O2 production.
They have shown that under the same level of H2O2 production following heat shock of 1
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h, 2 h and 3 h, only 3 h of heat shock exposure showed a complete inhibition of Cd-
induced toxicity whilst 1 h and 2 h of heat shock conferred little or no effect for
protection from toxicity of Cd.
Another key important aspect of H2O2 inducing cross tolerance is the localisation and
expression of antioxidant enzymes. Antioxidant enzymes are produced in particular
compartments and regulation of H2O2 production is controlled at localised spots (Veal et
al., 2007). This therefore shows how important the localised production of antioxidants
are to scavenge H2O2 and to further reduce the lipid peroxidation occurrence in plants.
However, in some cells it may be more important that the level of H2O2 be set
appropriately for H2O2 to signal cell division, differentiation, and migration to allow
survival at damaging levels. Hence, it is envisaged that a new approach that incorporates
a multitude of factors including the site of H2O2 production, the influence of antioxidants
when the timing of cross tolerance occurs, and the protein-protein interactions of H2O2
sensors, is required to give a whole picture of how H2O2 promotes cross tolerance in
different stresses.
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3.5 Conclusions
The application of drought, osmotic and salinity treament were found to have a strong
influence on the physiological changes of the rice plants, resulting in the loss of water
content, the fluctuation of H2O2 level, the decrease of chlorophyll content, and the
increase in lipid oxidation. In contrast, the application of cold stress was found to have
little influence on the the rice plants – a loss of water content was observed over a
transient period followed by the recovery of its water content during the cold stress
treatment.
From the previous and past studies, H2O2 is known to induce biological responses such as
antioxidant production and protein repair (Jamieson, 1998), stimulation of plant cell
death when challenged with pathogens, cell proliferation (Foreman et al., 2003) and also
lipid peroxidation (Gavino et al., 1981). However, to date, research in rice is yet to
determine whether the specific biological response is directly initiated by H2O2. This is
due to the variation in H2O2 levels required to initiate a particular biological response. As
an example, from this study it is apparent that different levels of cold stress (10 °C and 20
°C) can induce distinct responses in plants. Moreover, the biological response seems to
be concentration and timing specific to H2O2, as also reported by (Hsu and Kao, 2004).
Therefore, how the stress is perceived, how the signals are further transduced into a
series of responses, how fast and persistent the signalling machinery is activated, all
contribute to stress tolerance. To further explore the mechanism, different approaches
could be employed to study the complex events involving several interacting components,
such as the metabolites and protein response to H2O2 following mild and severe stress
treatment, or following short term and long term stress treatment. These studies are
important to further clarify the initial recognition of signal by the plants and the
subsequent transduction of these signals to trigger the physiological responses.
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4 Chapter Four: NMR profiling of rice (Oryza sativa) metabolites in
response to abiotic stresses
4.1 Aim
The metabolomic changes in leaves and roots of rice plants under drought stress, salinity
stress, osmotic stress and H2O2 treatment over several time points were evaluated with
1H NMR spectroscopy. The aim of this study was to compare the similarities and
differences in metabolite profile of rice plants undergoing different stresses in order to
fully understand the metabolic changes associated with stress signalling and perception.
4.2 Introduction
Metabolites present in living organisms are involved in cellular processes including energy
supply and storage, building blocks of DNA and RNA and as various signalling molecules.
These include sugars, major amino acids and organic acids coupled with macromolecules,
such as proteins and nucleic acids. It is known that proteins and metabolites are the main
effectors of a phenotype, affecting the function of the cells. A comprehensive study of
the metabolome is hence crucial to the understanding of cellular function, in addition to
the extensive study of DNA sequences (genome) and proteins (proteome). A complete
study of the dynamic behaviour of metabolites could reveal an in-depth knowledge of the
type and quantity of substances that exist and the conditions under which they are
present in the cells.
Metabolomic approaches have been applied extensively in plants of various species
including Arabidopsis (Kim et al., 2007; Le et al., 2006), Brassica rapa (Abdel-Farid et al.,
2007), cucumber (Yongdong et al., 2009), wheat (Graham, 2009), potato (Galindo et al.,
2009), opium poppy (Zulak et al., 2008) and rice (Fumagalli et al., 2009; Kusano et al.,
2007; Sato et al., 2008). Tarpley et al., (2005; 2007) reviewed the typical application of
metabolomic studies in the interpretation of genetic variation between plants or
between growth periods, the evaluation of food quality, and elucidation of the
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metabolome under different environmental constraints. Examples of stress-related
metabolomic studies include drought (Charlton et al., 2008; Dai et al., 2010; Fumagalli et
al., 2009), osmotic stress (van der Weerd et al., 2001), ozone (Cho et al., 2008), chemical
(Dubey et al., 2010), electric field (Galindo et al., 2009), and nutrient stress (Hirai et al.,
2004). The advantage of identifying stress-related biomarkers provides a new tool for
improving crop production with higher agronomic traits. However, metabolomics is not a
straightforward tool as comprehensive analysis involving complex molecules produced in
a short time frame is challenging.
Metabolomics requires advanced methodology to obtain large-scale measurements in a
short time and complex bioinformatic techniques to understand the output of the data.
Various methods are used to measure a large number of metabolites simultaneously;
mass spectrometry (MS) is one of the leading technologies in this field, with rapid and
accurate measurement of the molecular weight and quantities of many substances.
However, this method has a problem in distinguishing two or more substances with the
same molecular weight. Therefore, mass spectrometry is usually coupled with extra
separation techniques, such as gas chromatography (GC), liquid chromatography (LC) and
capillary electrophoresis (CE). These combinations are known as LC-MS, GC-MS and CE-
MS respectively. The chromatography column is capable of separating the same
molecular weight substances with different retention rates, hence allowing the
identification of these substances if the retention time of each substance is known
beforehand. Due to the unique behaviour of biological molecules, the charged substances
are mostly analyzed with CE-MS and LC-MS. Infusion techniques using MS lack accuracy
due to their inability to separate isomers and ion suppression effect. Moreover, the
characteristics of metabolites with high polarity, non-volatility and lack of appropriate
mobile phases compatible to MS shows the drawbacks of these techniques (Krishnan et
al., 2005).
A direct infusion analysis approach using nuclear magnetic resonance (NMR) has been
developed for powerful metabolome profiling (Zulak et al., 2008). NMR is a non-
destructive technique, which requires minimal sample preparation with high throughput
(hundreds of samples per day). NMR utilizes nuclei from substances with odd atomic or
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mass numbers which act as magnets and interact with an external magnetic field by a
process called nuclear spin (Hatada and Kitayama 2004). NMR has been successfully
applied in analytical chemistry, biochemistry, physiology and medicine. The application of
NMR dates back to 1952, when Felix Bloch and Edward Purcell group were awarded
Nobel Prize with the demonstration of molecular beams in both liquid and solids. Since
then, NMR appears as a versatile technique in analyzing liquid, solids, semi-solids, liquid-
crystal and gas for their molecule structure, composition, and dynamics. NMR is suitable
for studying cells as most of the cells in living organisms are built from hydrogen, nitrogen
and carbon that can produce resonance. It is therefore not surprising that it emerges as
the non-biased and standard metabolite profiling platform (Ward et al., 2003).
In this study, rice plants were exposed to various stresses namely, drought (5-9 days),
salinity (118.54mM NaCl for 4 h), osmotic (15% PEG for 4 h) stress and H2O2 (150 mM and
100 mM) before metabolites were extracted. Once the NMR spectra of the rice plants
were acquired, the datasets were baseline corrected, peak line width adjusted, phase-line
corrected and normalized. Identification and quantification of the metabolites require
comprehensive analytical methods that integrate together to map out the global outlook
of the metabolome status. Firstly, identification of metabolites from the complex NMR
spectra with the aid of two-dimensional nuclear magnetic resonance spectroscopy (2D-
NMR) was performed. Then the detailed behaviour of each identified metabolite was
closely observed with various analytical techniques. Lastly, the integration of each
specific metabolite with its possible signalling role in rice plants has been discussed.
4.3 Results
4.3.1 Identification of compound by NMR spectroscopy
In NMR spectroscopy, the chemical shift is the resonance frequency of a nucleus relative
to a standard. Atomic nuclei possess a magnetic momentum (nuclear spin), which gives
rise to different energy levels and resonance frequencies in a magnetic field, creating
chemical shifts. The first task to interpreting NMR spectra is to establish how many
resonances a compound will exhibit. Chemically inequivalent H atoms resonate at
different field strengths, while chemically equivalent Hs resonate at the same field
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strength. Therefore, a compound that possessed x number of 1H will not always shows x
number of resonances. Take valine as an example, the first resonance is doublet of COOH,
the second resonance composed of a multiple of three H atoms, and last resonance
occurs near δ≈1, with two-methyl protons giving rise to equal quartets (Figure 4-1).
Figure 4-1: A typical spectrum of valine in aqueous solution. The methyl region is shown expanded. The picture is taken from (Friebolin and Becconsall, 1993).
To identify a metabolite from a NMR spectrum, the three NMR parameters of the
metabolite, namely (1) chemical shift, (2) coupling pattern, and (3) integration, have to
match with those of authentic samples recorded in standard database. However, some
regions of the spectrum could heavily overlapped at the same chemical shifts, making
identification of metabolites difficult. 2D-NMR spectroscopy methods including 1H-1H
correlation spectroscopy (COSY), 1H-1H total correlation spectroscopy (TOCSY),
heteronuclear single-quantum correlation spectroscopy (HSQC), and heteronuclear
multiple-bond correlation spectroscopy (HMBC) provide bond connectivity information
between nuclei or between two types of nuclei and are routinely used to assign complex
spectra (Abraham, 1987; Friebolin and Becconsall, 1993).
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4.3.1.1 Homonuclear correlation spectroscopy sequence (COSY)
As its name suggests, homonuclear correlation shows the frequencies for a single isotope,
e.g. hydrogen (1H), along both axes. A typical COSY spectrum of aqueous phase leaf
metabolites ranging from δ 0-3 is shown in Figure 4-2. Typical COSY spectra show two
types of peaks, diagonal and cross peaks. A molecule that contributes to the same
frequency coordinate on each axis will appear along the diagonal of the plot and are
known as diagonal peaks; while cross peaks are contributed from molecules that have
different values of frequency and appear off the diagonal line. In short, the diagonal
peaks correspond to the peaks in 1D NMR while the cross peaks indicate that two
neighbouring nuclei are coupled that have two different chemical shifts.
From these diagonal and cross peaks, squares can be drawn to enable immediate
identification of the mutually coupled protons. Figure 4-2 shows ranges of amino acids
including isoleucine, leucine, threonine, lactate, alanine, asparagine, GABA, arginine,
glutamate, and glutamine. As an example, isoleucine can be distinguished from its
coupling of doublet at δ 0.99 to multiplets at δ 1.93 and δ 1.25, and a multiplet at δ 1.47
that coupled to a doublet at 1.02. The region between δ 3-5.5 contained a huge overlap
in sugar components such as glucose and sucrose with some important amino acids
overlapping in between the sugar components. However, metabolites could be
distinguished easily with the aid of the 2D display of nuclei coupling pattern. Taking
glutamine as an example, it could be easily distinguished from the sucrose peaks by its
multiplet at δ 2.14 that coupled to another multiplet at δ 2.46 and a triplet at δ 3.77,
which correspond to the three signals on the diagonal of the COSY spectrum.
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Figure 4-2: A typical 600MHZ COSY spectrum of leaf metabolites in aqueous phase obtained from control plants shown as contour plot. X and Y axis both represent the 1H nuclear resonance in the range of δ 0 to 3. This range of spectra contained metabolites of Asn: Asparagine, Gln: Glutamine, Glu: Glutamate, Ala: Alanine, Lac: Lactate, Thr: Threonine, Val: Valine, Ile: Isoleucine, Leu: Leucine, GABA: Gamma- amino-N-butyrate, Arg: Arginine.
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The identification of 1H NMR spectra from the chloroform phase is not as straightforward
as in aqueous methanol since the majority of compounds obtained from chloroform are
composed of lipids. It is known that the membrane of plant and animal cells are made up
of 50-100 types of lipids, which belong to different classes but related by the same motifs
(Adosraku et al., 1996). Therefore, it is not surprising that individual NMR lipids are yet to
be completely distinguished in the complex plant mixtures. Moreover, the study of lipid
analysis via prior separation by either TLC or HPLC is time-consuming. In this study, it was
found that the COSY spectrum of chloroform extracts contributed less but broader peaks
compared to the COSY spectrum of aqueous metabolite extraction (Figure 4-3). The
metabolites obtained from chloroform extraction consisted of mostly fatty acid chains,
which were determined from the area of –CH2C0 (lipid fraction 3) resonances at δ 2.31
and -CH3 at δ 0.97 (lipid fraction 7). However, numerous overlaps in the region near δ
0.97 made CH3 a better choice. The polyunsaturated lipid (PUFA) fraction can also be
distinguished by vinyl CH=CH groups at δ 5.35 (lipid fraction 1) and -CH2-CH2CO at δ 1.62
(lipid fraction 5).
The complex overlapping resonances at the region of δ 2.79 (lipid fraction 2) indicates the
presence of PUFAs with at least two unsaturated fatty acid chains. This triplet signal
arises from the allylic methylene protons within the series of double bonds in the chain
(=CH-CH2-CH=), which could be derived from linoleic acid. The crosspeaks at δ 5.39 and δ
2.79 confirmed the coupling between the methylene and vinyl protons of this
unsaturated fatty acid.
The presence of phosphatidic acids or diacylgycerophospholipids (DAGP) were observed
from the glycerol backbone as indicated by the multiplets at δ 5.22 (lipid fraction 11) and
a signal that arose from the Sn2 glycerol C-2 proton. The magnetically unequivalent
glycerol Sn1 methylene protons resonated at δ 4.41 and δ 4.15, while both glycerol Sn3
methylene proton resonated at δ 3.89. In order to distinguish between diverse
phosphatidic acids, resonances of protons from head group were necessary. The
presence of phosphatidylcholine (PC) was indicated by the characteristic of N+(CH3)3
proton singlet at δ 3.2 (Lipid fraction 8). The methylene protons head group –
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(OCH2CH2N+(CH3)3) resonated at δ 3.60 (-CH2N+(CH3)3) and δ 4.23 (-OCH2-) further
confirmed the presence of choline phospholipids (Adosraku et al., 1996). The presence of
phosphatidylglycerol (PG) was confirmed by the signal of glycerol head group CH2CHCH2
(lipid fraction 10). These were diagnosed from a set of signals at δ 3.88, δ 3.74, and δ 3.58.
Two cross peaks at δ 3.60, δ 3.74 and δ 3.80 were thus assigned to the PG.
Figure 4-3: Two–dimensional COSY spectrum of rice leaf metabolites in chloroform solution. The 600 MHz 1H NMR spectrum is shown at the top and left-hand edges. Key: LF: Lipid fraction.
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4.3.1.2 1H-1H total correlation spectroscopy (TOCSY)
TOCSY experiment uses a similar principle of COSY in that cross peaks of coupled protons
are observed, but in addition it shows the connection between nuclei by a chain of
couplings. These criteria added useful information for identifying the larger
interconnected networks of spin couplings. Figure 4-4 shows the 600MHz TOCSY
spectrum of the leaf metabolites in aqueous phase. The one-dimensional spectrum is
shown at the top and at the left–hand edge, with assignment indicated. Taking α-glucose
and β-glucose as examples, the TOCSY shows same cross peaks at δ 3.40 but it was then
differentiated by the cross peaks at δ 4.65, 3.25, 3.47, 3.78 for β-glucose.
Figure 4-4: Two–dimensional TOCSY spectrum of rice leaf metabolites in aqueous phase. The 600 MHz 1H NMR spectrum is shown at the top and left-hand edges with assignments from spectrum range δ1-5.5. Key: Suc: sucrose, Asn: Asparagine, Gln:Glutamine, Glu: Glutamate, Ala: Alanine, Lac: Lactate, Thr: Threonine, Val: Valine, Ile: Isoleucine, Leu: Leucine, GABA: Gamma- amino-N-butyrate, Arg: Arginine, Chol: Choline, α-glc: α-glucose, β-glc: β-glucose, IS: Indoxyl sulfate, Ino: Inosine, His: Histamine, Phe: Phenylalanine, Tyr: Tyrosine.
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4.3.1.3 Heteronuclear single-quantum correlation spectroscopy (HSQC)
Although many common metabolites can be distinguished with a single COSY or TOCSY
experiment, there are still some heavy overlapping regions that can be difficult to identify.
To unambiguously identify them, additional experiments, such as HSQC or HMBC are
needed. HSQC provides the coupling pattern of two different nuclei, in this case 1H and 13C, are to be analyzed with different pause frequencies. The resonances in the 1H and 13C
spectra can then immediately be assigned with confidence on the basis of their chemical
shifts and multiplicities. Figure 4-5 shows the HSQC of leaf sample in aqueous solution;
take GABA as an example, three peaks of 1H were observed with first 1H peak at δ 1.9
coupled to 13C at δ 26.4, second 1H peaks at δ 2.3 coupled to 13C at δ 37.2 and the last 1H
peak at δ 3.02 coupled to at δ 42.
Figure 4-5: Two–dimensional HSQC spectrum of rice leaf metabolites in aqueous solution. The 600 MHz 1H NMR spectrum is shown at the top and the 13C is shown at the left-hand edges with assignments from spectrum range δ1.2-4.5. Key: Suc: sucrose, Asn: Asparagine, Glu: Glutamate, Ala: Alanine, Lac: Lactate, Thr: Threonine, GABA: Gamma- amino-N-butyrate.
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4.3.1.4 Heteronuclear multiple-quantum correlation spectroscopy (HMBC)
Heteronuclear multiple-quantum correlation spectroscopy (HMBC) provides information
of complete coupling of 13C to neighbouring 13C and 1H to 13C. Valine can be distinguished
easily from four peaks of 1H, in which 1H at δ 0.983 coupled to 13C at δ 19.35, 1H at δ 1.03
to 13C at δ 20.73, 1H δ 3.62 to 13C δ 63.06, and 1H δ 2.27 to 13C δ 31.84 (Figure 4-6). The
two 1H of lactate were observed at δ 1.33 and δ 4.13 from COSY plot and the three
carbons of lactate were observed attached to them (Figure 4-6). They were 13C δ 71.25
and δ 185.34 that coupled to 1H δ 1.33 and 13C δ 23.31 that coupled at 1H δ 4.13.
