Proteomic Markers for Oxidative Stress: New Tools for ... - Springer Link

Proteomic Markers for Oxidative Stress: New Tools for Reactive Oxygen Species and Photosynthesis Research

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Ruby Chandna, Khalid Ul Rehman Hakeem, and Parvaiz Ahmad

Abstract

Abiotic stresses are the primary causes of crop loss worldwide. Photosynthesis is an important phenomenon that is particularly affected towards reactive oxygen species, generated during any stress condition in an organism. Oxidative stresses in plants leads to debilitation and death or to response and tolerance. The sub-cellular energy organelles (chloroplast, mitochondria and peroxisomes), responsible for major metabolic processes including photosynthesis, photorespiration, oxidative phosphorylation, b-oxidation and the tricarboxylic acid cycle, are much affecting centers in a plant cell by oxidative stresses. Plant adaptation to environmental stresses is dependent upon the activation of cascades of molecular networks involved in stress perception, signal transduction, and the expression of specific stress related genes and metabolites. Progress in genomics, proteomics and metabolomics results in more understanding of global cellular responses to oxidative stress on transcript, protein, and metabolite levels. Elucidating the function of proteins expressed by genes in stress tolerant and susceptible plants would advance our understanding of plant adaptation and tolerance to environmental stresses, but also may provide important information for designing new strategies for crop improvement.

R. Chandna Molecular Ecology Lab, Department of Botany, Jamia Hamdard, New Delhi 110062, India National Institute of Plant Genomics and Research, Aruna Asif Ali Marg, New Delhi 110070, India K.U.R. Hakeem () Molecular Ecology Lab, Department of Botany, Jamia Hamdard, New Delhi 110062, India e-mail: [email protected] P. Ahmad Department of Botany, A.S. College, University of Kashmir, Srinagar 190008, India e-mail: [email protected] P. Ahmad and M.N.V. Prasad (eds.), Abiotic Stress Responses in Plants: Metabolism, Productivity and Sustainability, DOI 10.1007/978-1-4614-0634-1_10, © Springer Science+Business Media, LLC 2012

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Keywords

Abiotic stress • Proteomics • Plant responses • ROS • Gene expression • Transgenics

1

Introduction

Plants are exposed to different abiotic stresses, such as high temperature, cold, drought, water deficit, salinity, heavy metals and mechanical wounding under field conditions. It is estimated that the yield of crop plants can potentially reduce by more than 50% under such stress conditions. Plants encountered by various stress conditions acts differently to reduce or minimise the adverse effects caused by these unfavourable conditions. Thus, molecular, biochemical and physiological processes activated by a specific stress condition might differ from those activated by a slightly different composition of environmental parameters. Therefore, a plant builds specific response in the given environmental conditions that it encounters. Transcriptome profiling studies of plants subjected to different abiotic stress conditions illustrated that various stress condition prompts a somewhat unique response, and little overlap in transcript expression could be found between the responses of plants to abiotic stress (Friso et al. 2004). Generally with the advent of any stress condition, reactive oxygen species/ intermediates (ROS/ROI) are produced. This is a family of many oxidative radicals which normally damage the cells and the whole plant. Reactive oxygen intermediates (ROIs) results from the excitation of O2 to form singlet oxygen or from the transfer of one, two or three electrons to O2 to form, respectively, a superoxide radical (O2−), hydrogen peroxide (H2O2) or a hydroxyl radical (HO−) (Shulaev and Oliver 2006). ROIs are capable of unrestricted oxidation of various cellular components irrespective of atmospheric conditions and can lead to the oxidative destruction of the cell (Melgar et al. 2009). There are many potential sources of ROIs in plants (Table 10.1). NADPH oxidases, amine oxidases and cell-wall-bound peroxidases are some of the new sources of ROIs have been identified in