Complicated metabolites such as sucrose also can be distinguished from the 2D spectrum.
A range of 1H were identified belonging to sucrose; they were 1H δ 3.75 and δ 5.46 that
coupled to 13C δ106, 1H δ 5.46 to 13C δ 75.3, 1Hδ 3.83 to 13C δ 71.9, 1H δ4.05 to 13C δ65.67
and 1H δ4.04 to 13C δ79.78.
Figure 4-6: Two–dimensional HMBC spectrum of rice leaf metabolites in aqueous solution. The 600 MHz 1H NMR spectrum is shown at the top and the 13C is shown at the left-hand edges. Key: Suc: sucrose, Asn: Asparagine, Glu: Glutamate, Lac: Lactate, GABA: Gamma- amino-N-butyrate, Val: Valine.
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4.4 Results
4.4.1 Rice metabolome
Table 4.1 lists the chemical shifts of metabolites found in rice leaves and roots obtained
from various extraction methods. Sucrose, asparagine, glutamine, glutamate, alanine,
lactate, threonine, isoleucine, GABA, choline, β-glucose, α- glucose, formate, inosine,
tyrosine, and histamine were generally found in aqueous extraction of both leaf and root
samples. Valine, leucine, arginine, indoxy sulfate, phenylalanine, fumarate, aconitic acid,
and cysteine were only found in leaf aqueous extracts; while glyceraldehyde,
phosphoenol-pyruvate, citrulline and carnitine were found in root extracts. Table 4.1 also
lists the lipid fractions collected from chloroform extraction of roots and leaves. In total,
39 metabolites have been identified from rice.
Table 4-1: Metabolites identified from aqueous and chloroform extract of rice leaves and roots. Metabolites (keya) H group δ H’ (multiplicity) Plant organ/
extraction 1. Sucrose (Suc) GH1, GH2, GH3, GH4,
GH5,6, FH2,FH3, FH4, FH5, FH6
5.42(d), 3.57(m), 3.81(d), 3.83, 3.86, 3.76, 3.67, 3.48,4.22, 4.05
Leaf, root/ methanol
2. Asparagine (Asn) α-CH, half β –CH2, half β –CH2
3.9 (dd), 2.81 (dd), 2.69 (dd) Leaf, root/ methanol
3. Glutamine (Gln) α-CH, β –CH2, у –CH2 2.14(m), 2.46(m), 3.77(t) Leaf, root/ methanol 4. Glutamate (Glu) βCH2, у CH2,α CH 2.1(m), 2.36(m),3.77(t) Leaf, root/ methanol 5. Alanine (Ala) CH3, CH 1.48(d), 3.79(qt) Leaf, root/ methanol 6. Lactate (Lac) CH3, CH 1.33(d), 4.11(q) Leaf, root/ methanol 7. Threonine (Thr) CH3, αCH, βCH 1.33(d), 3.59(d), 4.26(dq) Leaf, root/ methanol 8. Valine (Val) αCH, β-CH, у-CH3, у’-
CH3 3.62(d), 2.28(m), 0.98(d), 1.03(d) Leaf, methanol
9. Isoleucine (Ile) α- CH, β-CH, half у- CH2, δ- CH3,β- CH3
3.68(d), 1.93(m), 1.25(m), 1.47(m), 0.94(t), 1.02(d)
Leaf, root/ methanol
10. Leucine (Leu) Α-CH, β- CH2, y- CH,
δ- CH3 3.72(t), 1.96(m), 1.63(m), 1.69(m), 0.91(d), 0.94(d)
Leaf / methanol
11. Gamma-amino-N- butyrate (GABA)
β CH2, α CH2, y CH2 1.9(q), 2.3(t), 3.02(t) Leaf, root/ methanol
12. Arginine (Arg) y- CH2, β CH2, ∆ CH2,
CH 1.66(m), 1.73(m), 1.92(m), 3.25(t), 3.77(t)
Leaf / methanol
13. Choline (Chol) CH3, CH2NH, CH2OH 3.21(s), 3.52 (m),4.07 (m) Leaf, root/ methanol 14. β-glucose (β-glc) 1-H, 2-H, 3-H, 4-H,
5-H, CH2(i), CH2(ii) 4.65(d), 3.25(dd), 3.47(t), 3.40(t), 3.47(ddd), 3.78(dd), 3.90(dd)
Leaf, root/ methanol
15. α- glucose (α-glc) 1-H, 2-H, 3-H, 4-H, 5-H, CH2(i), CH2(ii)
5.24(d), 3.56(dd), 3.70(t), 3.40(t), 3.83(m), 3.72(dd), 3.85(m)
Leaf, root/ methanol
16. Formate CH 8.45(s) Leaf, root/ methanol 17. Indoxyl sulfate (IS) 5C-CH, 6-CH, 4-CH,
7-CH 7.2 (dd), 7.38 (s), 7.5 (d), 7.7 (d) Leaf / methanol
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Metabolites (keya) H group δ H’ (multiplicity) Plant organ/ extraction
18. Inosine (Ino) CH2(ii), CH2 (i) , H5’, H4’,H8,H2
3.85(dd), 3.92(dd), 4.28(Q), 4.44(t), 6.1(d), 8.24(s), 8.34(s)
Leaf, root/ methanol
19. Histamine (His) β CH2, α CH2, C5 3.05(t), 3.31(t), 7.17(s), 8.04(s) Leaf, root/ methanol 20. Phenylalanine (Phe) β CH2 (ii), β CH2 (i), α
CH, H 3.13(dd), 3.28(dd), 4(dd), 7.39(m)
Leaf/ methanol
21. Tyrosine (Tyr) 2,6-CH, 3,5-CH, CH2,
α-CH 7.20(d), 6.91(d), 3.95(dd), 3.20(dd), 3.06(dd)
Leaf, root/ methanol
22. Fumarate CH 6.53(s) Leaf/ methanol 23. Aconitic acid
(Aconitate) CH2, CH 3.17(s), 5.92 (s) Leaf/ methanol
24. Cysteine (Cys) CH 4.02 (t), 3.07 (dd), 2.76 (dd) Leaf/ methanol 25. Glyceraldehyde
(GA) CHOH, CH2OH 3.68 (dd), 3.56 (m) Root/ methanol
26. phospho(enol)-pyruvate (PEP)
CH2, CH2 5.26(t), 5.42(t) Root/methanol
27. Citrulline (Cit) ∆CH2, α-CH, y CH2, β CH2
3.15(t), 3.82(t), 1.48(m), 2.10(m) Root/methanol
28. Carnitine CH2COOH, CH3, NCH2
2.44(dd), 3.23(s), 3.43(m) Root/ mthanol
29. Lipid fraction 1 (LF1)-PUFA
CH=CH 5.35(bs) Leaf/chloroform
30. Lipid fraction 2 (LF2)-PUFA
=CH-CH2-CH= 2.79(bs) Leaf/chloroform
31. Lipid fraction 3 (LF3)-FA
-CH2-C0 2.32(bs) Leaf/ chloroform
32. Lipid fraction 4 (LF4)
CH2-C=C 2.07(bs) Leaf/chloroform
33. Lipid fraction 5 (LF5)-PUFA
CH2-CH2CO 1.62(bs) Leaf/chloroform
34. Lipid fraction 6 (LF6)
-(CH2)-n CH3 1.34(bs) Leaf, root/chloroform
35. Lipid fraction 7 (LF7)—Fatty acid (FA)
CH3 0.97(bs) Leaf, root/ chloroform
36. Lipid fraction 8 (LF8)- phosphatidylcholine (PC)
N+(CH3)3, CH2 N+(CH3)3, OCH2
3.22(s), 3.60, 4.23 Leaf/chloroform
37. Lipid fraction 9 (LF9)--sphingolipids
5.75(m), 5.48(q) Root/chloroform
38. Lipid fraction 10 (LF10)- phosphatidylglycerol (PG)
CH2CHCH2 3.88, 3.74, 3.58, 3.60 Leaf/chloroform
39. Lipid fraction 11 (LF11)- diacylgycerophospholipids (DAGP)
5.21, 4.41,4.15,3.89 Leaf, root/chloroform
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Figure 4-7 shows the assignment and the position of metabolites in 1D spectra obtained
from aqueous extracts of leaf samples after treatment with hydrogen peroxide, drought,
salinity and osmotic stress compared to its control. Each experiment shows almost the
same profile of metabolites with changing concentrations as seen by different peak
heights. Figure 4-8 shows the metabolites assigned in roots extracted from aqueous
solution. Fewer metabolites were observed in the region of 6-9 ppm in roots compared to
leaf. The majority of metabolites found in both leaves and roots were amino acids,
organic acids and sugars. Both leaf and root samples showed heavy overlapping peaks in
the region between 3.0-4.5 ppm.
Metabolites assigned from the chloroform-extracted samples contained mostly lipid
fractions in both leaves and roots (Figure 4-9 and Figure 4-10 respectively). Chloroform
dissolves non-polar molecules, such as those lipid fractions obtained from leaf and root
samples, which have almost no polarity in the bonds due to the equal sharing of electrons
between their atoms. There were heavy overlaps of broad regions in spectrum peaks
between 0.5 -2.5 ppm for leaf samples and 0.5-2.0 ppm for root samples, which makes
the identification difficult. Therefore, 2D experiments were performed, to provide
coupling information in addition to carbon and hydrogen position, which would then be
compared to standard metabolites provided in the library.
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Figure 4-7: Examples of typical 1D-NMR spectrum of leaf samples extracted in aqueous solution. a) 4 h control plants b) H2O2 (10 mM, 30 min) treated plants, c) drought (7 days) stressed plants, d) salinity (118.54 mM, 4 h) treated plants, and e) Osmotic (15% PEG, 4 h) stressed plants. Each spectrum is a representation of six biological replicates. Key: Suc: sucrose, Asn: Asparagine, Gln:Glutamine, Glu: Glutamate, Ala: Alanine, Lac: Lactate, Thr: Threonine, Val: Valine, Ile: Isoleucine, Leu: Leucine, GABA: Gamma- amino-N-butyrate, Arg: Arginine, Chol: Choline, α-glc: α-glucose, β-glc: β-glucose, IS: Indoxyl sulfate, Ino: Inosine, His: Histamine, Phe: Phenylalanine, Tyr: Tyrosine, Cys: cysteine
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s
Figure 4-8: Examples of typical 1D-NMR spectrum of root samples extracted in aqueous solution. a) 4 h control plants, b) H2O2 (10 mM, 30 min) treated plants, c) drought (7 days) stressed plants, d) salinity (118.54 mM, 4 h) treated plants, and e) Osmotic (15% PEG, 4 h) stressed plants. Each spectrum is a representation of six biological replicates. Key: Suc: sucrose, Asn: Asparagine, Gln: Glutamine, Glu: Glutamate, Ala: Alanine, Lac: Lactate, Thr: Threonine, Val: Valine, Ile: Isoleucine, Leu: Leucine, GABA: Gamma- amino-N-butyrate, Arg: Arginine, Chol: Choline, α-glc: α-glucose, β-glc: β-glucose, Ino: Inosine, His: Histamine, Tyr: Tyrosine, PEP: Phospho(enol)pyruvate, GA: Glyceraldehyde, Cit: Citrulline.
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Figure 4-9: Examples of typical 1D-NMR spectrum of leaf sample extracted in chloroform. a) 4 h control plants, b) H2O2 (10 mM, 30 min) treated plants, c) drought (7 days) stressed plants, d) salinity (118.54 mM, 4 h) treated plants, and e) Osmotic (15% PEG, 4 h) stressed plants. Each spectrum is a representation of six biological replicates. Key: LF1: lipid fraction of CH=CH, LF2: lipid fraction of =CH-CH2-CH=, LF3: lipid fraction of –CH2-CO, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO, LF6: lipid fraction of –(CH2)n-CH3, LF7: lipid fraction of CH3, LF8: lipid fraction of Choline head, LF10: lipid fraction of glycerol head, LF11: lipid fraction of glycerol backbone.
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Figure 4-10: Examples of typical 1D-NMR spectrum of root samples extracted in chloroform. a) 4 h control plants, b) H2O2 (10 mM, 30 min) treated plants, c) drought (7 days) stressed plants, d) salinity (118.54 mM for 4 h) treated plants, and e) Osmotic (15% PEG, 4 h) stressed plants. Each spectrum is a representation of six biological replicates. Key: LF1: lipid fraction of CH=CH, LF2: lipid fraction of =CH-CH2-CH=, LF3: lipid fraction of –CH2-CO, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO, LF6: lipid fraction of –(CH2)n-CH3, LF7: lipid fraction of CH3, LF8: lipid fraction of choline head, LF10: lipid fraction of glycerol head, LF11: lipid fraction of glycerol backbone.
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4.4.1.1 Metabolome under drought stress
Leaf and root samples of plants imposed with drought stress for 5, 7 and 9 days, were
separated and freeze dried. PCA and PLSDA were carried out on the centred normalize
NMR dataset. PCA is an unsupervised clustering method that requires no knowledge of
the dataset and acts to reduce the dimensionality of multivariate data while preserving
most of the variance within the data (Goodacre et al., 1999). In contrast, PLSDA is a
supervised method of classification by which 80% of the data is used to build a
classification model. The rest of the data are then used to evaluate the quality of the
predictive model (Trygg et al., 2006). The score plots of aqueous or chloroform extracted
leaf and root samples are shown in Figure 4-11. In the leaf sample obtained from aqueous
extraction, control and treated metabolome were clearly separated (Figure 4-11 a). The
PLSDA maximizes the separation and hence clear clustering of control and treated plants
were observed (Figure 4-11 b). Similar result was found in PLSDA score plot of chloroform
extracts of leaf samples (Figure 4-11 d). However, the root metabolome obtained from
aqueous extracts showed no significant difference at day 5; the variation between
samples itself were bigger than the variation applied from drought. Nevertheless,
separations were observed at day 7 and 9 (Figure 4-11 e, f). In the PCA clustering of root
samples extracted from chloroform, the control samples shifted from day 5, day 7 and
day 9 toward the centre of both the components, while treated samples also clustered
into three groups close to the centre of components (Figure 4-11 g). While taking the
data for higher classification (PLSDA), no clear separation was observed (Figure 4-11 h).
Together, the PCA and PLSDA analyses show that metabolome of leaf but not root from
both aqueous and chloroform extracts were significantly affected by drought.
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Figure 4-11: PCA and PLS-DA of metabolite profiling data under different drought stress levels. Plate a), b), c) and d) show the clustering of leaf sample obtained with aqueous and chloroform extraction; while plate e), f), g), and h) shows the clustering of roots sample obtained from aqueous and chloroform extraction.
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To classify the metabolites according to their pattern of alteration, the data set was
subjected to hierarchical cluster analysis (HCA). The graphical output of HCA is a
dendrogram, which is a tree-like chart that allows visualization of clustering (Hartigan,
1975). The output of HCA is usually a display coupled with a heat map - a graphical
colour-coding that represents values taken by a variable in a hierarchy. Figure 4-12 shows
a dendrogram created by HCA coupled with a heat map coloured according to the
relative fold change to control of each metabolite, in which green and red represent
increase and decrease respectively of treated: control ratio. Both horizontal dendrograms
that represent drought treated leaves and roots show a shift at day 7. In leaf samples,
HCA generated three clusters (Figure 4-12 a). The first cluster consisted of Sucrose, α-
glucose, lipid fraction of 1, 2, 4, 6, 7, and 10; which may contribute to the shift at day 7
due to the huge increment of their metabolites at this period (Figure 4-12 a). The second
cluster consisted of β-glucose, asparagine, formate, leucine, lactate, inosine, fumarate,
lipid fractions 2, 3, 5, 8, 9 and 11; in which the transient decrease of lipid fraction 2, 3, 5,
8, leucine, lactate and inosine were observed at day 7 (Figure 4-12 a). Metabolites in the
third cluster increased by more than 2 fold in leaves throughout the drought treatment;
this cluster included alanine, glutamine, glutamate, aconitate, arginine, cysteine,
isoleucine, valine, indoxyl sulfate, tyrosine, GABA, histamine, phenylalanine, and choline
(Figure 4-12 a).
The root samples were also clustered into three groups; first cluster consisted of formate,
threonine, lactate, tyrosine, and asparagine that increased throughout the drought
treatment (Figure 4-12 b). Phosphoenolpyruvate, carnitine, choline, citrulline, glutamate,
GABA, histamine, arginine, α-glucose, β-glucose, isoleucine, alanine and lipid fraction 5
were metabolites that were listed under cluster two; in which phosphoenolpyruvate,
carnitine, choline, citrulline, glutamate, GABA, histamine, arginine, α-glucose showed a
constant increase between day 7 and day 9 (Figure 4-12 b). Cluster 3 consisted of
glyceraldehyde, inosine, glutamine, sucrose, lipid fraction 1, 2, 3, 4, 6, 7, 9, 10, and 11,
with majority of lipid fractions decreasing more than 2 fold compared to the controls
(Figure 4-12 b).
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Figure 4-12: Pearson correlation coefficient- based heat map coupled with clustering dendrogram showing a) leaf and b) root metabolites expression under 5, 7 and 9 days of drought stress. The vertical axis represents metabolites that were used to cluster the samples (horizontal axis). The color code represents matrix of log2 transformation of treated to control ratio, ranging from green color representing underexpression to red color representing overexpression. 1, 2 and 3 are the different clusters based on HCA. Key: Suc: sucrose, Asn: Asparagine, Gln:Glutamine, Glu: Glutamate, Ala: Alanine, Lac: Lactate, Thr: Threonine, Val: Valine, Ile: Isoleucine, Leu: Leucine, GABA: Gamma- amino-N-butyrate, Arg: Arginine, Chol: Choline, α-glc: α-glucose, β-glc: β-glucose, Ino: Inosine, His: Histamine, Tyr: Tyrosine, PEP: Phospho(enol)pyruvate, GA: Glyceraldehyde, Cit: Citrulline, LF1: lipid fraction of CH=CH, LF2: lipid fraction of =CH-CH2-CH=, LF3: lipid fraction of –CH2-CO, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO, LF6: lipid fraction of –(CH2)n-CH3, LF7: lipid fraction of CH3, LF8: lipid fraction of Choline head, LF10: lipid fraction of glycerol head, LF11: lipid fraction of glycerol backbone.
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To further examine and better represent the specific response of metabolites over the
drought stress period, metabolites that showed more than 0.5 fold changes were log 2
transformed and plotted as bar charts for leaf (Figure 4-13) and root (Figure 4-14). In leaf
samples, there were 22 metabolites that showed more than 0.5 fold change compared to
control. The majority of amino acids were found to increase transiently at day 5 followed
by a decrease at day 7 and 9 following drought treatment. These amino acids included
arginine, glutamine, alanine, valine, glutamate, isoleucine, phenylalanine, GABA, tyrosine,
histamine, and cysteine. Interestingly, sucrose, α-glucose, fumarate, lipid fractions 1, 2, 4
and 10 were found to decrease under drought stress. A noticeable shift of majority of the
metabolites at day 7 compared to day 5 was observed. Groups of metabolites showing a
huge decrease at day 7 consisted of the amino acids arginine, glutamine, alanine, valine,
glutamate, isoleucine, phenylalanine, GABA, tyrosine, histamine, cysteine, and organic
acids like aconitate and indoxyl sulfate (Figure 4-13). What is intriguing is that this group
belonged to group three in the heat map (Figure 4-12 a). Even though concentration of
metabolites of this group was at levels higher than control, they experience a decline at
day 7.
There were also metabolites that showed increase with time, namely α-glucose, lipid
fraction 5 and 9. On the other hand, metabolites that showed specific changes on day 9
were also observed; they were phenylalanine, lipid fraction 1, 2, 4, 5 and 10. More
specifically, LF5 (PUFA) was the only metabolite that showed an increase at day 9
following drought.
In the root samples, 25 metabolites were identified showing more than 0.5 fold change
(Figure 4-14). Metabolites that increased following drought treatment were arginine,
glutamate, choline, carnitine, inosine, tyrosine, asparagine, threonine, glyceraldehyde,
phosphoenolpyruvate. Majority of lipid fractions showed reduction in concentration over
drought treatment. Examples of metabolites that showed a transient decrease at day 7
were isoleucine, alanine, β-glucose, and lipid fraction 5. At the same time, metabolites
such as choline, glyceraldehyde, inosine, tyrosine, threonine, lipid fraction 1, 2, 9 and 11
showed transient increment. At day 9, only arginine, alanine, glutamate, and asparagine
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showed visible increase. This is unlike that seen in leaf samples where majority of the
metabolites decreased at day 9.
Figure 4-13: Metabolites of leaf that changed at least 0.5 fold at 5, 7 and 9 days of drought treatment. The data were ratio of treated vs control metabolites with log 2 transformation. Key: Ala: Alanine, Arg: Arginine, Cys: Cysteine, GABA: Gamma- amino-N-butyrate, Gln: Glutamine, Glu: Glutamate, His: Histamine, Ile: Isoleucine, IS: Indoxyl sulfate, Phe: Phenylalanine, Tyr: Tyrosine, Val: Valine, Suc: Sucrose, α-glc: α-glucose, LF1: lipid fraction of CH=CH, LF2: lipid fraction of =CH-CH2-CH=, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO, LF9: Lipid fraction of sphingolipids andLF10: lipid fraction of glycerol head.
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Figure 4-14: Metabolites of roots that changed at least 0.5 fold at 5, 7 and 9 days of drought treatment. The data were ratio of treated and control metabolites with log 2 transformation. Key: GA: Glyceraldehyde; Formate, Asn: Asparagine, Glu: Glutamate, Ala: Alanine, Thr: Threonine, Ile: Isoleucine, Arg: Arginine, Chol: Choline, α-glc: α-glucose, β-glc: β-glucose, Ino: Inosine, Tyr: Tyrosine, PEP: Phospho(enol)pyruvate, GA: Glyceraldehyde, Cit: Citrulline, LF1: lipid fraction of CH=CH, LF2: lipid fraction of =CH-CH2-CH=, LF3: lipid fraction of –CH2-CO, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO, LF6: lipid fraction of –(CH2)n-CH3, LF7: lipid fraction of CH3, LF10: lipid fraction of glycerol head, LF11: lipid fraction of glycerol backbone.
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4.4.1.2 Metabolome after salinity stress
In vitro rice plants were treated with salt stress by transferring them into 118.54 mM
NaCl solution for 4 h. This dose and time of treatment was chosen because this
combination appeared to show recovery of lipid peroxidation after the first peak of H2O2
production.
The PCA and PLSDA score plots of saline treated leaves and roots are shown in Figure
4-15. PCA indicated that salinity treatment has little or no effect on constitutive
metabolite composition. However, metabolome of control plants appeared to cluster
together while treated plants were more spread out following salinity stress (Figure 4-15
a, c, e and f). In PLSDA, metabolites from salt treatments of leaf and root extractions
showed significant separation from control (Figure 4-15b,d, f, and h). Metabolites that
contribute to the separation in PLSDA with more than 0.5 fold changes were log 2
transformed and plotted as bar charts (Figure 4-16(a) leaf and (b) root).
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Figure 4-15: PCA and PLSDA of metabolite profiling data following salinity stress (118.54 mM NaCl). Plates a), b), c) and d) shows the clustering of leaf samples obtained with aqueous and chloroform extraction; while plates e), f), g), and h) shows the clustering of root samples obtained from aqueous and chloroform extraction.
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Surprisingly, a total of only eight metabolites from leaves and roots showed a 0.5 fold
change following salinity stress. In the leaf sample, 3 out of 4 metabolites showed a
decrease relative to control. Only lipid fraction 5 showed more than 0.5 fold increase.
Significant decrease in glutamate, α-glucose, and lipid fraction 4 were observed after 4 h
of NaCl treatment (Figure 4-16 a). In the root sample, no metabolites showed an
increment of more than 0.5 fold. However, reduction of glutamate, and lipid fractions 1, 9,
and 10 were observed (Figure 4-16 b).
Figure 4-16: Metabolites of a) leaves and b) roots that changed at least 0.5 fold after 4 h of salinity (118.54 mM NaCl) treatment. The data are presented as ratio of treated compared to control metabolites with log 2 transformation. Key: Glu: Glutamate, alpha-glc: alpha-glucose, LF1: lipid fraction of CH=CH, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO (PUFA), LF9: lipid fraction of sphingolipids, LF10: lipid fraction of phosphatidylglycerol.
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4.4.1.3 Metabolome after osmotic stress
Rice plants were subjected to osmotic stress treatment (15% PEG), which is equivalent to
the same osmolarity created by 118.54 mM NaCl solution. The in-vitro grown rice plants
were transferred into 15% PEG solution for 4 h before the separation of leaves and roots
for extraction. The PCA and PLSDA score plots are presented in Figure 4-17. Four hours of
osmotic stress contributed to a significant separation of control group from treated group
in PCA of leaf (Figure 4-17 a and c) and root samples (Figure 4-17 e and g). Further PLSDA
showed significant separation of control and treated groups but also showed more in-
depth information of the control plant status, in which controls were more clustered
together, reflecting the stability of control group metabolites throughout the experiment.
Metabolites responsible for the classification in the PCA and PLSDA score plots were log 2
transformed and plotted as bar charts in Figure 4-18 (a) leaf and (b) root. In the leaf
sample, apart from glutamate and inosine, the rest of the metabolites that were
identified in this study showed a significant reduction. Most of the metabolites identified
comprised of lipid fractions (Figure 4-18 a). Other than lipid fractions, choline, asparagine
and formate were also metabolites that showed at least 0.5 fold reduction versus control.
More metabolites in the root showed a significant increment when in direct contact with
saline water; alanine, histamine, citrulline, lipid fraction 4, 3 and 2 were examples of
these (Figure 4-18 b). Whereas lipid fractions 9, 10, 11, threonine, phosphoenolpyruvate,
arginine, and formate showed 0.5 fold reduction. Interestingly, formate and lipid fraction
9 showed similar decrease in both the leaf and root under osmotic stress.
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Figure 4-17: PCA and PLSDA of metabolite profiling data from osmotic stress treatment (15% PEG for 4 h). Plate a), b), c) and d) shows the clustering of leaf samples obtained with aqueous and chloroform extraction; while plate e), f), g), and h) shows the clustering of root samples obtained from aqueous and chloroform extraction.
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Figure 4-18: Metabolites of a) leaf and b) root that changed at least 0.5 fold after 4h of osmotic (15% PEG) treatment. The data are presented as ratio of treated versus control metabolites with log 2 transformation. Key: Chol: Choline, Asn: Asparagine, glu: glutamate, Ino: Inosine Ala: Alanine, Arg: Arginine, His: Histamine, Cit: Citrulline, Thr: Threonine, PEP: Phospho(enol)pyruvate, , LF4: lipid fraction of CH2-C=C, LF3: lipid fraction of –CH2-CO, LF2: lipid fraction of =CH-CH2-CH=, LF10: lipid fraction of glycerol head, LF11: lipid fraction of glycerol backbone, LF9: lipid fraction of sphingolipids.
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4.4.1.4 Metabolite changes following treatment with H2O2
To evaluate the possible route of stress-induced metabolite changes, H2O2 treatment was
employed. H2O2 is well known for its role as a central signalling molecule for metabolic
defence against oxidative stress (Baty et al., 2005; Foyer et al., 1997; Libik et al., 2007).
Treatment of plants with H2O2 for stress signalling analysis has been reported (Cheng et
al., 2007; de Azevedo Neto et al., 2005; Liu, 2010). In this study, rice plants were treated
with high dose (50 mM) and low dose (10 mM) of H2O2 for 15 min, 30 min, 1 h and 5 h.
Metabolome analysis was then performed to evaluate the effects of H2O2 on metabolite
changes. The PLSDA score plots of leaf samples treated with H2O2 at 15 min, 30 min, 1 h
and 5 h are shown in Figure 4-19. In the aqueous extraction, a clear separation could not
be achieved by PLSDA for the first 15 min treatment (Figure 4-19 a). These results
indicate that the slight increase of H2O2 following treatment produced little or no effect
on the primary metabolism in rice leaf. After 30 min of treatment, metabolome were
found clusterred into 3 groups, namely: control, low dose and high dose (Figure 4-19 b).
In the chloroform extraction, which mostly consisted of lipid fractions, clear separation of
control and treated groups were observed as early as 15 min immediately after the
treatment (Figure 4-19 e). The separation was transient as it showed recovery between
30 min to 1 hour, in both high and low dose treatment (Figure 4-19 f and g). Nevertheless,
PLSDA clustered into three groups at the end of treatment (Figure 4-19 h). Even though
both samples showed distinct clusters, the cluster in Figure 4-19 e and h provides
different information on metabolome behaviour. Figure 4-19 e shows that the treated
group was totally shifted from those in control by a second component vertically. Figure
4-19 h on the other hand shows that the shift of metabolome was affected by the
treatment strength with high dose shifting further from control compared to lower dose
treatment.
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Figure 4-19: PLSDA score plots of leaf metabolite profiling data after treating with H2O2: low dose 10 mM (yellow), high dose 50 mM (red) and control (blue). Plate a), b), c) and d) show the clustering of leaf samples obtained with aqueous extraction at 15 min, 30 min, 1 h and 5 h. Plate e), f), g), and h) shows the clustering of leaf sample obtained from chloroform extraction at 15 min, 30 min, 1 h and 5 h.
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Metabolites that contributed to the separation of PLSDA following high dose H2O2
treatment were plotted in Figure 4-20. Eighteen metabolites showed at least a 0.5 fold
change compared to their respective controls, in which majority showed huge changes at
30 min of treatment with high dose of H2O2. At this time point, formate, sucrose, valine,
GABA, lactate, isoleucine, and α-glucose showed significant increment while asparagine,
glutamate, glutamine and all the LFs except LF1 showed significant reduction. More
interestingly, after the huge reduction observed, the lipid fractions recovered to their
control levels at all time points.
Figure 4-20: Metabolites of leaf that changed at least 0.5 fold after high dose (50 mM) treatment. The data are presented as ratio of treated to control metabolites with log 2 transformation. Key: Suc: sucrose, Asn: Asparagine, Gln:Glutamine, Glu: Glutamate, Lac: Lactate, Val: Valine, Ile: Isoleucine, GABA: Gamma- amino-N-butyrate, Arg: Arginine, α-glc: α-glucose, LF1: lipid fraction of CH=CH, LF2: lipid fraction of =CH-CH2-CH=, LF3: lipid fraction of –CH2-CO, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO, LF6: lipid fraction of –(CH2)n-CH3, and LF7: lipid fraction of CH3.
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Roots that were in direct contact with the H2O2 solution were significantly affected as
three distinct clusters were observed at all times. The PLSDA score plots of root samples
extracted with aqueous methanol are shown in (Figure 4-21 a,b,c and d). The clustering of
low and high dose samples explains the shift in metabolites due to the different amounts
of H2O2 applied. Again, controls were closely clustered which could suggest the stability of
metabolome under normal conditions. On the other hand, PLSDA score plots of root
metabolome extracted from chloroform, are shown in Figure 4-21 e, f, g and h. Significant
difference between metabolites from low dose of H2O2 compared to control were
observed at 15 min, 30 min and 5 h of treatments; while high dose showed no significant
difference at all time points.
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Figure 4-21: PLSDA score plots of root metabolite profiling data after treating with H2O2: low dose 10 mM (yellow), high dose 50 mM (red) and control (blue). Plate a), b), c) and d) shows the clustering of root sample obtained with aqueous extraction at 15 min, 30 min, 1 h and 5 h respectively. Plate e), f), g), and h) shows the clustering of root sample obtained from chloroform extraction at 15 min, 30 min, 1 h and 5 h.
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The change in the metabolites obtained via aqueous extraction was affected in only 14
metabolites, shown in Figure 4-22. An increase in α-glucose while a reduction in
asparagine, tyrosine, and glutamine were observed in the 15 min treatment. At 30 min
after treatment, accumulation of GABA was observed and proceeded until the end of the
treatment, while sucrose acted in an opposite manner with a decrease following 5 h
treatment. Amongst all the root metabolites extracted from chloroform, seven types of
lipid fractions were found to show more than 0.5 fold changes over time, with majority
showing a transient decrease at 30 min.
Figure 4-22: Metabolites of roots that changes at least 0.5 fold after high dose (50 mM) treatment. The data are presented as ratio of treated to control metabolites with logged 2 transformation. Key: Asn: Asparagine, GABA: Gamma- amino-N-butyrate, Gln:Glutamine, Glu: Glutamate, Tyr: Tyrosine, α-glc: α-glucose, Suc: sucrose, LF1: lipid fraction of CH=CH, LF2: lipid fraction of =CH-CH2-CH=, LF3: lipid fraction of –CH2-CO, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO, LF6: lipid fraction of –(CH2)n-CH3, and LF7: lipid fraction of CH3.
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More than 0.5-fold change were observed in nine metabolites following treatment with
low dose (10 mM) H2O2 (Figure 4-23); the metabolites consist of glutamine, glutamate
and seven lipid fractions. Glutamine and glutamate showed consistent reduction in low
dose H2O2 treatment (Figure 4-23), which is a similar response seen under high dose H2O2
treatment (Figure 4-20).
Figure 4-23: Metabolites of leaf that changed at least 0.5 fold after low dose (10 mM) treatment. The data are presented as ratio of treated and control metabolites with log 2 transformation. Key: Gln:Glutamine, Glu: Glutamate, LF1: lipid fraction of CH=CH, LF2: lipid fraction of =CH-CH2-CH=, LF3: lipid fraction of –CH2-CO, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO, LF6: lipid fraction of –(CH2)n-CH3, and LF7: lipid fraction of CH3.
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The metabolites obtained from roots that respond to low dose of H2O2 treatment are
shown in Figure 4-24. The result clearly demonstrated that the root metabolome was
more rapidly affected compared to leaf, with 18 metabolites changing at least 0.5 fold
compared to control. These metabolites were α-glucose, sucrose, asparagine, tyrosine,
valine, threonine, formate, histamine, indoxyl sulfate, GABA, glutamine, arginine, lipid
fraction 7, 6, 5, 4, 3,and 2. Interestingly, four metabolites, namely α-glucose, asparagine,
GABA and glutamine, were observed in high dose treatment of roots with the same
response. In the first 15 min of low dose treatment, only α-glucose and LF5 showed
intensive increment; while tyrosine, histamine, arginine, GABA, glutamate, and
asparagine showed reduction. The noticeable change following 30 min treatment was the
reduction of asparagine and glutamine. At much later time points, 1 h to 5 h, increment
of tyrosine, arginine, GABA, and asparagine were prominent. The sucrose content in roots
was kept at consistently low levels which were opposite to that in leaf following high dose
of H2O2. Prolonged H2O2 treatment led to a massive reduction of lipid fraction 2, 3, 4, 5, 6,
and 7 at 5 h of treatment in low dose treatment (Figure 4-24). This phenomenon was
absent in the root sample treated with high dose, which showed a recovery instead.
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Figure 4-24: Metabolites of root that changed at least 0.5 fold after low dose (10 mM) treatment. The data are presented as ratio of treated and control metabolites with logged 2 transformation. Key: Arg: Arginine, Asn: Asparagine, GABA: Gamma- amino-N-butyrate, Gln:Glutamine, Glu: Glutamate, His: Histamine, IS: indoxyl sulfate, Thr: Threonine, Tyr: Tyrosine, Val: Valine, α-glc: α-glucose, Suc: sucrose, LF2: lipid fraction of =CH-CH2-CH=, LF3: lipid fraction of –CH2-CO, LF4: lipid fraction of CH2-C=C, LF5: lipid fraction of CH2-CH2CO, LF6: lipid fraction of –(CH2)n-CH3, and LF7: lipid fraction of CH3.