plants recently. They are involved and in the production of ROIs during processes such as programmed cell death (PCD) and pathogen defense (Mittler 2002). In addition to the enhanced production of ROIs during stress that can pose a threat to cells, ROIs also act as signals for the activation of stress-response and defence pathways (Mittler et 2006). Thus, ROIs can be viewed as cellular indicators of stress and as secondary messengers involved in the stress-response signal transduction pathway. ROI-induced cell death can result from oxidative processes such as protein oxidation, membrane lipid peroxidation, enzyme inhibition and nucleic acid damage. ROIs participate in signaling events by at least two different mechanisms, to regulate their intracellular ROI concentrations by scavenging of ROIs: fine modulation of low levels of ROIs for signaling purposes, and second that will enable the detoxification of excess ROIs, especially during stress (Melgar et al. 2009). Therefore, ROI scavenging mechanisms can change drastically depending upon the physiological condition of the plant and the integration of different environmental, developmental and biochemical stimuli. In plants, the reaction centres of photosystem I (PSI) and photosystem II (PSII) in chloroplast thylakoids which are the major generation site of reactive oxygen species (ROS). PSII is the catalytic centre that generates the oxygen in the atmosphere from light-driven oxidation of water (Barber 2008). Therefore, the photo production of ROS is largely affected by physiological and environmental factors and a target of many abiotic stress conditions such as heavy metals, photoinhibition, drought, ozone, high and low temperature. The photosynthesis rate is enhanced under the conditions where photon intensity (P) is in excess of that required for the CO2 assimilation. More recently some workers have shown that pathogen, such as virus and fungi, disturb the PSII photochemistry and induce photoprotective

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Proteomic Markers for Oxidative Stress...

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Table 10.1 Producing, scavenging and avoiding reactive oxygen intermediates in plants Mechanism Production Photosynthesis ET and PSI or II Respiration ET Glycolate oxidase Excited chlorophyll NADPH oxidase Fatty acid b-oxidation Peroxidases, Mn2+ and NADH Scavenging Superoxide dismutase Ascorbate peroxidase Catalase Glutathione peroxidise Peroxidases Ascorbic acid a-Tocopherol Carotenoids Avoidance Anatomical adaptations C4 or CAM metabolism Chl movement Suppression of photosynthesis PS and antenna modulations Alternative oxidases

Localization

Primary ROI

References

Chlorophyll Mitochondria Peroxisomes Chlorophyll Plasma membrane Peroxisomes Cell wall

O2− O2− H2O2 O2− O2− H2O2 H2O2, O2−

Asada (1999) Dat (2000) Asada and Takahashi (1987) Corpas (2001) Grant and Loake (2000) Corpas (2001) Grant and Loake (2000)

Chlorophyll, peroxisomes, cytochrome, apoplast Chlorophyll, peroxisomes, cytochrome, apoplast Peroxisomes Cytosol Cell wall, cytosol, vacuoles Chlorophyll, peroxisomes Membranes Chlorophyll

O2−

Bowler (1992)

H2O2

Asada (1999)

H2O2 H2O2, ROOH H2O2 H2O2, O2 ROOH, O21 O21

Willekens (1997) Dixon (1998) Asada and Takahashi (1987) Dixon (1998) Asada and Takahashi (1987) Asada and Takahashi (1987)

Epidermis Chlorophyll Chlorophyll Chlorophyll Chlorophyll Chlorophyll

O2−, H2O2 O2−, H2O2 O2−, H2O2 O2−, H2O2 O2−, O21 O2

Mullineaux and Karpinski (2002) Mittler et al. (2001) Mullineaux and Karpinski (2002) Mullineaux and Karpinski (2002) Mittler et al. (2001) Mullineaux and Karpinski (2002)

Mittler (2002); Trends in Plant Science

mechanisms in order to preserve the integrity of this complex in the host plant (Asada 2006). Thus, PSII is a target within the photosynthetic apparatus for both biotic and abiotic stress conditions. Under both stress situations a common response pattern occurs consisting of the enhancement of non photochemical-quenching related to energy dissipation before being trapped by the PSII reaction centre (Hideg et al. 2002). Photosynthetic chain supplies metabolic energy and redox power for CO2, nitrate and sulphate assimilation by plants, thus a healthy photosynthetic activity is critical for the plant growth and nutrition (Chaves et al. 2009). Under normal growth conditions active oxygen species (AOS) are formed at low rate in photosynthetic cells as by-products of aerobic metabolism, but many stresses can produce a dramatic increase in the rate of AOS production, being the chloroplasts the major AOS source in the leaf cells. An efficient removal of AOS from chloroplasts is critical since H2O2 concentrations as low