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4.4.1.5 Comparison of metabolite profile across stresses
It is well known that H2O2 plays a central role in responses to both biotic and abiotic
stresses in plants. Many studies have proved that the H2O2 burst after exposure to stress
depends on the strength and duration of the imposed stress (Neill et al., 2002). Hence, to
unveil the consequence of metabolites resulting from H2O2 signalling, perturbation of
metabolites due to treatment with H2O2 was compared to metabolite behaviour under
other stresses (Table 4.2).
A total of 18 metabolites showed significant changes in leaf and root under H2O2
treatment. Most of the metabolites in leaves and roots experienced reduction rather
than increment corresponding to H2O2. In leaf samples, most metabolites that showed
reduction were of lipid fractions; lipid fraction 1 to 7 all reduced massively in high dose of
H2O2 and osmotic treatment, while in salinity stress, only LF 4 showed reduction. In fact,
LF4 was the only LF that appeared to be reduced across high dose H2O2, drought, osmotic
and salinity stress. Increment of valine, GABA and isoleucine was observed in high dose of
H2O2 and drought stress. A reduction of glutamine in osmotic stress and glutamate in
salinity correlated with reduction following treatment with a high dose and low dose of
H2O2 respectively. Moreover, a high dose of H2O2 treatment appears to induce more
dynamic changes compared to a low dose of H2O2 in rice plants.
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Table 4-2: Integration of metabolite responses in drought, osmotic and salinity compared to H2O2 treatment.
Leaf H2O2 Drought Osmotic Salinity Low High
Suc Χ ↑ ↓ X X Asn X ↑ X ↓ X
α-glc X ↑ ↓ X ↓ Val X ↑ ↑ X X
GABA X ↑ ↑ X X Formate X ↑ X ↓ X
Arg X ↓ ↑ X X Gln ↓ ↓ ↑ ↓ X Glu ↓ ↓ ↑ ↑ ↓ Ile X ↑ ↑ X X Lac X ↑ X ↓ X LF1 ↑ ↓ ↓ ↓ X LF2 ↓ ↓ ↓ ↓ X LF3 ↓ ↓ X ↓ X LF4 ↑ ↓ ↓ ↓ ↓ LF5 ↓ ↓ ↑ ↓ ↑ LF6 ↓ ↓ X ↓ X LF7 ↓ ↓ X ↓ X
Root Low High Drought Osmotic Salinity Suc ↓ ↓ X X X Asn ↓ ↓-↑ ↑ ↓ X
α-glc ↑ ↑ ↑ ↓ X Val ↓ X X X X
GABA ↑ ↑ X ↑ X Formate ↑ X ↓ ↓ X
Tyr ↓-↑ ↓-↑ ↑ ↓ X Thr ↑ X ↑ ↓ X His ↓ X X ↑ X IS ↓ X X X X
Gln ↓-↑ ↓-↑ X X X Arg ↓-↑ X ↑ ↓ X LF1 X ↓ ↓ X ↓ LF2 ↓ ↓ ↓ ↑ X LF3 ↓ ↓ ↓ ↑ X LF4 ↓ ↓ ↓ ↓ X LF5 ↑-↓ ↓ ↓ ↓ X LF6 ↓ ↓ ↓ ↑ X LF7 ↓ ↓ ↓ ↑ X
The first column showed metabolites that obtained in low and high dose of H2O2 treatment and its perturbation under different stresses. The X represents metabolites that showed no changes under the stress, ↓ represents decrease of metabolites, while ↑ represents increase of metabolites over stress treatment. The blue boxes highlight metabolites that were changed under H2O2 treatment and other stresses.
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In root samples, drought and H2O2 treatment reduced the levels of LF 2, 3, 4, 5, 6 and 7;
LF4 and LF5 also decreased in osmotic stress treatment, but LF 2,3,6 and 7 increased
following osmotic stress. LF2-7 showed a decrease in both leaves and roots in H2O2
treatment, but they only showed a decrease in osmotic stress for leaves and in drought
stress for roots. Asparagine, tyrosine, α-glucose, threonine, arginine and LF 1, 2, 3, 4, 5, 6,
and 7 were common in H2O2 and drought stress. An increase of GABA was observed
following H2O2 and osmotic stress, whilst a reduction of glutamine was common to H2O2
and salinity stress.
Metabolites that showed no changes in H2O2 treatment but instead showed changes in
drought, osmotic and salinity stress treatment are shown in Table 3.3. Aside from lipid
fraction 10 and 11, no other metabolites in salinity that overlapped with either drought
or osmotic stress. In leaf extracts, nine metabolites specifically responded to drought
stress and four types of metabolites were only responding to osmotic stress. In the nine
metabolites that were found in drought, seven of them increased, consisting of alanine,
phenylalanine, tyrosine, histamine, indoxysulfate, cysteine, and aconitate. Fumarate and
lipid fraction 10 were found to reduce in drought stress. Under osmotic stress, choline,
lipid fraction 8 and 9 were found to decrease while increment of inosine was observed. In
root extracts, 12 metabolites were observed, and they showed changes in drought,
osmotic, and salinity treatment. Two metabolites, alanine and choline, were found to
increase in both drought and osmotic treatments. Lipid fraction 11 showed a decrease in
osmotic and salinity treatment, whilst LF9 only showed a reduction in osmotic stress
treatment.
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Table 4-3: Changes of metabolites in drought, osmotic and salinity stress independent of H2O2. The ↓ represents decrease of metabolites while ↑ represents increase of metabolites and Χ represents no changes in treated versus control plants.
Leaf Drought Osmotic Salinity Ala ↑ Χ Χ Phe ↑ Χ Χ Tyr ↑ Χ Χ Hist ↑ Χ Χ Fumarate ↓ Χ Χ IS ↑ Χ Χ Cys ↑ Χ Χ Aconitate ↑ Χ Χ LF10 ↓ Χ ↓ Ino Χ ↑ Χ Chol Χ ↓ Χ LF9 Χ ↓ Χ LF8 Χ ↓ Χ Root Drought Osmotic Salinity Ile ↑ Χ Χ Ala ↑ ↑ Χ Chol ↑ ↑ Χ Carnitine ↑ Χ Χ β-glc ↓ Χ Χ GA ↑ Χ Χ Ino ↑ Χ Χ PEP ↑ ↓ Χ LF10 ↓ ↓ Χ Cit Χ ↑ Χ LF11 Χ ↓ ↓ LF9 ↓ ↓ ↓
4.5 Discussion
4.5.1 Drought stress
Rice plants were drought stressed by withholding the water supply for 11 days. The
metabolite profile of leaves and roots at day 5, 7 and 9 of drought treatment were
selected for analysis owing to the intense physiology changes observed at these time
points.
PCA score plots of leaf samples (Figure 4-11) showed that metabolites from all drought
treated plants shifted away from the control metabolites. A similar shift in the
metabolome was observed in pea (Pisum sativum L.) under drought stress (Charlton et al.,
2008). From the PCA of drought-treated roots, the impact of water deficit was only
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observed from day 7 onwards. The metabolites that contributed to a shift at day 7 were
those from cluster three in the HCA (Figure 4-12a), which showed higher concentrations
than those in control throughout the drought treatment. Interestingly, these metabolites
were found to be amino acids such as glutamate, glutamine, arginine, alanine, valine,
cysteine, isoleucine, tyrosine, histamine, and phenylalanine. Under drought conditions,
proteins undergo catabolism and breakdown into amino acid subunits (Henckel, 1964).
The accumulation of various amino acids following drought treatment was also observed
in Brassica napus (Good and Zaplachinski, 1994), Astragalus tennessessensis (Baskin and
Baskin, 1974), conifer (Cyr et al., 1990) and rice (Fumagalli et al., 2009). The phenomenon
of irreversible protein degradation to amino acids has been reported in peanut (Barnett
and Naylor, 1966). Accumulation of amino acids is noted to be a mechanism of enhancing
protection against drought stress (Cyr et al., 1990; Good and Zaplachinski, 1994). The
increment of amino acids has also been reported in drought treated plants (Barnett and
Naylor, 1966; Cyr et al., 1990) and it was reported that the amino acids may act as
osmolyte and enzyme regulators in drought stress treatment (Rai, 2002).
Accumulation of GABA has been previously observed in plant tissues exposed to a variety
of stresses due to its association in various physiological responses, including the
regulation of cytosolic pH, carbon fluxes into the TCA cycle and nitrogen metabolism,
protection against oxidative stress, osmoregulation and signalling (Kinnersley and Turano,
2000). For example, glutamate and glutamine affect nitrogen metabolism under stress
conditions (Masclaux-Daubresse et al., 2006; Wang et al., 2007). Alanine and valine have
been found to accumulate under drought stress (Ranieri et al., 1989; Saunier et al., 1968)
and confer a function towards ammonia storage during wilting.
The reduction of formate and fumarate were correlated with the increase of essential
amino acids threonine, isoleucine and alanine, which are mediated by the pyruvate
oxidation pathway. Moreover, isoleucine is well documented as a metabolic donor that
feeds the TCA cycle in plants (Mooney et al., 2002). On the other hand, major reduction
in PUFA lipid fractions were observed. The decline of lipid fractions was reported in
different peanut cultivars over drought stress (Lauriano et al., 2000). Acyl lipids are a
major component of plant membranes (Webb and Green 1991), contributing to more
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than 25% of the thylakoid membrane (Wada and Murata, 2007), especially the
phosphatidylglycerol that surrounds chloroplast (Frentzen, 2004; Jordan et al., 1983). The
decrease of lipids leads to an increase in membrane permeability (Navari-Izzo et al., 1993)
that results in electrolyte leakage and ultimately cell death (Pennazio and Sapetti, 1982).
Thus, the changes in lipid content and composition could be a useful feature in observing
the membrane deterioration. In this study, a significant increase of lipid degradation was
observed on day 5 compared to day 9 of drought stress. Lipid oxidation products can be
very reactive with protein-bound amino acids such as tryptophan (Ge et al., 2011),
histidine (Matoba et al., 1982) and tyrosine ((Bartesaghi et al., 2010). The role of tyrosine
in controlling the ion release in guard cell under stress has been well described
(MacRobbie, 2002). Thus it was not surprising to observe the accumulation of tyrosine
following drought stress of rice leaves in the first 5 days, followed by a decrease, which
coincided with an increase in LFs when drought progressed.
Cysteine, a thiol-containing amino acid, is known to possess antioxidant properties as a
precursor of glutathione. Cysteine forms conjugates with free radicals or trace elements,
thereby functioning as a detoxifying agent (Baker and Czarnecki-Maulden, 1987). Increase
in cysteine following drought stress in this study could function as H2O2 scavenger when
H2O2 synthesis increased. Histidine is one of the metabolites, which is rarely detected
unless under the conditions that promote protein breakdown. The histidine-mediated
over expression of histidine kinase has the novel role in regulating water stress response
(Wohlbach et al., 2008). Therefore, the increment of its precursor, histamine suggests
that the increment of histamine might play a positive role in water stress signalling
process.
An accumulation of arginine and a decline in phosphatidylglycerol lipid (lipid fraction 10)
are considered as indicators of phosphate shortage in leaves (Etiene and Carol, 1986;
Frentzen, 2004). Plants are not able to produce phosphate on their own and its only way
of phosphate intake is through osmosis in its roots. In addition, phosphate has to undergo
various pathways such as biosynthesis of energy transfer molecules (ATP and NADP) in
order to replenish the main components of nucleic acids and amino acids and also for the
building of phospholipid membranes. This is likely to lead to phosphate shortage,
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especially when plants were not able to uptake phosphate under drought condition
(Wittenmayer and Merbach, 2005).
Drought tolerant plants usually show an increment of solutes such as sucrose, choline,
proline and glucose (Charlton et al., 2008; Dai et al., 2010). These solutes are of great
importance to maintain the water levels in the plant. Moreover, the high hydrophilicity of
these solutes function as water molecules around nucleic acid, protein and membrane
during water shortage (Hoeskstra et al., 2001). However, a contrary result was observed
in this study with a huge reduction of sucrose and α-glucose observed as early as day 5 in
drought stressed leaves. Since sucrose and glucose are considered as end products of
Calvin cycle, their reduction indicates that photosynthesis and carbohydrate metabolism
were suppressed. Nevertheless, substantial choline and α-glucose were observed in the
roots. The different responses seen in the leaf and root might be due to the roots sensing
the dryness of soil before the leaves.
In summary, the results indicate that the shorter drought period of 5-7 days was likely to
have activated the amino acids pathways. When the drought stress was prolonged to 9
days, carbon and phosphate deficiency were observed with reduction of fatty acid
metabolism. Taken together, signalling cascades may be induced at the short term
exposure to drought at day 5-7. However, oxidation of lipid fractions and a shift in energy
synthesis and conservation at prolonged drought stress were observed at day 9,
suggesting that the exposure of plants to prolonged drought leads to reduction in
photosynthesis and respiration simultaneously, causing energy deprivation and retarding
growth.
4.5.2 Salinity stress
Plants respond to salinity in a two-phase process: a rapid osmotic phase at the beginning
that inhibits the growth of young leaves, and slower ionic phase that accelerates the
senescence of mature leaves (Munns and Tester, 2008). The two phases effect of salinity
are not obvious if the species are particularly sensitive to Na+. For example, rice root
experiences ion leakage under salinity stress and takes up Na+ immediately. The rapid
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uptake of Na+ bypasses the osmotic stress level and cause ionic stress to the plant by the
production of ROS (Gong et al., 2006). The production of ROS, especially H2O2 can either
act as a signalling molecule at low concentrations or create oxidative stress at higher
concentrations. In fact, de Azevedo Neto et al., (2005) has reported that pre-treatment
with H2O2 acclimated maize plants under salinity conditions. However, to some extent,
the presence of this ROS enables oxidation to occur on the lipid membranes. There were
signs of lipid peroxidation, observed in the degradation of lipid fractions in both leaf and
root of rice following salt treatment, which is in agreement with results from salt treated
Populus cathayana (Lu et al., 2009). The degradation of lipid fractions always occurs
concurrently with the accumulation of amino acids (Ayala-Astorga and Alcaraz-Melendez,
2010). Nevertheless, no increment of amino acids was observed, probably due to plants
undergoing nitrogen deficiency under salt stress that inhibited the production of amino
acids (Soussi et al., 1998).
Under salt stress, the complex ionic responses inhibit the uptake and production of
essential nutrients, as an example, an imbalance of NO3- and K+ have been reported in
rice plants (Castillo et al., 2007; Debouba et al., 2006; Song et al., 2006). This lead to
nutrient defiency and hence affecting the growth and development of plants (Song et al.,
2006) by inhibiting photosynthesis and protein synthesis (Di Martino et al., 2003; Santos
et al., 2001). K+ is essential for osmoregulation and protein synthesis; its deficiency could
lead to a reduction in amino acid production. Nevertheless a contradictory result was
observed in this study; as amino acids were maintained at normal levels, it is possible that
the plants experienced little or no K+ deficiency. The constant level of K+ was also
reported by Siringam et al (2011) following salt stress.
A noticeable decrease in glutamate was observed in leaves and roots under salt stress.
Glutamate is well known to be the most relevant metabolite for salt and water stress
tolerance in addition to being a central molecule in amino acid metabolism (Forde and
Lea, 2007). There has also been a report of glutamate-mediated synthesis of the
antioxidant glutathione (Jez et al., 2004). Glutathione reacts as an antioxidant as well to
quench ROS and is well known for its ability in eliminating H2O2 in the ascorbate-
glutathione cycle (Foyer et al., 1997). Interestingly, despite mostly increasing under
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various environmental stresses, a reduction of glutathione synthetase was observed in
H2O2 treated canola root (Lappartient and Touraine, 1997). This result coincides with the
reduction in glutamate after salt stress treatment of rice as observed in the present study.
The changes in sucrose content following salinity treatment has also been reported in the
study of three rice cultivars with different salinity tolerance (Pattanagul et al., 2008), in
which sucrose and starch accumulation were observed only in plants that physically
showed salinity stress while those with more resistance to salt showed stagnant level of
sucrose (Pattanagul et al., 2008). Kim et al.,(2007) suggest that the accumulation of
sucrose in plants occurred only when exposed to long-term salt stress. This also suggests
that sugar metabolism is time and cultivar-dependent in response to salt stress (Fumagalli
et al., 2009).
Despite the clear distinction between the control and the salt treated metabolome,
comprehensive quantification of organic and mineral solutes showed little fluctuation of
metabolites content (Figure 4-16). Together, the relatively stable nature of organic and
compatible solutes may suggest the possibility of these rice plants coping with a short-
term salt stress with protective strategies rather than through ionic or osmotic
adjustment (Lugan et al., 2010).
4.5.3 Osmotic stress
The use of PEG to induce osmotic stress dates back to 1970 in maize (Lawlor, 1970). Since
then, it has been used in various plant types to assess the osmotic effect on plant systems
including banana (Chai et al., 2005), wheat (Kerepesi and Galiba, 2000), potato (Tao and
Xi-Yao, 2009) and in rice (Hsu and Kao, 2003; Xiong et al., 2010). However, information of
the metabolome status under PEG-induced osmotic stress is limited; most evidence is the
analysis of targetted metabolite groups after PEG treatment in rice (Pandey et al., 2004).
The PCA alone showed a distinctive separation between metabolites from the control
group and 15% PEG treated group. The separation between these groups is likely due to
the decline of asparagine and formate and the increase in inosine and glutamate. Since
these metabolites play a crucial role in nitrogen and carbon balance in plants (Hourton-
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Cabassa et al., 1998; Masclaux-Daubresse et al., 2006; Miflin and Habash, 2002), it can be
concluded that osmotic stress affected the nitrogen and carbon metabolism to some
degree. Change in nitrogen metabolism was also observed in rice plants treated with 27
and 30% of PEG 6000 (Pandey et al., 2004). Nevertheless, a contradictory increase in
compatible solutes and free sugars were reported. Although different stress levels were
applied, results from this study and that of (Pandey et al., 2004) reveal important carbon
and nitrogen function in sensing and signalling of osmotic stress.
Alteration of soluble carbohydrate content has been reported in wheat that was treated
for 7 days with 18% PEG 4000 (Kerepesi and Galiba, 2000). In this study, the overuse of
carbon can be observed from the huge reduction of formate in both leaves and roots.
Formate is a substrate for formate dehydrogenase that is involved in O2 uptake coupled
to ATP synthesis (Hourton-Cabassa et al., 1998). The large reduction in lipid fractions
implies that lipid were probably broken down in order to maintain a constant supply of
lipid to carbon backbone and to balance up the carbon metabolism. These lipid fractions
supply the energy for plants and feed the carbon back into the TCA cycle. The increment
of alanine suggests an apparent increase of keto acid for lipid synthesis, thereby
balancing the breakdown of lipids (Forde and Lea, 2007).
Inosine, one of the purine nucleosides that is widely found in plants, was found to
increase following osmotic stress. Its function includes acting as precursors of nucleic acid
(DNA and RNA), participation in bioenergetic processes (ATP synthesis) and the synthesis
of macromolecules such as polysaccharides, phospholipids and glycolipids (Stasolla et al.,
2003). Hence, the accumulation of inosine is likely to have activated the lipid biosynthesis
in order to produce phospholipids and glycolipids for replacing the reduction of these
lipids during carbon starvation following osmotic stress.
A significant decrease of choline was observed in osmotically stressed plants. Apart from
being a component of phospholipids, choline also plays a significant role in plant osmotic
regulation (McNeil et al., 2001). Choline acts as a precursor for the osmoprotectant
glycine betaine which confers tolerance to water stress (GlyBet) (Di Martino et al., 2003).
Surprisingly, choline that should increase under osmotic conditions, showed signs of
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reduction instead in rice plants treated with PEG. It is possible that the low amount of
choline presented was caused by the low amount of phosphoethanolamine (lipid fraction
9) available; lower amounts of phosphoethanolamine would limit the conversion of
phosphocholine via dephosphorylation to free choline. This implies an imbalance in
phospholipid regulation under osmotic stress.
In summary, osmotic stress appears to affect the nitrogen metabolism and re-
programming of the cell membrane that is composed of various lipid components. These
findings have important implications for understanding the biochemical and molecular
mechanisms of plant adaptation and response to osmotic stress.
4.5.4 H2O2 and overall integration
H2O2 plays a significant role in sensing and signalling of stresses in plants (Veal et al., 2007).
It was hypothesised in this study that if H2O2 is produced internally during the stress and is
able to change the metabolome to adapt to the stress environment, the external supply of
H2O2 might induce similar changes in the plant metabolome under optimum conditions.
The examination of the metabolite changes would then provide useful information of how
stress information is sensed and perceived via H2O2. However, the comparison of
metabolites should be considered to be qualitative rather than quantitative due to the
uncertainties concerning the amount of H2O2 actually entering and inducing the responses
in plant cells.
From Table 3.2, stress related metabolites that were observed in drought, salinity and
osmotic stress were identified and its relationship with H2O2 sensitive metabolites are
presented in a global map of the metabolic pathways (Figure 4-25). In leaf samples,
common metabolites observed over all treatments were glutamate, LF4 and LF5. A
metabolite that was common to H2O2 treatment, drought and salinity was α-glucose,
while glutamine, LF 1 and 2 were common in drought, osmotic and H2O2 treatment. In
root samples, majority of metabolites observed were common across H2O2 treatment,
drought and osmotic stress, namely asparagine, α-glucose, formate, tyrosine, threonine,
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arginine, and LFs 2,3,4,5,6,7. This suggests some possible overlapping in signalling
pathways.
It has been reported in wheat that the addition of H2O2 to the roots could promote
vigorous root growth that leads to the development of a fully functional root system,
which can thus promote a higher nitrogen uptake by the wheat (Hameed et al., 2004). In
addition, the author reported that the increase in H2O2 production under K+ deficiency
environment could promote and induce high-affinity K+ transport activity in the root. It
has also been reported that the application of H2O2 could lead to the restoration of high-
affinity K+ transport activity in roots under K-sufficient conditions (Shin and Schachtman,
2004). On the other hand, Davies and Zhang (1991) reported that the root of a plant,
when subjected to drought stress, could sense the soil dryness and in turn lead to
chemical changes of the plant, e.g. accumulation of ABA, pH, and changing of cytokinin
level. The information of these chemical changes could be conveyed to shoots that leads
to subsequent control of the leaf system for better water conservation (Blackman and
Davies 1985). Therefore, it is thought that the different responses observed in roots and
leaves following the application of different stress and H2O2 treatments could be
attributed to the development of a fully functional root system for stress adaptation.
GOGAT cycle produces glutamate, an important substance in the biosynthesis of
nitrogenous compounds such as amino acids, nucleotides and chlorophyll (Masclaux-
Daubresse et al., 2006). Therefore, the levels of glutamine and glutamate are crucial for
cell stress signalling, perhaps more precisely in nitrogen stress homeostasis (Debouba et
al., 2006; Masclaux-Daubresse et al., 2006; Miflin and Habash, 2002). In this study,
glutamate showed a decrease in H2O2 and osmotic stress treatment, but an increase of
glutamine was observed in drought stress. This suggests the possible regulation of
ammonia assimilating pathway and the low level of glutamate/ glutamine in H2O2
treatment may induce carbon assimilation, suggesting that the regulation of GS/GOGAT is
to provide substrate for carbon metabolism, a result yet to be confirmed.
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In this study, major reduction of PUFA lipid fractions, namely LFs 1, 2, 4 and 5 occurred in
leaf and root samples were observed. It is hence very obvious that the production of H2O2
under stress attacks the C=C double bonds of unsaturated fatty acids side chains. The
changes in PUFA levels draw attention to the key enzyme that catalyzes the oxidation of
fatty acids, Lipoxygenase (linoleate:oxygen oxidoreductase, EC 1.13.11.12) LOX (Rosahl,
1996; Siedow, 1991). The regulation of LOX was also actively found under water stress
and wounding conditions (Bell and Mullet, 1991). It is therefore important to assess the
function of key LOX enzymes during abiotic stress responses in rice. Nevertheless, there
were changes of lipid fractions that do not overlap with any of the lipid fractions obtained
from H2O2 treatment. These lipids were LFs 8, 9, 10 and 11. Sphingolipids (LF9) was found
to be reduced in roots under drought, osmotic and salinity stress. The regulation of
sphingolipids confers stability to plant membranes, contributing to acclimation to
drought stress (Norberg et al., 1996). Phosphatidylglycerol (LF10) was found in leaf under
drought and salinity stress conditions, and it was also found in roots following drought
and osmotic stress. This metabolite has long been known as an important component of
plant photosynthetic membranes (Wada and Murata, 2007). A decline of
phosphatidylcholine (LF8) (one of the phospholipids) was observed in the leaf of osmotic
treated rice and diacylgycerophospholipids (LF11) was observed in the roots when
treated under osmotic and salinity stress conditions. The detection of
phosphatidylglycerol and diacylgycerophospholipids in salt stress treatment is important,
considering that phospholipid signalling response has been reported in salt treated rice
(Darwish et al., 2009). Therefore, it is envisaged that future work could be performed to
identify the function of these key metabolites.
To correlate the changes of metabolites by H2O2 to other stresses, one must consider the
H2O2 production sites, which contribute to various metabolite turnovers in each of the
plant’s compartment (Blokhina et al., 2003), especially the discrete zones of cell division,
expansion, full photosynthesis activity and senescence. In addition, most of the
metabolites pathways are often overlapped, so it is wise to determine the most
influential pathway of the overall leaf followed by comparison with the metabolite
pathway in steady state condition, rather than targeting the metabolite pathway of the
different compartments, which may produce different levels of metabolites and result in
150
generating a biased metabolite model that gives a false implication of the overall changes
of metabolites by H2O2 (Lunn, 2007). Furthermore, it should be noted that the plants
were subjected to different stress treatments (salinity, osmotic, and H2O2) for only a short
period of time, so it is thought that the stress treatments would only result in small
changes of metabolites and subsequently lesser effects of cell death and senescence in
each compartment. Therefore, it was decided to adopt an approach to first identify the
most influential pathway under stress. The detail characteristic of the most influencial
pathway can then be further examined at each sub-cellular compartment. Alternatively,
one could analyze the metabolite turnover under each stress by correlating the
metabolite data with pos-tranductional modification in a more comprehensive manner.
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Figure 4-25. Representation of the metabolic network of Oryza sativa under drought, osmotic and salinity stress corresponding with changes elicited by H2O2 treatment. The blue and green colours show the metabolites that change significantly in H2O2 treatment while metabolites changing under drought, osmotic and salinity stress were labelled with brown, pink and yellow circles respectively.
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4.6 Conclusion
In conclusion, NMR-based metabolic profiling in rice has identified dynamic metabolic
changes under different stress factors. In addition, there appears to be a few metabolites
common to drought, osmotic and salinity stress, as well as exogenous H2O2. The
advantage of this study is that it provides an un-targeted approach to study global
metabolite changes after different stresses.
Drought stress has an influence on most of the metabolite groupings, including amino
acid biosynthesis, photorespiration, fatty acid metabolism and citric acid cycle. Compared
to salinity stress, more prominent changes were observed following PEG treatment
(osmotic), as also reported by (Kerepesi and Galiba, 2000). Apart from glutamate, LF4 and
LF5 that were found in almost all stresses, LF 9 and 11 were both unique and common to
salinity and osmotic stress, indicating some overlap in the signalling pathway.
The role of root in stress adaptation has rarely been reported due to lack of information
on the transcriptional, metabolomics, and proteomics level under stress conditions. In
this study, the common overlap of nitrogen metabolite pathway observed under osmotic,
drought and exogenous application of H2O2 implies the importance of these pathways in
stress adaptation. Hence, improving the factors regulating nitrogen metabolism could
increase the chances of survival of rice plants under water stress conditions.
The function of fatty acid metabolism in signalling is always being neglected as it is
difficult to be identified and quantified (Bonzom et al., 1999; Gunstone and Shukla, 1995).
However, it was found that lipid metabolism was altered in response to stress, suggesting
that lipid metabolism plays a role in various signalling processes (Bartesaghi et al., 2010;
Darwish et al., 2009; Domonkos et al., 2008; Frentzen, 2004). Therefore, lipid metabolism
under stress and the LOX enzyme that catalyses its activity could be the subject of future
research to shed more light on the role of lipid metabolism in stress adaptation.
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5 Chapter Five: Targeted proteomics to identify oxidant-sensitive
cysteine thiols following abiotic stress
5.1 Aims
This study focuses on the method development and identification of post-translational
modifications of proteins, particularly the oxidation of thiols, following stimulation of rice
plants with exogenous H2O2 or abiotic stresses. This would lead to identification of
oxidation sensitive proteins that are important in stress signalling, for further functional
analysis.
5.2 Introduction
Reversible oxidative modification of protein thiols serves an important role in plant cell
signalling (Ghezzi and Bonetto, 2003). Under mild stress, protein thiol modification serves
as redox sensors and signal transducers for stress adaptation. However, under severe
oxidative stress, many proteins undergo irreversible oxidative modifications causing
protein degradation and damage (Ghezzi and Bonetto, 2003). Therefore, redox
proteomics enables identification of those proteins whose thiols are modified via
oxidative conditions, but having a signalling function. The oxidation process and examples
of the signalling molecules such as GPX and ETR1 identified in plants has been described
in Introduction Section (1.3.3). Among all the amino acids, cysteine is particularly
susceptible to oxidation, owing to its nucleophilic property (Di Simplicio et al., 2003).
Cysteine thiol plays essential roles in maintaining the integrity of protein structure and
function. Redox sensitive cysteines possess acidic pKa and are likely to deprotonate under
physiological pH, therefore making them susceptible to oxidant challenge.
Reversible cysteine oxidation modification including disulfide bond formation (Houston et
al., 2005), sulfenation (Charles et al., 2007; Saurin et al., 2004), nitrosylation (Lindermayr
et al., 2005) and glutathionylation (Michelet et al., 2005) have been identified to function
as cellular protective mechanism to prevent further oxidation of proteins that cause
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irreversible oxidative damage. Examples of irreversible modifications of cysteine residues
are sulfonic acid and 4-hydroxynonenal adduct formation (Ghezzi and Bonetto, 2003).
Recent proteomic studies in rice were mainly performed using two-dimensional gel
electrophoresis (2 DE) to identify the increase or decline of stress sensitive proteins
(Abbasi and Komatsu, 2004; Ali and Komatsu, 2006; Cui et al., 2005; Makoto and Setsuko,
2007). At the same time, limited studies have been performed to identify oxidative
sensitive proteins in rice plants due to technical challenges for both accurate proteomic
identification and sensitive quantification of cysteine thiols. Therefore, this study focuses
on setting up a gel-based approach in conjunction with a thiol-sensitive fluorescent dye
(5’-IAF) to identify proteins modified under various stresses as well as following H2O2
treatment.
5.3 Results
5.3.1 Protein extraction methods
To establish reproducibility and representation of the total proteome, several types of
extraction methods were tested, namely: phenol extraction, Tris-HCl extraction, and Mg-
NP40 extraction. The protein pellet obtained from phenol extraction and Tris-HCl were
dissolved in SDS sample buffer whilst the protein pellet obtained from Mg-NP40
extraction was washed and dissolved in PEG or SDS sample buffer (termed ‘PEG in SDS’)
(see Section 2.6.1).
The protein pellets were then electrophoresed on a 1D SDS PAGE (Figure 5-1). It was very
obvious that proteins from the rice leaves ranged from 100 to 10 KDa. Significant increase
in the number of proteins obtained with clearer bands was observed with the phenol
extraction method (lane 2). Moreover, high abundance of protein bands at lower
molecular weight ranging from 20 to 10 KDa was observed. The PEG fractionation process
interfered with Mg-NP40 resulting in very low protein recovery. In comparison with
phenol extraction, Tris-HCl extraction, which is routinely used in Arabidopsis, did not yield
enough proteins (lane 1). Therefore phenol extraction was the choice of method for
subsequent experiments.
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Figure 5-1: Coomassie-stained gel showing the different extraction methods to obtain pure rice total protein, with equal amounts of protein loaded. Lane 1: Tris-Hcl extraction, lane 2: phenol extraction, lane 3: Mg-NP40 extraction, lane 4: Mg-NP40 extraction then fractionation with PEG, lane 5: Mg-NP40 extraction of PEG in SDS sample buffer. 5.3.2 Method development for identification of thiol modification proteins
Cells respond to oxidants by activating a number of regulatory proteins, which contain
critical cysteine residues that are sensitive to oxidation, resulting in the formation of
sulphenic acids, disulphides, sulphinic, and sulphonic acid formation. Interestingly,
sulphenic acids, disulphides, and sulphinic transformations are readily reversible through
interaction with antioxidant systems such as GSH, glutaredoxin and thioredoxin, making
them well suited for signal transduction (Dickinson and Forman, 2002; Poole et al., 2004).
Therefore, monitoring oxidized thiols action should provide information on how the
redox signals are transmitted in cells following stimulation via ROS or ROS-generating
stimuli. This study is for developing a method suitable for identification of thiol oxidation
in rice by fluorescent labeling of reduced and oxidized thiols in combination with two-
dimensional SDS PAGE.
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5.3.3 Labelling of reduced proteins by 5'IAF tag
Fluorescent probes such as 5’IAF (5-iodoacetamidofluorescein) that react only with free –
SH groups has been widely used for the labelling of reduced proteins and peptides (Wu et
al., 1998). After the H2O2 pre-treatment, sulfhydryl-containing peptides and proteins may
oxidize in solution and hence inhibit the reaction with 5’IAF (Figure 5-2). Since proteins
are mostly in reduced form, a decrease of fluorescent labeling should be observed in
oxidized thiols since the 5’IAF is unable to bind with oxidized thiols (Figure 5-2). In this
study, plants were pre-treated with H2O2 for 15 min, 30 min, 1 h, and 5 h prior to
extraction of proteins and labelling with 5’IAF (Figure 5-3).
Figure 5-2: Schematic illustration of 5-iodoacetamidofluorescein (5’IAF) tagging of thiol proteins. SH represents reduced thiol, S-S represents intramolecular disulphides, -SOH is sulphenic acid, - SO2H is sulphinic acid. After treatment with 5’IAF, free thiols become fluorescently labelled, while oxidised amino acids are not labelled. Picture is adapted and modified from (Baty et al., 2005).
157
With the same amount of protein loaded (Figure 5-3 b), significant changes in
fluorescence of proteins compared to control were observed in lane 6 (50 mM H2O2
treatment for 30 min) and lane 9 (50 mM H2O2 treatment for 1 h). The largest protein
labelled was detected around the size of 75 KDa, whilst the smallest size of protein
fractions was in the range of 10 to 15 KDa. The most abundant proteins, indicated by its
high intensity, were found with a size of approximately 50 KDa. Compared to the 50 mM
treatment for 30 min and 1 h, only minor changes were found in other H2O2 treatments.
Therefore it appeared that 5’IAF was capable of detecting thiol modifications of rice
proteins; nevertheless the method needed refining to get a clear indication of the
changes in the proteome following H2O2 treatment.
Figure 5-3: Gel show different fluorescent labelling under UV light after tagging with 5'IAF. The proteins were obtained from plants treated with low dose (10 mM) and high dose (50 mM) H2O2 for 15 min, 30 min, 1 h and 5 h respectively. Lane 1: control 15 min, lane 2: 10 mM H2O2 15 min, lane 3: 50 mM H2O2 15 min, lane 4: control 30 min, lane 5: 10 mM H2O2 30 min, lane 6: 50 mM H2O2 30 min, lane 7: control 1 h, lane 8: 10 mM H2O2 1 h, lane 9: 50 mM H2O2 1 h, lane 10: control 5 h, lane 11: 10 mM H2O2 5 h, lane 12: 50 mM 5 h. B. Coomassie stained gel containing the same samples as A. MW markers indicated in kDa.
5.3.4 Selected 2D experiments
5.3.4.1 Treatment with 10 mM H2O2 for 15 min
Different time periods and stress levels that contributed to most changes in the oxidation
pattern of proteins were selected from the previous 1D H2O2 experiments, for further
analysis. In order to perform a detailed investigation on the IAF-labelled proteins, it was
necessary to perform 2D gel electrophoresis. The reproducible gel maps of 2-DE
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proteomic analysis of rice leaf after 15 min of 10 mM H2O2 treatment are presented in
(Figure 5-4). The 2D gels were obtained by analysing proteins extracted using a modified
phenol protocol and proteins labelled using 5’IAF.