as 10 mM can inhibit photosynthesis by 50% (Tuberosa and Salvi 2006; Sanchez et al. 2007). Chloroplasts in higher plants contain photosynthetic thylakoid membranes with four multisubunit protein complexes (PSI, PSII, ATP synthase, and cytochrome b6f complexes), each with multiple cofactors (Chaves et al. 2009). These four complexes are composed of at least 70 different proteins that perform the biogenesis, maintenance, and regulated breakdown of the photosynthetic complexes (Wollman et al. 1999). Abiotic and biotic stresses bring about numerous changes in the protein expression. The thylakoid membrane system also must adjust to changes in stress conditions. This requires short-term responses, such as state transitions and a build up of quenching components, whereas long-term responses bring change of PSI/PSII ratios (Aro and Andersson 2001). These regulators are typically expressed at much lower levels than components of the photosynthetic apparatus, making

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their biochemical identification quite challenging (Zhou et al. 2007). Thus, the thylakoid membrane system must be protected against abiotic stresses, of which oxidative stress is one of the most prominent (Friso et al. 2004). Oxidative damage results from incomplete detoxification of ROS. To prevent and respond to oxidative stress, an antioxidative defense system is expressed in the chloroplast, consisting of proteins and scavenging molecules (Froehlich et al. 2003). Several of these proteins like thylakoid ascorbate peroxidase (Yabuta et al. 2002), three M-type thioredoxins (IssakidisBourguet et al. 2001), peroxiredoxins (Konig et al. 2002), and superoxide dismutases (SODs) are associated with the thylakoid membrane. These proteins are with expression levels at two to three orders of magnitude lower than the photosynthetic apparatus (Peltier et al. 2002). If protection of the thylakoid membrane system is not complete, damage to components such as proteins, lipids, and cofactors will occur, leading to irreversible changes. Cold stress or drought, combined with high light conditions, result in enhanced production of ROS by the photosynthetic apparatus because these conditions limit the availability of CO2 for the dark (Sharkey 2005). So, there is a need to understand the basis of stress tolerance, the diversity of the stress response and its utility for the survival of plants. As the plants sense the change in environmental conditions there is an initial perception, a signal is relayed via several signal transduction cascades (Melgar et al. 2009). Various strategies have been employed to isolate the genes that are involved in the stress response. Information about stress-responsive genes has been obtained largely using conventional approaches. However, the challenge still remains to integrate the function of these genes logically to generate a global understanding of the stress response process (Valliyodan and Nguyen 2006). Plant cells defend against stresses by altering their expression of genes and, consequently, their proteome. Gene expression varies according to the type and severity of the stress and the developmental stage of the plant. Analysis of the proteome is a powerful tool for linking gene expression to cell metabolism. It also provides the possibility of studying individual organelles

within the cell (Vinocur and Altman 2005). Proteomics, with its growing collection of technologies for extraction and identification of proteins and for studies of their interactions, permits the elucidation of the mechanisms that are involved in the responses of cells to abiotic stresses. In this review, the use of such proteome and genome-wide strategies in understanding the basis of abiotic stress tolerance is discussed.

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Tailoring a Response to a Particular Stress Situation

Global expression profiling reveals the information of the initial repertoire of genes involved in the abiotic stress response that can be used to establish the roles of genes in abiotic stress and to identify their mechanisms of action (Lin et al. 2004). This step acts as a sieve to identify a smaller subset of genes which can be employed to draw up a final list of candidate genes using a multipronged strategy dependent on several check points, including the association with abiotic stress-related QTLs, mutant phenotypes and alleles from wild relatives (Valliyodan and Nguyen 2006). Several components of regulatory systems are activated on the perception of stress. This leads to the production of effecter molecules which are directly involved in mitigating stress. Regulatory systems are represented by transcription factors and signal transduction components such as kinases and phosphatases (YamaguchiShinozaki and Shinozaki 2006). Studies done by Liu et al. (1998) and Nakashima et al. (2000) showed that the ABA-independent regulatory cascade is mainly represented by the DREB1 (cold) and DREB2 (salt, dehydration) families of proteins. Another major abiotic stress signal transduction pathway is the ABA-dependent pathway. This pathway is mainly associated with the bZIP class of transcription factors called ABRE-binding factors, ABFs (Choi et al. 2000; Vinocur and Altman 2005). Vij and Tyagi (2007) studied the expression profiling of mutants and transgenics overexpressing transcription factors can aid in an understanding of their mechanism of action. They showed that the analysis of the CBF regulon in Arabidopsis revealed 306 cold-respon-