There were up to 42 detectable and reproducible spots observed on each 2-DE gel in
accordance with three controls and three sample replicates. The reproduction of the gels
was as shown in Figure 5-4. It should be noted that some spots were present on the gels
of the control samples, but they were found absent in treated samples (Figure 5-4).
Changes of fluorescent intensity were observed in 25 proteins through a spot to spot
comparison to identify the protein spots that showed difference in intensity. The protein
spots on the gels were subjected to in-gel trypsin digestion and analyzed by MALDI-TOF
mass spectrometry. Database searches were performed using Mascot. Only 10 protein
spots were identified and listed in Table 5.1. A total of 7 spots were found reduced in
fluorescent intensity and 3 spots were found increased in fluorescent.
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Figure 5-4: 2-DE analysis of proteins from plants treated with 10 mM H2O2 for 15 min. A total of 150 µg proteins were extracted and labelled with 5'IAF before separation by 2-DE. Following electrophoresis, gels were scanned under UV light to produce fluorescence gels showing (ai) untreated plant as control, and (aii, aiii, aiv) are 3 different batches of biological replicate of the treated plant. After fluorescent scanning, gels were stained with Coomassie blue to obtain Coomassie blue gel showing (bi) untreated plant as control, and (bii, biii, biv) are 3 different batches of biological replicate of the treated plant.
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Table 5-1: Identities of H2O2 responsive proteins following 15 min of 10 mM H2O2 treatment by MALDI-TOF MS.
Changes CBB=proteins changes under Coomassie brilliant blue gels, changes fluorescence= proteins changes under fluorescence gels, mascot score shows the total ion score and peptide matches show number of peptide matched versus peptide submitted for analysis.
Spot number
Changes CBB
Changes fluorescence
Protein name Accession number
Mascot score Peptide matches
Predicted MW
Observed MW
1 / ↓ Salt stress root protein RS1 Q0JPA6
251 5(4) 21788 22860
2 / ↑ S-adenosylmethionine synthase P0A817
59 6(2) 42459 42250
5 ↑ ↑ Mitochondrial outer membrane protein porin
EF576087
38 1(1) 29202 26875
8 ↓ ↓ Enolase D17767
397 10(6) 48290 52000
15 / ↑ Ribulose bisphosphate carboxylase large chain
P58348
50 1(1) 53070 46250
26 / ↓ L-ascorbate peroxidase 1, cytosolic DP000009
366 19(14) 27255 25000
27 / ↓ Triosephosphate isomerase, cytosolic P48494
173 9(6) 27278 24100
28 / ↓ L-ascorbate peroxidase 2, cytosolic Q9FE01
202 5(4) 27217 31000
32 / ↓ Ribosome-recycling factor, chloroplastic A3BLC3
66 1(1) 29692 26875
19 / ↓ Ribulose bisphosphate carboxylase small chain
P16881
58 2(0) 19850 18500
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5.3.4.2 Treatment with 50 mM H2O2 for 30 min
Following the treatment of 50 mM H2O2 for 30 min, 15 protein spots were detected on
control gels while less protein spots were observed in fluorescent treated gels (Figure
5-5). Moreover, less protein spots were observed from this experiment compared to the
15 min treatment with 10 mM H2O2 (Figure 5-5). The protein spots were subjected to
trypsin digest and MALDI-TOF mass spectrometry and only 4 protein spots were
identified by Mascot analysis (Table 5.2). Surprisingly, all protein spots showed down-
regulation following treatment with 50 mM H2O2.
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Figure 5-5: : 2-DE analysis of proteins from plants treated with 50 mM H2O2 for 30 min. A total of 150 µg proteins were extracted and labelled with 5'IAF before separation by 2-DE. Following electrophoresis, gels were scanned under UV light to produce fluorescent gels showing (ai) untreated plant as control, and (aii, aiii, aiv) 3 different batches of biological replicate of the treated plant (50 mM H2O2 for 30 min). After fluorescent scanning, gels were stained with Coomassie blue to obtain Coomassie blue gel showing in (bi)- untreated plant as control, and (bii, biii, biv) are 3 different batches of biological replicate of the treated plant.
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Table 5-2: Identities of H2O2 responsive proteins following 30 min of 50 mM H2O2 treatment by MALDI-TOF MS.
Spot Number
Changes BCC
Changes fluorescent
Protein name Accession number
Mascot score
Peptide matches
Predicted MW
Observed MW
3 ↓ ↓ BURP domain-
containing protein 1
Q6I5B3
27 3(1) 28368 37000
5 / ↓ Full Ribosome-recycling
factor, chloroplastic
A3BLC3
66 4(2) 29692 35500
9 / ↓ Triosephosphate isomerase, cytosolic
P48494
37 2(1) 27278 35500
8 / ↓ Cysteine synthase
AC087852
46 3(1) 33933 27666
Changes CBB=proteins changes under Coomassie brilliant blue gels, changes fluorescence= proteins changes under fluorescence gels, mascot score shows the total ion score and peptide matches reprecent the number of peptide matched versus peptide submitted for analysis.
5.3.4.3 Treatment with 10 mM H2O2 for 1 h
The fluorescent proteomic expression profile following 1 h of 10 mM H2O2 treatment is
presented in Figure 5-6, whist its coomassie blue comparative gels are presented in
Figure 5-6. A total of approximately 30 protein spots were observed in Coomassie gels
and relatively less protein spots were observed in fluorescent gels. There were
approximately 8 protein spots detected in the fluorescent control gels, but more protein
spots were observed in H2O2 treated fluorescent gels. The protein spots were then
selected for further analysis and a protein spot was identified via Mascot and listed in
Table 5.3.
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Figure 5-6: : 2-DE analysis of proteins from plants treated with 50 mM H2O2 for 1 h. A total of 150 µg proteins were extracted and labelled with 5'IAF before separation by 2-DE. Following electrophoresis, gels were scanned under UV light to produce fluorescent gels showing (ai) untreated plant as control, and (aii, aiii, aiv) 3 different batches of biological replicate of the treated plant (50 mM H2O2 for 1 h). After fluorescent scanning, gels were stained with Coomassie blue. (bi) represents untreated plant as control, and (bii, biii, biv) are 3 different batches of biological replicate of the treated plant.
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Table 5-3: Identities of H2O2 responsive proteins following 30 min of 50 mM H2O2 treatment by MALDI-TOF MS. Spot
number Changes BCC
Changes fluorescent
Protein name Accession number
Mascot
score
Peptide matche
s
Predicted MW
Observed MW
16 / ↓ Ribulose bisphosphate carboxylase
small chain A, chloroplastic
P16881
58 3 19850 23000
Changes CBB=proteins changes under Coomassie brilliant blue gels, changes fluorescence= proteins changes under fluorescence gels, mascot score shows the total ion score and peptide matches reprecent the number of peptide matched versus peptide submitted for analysis.
5.3.5 Labelling oxidized proteins by blocking of reduced proteins with NEM
Since most of the proteins are in reduced form rather than oxidized form, it is more
appropriate to detect proteins in the oxidized form in order to avoid a bias in the analysis
due to the overlay of reduced proteins. The above approach of directly labelling proteins
with 5’IAF clearly works; however there are a number of “background” proteins labelled
with this dye because these proteins are reduced and therefore accessible to the dye.
Therefore, a method has been developed in which the reduced form of proteins (free
thiols –SH) were first blocked by N-Ethylmaleimide (NEM) before the total proteins were
reduced and tagged with 5’IAF (Baty et al., 2005). The NEM reacted to the –SH residue of
thiol groups and bound to them. This reaction disabled the –SH free thiol from further
reacting with 5’IAF. However, to detach oxidised cysteine groups, which may form
disulfide bonds that are incapable of reacting with 5’IAF, additional reduction steps are
needed. Figure 5-7 illustrates the process of the detection of reduced and reversible
oxidized thiol proteins. In this study, DTT was applied to reduce these proteins and
optimization of the DTT concentration was examined (Figure 5.8).
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Figure 5-7: Schematic illustration of 5-iodoacetamidofluorescein (5’IAF) tagging of thiol proteins. SH represents reduced thiol proteins, S-S represents intramolecular disulphides, -SOH is sulphenic acid. The function of NEM is to block reduced thiol proteins and the DTT reduces reversibly oxidised thiol proteins. The reduced thiols after treatment with 5’IAF, become fluorescently labelled. Since the free thiols were blocked by the NEM, they are not fluorescence. Picture is adapted and modified from (Baty et al., 2005).
With the same amount of proteins loaded from control leaf extracts (Figure 5-8), higher
intensity of 5’IAF fluorescence was observed following 1 mM DTT pretreatment, whilst
the application of 10 mM DTT completely inhibited the reaction of 5’IAF (Figure 5-8). The
IAF lane on Figure 5-8(a) indicates proteins labeled directly with IAF with no NEM/DTT
pre-treatment. This technique showed a significant reduction of fluorescent intensity and
enabled more precise detection of the oxidized proteins.
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Figure 5-8: Gels show the different amounts of DTT added to maximize the 5'IAF tagging. Extracted proteins were first pre-treated with 200 mM NEM and then treated with different amount of DTT before labelling with 5'IAF. Lane 1: no DTT was added, lane 2: 1 mM DTT was added, lane 3: 2.5 mM DTT was added, lane 4: 5 mM DTT was added, lane 5: 10 mM DTT was added and lane 6: proteins were directly labelled with 5'IAF tag. MW markers indicated in kDa. (a) UV fluorescent image (b) Coomassie stained gel.
This technique was then applied to proteins extracted from H2O2 treated samples that
showed changes in the previous experiment (Figure 5-9). Compared to the Coomassie
blue gel in Figure 5-9 (b), only few protein bands were highlighted by fluorescent tag,
showing the oxidation-specific selection of this technique. Same amounts of proteins
were loaded into each well, indicated by same amount of staining intensity in the
Coomassie blue stained gel; based on this it was obvious that 15 min of 10 mM H2O2
treatment increased the oxidation of thiol groups the most. New protein bands were
observed in lane 2 at approximately 50 KDa, 37 KDa, 25 KDa, 18 KDa, 17 KDa and 12 KDa.
Interestingly, proteins of different sizes were observed following 30 min of 50 mM H2O2
treatment. The protein bands at 37 KDa, 30 KDa, 25 KDa, 18 KDa and 15 KDa observed in
15 min of 10 mM H2O2 treatment disappeared in lane 4 but accumulation of proteins was
observed at around 30 KDa - 32 KDa. Following 1 h of H2O2 treatments, change of
fluorescent intensity in low dose and high dose of H2O2 treatment were observed.
Increased intensity was observed in high dose treatment, whereas decreased intensity
was observed in low dose treatment.
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Figure 5-9: Gel shows difference level of fluorescent intensity observed under UV light, following treatment with H2O2 and tagged with 5’IAF. (a) Proteins were obtained from lane 1: control 15 min, lane 2: 10 mM H2O2 treatment for 15 min, lane 3: control 30 min, lane 4: 50 mM H2O2 treatment for 30 min, lane 5: control 1 h, lane 6: 10 mM H2O2 treatment for 1 h, and lane 7: 50 mM H2O2 treatment for 1 h; (b) with the same amount of proteins loading shown in Coomassie blue gel.
5.3.5.1 Drought stress
The well-developed method using 5’-IAF with additional NEM and DTT treatment steps
was applied to various abiotic stresses in order to test the feasibility of the technique.
Plants were first treated with 13 days of drought by withholding their water supply. The
proteins obtained after extraction were analysed using 1D SDS-PAGE (Figure 5.10). The
highest abundance of proteins was observed at 50 KDa.
An increased in protein oxidation was observed over the drought treatment period
(Figure 5.10 a). The increased of fluorescence intensity shown from day 1 to day 3
indicated the increase of oxidation. Nevertheless, a significant decrease in oxidation (or
more availability of free thiols) was observed on day 9 and day 13. This suggests a
transient or reversible oxidation of proteins at these time points.
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Figure 5-10: Labelling of oxidase proteins from drought stress sample by blocking of reduced proteins with NEM before 5’IAF labelling. Lane 1: control day 1, lane 2: 1 day of drought, lane 3: control day 3, lane 4: 3 days of drought, lane 5: control day 5, lane 6: 5 days of drought, lane 7: control day 7, lane 8: 7 days of drought, lane 9: control day 9, lane 10: 9 days of drought, lane 11: control day 11, lane 12: 11 days of drought, lane 13: control day 13, and lane 14: 13 days of drought. (a) UV fluorescent image (b) Coomassie stained gel.
5.3.5.2 Osmotic stress
In osmotic treatment induced by 15% PEG, 50 KDa proteins showed huge increment of
oxidation. With same amount of proteins loaded (shown by the similar pattern and
amount presented in Coomassie blue gel), it is very likely that the increment was due to
change in oxidation status of the proteins (Figure 5-11). ASA pre-treatment (lane 2)
caused significant reduction in protein oxidation compared to control, as seen by a
decrease in fluorescence (lane 1). Interestingly, ASA pre-treatment combination either
following water treatment or PEG treatment caused a decrease in fluorescence intensity
of 37 KDa proteins (Figure 5-11). This experiment clearly shows that removal of H2O2 via
ASA reduces the oxidation of proteins following osmotic stress.
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Figure 5-11: Labelling of oxidase proteins from 15% PEG treated sample by blocking of reduced proteins with NEM before 5’IAF labelling. Lane1: control, lane 2: ascorbic acid pre-treatment and then treated with water, lane 3: 15% PEG treatment for 4 h, lane 4: ascorbic acid pre-treatment and then treated with 15% PEG for 4 h. (a) UV fluorescent image (b) Coomassie stained gel.
5.3.5.3 Salinity stress
The effect of H2O2 on protein modification related to stress signalling was further
examined with ASA pre-treatment before NaCl treatments. Compared to the Coomassie
blue stained gels (Figure 5.12b), it is clear that proteins of plants pre-treated with ASA
showed less fluorescence intensity compared to water controls or NaCl treatment (Figure
5.12a). It can be seen from the fluorescent image that ASA pre-treatment leads to the
decrease of oxidation in 50 KDa proteins, moreover these proteins show a decrease of
oxidation with ASA pre-treatment even before treatment with NaCl stress. Similar
responses were observed in proteins approximately at 37 and 17 KDa. There were several
protein bands that only appeared in NaCl treatment. These protein bands were observed
at approximately 27 KDa and 12 KDa, suggesting that they are NaCl specific proteins.
However overall the difference in oxidised proteins between the control samples (lane 2)
and NaCl samples (lane 4) are not obvious, although there may be subtle changes difficult
to detect using 1D gels.
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Figure 5-12: Labelling of oxidase proteins from salinity treated (118.54 mM NaCl) sample by blocking of reduced proteins with NEM then labeled with 5’IAF. Lane1: Ascorbic acid pre-treatment followed by 4 h water treatment; Lane 2: 4 h water treatment as control; lane 3: Ascorbic acid pre-treatment followed by 118.54 mM NaCl treatment for 4 h; lane 4: 118.54 mM NaCl treatment for 4 h. (a) UV fluorescent image (b) Coomassie stained gel.
5.3.5.4 Cross-talk experiment
The physiological studies revealed that cross-talk occurs between cold and salinity stress
(chapter 3 – 3.3.7). Therefore, the oxidation responses due to crosstalk between two
stresses were tested (Figure 5-13). Plants that were treated with 4 h of 10 °C cold stress
prior to 4 h 118.54 mM NaCl treatment showed a massive decrease in fluorescent
intensity compared to the control, and the cold and salinity treatment on its own. The
proteome from control and cold treatment showed almost a similar pattern, possibly due
to a 4 h recovery period offered to plants after cold treatment before protein extraction
(see chapter 3 for details). Proteins from the NaCl treatment alone did not appear to
show an oxidation pattern, similar to that seen in Figure 5.12. In the crosstalk experiment
(lane 1), clear bands showed around 25 KDa to 35 KDa under Coomassie were absent in
the fluorescent image (Figure 5-13). Interestingly, a protein band at 12 KDa that was
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absent in the control was found present in all stress combinations. However, this protein
was not present in the fluorescent image, suggesting it was induced by stresses that were
not sensitive to the oxidative response. Overall, it was obvious that the cold pre-
treatment caused a decrease in the oxidation pattern of the proteins found with NaCl
stress alone (lanes 1 vs 3).
Figure 5-13: 5’IAF labelling of oxidised proteins following NEM/DTT treatment, proteins were obtained from plants that were exposed to corsstalk experiment. Lane 1: Plant pre-treated with 4 h at 10 °C then with 4 h 118.54 mM NaCl, lane 2: control plants, lane 3: Plants treated with 118.54 mM NaCl for 4 h, and lane 4: Plants treated with 10 °C for 4 h + 4 h of water treatment. (a) UV fluorescent image (b) Coomassie stained gel.
5.4 Discussion
To identify post-translational modification of proteins responsive to oxidative conditions,
5’IAF was chosen, based on several successful examples in identification of oxidative
sensitive proteins (Baty et al., 2005; Cuddihy et al., 2008). Through this technique, two
messages can be retrieved: the changes in protein abundance and the changing of
proteins based on their post-translational oxidative modification. The changes of protein
spots observed in Coomassie blue gel were referred to as changing in abundance while
those changing under fluorescence without the increase or decrease in Coomassie stain
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were those important in oxidative modification. Proteins that are modified by oxidative
conditions at the final step of translation will then affect various downstream processes
including controlling the behaviour of proteins, modifications of transcription factors and
gene expression (Sweetlove and Mittller, 2009).