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sive genes; only 70% were part of the CBF regulon which included 15 transcription factors. ZAT12 has been identified as a new cold-responsive regulon which functions as a negative regulator of CBF2 (Vogel et al. 2005). In addition, studies with another cold-responsive regulon, i.e. ICE1, have shown that it acts as a master switch controlling many CBF-dependent and CBFindependent regulons (Lee et al. 2005). Microarray analysis performed for transgenic Arabidopsis plants constitutively expressing DREB2A, a key transcription factor involved in salt and drought stress regulation showed 21 genes were upregulated by DREB2A over-expression (Sakuma et al. 2006). Such microarray analyses to decipher abiotic stress regulation have also been performed for signalling components, such as kinases and phosphatases (Umezawa et al. 2004; Osakabe et al. 2005). The significance of this approach is highlighted by the availability of several reports on contrasting stress-tolerant relatives of Arabidopsis, tomato, barley and maize (Bohnert et al. 2006). Similar studies performed by Vinocur and Altman (2005) to compare the gene expression patterns in desiccation-tolerant (ice plant) and non-tolerant plants have shown that the difference is in the expression pattern, irrespective of presence or absence of particular genes. The complete genome sequence of rice and Arabidopsis and emerging sequence information for several other plant genomes, have given rise to the use of tools which can aid in the determination of the function of many genes simultaneously (The Arabidopsis Genome Initiative 2000; International Rice Genome Sequencing Project 2005). Functional genomics employs multiple parallel approaches, with the use of mutants and transgenics coupled with transcript profiling, to study gene function in a high throughput mode (Kamal et al. 2010). This change in the approach to the study of the abiotic stress response has undoubtedly reduced the time of completion of an otherwise arduous task in plants with well-established enomics platforms, such as Arabidopsis (Denby and Gehring 2005; Yamaguchi-Shinozaki and Shinozaki 2006). Besides molecular approaches, proteomics, based on the recent developments of 2DE, Mass spectroscopy and bioinformatics, offers a com-

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plementary insight into protein expression and regulation within abiotic and biotic stresses in plants (Kamal et al. 2010). Proteins are physically and chemically much more diverse than nucleic acids, which hinders the quantitative analysis of complex samples of proteins. In addition, due to different RNA splicing and post-translational modifications, it is expected that for a given organism the number of protein species exceeds several folds the number of genes. Another level of complexity arises when considering the potential number of protein–protein interactions in an organism modified by developmental events and physiological constraints (Shulaev and Oliver 2006). After reviewing past and recent studies, the potential of proteomics for an integrated understanding of the processes involved will provide a basis for future proteome comparisons of biotically and abiotically challenged plants

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Proteomics: The Analysis of Genomic Complements of Proteins

Proteomics, the systematic analysis of (differentially) expressed proteins, is a tool for the identification of proteins involved in cellular processes – has burst onto the scientific scene with stunning rapidity over the past few years, perhaps befitting a discipline that can enjoy the virtually instantaneous conversion of a genome sequence to a set of predicted proteins (Toorchi et al. 2009). Every fragment of DNA behaves biochemically much like any other, proteins possess unique properties, and such individuality creates an enormous hurdle for methodologies that seek to assign an activity to sets of proteins that may number in the thousands (Nouri et al. 2011). Proteomics is a powerful tool for investigating the molecular mechanism/s of plant response/s towards different stress conditions, and it provides a path for increasing the efficiency of indirect selection for inherited traits (Fig. 10.1). Besides the enzymes, transport and regulatory proteins are also involved in combating any adverse condition, which makes the proteome an essential topic for studying metabolic pathways. Proteomics technology is based on high-throughput techniques for the

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GENOME - WIDE INFORMATION

Sequence, ESTs, markers

GLOBAL EXPRESSION ANALYSIS OF ABIOTIC STRESS RESPONSE IN SENSITIVE AND TOLERANT PLANTS Microarray, SAGE, MPSS, 2DGE, MALDI-TOF, Yeast two-hybrid

VALIDATION OF FUNCTION AND STRESS RESPONSE MECHAN IS MS

Overexpression, Knockout, Antisense, RNAi, Allel/QTL mapping

GENETIC ENHANCEMENT OF CROP PLANTS

Molecular Breeding/ Transgenics

VARIETY DEVELOPMENT AND FIELD TRAILS

Fig. 10.1 A typical functional genomics approach to improve crop performance under abiotic stress conditions. 2GE two-dimensional gel electrophoresis, EST expression sequence tag, MALDI-TOF matrix assisted laser desorption/ionization-time of flight, MPSS massively parallel signature sequencing, QTL quantitative trait locus, SAGE serial analysis of gene expression (Vij and Tyagi 2007)

separation and identification of proteins, allowing an integral study of many proteins at the same time. For the separation of protein mixtures the most powerful technique available is two-dimensional polyacrylamide gel electrophoresis (2D-PAGE): after separation, proteins can be subsequently iden-

tified by mass spectrometry (mass spectrometry) (Vij and Tyagi 2007). The increasing amount of genome sequence data has to be followed by deciphering the function of the genes and proteins. Studying differential expression by proteomics is a complementary tool for functional analysis. Several proteomic studies have been performed on the responses of plants to various abiotic stresses, knowledge about the stresses leading to improving plant tolerance is inadequate (Table 10.1). In studies on stress, it should be noted that under natural conditions adverse environmental factors are almost never present as individual entities; on the contrary, they tend to occur together, a factor that should be considered when designing comprehensive studies (Sobhanian et al. 2010). The involvement of several signalling molecules and the activation of signalling pathways in the cells under stress conditions have been partly studied, but an understanding of the signal transduction pathways that mediate responses to stresses remains a challenge (Ahsan et al. 2010). The application of highthroughput proteomics approaches is expected to accelerate progress in our understanding of these signalling elements. Elucidation of the signalling networks through proteomics should pave the way for more rational engineering of stress tolerant soybean plants (Nouri and Komatsu 2010). Proteomics experimental plan consists of four steps: sample preparation, protein separation, identification and function analysis (Fig. 10.2). The important features of proteome analysis are high-throughput techniques for protein separation and identification. 2D-PAGE is the method of choice for separating proteins from a complex mixture in a fast and reproducible way. This technique is able to separate thousands of proteins in single experiment. Subsequently, the separated proteins can be analysed by MS for protein identification. In order to reduce the manual handling steps and to increase the high-throughput of proteomics, combinations of capillary electrophoresis, High Performance Liquid Chromatography (HPLC) and MS are being developed (Rohde et al. 1998; Manabe 1999). Two-dimensional (2D) electrophoresis (O’Farrell 1975), separates polypeptides according to their pI

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Stressed

Control Protein extraction, rem obilization and digestion

PROTEIN EXTRACTION AND SAMPLE PREPERATION ISOELECTRIC FOCUSSING REDUCTION AND ALKYLATION

SDS PAGE (2nd -dimenstional), VISUALIZATION (Coomassie BB/Silver staining) pH 3-10

pH 3-10 Repressed Induced

Over expressed Absent

Protein identification

DIFFRENTIALLY EXPRESSED SPOTS EXCISED AND DIGESTED

HPLC/MASS SPECTROSCOPY AND PROTEIN IDENTIFICATION

LC-MS PEPTIDE MASS FINGERPRINTS OF TRYPTIC DIGEST OF DIFFERENTIALLY EXPRESSED PROTEINS AND ITS IDENTIFICATION USTING MASCOT SOFTWARE

Fig. 10.2 Schematic overview of proteomics

in the first dimension (isoelectro-focalization) and to their molecular weight in the second dimension (SDS-PAGE), has been the most powerful technique for the separation of complex mixtures of proteins. Methods of identification MALDI-TOF (matrix-assisted laser desorption-ionization-time of flight) instruments allow the high-precision measurement of the masses of peptides resulting from the digestion of a protein by an endoprotease. The masses of several peptides can be compared with those predicted from sequences in databases. This technique of fingerprinting is particularly applicable for proteins from organisms with genomes or cDNAs entirely or largely sequenced.