5.4.1 Protein extraction
Compared to yeast or animal proteomics, plant proteomics comparatively lags behind in
the amount of research publications mostly due to the complexity of the sampling
process. The phenol extraction showed advantages in providing a thorough and complete
cleansing that can remove most of the impurities such as interfering pigments that bound
to the proteins. Identification of thiol groups on proteins using 5’IAF tagging has been
widely used in jurkat cells (Baty et al., 2005; Cuddihy et al., 2008), but applying it to plants
is a relatively new approach (Desikan et al., 2009; Hancock et al., 2005). 5’IAF is capable
of binding to the free (reduced) thiols and causes fluorescence, whereas they are
incapable of binding to the oxidised thiols. Therefore, a decrease in intensity of
fluorescence is expected in plants following abiotic stresses due to oxidation of proteins
via H2O2. In the early experiment in this study it was observed that most of the protein
bands in the control samples were fluorescent when labelled with 5’IAF. This suggests
that most of the proteins were in reduced form and their abundance make the
identification of oxidised proteins difficult, the amount of which is present in a smaller
proportion. This was clear from the 1D analysis; nevertheless it was hoped that a clearer
separation of proteins using 2D SDS-PAGE might allow identification of oxidised proteins
better.
5.4.2 2 DE experiment
The protein spots identified in H2O2 treatment (by changes in their oxidative status) were
grouped according to their functions (Table 5.4). Some of these proteins were also found
in other reports, nevertheless, it should be noted that the regulation of proteins from
other studies were based on the increase or decrease of the protein amount rather than
PTM. The proteins detected under H2O2 treatment were classified into six major
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regulatory functions. A total of five identified proteins in response to H2O2 stress was
found related to leaf protective response in rice plants and this could be classified into
three functional categories: cell rescue/ defence, amino acid metabolism, and redox
homeostasis. All the five identified proteins showed down-regulation in fluorescence
following H2O2 treatment, i.e. were oxidised. S-adenosyl-methionine synthase, which was
found to be oxidised in H2O2 treatments, were down regulated following drought and
salinity treatment. This protein catalyzes the biosynthesis of S-adenosyl methionine
which regulate synthesis of methionine and amino acids of aspartate family was related
to tolerance to cold, salt, and abscisic acid stress tolerance (Cui et al., 2005; Espartero et
al., 1994). It is possible that oxidation of this protein results in its later breakdown,
thereby modulating the tolerance mechanisms.
Cysteine synthase is responsible for the final step in cysteine biosynthesis, the key-
limiting step in producing glutathione that is usually related to adverse stresses (May et
al., 1998). It was oxidised with H2O2 and decreased in abundance following drought (May
et al., 1998). Ascorbate peroxidase (APX) was found to be oxidised by H2O2 and down
regulated in response to osmotic stress (Liu, 2010). Similar finding of decrease in
abundance of these proteins was observed in rice seedling that were also treated with
different conditions of H2O2 treatment (Wan and Liu, 2008). APX is an important H2O2
scavenging enzyme, therefore the oxidation by H2O2 of APX could indicate inactivation of
this antioxidant activity, thereby contributing to an increase in H2O2 accumulation, which
can cause oxidative stress to plants. However oxidation of APX could also lead to
activation of the enzyme, in which case reduced H2O2 levels would be expected. The
profile of this protein in response to the abiotic stresses studied would be interesting to
follow up.
The exposure to H2O2 was found to regulate the photosynthetic function of rice seedlings.
Ribulose bisphosphate carboxylase (Rubisco) is a very inefficient enzyme with expression
level of 30-55% under normal condition (Makino et al., 2000). Under normal condition,
the Rubisco and its degraded fragments should remain in dynamic balance. Nevertheless,
the treatment with H2O2 was found to induce modification of all photosynthetic proteins
that were identified. The imbalance of Rubisco and its derivatives are likely to have
175
caused major damage to photosynthetic function leading to declined photosynthetic
activity in plants. In correlation with photosynthesis, the oxidation of triosephosphate
isomerase, which is responsible for carbohydrate synthesis, was also observed. This
phenomenon suggests that low calvin cycle activity provided limited substrate for
carbohydrate synthesis.
Table 5-4: Identification and functional classification of the proteins modified following H2O2 treatment and its response under other stresses in rice plants. Proteins ID Oxidation
Drought Salinity Osmotic
Cell rescue /defence Salt stress root protein RS1 ↑ ↑1 Amino acid metabolism S-adenosylmethionine synthase ↑ ↓3↓4↓5 Cysteine synthase ↑ ↓2 Redox homeostasis L-ascorbate peroxidase 1, cytosolic ↑
L-ascorbate peroxidase 2, cytosolic ↑ ↑2 ↑5 ↓6 Photosynthesis Ribose-recycling factor, chloroplastic ↑
Ribulose bisphosphate carboxylase small chain
↑ ↓1 ↑6
Ribulose bisphosphate carboxylase large chain
↑ ↓1 ↑4 ↑6
Full Ribosome-recycling factor, chloroplastic
↑
Enolase ↑ ↓5 Carbohydrate synthesis Triosephosphate isomerase, cytosolic ↑ ↓, ↑2 ↑5 Others Mitochondrial outer membrane protein porin ↓
BURP domain-containing protein 1 ↑ Vacuolar cation/ proton exchanger 1a ↑ ↑2 ↑6 References: 1(Ali and Komatsu, 2006), 2 (Liu and Bennett, 2011), 3(Chen et al., 2009), 4(Parker et al., 2006), 5(Yan et al., 2005), 6 .
The discovery of proteins sensitive to H2O2 by fluorescent tagging of free thiols and 2-DE
gels provides new insights into the stress defence mechanism in rice. Nevertheless, this
study presents lower recovery of protein spots in 2-DE when compared to the studies
performed by (Wan and Liu, 2008), who investigated the protein changes provoked by
H2O2 through examining the abundance of the proteins. The loss of protein spots in this
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study is likely due to the changes of protein solubilisation in IEF strips influenced by 5’IAF
tag, despite the effectiveness of this technique in identifying oxidised thiols.
Therefore, an intermediate procedure was needed to first block the free thiol or reduced
proteins with NEM in order to reduce the background fluorescence caused by labelling of
constitutive free thiols in proteins. This was followed by treating the oxidized proteins
(labelled with NEM) with DTT via reduction. These steps enable only the tagging of
oxidised thiols with 5’IAF (Baty et al., 2005). To examine oxidative sensitive proteins,
plants were treated with external H2O2 as a way to mimic the production of ROS. The rice
plants treated with H2O2 showed increase in fluorescent intensity following treatment
with 10 mM H2O2 for 15 min, 50 mM H2O2 for 30 min, 10 mM and 50 mM H2O2 for 1h.
This suggests that treatment period and H2O2 dosage contributed to the increase of
protein oxidation in plants. Moreover, the increase in fluorescence was not caused by
increase in the relative abundance of proteins, observed as the same intensity in
Commassie stained gels, suggesting that the 5’IAF labelled proteins were indeed modified
in some way.
An increase in fluorescence intensity was also observed in some oxidised proteins
following abiotic stress. Even though an increase of protein oxidation following 7, 9 and
11 day of drought stress were also observed, the protein profiles (based on their protein
bands produced) were different upon H2O2 treatment and drought treatment. This
suggests that plants undergo different post-translational modification responses to
different stress challenges.
To further investigate whether it is the H2O2 that contributes to these proteomic changes,
plants were treated with ASA, an agent that scavenges the production of H2O2 (Athar et
al., 2009). Subsequently, the response of oxidative sensitive proteins under low or no
H2O2 condition were examined following abiotic stresses such as PEG and NaCl. With ASA
pre-treatment, less protein oxidation was observed following treatment with PEG and
NaCl. This suggests that H2O2 is the key that leads to the modification of thiols in selected
proteins, without increasing the abundance of the protein content. This study has
provided an insight into the occurrence of thiol modifications of proteins following stress
177
stimulation of rice plants. It is nevertheless important to establish the functional
significance of the oxidative modifications of the proteins identified following stress
application.
Proteomic analysis of thiols modification using 5’IAF was first time applied in cross-talk
analysis of rice plants. This experiment clearly showed the changes of cell signalling by
H2O2, which was found elicited by cold stress, thereby offering corss-talk to the later
salinity stress treatment. This resulted in less oxidation compared to single stress of
either cold or salinity stress. The next challenge will be to identify the proteins that confer
to these changes, and to examine their role in plant stress signalling mechanisms.
5.5 Conclusion
The ability to compare the dynamic changes in the proteome is an exciting new
technology added to plant sciences. To understand the effect of H2O2 on rice seedlings,
5‘IAF coupled with 2-DE gels were developed to examine these changes in rice plants. A
total of 14 proteins were found sensitive to H2O2, in which salt stress responsive protein,
triosephosphate isomerase, mitochondrial outer membrane protein, BURP domain-
containing protein and vacuolar cation/ proton exchanger seem to be the novel proteins
that did not appear in other H2O2 treatment dataset in rice plants (Wan and Liu, 2008).
Accumulation of H2O2 in plant cells was found to induce protein modifications as seen by
treatment with osmotic and drought stress with ASA. Therefore it is obvious that H2O2
generated from these stimuli cause redox modifications of proteins. The identification of
the oxidative protein from 1D gels was not possible; therefore further experiments are
needed to perform alternative analyses to identify what these proteins are. The functions
of the modified proteins need to be evaluated to assess their role in various metabolic
pathways. A combination of H2O2 production, protein modification alongside metabolite
analyses should provide useful information to produce stress tolerant plants in the future.
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6 Chapter six: Project overview and general discussion
6.1 Background to the study
Being one of the most important crops in the world that supports more than half of the
world’s population, the deficit in rice yields has become a major concern for modern
society in order to keep up with the pace of population growth. To achieve this, a series
of useful agronomical traits were introduced into rice plants for breeding purposes
(Khush, 2003). In order to find the right genetic target to improve rice growth and yield, it
is important to determine the key signalling pathways that occur under different stress
conditions, including the cross talk adaptation that may occur. This allows identification
of common regulators of multiple stress tolerance, thereby increasing the potential to
improve breeding practices.
A similar feature observed in all stressed plants is the increment of ROS, particularly H2O2
that is well recognized as a stress signaling molecule. ROS participates not only in signal
transduction for stress adaptation, but it is also capable of modifying cellular components
and causing cellular damage (Halliwell, 2006). Therefore, it is important to examine the
cellular responses under conditions where the rise of H2O2 is observed. This evaluation is
important to assess the different possible routes of H2O2 mediated signalling pathways,
which provides useful information to underpin the significant genetic traits for breeding
more stress tolerant plants (Yamamoto et al., 2009). Therefore, work has been performed
in this study to quantify H2O2 production in rice plants under different stress conditions.
The work was followed by examination of the cellular changes under conditions where
the highest production of H2O2 was observed. This was performed to investigating the
possible correlation between H2O2production and the physiological changes observed.
6.2 General observations
Techniques such as in vitro tissue culture, metabolites and protein extraction, NMR and
MS/MS for metabolite and proteomic analysis were successfully optimized for the above
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studies. Typically, changes in cellular metabolism was observed at the beginning of the
stress treatment, revealing the activation of stress adaptation mechanisms. Espercially in
drought stress that an increment of H2O2 levels was observed over a prolonged drought
period, indicating that more damage has resulted to the plant. Under the elevated H2O2
conditions, oxidized products can cause cellular damage or act as important secondary
signaling molecules. The fate of H2O2 modified components such as proteins and
metabolites could reveal the different response strategies initiated by the plant when
subjected to the stress conditions imposed by drought, osmotic, salinity, and cold in this
study. This would further implicate a role for H2O2 in either signalling towards adaptation
or oxidative stress-related damage to the plant. In order to elucidate this, different
abiotic stresses were imposed to rice plants.
6.3 Drought stress
Following exposure to drought stress by withholding water supply, rice plants showed
morphological signs of wilting on day 5; this corroborates with the reduction in relative
water content found in the leaf. At the same time, drought stress was found to have a
significant effect on chlorophyll content, reflecting changes in photosynthetic activity
(Kura Hotta, 1987) (Figure 3.4). There wasa cler shiftin metabolite clustering on day 5 of
drought treatment (Figure 4.11), and the oxidation of free thiols was also observed in the
same period (Figure 5.10). At this time point, an increment of a group of amino acids was
observed, consisting of arginine, glutamine, alanine, valine, glutamate, isoleucine,
phenylalanine, Gaba, tyrosine, histamine and cysteine. According to Moller et al., (2007),
the changes in amino acids under stress condition act as a signalling response, or as a
mechanism to repair the damages incurred by the stress condition. Nevertheless, a shift
in metabolome was observed on day 7. The ‘shift’ was likely due to the transient
reduction of these amino acids, although their concentrations at a higher level than
control throughout the drought stress treatment (Figure 4.13). According to Palma et al.,
(2002), proteins may breakdown into amino acids for transportation into the cell through
the plasma membrane and ultimately polymerisation into new proteins for adaptation
purposes. The key indication of the early dehydration sensing was the increment of
histamine, which is a final product of histidine biosynthesis. Histidine-mediated
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overproduction of histidine kinase in dehydration tolerance has been well reported
(Wohlbach et al., 2008).
At day 7, almost two-fold increment of free thiol oxidation compared to the control was
observed. Nevertheless, a decrease in thiol oxidations was observed on day 9 of drought
treatment (Figure 5.10). The detection of low H2O2 production and the observed
reduction of thiol oxidation when the drought treatment was extended to 9 days suggest
that any H2O2 produced on day 7 were likely to have converted into more toxic radicals
such as HO• (Lee et al., 2009) or: H2O2 has been removed, or alternatively NO production
compensates for H2O2 removal (Neill et al., 2002). Other than conversion from H2O2, the
production of HO• could also result from the extensive PUFA peroxidation observed with
the decline in lipid fraction 1 and 2 on day 9 (Figure 4.13). PUFAs such as linoleic acid and
linolenic acid are the building blocks for plant thylakoid membrane (Moller et al., 2007),
therefore PUFA peroxidation would lead to the damage of thylakoid membrane, the
increase of membrane permeability (Pennazio and Sapetti, 1982) and ion leakage
(Halliwell, 2006). HO• represents the most reactive radical that could attack proteins
involved in photosynthesis such as Rubisco (Nakano et al., 2006). Interestingly, the
physiological studiesalso showed the decline in chlorophyll level compared to the control
(Figure 3.4), suggesting a decline in photosynthetic activities. This led to the reduction in
sugar metabolism such as sucrose and α-glucose, causing less carbon metabolism and
inducing carbon deficiency in plants on day 9 post-treatment (Figure 4.13).
The occurrence of lipid peroxidation was further confirmed through the measurement of
the production of malondialdehyde (MDA) – a by-product of PUFA peroxidation. The
examination of the MDA level (Figure 3.5) suggests that lipid peroxidation propagated by
attacking PUFAs results in the degradation of PUFAs into small lipid fractions (Sevanian,
1985).
The damage events that occurred following 9 days post-drought treatment seemed to be
permanent as no signs of water and photosynthesis recovery were observed. Moreover,
the lipid peroxidation remained high until the end of 13 days of drought treatment. At
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this stage, the plants appeared to have senesced considerably. The proteomic results
revealed the highest level of thiol oxidation on day 11 following the drought treatment
(Figure 5.10). This high level of thiol oxidation was reported to be irreversible and
damaging (Ghezzi and Bonetto, 2003). The result corroborates with the increment of DAB
staining (Figure 3.1) and H2O2concentration (Figure 3.2) observed on day 11.
The results from this study are in agreement with the findings from the proteomic study
of rice plants challenged with drought stress by Ali and Komatsu (2006), who described
the regulation of photosynthetic subunits such as chloroplast ATPase, Rubisco small and
large subunits, light harvesting complex chains, and photosystem complex protein after
10 days of drought stress. The increase of these proteins suggests the induction of
chloroplast stress that is in agreement with the decline of chlorophyll content in this
study (Figure 3.4).
A genomic study of rice plants following drought treatment suggests that the majority of
genes expressed under drought stress regulate cellular metabolism, signal transduction,
and protein regulation (Gorantla et al., 2007). This is in agreement with the massive
change in amino acid content, and the fluctuation in sugar and lipid metabolism that
were observed in this study. Severe water stress is expected to induce the reduction of
plant growth by initiating various physiological and biochemical processes, including
photosynthesis, respiration, translocation, ion uptake, carbohydrate and nutrient
metabolism (Jaleel et al., 2009). Such changes may pose a huge impact on the
performance and end-use of the plant.
In conclusion, the results from the physiological, metabolomic, and proteomic studies
provide a typical model of the occurrence of drought stress perception and signalling in
plants as a result of adaptation to different stress conditions up to 7 days of drought. The
reactions of plants to water stress differ significantly at various organizational levels
depending upon the intensity and duration of stress. The results also showed step-by-
step damage observed in plants through the arrest of photosynthesis, the disturbance of
metabolism, and finally the death of the plant in prolonged drought conditions.
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6.4 Osmotic stress
Using PEG as an osmotic stress treatment induced the activation of different signalling
pathways compared to drought induced by withholding water supply, resulting in more
rapid H2O2 production and alteration of different metabolomic pathways. The highest
H2O2 production was observed following 4 h of 15% PEG treatment (Figure 3.6). The
increment of H2O2 production at this time point was found to be inversely correlated with
the reduction in chlorophyll content. The reduction in total chlorophyll content was likely
due to the stomata closure that might have reduced the photosynthetic activity. ABA
signalling mediated by H2O2 inducing stomata closure is a normal phenomenon observed
under water stress in order to avoid water loss from leaves (Zhang et al., 2001). Low level
of water loss observed at this time point (Figure 3.7) suggests the occurrence of stomata
movement to protect plants from dehydration. At the same time, plants showed
downstream effect of calvin cycle such as the decrease of formate and the accumulation
of glutamate. Formate is a substrate for formate dehydrogenase, which plays a role in O2
uptake coupled to ATP synthesis (Hourton-Cabassa et al., 1998), while the accumulation
of glutamate is a biomarker for nitrogen deficiency (Miflin and Habash, 2002).