The peptides isolated after proteolytic cleavage are fragmented by collision in the MS instrument, and the masses of the fragments are measured. They can be compared to the masses expected from databasestored sequences, and sequence stretches can be deduced from the mass differences between fragments ranked in order of size. Although ESI-MS/ MS is not yet a high-throughput technique, it is more appropriate than MALDI-TOF for the identification of proteins from organisms in which few cDNAs have been sequenced: micro-sequences allow cross-species identification, while sequence polymorphism limits the efficiency of MALDITOF fingerprinting (Nouri and Komatsu 2010).

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3.1

Sub-cellular Proteome Analysis of Plants

An adverse abiotic environmental condition is cause of severe oxidative stress in plants, leading to debilitation and death or to response and tolerance. The sub-cellular energy organelles (chloroplast, mitochondria and peroxisomes) in plants those are responsible for major metabolic processes including photosynthesis, photorespiration, oxidative phosphorylation, b-oxidation and the tricarboxylic acid cycle (Aken et al. 2009). Over the past few years a number of studies have begun to characterize the expressed proteins of plant including whole plant/organ proteomes and the subcellular proteomes of organelles such as the chloroplast mitochondrion, peroxisome and nucleus (Lilley and Dupree 2007). These sub-cellular proteomic studies are a greater dynamic range of proteins are often able to be identified since they generally have the advantage of assessing less complex protein subsets than whole tissue extracts. This range of proteomics studies has provided a basis for the establishment of quantitative proteomic through which the protein profile of plants exposed to abiotic stress, pathogen attack or mutation can be acquired (Komatsu and Ahsan 2009). Stresses significantly alter plant metabolism, growth and development and ultimately lead to plant death. Plants in order to survive in these adverse conditions have developed various responses. The signal transduction pathways that elicit these responses, or the way in which plants perceive these environmental stresses, are not well understood. Proteomics is already proving to be a valuable tool in the functional characterization of plants to interpret the stress response of plants (Hongsthong et al. 2009). A number of proteomics studies have assessed the affect of environmental stress on organelle proteomes. Each organelle of a plant cell generally has its own specific functions in addition to communicating with other parts of the cell. A study of an enriched fraction of a desired organelle offers many advantages in proteome research. The analysis of proteome of organelles and sub-cellular fractions is one of the most informative approaches to the functional analysis of living cells. The study of

cell organelles is usually subject to two major constraints: the availability of an appropriate purification technique, and the verification of the purity of extract (Nouri et al. 2011). The evaluation of the purity or the degree of contamination of organelle extracts is necessary step; otherwise any novel proteins identified by proteomics analyses cannot be definitely assigned to a particular organelle (Komatsu and Ahsan 2009). Mitochondria, chloroplasts and peroxisomes are involved in either the reduction of oxygen or the oxidation of water as part of their normal metabolic activity. Because of this, these are known to be significant sources of ROS in plant cells, even under optimal growth conditions (Nouri et al. 2011). However, under extreme conditions these ROS synthesis rates can increase, inducing an oxidative stress in both organelle and on whole cell functions. Modification of ascorbate/glutathione cycle components, SODs, peroxiredoxins, and catalase indicate significant changes in ROS levels in each of the organelles during environmental stress. Changes in abundances of the mitochondrial (At2g05710 and At4g26970) isoforms of the ROS sensitive enzyme aconitase were observed to occur following exposure to cadmium and salt (Friso et al. 2004). In chloroplasts, the reaction centers of PSI and PSII in the thylakoid membrane are the main sites of ROS generation. Photoreduction of oxygen to superoxide occurs in PSI (following the Mehler reaction), in PSII, oxygen of the ground (triplet) state of oxygen (3O2) is excited to the excited singlet state of oxygen (1O2) by the P680 reaction center chlorophyll (Chl) (Bae et al. 2003). The photo production of ROS is affected by physiological and environmental factors. The rate is enhanced under the conditions where photon intensity is in excess of that required for the CO2 assimilation. During condition of photon excess, a variety of systems suppress ROS production in chloroplasts including photorespiration, the cyclic electron flow through PSI or PSII, and the down-regulation of PSII quantum yield by the xanthophyll cycle and the proton gradient across the thylakoid membrane (Kamal et al. 2010). Thus the chloroplast has the potential to be a variable source of ROS during environmental

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stress, and much of the damage to chloroplast proteins during stress may be linked to its changed rate of ROS synthesis (Taylor et al. 2009). Superoxide produced in mitochondria by peripheral single electron transfers from reduced components in the respiratory electron transport chain (ETC) to oxygen, the ubiquinone pool and components in complex I and III have been implicated in mitochondrial superoxide production (Aken et al. 2009). It is observed that 3–5% of oxygen consumption by mitochondria is due to single electron superoxide formation, while the majority of oxygen consumption is four electron reduction of oxygen to water (Xu et al. 2006). The rate of superoxide production by mitochondria depends on the redox poise of ETC components, and on the concentration of oxygen. During hypoxic conditions, ROS production by mitochondria is low, and is elevated when respiration inhibitors block the ETC and cause over-reduction of earlier components, and can be altered by environmental factors of chemicals that alter the rate of these peripheral electron transfer reactions. Notably, nitric oxide is a potent inhibitor of the mitochondrial ETC and its generation during plant stress may be critical in the elevation of ROS production from mitochondria in plants, just as it is in animals (Taylor et al. 2009). Peroxisome is a house of variety of oxidases that reduce oxygen to hydrogen peroxide and concomitantly oxidize a variety of compounds; these enzymes include urate oxidase, glycolate oxidase, xanthine oxidase and acyl-CoA oxidases. Peroxisome ROS formation is a stoichiometric and linear consequence of the rate of the metabolic pathways containing hydrogen peroxide-producing enzymes. Acyl-CoA oxidases operate in b-oxidation of fatty acids and hormones which will vary depend on the stage of plant development, while glycolate oxidase operates in the photorespiration pathway which depends on light, the photosynthetic rate, and the CO2 concentration in the tissue (Agrawal et al. 2010). Studies done by Hoa et al. (2004), on soybean organelles performed for mitochondrial fractions from roots and nodules, peribacteroid membranes (Panter et al. 2000), and etiolated cotyledon peroxisomes (Arai et al. 2008). In relation

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to abiotic stress responsive proteins in plant organelles and subcellular fractions, the effects of osmotic stress on the plasma membrane, of flooding on the cell wall and plasma membrane, and of ozone on chloroplasts have also been focussed. In proteome analysis of sub-cellular organelles, the purification of protein extracts and the verification of their purity determine the validity of the results. Plasma membrane extracts were purified by using a two-phase partitioning method and their purity was verified by measurement of the activity of P-type ATPase (Komatsu et al. 2009; Nouri and Komatsu 2010). A cell wall fraction was obtained by using calcium chloride, and its purity was confirmed by assaying the activity of glucose-6-phosphate dehydrogenase (Komatsu et al. 2010). Chloroplasts from soybean leaves were purified by using a Percoll gradient, and their purity was assayed by immunoblot analysis using specific antibodies (Ahsan et al. 2010). Although techniques for organelle proteomics continue to be improved, achieving to a pure fraction free of contaminants from other parts of the cell remains a challenging problem. This aspect is particularly important under stress conditions where several proteins, such those related to quality control, defense, or metabolism, can migrate through secretary pathways in the cell to cope with the imposed stress. In such cases, protein localization may be capable of verifying the existence of a given protein in a specific fraction (Lilley and Dupree 2007). Completion of annotation of the important crop plants genome and the corresponding database information should result in further improvements and the analysis of the proteome of subcellular organelles.