The huge accumulation of H2O2 observed between 2-4 h post 15% PEG treatment
resulted in the increment of lipid peroxidation that was maintained at a high level until 8
h post-treatment (Figure 3.9). The lipid peroxidation caused the degradation of lipids into
lipid fragments as shown by decrease of various lipid fractions in NMR experiment (Figure
4.18). To further investigate the role of H2O2 in mediating signal perception under
osmotic stress conditions, plants were pre-treated with ASA in order to scavenge the
production of H2O2. H2O2 production was found to reduce following ASA pre-treatment
compared to the treatment with PEG on its own. Less thiol oxidation was observed in ASA
treated plants (Figure 5.11), possibly due to lower amount of H2O2 reacting with free
thiols. However, a high rate of lipid peroxidation and relatively low chlorophyll content
were observed. It is thought that the toxic effect resulted from the high level of H2O2
production was not fully eliminated by ASA. It is also possible that other ROS could
contribute more towards the lipid peroxidation observed. In addition, the occurrence of
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lipid peroxidation is likely to cause damage to the thylakoid membrane and further inhibit
photosynthetic activity (Quartacci et al., 1995).
Regulation of inosine and choline seemed to be osmotic specific as they were not
identified in other stress treatments, even by H2O2 treatment (Table 3.2). Choline plays a
critical role in plant osmotic regulation while acting as a component of phospholipids
(McNeil et al., 2001). Phospholipids interact with DNA binding proteins that are needed
for DNA replication, transcription and recombination (Kazuhisa, 1994). The decrease of
choline together with the increase of inosine is interpreted as a sign of DNA repair.
Inosine is one of the purine nucleosides that are widely found in plants, and its increment
suggests the up-regulation of DNA and RNA synthesis (Stasolla et al., 2003) under osmotic
stress condition. Thus, it is likely that the DNA damage and repair system were activated
through the observation of the decrease of choline and the absence of inosine.
In sumary, the osmotic stress caused rapid dehydration compared to drought stress.
Osmotic stress affected the physiology of rice plants by first rapidly increasing the H2O2
production 4 h post-treatment. The accumulation of H2O2 increased the lipid peroxidation,
probably leading to the consequence of thylakoid membrane damage, which was
observed by reduced chlorophyll content. Due to the limited photosynthesis activity,
carbon deficiency was observed in metabolites.
6.5 Salinity stress
According to Munns and Tester (2008), the response of plants to salinity stress occurs in
two phases – a rapid osmotic phase at the beginning followed by a slower ionic phase.
This corroborates with the rapid H2O2 production observed at the initial stage of the test
followed by a reduction to a level almost comparable to the control in the subsequent
stage of the test (Figure 3.10). Stress was observed at 1 h after the treatment of NaCl,
during which the chlorophyll level of plants were found to reduce to almost half of that
shown by the control, and it remained at the low level throughout the 32 h of treatment
period. Moradi and Ismail (2007) also reported that reduction of photosynthesis activity
was observed as a result of stomata closure following the salinity stress treatment.
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However, it should be noted that the low level of chlorophyll content resulted from the
salinity treatment may not lead to membrane damage (e.g. destruction on thylakoid
membrane) as shown by PEG and drought stress treatment, considering that very low
level of lipid peroxidation was observed in the salinity treatment (Figure 3.13).
Furthermore, no sign of degradation or decrease of PUFAs (active substrates for H2O2
oxidation) was observed (Thompson et al., 1998).
The proteomic result (Figure 5.12) suggests that the thiol oxidation following NaCl
treatment was reversible and presented no harm to plants, as only permanent
irreversible oxidation of protein would produce toxic by-products that could damage the
cell of plants (Ghezzi and Bonetto, 2003). Physiologicalstudies following ASA pre-
treatment also suggest the reversibility of oxidation caused by H2O2, as shown by the
recovery of water and chlorophyll content (Figure 3.23 and Figure 3.24) and the reduction
of lipid peroxidation (Figure 3.25). Proteins oxidised under salinity conditions could also
result from the oxidation of free thiols that act as second messengers (Biteau et al., 2003).
The key metabolite change observed was the decrease of glutamate, as also reported by
Fumagalli et al. (2009), while decrease of glucose was also observed at the same time.
Changes in sugar metabolism following salinity stress treatment is a controversial issue in
the literature (Kerepesi and Galiba, 2000; Kim et al., 2007; Pattanagul et al., 2008). Higher
concentration of sugar in cytoplasm could exert a feedback inhibition on carbon
metabolism that results in lower CO2 assimilation, while the expression of Rubisco could
be repressed by excess amount of sugars in cytoplasm (Pattanagul et al., 2008). Therefore,
the reduction in chlorophyll content and metabolic alterations by reducing the sugar
accumulation may contribute to salt tolerance under salinity condition.
Interestingly, it was observed that early detection of salinity stress by the plant led to
subsquent adaptation, as seen by a correlation between the reduction in H2O2 production
at 4 h and the recovery of chlorophyll and water content at 8 h. Moreover, only few
metabolite changes were observed and this probably indicates that the dosage and NaCl
exposure period was not sufficience to cause stress on the rice plants. Thus, it is thought
that the responses observed could be due to H2O2 mediated acclimation of salt stress; the
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contribution of exogenous H2O2 to the tolerance of salinity has been reported by de
Azevedo Neto et al. (2005).
6.6 Crosstalk between stresses
Both soil grown plants and tissue culture plants produced two peaks of H2O2 when
subjected to cold stress condition, but the peaks were observed at different timings. In
this study, the first peak of H2O2 that occurred following 4 h of cold stress treatment is
thought to work as a signalling molecule in stress adaptation at the later time points. This
is derived from the unchanged level of water and chlorophyll content compared to the
control following the first peak of H2O2 (Figure 3.19 and Figure 3.20). It has been reported
that cold stress pre-treatment would induce cross-tolerance to freezing stress at a certain
time after the treatment (Hung et al., 2007). Proteomic result also showed that new
proteins were found following the introduction of cold stress treatmen (Figure 5.13). This
suggests that the production of new proteins could provide protection to plants from cold
stress.
Therefore, experiments were designed and performed in which 4 h cold pre-treatment
was applied prior to the treatment with NaCl. The physiology study showed that cold pre-
treatment reduced salinity-induced H2O2 production, but did not increase the water or
chlorophyll content (Figure 3.27 and 3.28). This indicates that the H2O2 increase elicited
by cold pre-treatment for 4 h was not fully utilised to protect the plant from subsequent
NaCl treatment. To fully control the stress tolerance mechanism, the timing of treatment
period is also a crucial issue. In another independent cadmium toxicity study it was
observed that the production of H2O2 at different timings plays an important role in
controlling the stress tolerance mechanism in rice, even though the same level of H2O2
production was observed (Hsu and Kao, 2007).
The plants treated with both NaCl and cold pre-treatment showed a higher lipid
peroxidation rate than control plants; however, they showed a lower lipid peroxidation
when compared to plants treated with NaCl on its own (Figure 3.29). At the same time,
highest thiol oxidation was observed following NaCl stress alone, whilst lowest thiol
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oxidation was observed in NaCl stress with 4 h pre-cold treatment. It has been reported
that plants would change the fluidity of the plasma membrane in order to protect
themselves from cold treatment (Uemura and Steponkus, 1999). The changing
membrane composition and its fluidity were reported as an important regulator of
photosynthesis function under cold stress (Frentzen, 2004; Jordan et al., 1983). However,
it has also been reported that the higher lipid peroxidation compared to control following
cold stress treatment may provide protection in reducing the lipid peroxidation caused by
subsequent NaCl treatment (Sardesai and Babu, 2000; Uemura and Steponkus, 1999).
Furthermore, many reports have indicated that the potential of cold stress in cross-
tolerance mainly occurs by regulation of H2O2 (Cheng et al., 2007; Hung et al., 2007;
Kazemitabar et al., 2003). Therefore this study is the first to offer more insight into H2O2
regulation of cross-tolerance between cold and salinity stress.
6.7 H2O2 induced responses and its correlation with other stresses
To analyze the H2O2 responsive pathways, plants were treated exogenously with high
dose (50 mM) and low dose (10 mM) of H2O2. The exogenous application of H2O2 is
expected to be absorbed by plants and create an environment that mimics the
production of H2O2 under stress condition. However how much of the applied H2O2
enters plant cells is not known and therefore concentrations are hard to compare, for
exogenous versus endogenous H2O2. The redox state produced following H2O2 treatment
is expected to elicit cellular changes at proteomic and metabolites level. Therefore, the
proteomic and metabolomic fluctuations following H2O2 treatment were examined at
different time points. 1 DE proteomic analysis was performed to determine the suitable
doses and the treatment period that showed the most prominent oxidative responsive
activity. This was followed by examination with 2 DE proteomic analysis.
The treatment of rice plants with low dose (10 mM) of H2O2 showed protein thiol
oxidation as early as 15 min after the beginning of the treatment (Figure 5.3). These
proteins were determined from the 2 DE analysis to be triosephosphate isomerase,
adenosylmethionine synthase, Rubisco, and ascorbate peroxidase (Figure 5.4). These
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proteins play an important role in carbohydrate, redox balance, amino acid, and
photosynthesis as indicated in Table 5.1. The results imply that the treatment with 10
mM of H2O2 may lead to chloroplastic stress that could have changed the photosynthetic
activity, either by activating protective mechanisms, or acting as signalling molecules.
NMR analysis revealed that the treatment with a low dose of H2O2 at a short period
resulted in a decrease in glutamate and glutamine and an increase of lipid fraction 4 in
leaves, and a decrease of asparagine and an increase of formate in the roots. It has been
reported that the decline in glutamate, glutmine, and asparagine in plants is an indicator
of a decrease in the nitrogen metabolism (Masclaux-Daubresse et al., 2006; Ta et al.,
1985). Amino acid synthase (i.e. adenosylmethionine synthase) has been reported to
have the ability to adjust the nitrogen pathway for pea development (Gomez-Gomez and
Carrasco, 1998). Therefore, it is thought that the detection of oxidation of
adenosylmethionine synthase in the plants in this study, combined with the proposed
decrease in nitrogen metabolism, is likely to have adjusted the nitrogen biosynthesis
pathway. The over expression of S-adenosylmethionine synthase gene has also been
shown to provide tolerance to salt streass as reported in tobacco plants (Qi et al.,(2010).
Therefore oxidative modification of S-adenosylmethionine synthase could be a
mechanism by which rice plants adapt to abiotic stress, leading to changes in nitrogen
metabolism.
When the 10 mM H2O2 treatment was extended to 1 h, there was an increase in
oxidation level of thiol-containing proteins in the treated plants compared to the control
(Figure 5.3). Interestingly, a distinct separation of metabolome was observed for plants
treated with 1 h of 10 mM H2O2 compared to the control plants (Figure 4.19) and was
later identified as a result of alteration in lipid fractions. The key protein target observed
from the proteomic study was the oxidation of Rubisco (Table 5.3). It has been shown
that the toxic effect of H2O2 targetting and oxidizing Rubisco leads to inefficient
photosynthesis (Nakano et al., 2006). In addition, limited photosynthesis may lead to lipid
peroxidation (Moradi and Ismail, 2007); this corroborates with the increase of lipid
fraction observed in both leaves and roots.
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Proteomic results following treatment with 50 mM H2O2 for 30 min (Figure 5.5) identified
the oxidation of Rubisco subunits, Triosephosphate isomerase, and cysteine synthase
(Table 5.2). These proteins were important in photosynthesis, carbon metabolism and
amino acid synthesis respectively. The result of proteomic study was found to correlate
with the massive metabolite alteration in photosynthesis and carbon metabolism (Figure
4.20). Formate has been reported to have some influence in carbon assimilation (Hanson
and Roje, 2001) and sucrose, alpha-glucose, and lactate were determined to be the by-
product of Calvin cycle and carbohydrate metabolism (Salerno and Curatti, 2003). The
influence of water stress on the alteration of carbon assimilation has been widely
reported in the literature (Gao et al., 1998; Kerepesi and Galiba, 2000; Saibo et al., 2009;
Sharkey and Seemann, 1989). The results found in this study was in agreement with those
papers that reported the shift in carbon metabolism under drought, osmotic and H2O2
treatment. H2O2 mediated alteration of photosynthesis in rice plants has been reported
by Wan and Liu (2008). The authors reported that 31% of the total proteins represented
the photosynthetic proteins that were modified under the influence of H2O2, comprising
the largest fraction of the total proteins. These H2O2-modified photosynthesis proteins
provide information on photosynthetic processes involved in H2O2 signalling. However, it
should be noted that H2O2 mediated alteration of photosynthesis function may occur via
different mechanisms. Considering that alteration of lipid fraction was observed in H2O2
treatment, it is expected that changes of membrane structure and permeability would
also occur at the same time (Domonkos et al., 2008). These could lead to ion leakage into
chloroplast (Domonkos et al., 2008) and affect the photosynthesis system (Jordan et al.,
1983). The regulation of lipid fraction could also be due to the H2O2 mediated stomata
closure, as Wan and Liu (2008) reported the decline in photosynthesis rate and stomatal
conductance in rice seedlings following H2O2 treatment. However what is not yet clear is
whether H2O2 directly modifies Rubisco via its thiol residues, thereby causing a reduction
in photosynthesis.
In addition to photosynthesis, other key mechanism that may be influenced by H2O2 are
the regulation of the GOGAT cycle, lipid modification and carbohydrate metabolism. The
GOGAT cycle was found to be regulated when the plants were subjected to drought,
salinity and osmotic stress; this implies the correlation of glutamate and glutamine
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exchange cycle with water stress regulation. The activation of GOGAT cycle as an
indicator of perturbation of nitrogen assimilation following salinity, osmotic and drought
stress treatment have been reported (Saibo et al., 2009; Soussi et al., 1998).
Alteration of lipid fraction was observed in leaf and root in all the stress treatments,
particularly the lipid fraction 1, 2, 4 and 5. The regulation of PUFAs observed in this study
indicates the importance of lipoxygenase (LOX), which is a key enzyme that catalyses the
oxidation of fatty acids (Rhoads et al., 2006; Rosahl, 1996). LOX has been identified in
plants under water and wounding stress conditions (Bell and Mullet, 1991), however, the
correlation of LOX and H2O2 in plant stress signalling is yet to be investigated.
6.8 Summary of metabolic pathways affected
Figure 4.25 illustrates the global metabolite network activated under drought, osmotic,
salinity and H2O2 treatments and the metabolites were involved in three major functions,
including energy and photorespiration metabolism, lipid metabolism, and amino acid
metabolism.
Energy and photorespiratory metabolism
: Drought and H2O2 stress were found to have a
strong influence on the energy-related metabolism in rice plants, such as the changes of
glucose and sucrose in leaves and roots. Glycolysis provides energy for plants to survive
drought and hence may interrupt the energy used for other purposes such as growth and
productivity.
Lipid/ fatty acid metabolism:
1995
In particular, lipid fraction from 1 to 7, which consists of
PUFAs and fatty acids, were detected under drought, osmotic, and H2O2 treatment,
suggesting that the alteration of these lipid fractions may be derived from the attack of
H2O2 on the double bonds in the lipid structure. According to Quartacci et al. ( ), the
alteration of lipid membranes was found to have an influence on the composition of
thylakoid membranes and may lead to senescence (Thompson et al., 1998). The
alteration of sphingolipids, PC, PG and DAGP was found to be initiated independent of
H2O2 (Table 4.3). This implies that they play a different role than the PUFAs and fatty acid
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fractions, e.g. involvement in stress signalling (Darwish et al., 2009), change of membrane
structure and ion channel changes under stress conditions (Tucker and Baukrowitz, 2008),
or photosynthesis regulation (Frentzen, 2004).
Amino acids:
Joshi, 2010
A broad range of amino acids were observed to be induced in the drought
stress treatment, while some of them also showed response to osmotic stress treatment.
Glutamate, which acts as a stress biomarker, was observed in all stress treatments.
Changes in asparagine, threonine, glutamine, alanine, and histamine were observed
following osmotic and drought treatment, indicating the involvement of these molecules
in response to water stress. The results also revealed the influence of water stress on
nitrogen metabolism. In this study, BCAAs were found increase only in drought stress
samples, this has been suggested as the result of protein turnover ( ).
In conclusion, this study has been performed with the aim to observe H2O2 production
under different stresses or combination of stresses and its consequence on the
physiology side of the plants at a cellular level. The results discussed the possible roles of
H2O2 and the fate of proteins and metabolite pathways in relation to plant stress
adaptation. However, future studies could be performed to further verify the function of
H2O2 in plant stress signalling mechanism.
6.9 Future studies
1. Identification of oxidative sensitive proteins.
In this study, a technique to identify oxidative modified proteins was optimized and
applied to rice plants. Proteins have only been identified in response to H2O2 treatment,
therefore it is envisaged that future work could be performed using this technique to
identify the thiol modified proteins following various stress treatments. The early
proteomic studies here show a differential modification of proteins following various
stresses. A different approach of using isotope-coded affinity tag (ICAT) (Griffin et al.,
2001) could also be adopted to further verify the result obtained under 2 DE. The work
may allow the identification and a more complete characterisation of H2O2 responsive
proteins, which is believed to play an important role in stress signal transduction. The
191
identification of the protein biomarkers provides clues as to which metabolic pathways
are involved in specific stress adaptation and also provides candidate markers for
developing new stress-tolerant crops in the future.
2. Cross-tolerance between stresses
The results of cold and salinity treatment suggest the possible occurrence of cross-
tolerance via H2O2 between stresses. The co-interaction of stresses is a common feature
in nature and it may have an influence on how the plants react to stress, and thus
inducing different responsive pathways compared to the application of single stress.
Hence, future studies could investigate the cross-tolerance between multiple stresses in
order to mimic more closely the stressed environment that occurs in nature. It is also
envisaged that further experiments could be performed in order to identify the type of
lipids that may change following the treatment of cold stress. This evaluation may
provide useful information in plant cross-tolerance. In addition, various H2O2 production
times could be further tested in order to identify the most efficient level of cold stress
crosstalk (Hsu and Kao, 2004).
3. Confirmation of the proposed biological pathways under different stress conditions
A great number of metabolites and proteins were found to be altered under drought,
salinity, and osmotic treatments, and the corresponding changes of the physiological
status of plants were also presented and discussed. Possible stress responsive and
signalling pathways that account for these changes have been proposed and thus it is
envisaged that future work could be performed to confirm the stimulation of these
processes under a certain stress condition. This could be achieved by measuring the
enzymatic activities of proteins and corresponding changes in targeted metabolites in
vivo. Future work will also involve a genetic approach targeting key genes that regulate
multiple stress responses in rice.
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