3.2

Proteomics to Identify Targets Beyond the Gene

Analysis of the proteome provides a direct link of genome sequence with biological activity (Pandey and Mann 2000; Agrawal and Rakwal 2006). It helps us to understand analysis of the proteome including the knowledge of entire protein repertoire as well as the expression levels, post-translational modifications and interactions, to understand

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the cellular processes at the protein level (Peck 2005). The combination of (MS) with twodimensional gel electrophoresis (2DGE) in the 1990s proved to be useful for proteome analysis. Matrix-assisted laser desorption/ionization-time of flight (MALDI-TOF) and electrospray ionization (ESI) are the two most commonly used MS techniques (Mann and Pandey 2001). Bae et al. (2003) used 2DGE and MALDI-TOF-MS to study the Arabidopsis nuclear proteome and changes in the nuclear proteome in response to cold stress. One hundred and eighty-four protein spots were identified, with the expression of almost 30% of these proteins altered in response to cold stress. These included several proteins previously reported to be involved in stress, including heat-shock proteins, transcription factors (AtMYB2 and OBF4), DNA-binding proteins (DRT102 and Dr1), catalytic enzymes (phosphoglycerate kinase, serine acetyltransferase and glyceraldehyde-3-phosphate dehydrogenase), syntaxin, calmodulin and germin like proteins. Salekdeh et al. (2002) studied the proteome analysis of rice and the changes in response to drought stress. More than 1,000 protein spots were identified on 2DGE, with the expression of 42 proteins altered by stress. The identities of 16 of these drought-responsive proteins were established by MS. Similarly, studies performed by Cui et al. (2005) on rice cold stress proteome, proteins from unstressed seedlings were compared with those from seedlings exposed to temperatures of 15, 10 and 5°C. Of a total of 1,700 protein spots separated by 2DGE, 60 proteins were up-regulated with a decrease in temperature. The identities of 41 of these proteins were established by MALDITOF-MS or ESI/MS/MS, and these mainly included chaperones, proteases, detoxifying enzymes, and enzymes linked to cell wall biosynthesis, energy pathways and signal transduction. The results obtained emphasize the importance of maintaining protein quality control via chaperones and proteases, together with an increase in cell wall components, during the cold stress response. The availability of a rice proteome database (http://gene64.dna.affrc.go.jp/RPD) which catalogues information from 23 reference maps of 2DGE analysis of proteins from diverse biological samples. The database contains, in total, 13,129

identified proteins and the amino acid sequences of 5,092 proteins (Komatsu 2005). Salt stress-responsive proteins were also studied in the rice root proteome. Around 54 proteins, whose expression changed in response to salt stress, were obtained. Twelve among them were identified by MS, 50% of which were novel saltresponsive proteins, such as UGPase, Cox6b-1, GS root isozyme, a-NAC, putative splicing factor-like protein and putative ABP (Yan et al. 2005). In the study of tobacco leaf apoplast proteome in response to salt stress, 20 proteins were identified whose expression has changed in response to stress. These included several wellknown stress associated proteins, together with chitinases, germin-like protein and lipid transfer proteins (Dani et al. 2005). In addition to this, a large number of other interactions were established for proteins, involved in different stress conditions and, on many occasions, this interaction network also overlapped with proteins involved in development, showing the complexity of the stress response using proteome analysis studies (Vij and Tyagi 2007; Kamal et al. 2010). A large number of bioinformatics tools are available for plant proteome analysis. These include the Proteins of Arabidopsis thaliana Database (PAT) (http:// www.pat.sdsc.edu/), MIPS Arabidopsis thaliana Database (MAtDB) (http://mips.gsf.de/proj/thal/db) and Rice Proteome Database (RPD) (http:// gene64.dna.affrc.go.jp/RPD/main_en.html).

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Towards Making Plants Abiotic Stress Tolerant

It must be kept in mind that the basic task of the identification of key gene(s), whose manipulation will ultimately affect crop performance in response to abiotic stress, is highly complex and difficult to decipher because of the polygenic nature of the abiotic response. In addition, the plant’s response to each stress is unique, and thus the response to multiple stresses will also be different. Indeed, global expression profiling of a plant’s response to abiotic stress conditions has shown that, although overlap may occur for different abiotic stresses, such as cold, salt, dehydration, heat, high light and

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mechanical stress, a set of genes unique to each stress response is also seen. Even in the case of ROS, which are known to play a central role in both biotic and abiotic stress conditions, it has been shown that different genes of the ROS network respond differently to different stress treatments (Mittler 2006; Noctor 2006; Melgar et al. 2009). However, most of the studies carried out to investigate the performance of plants under abiotic stress conditions have not focused on this aspect, making it an important area of concern, especially as it is known that plants are exposed to multiple environmental stresses in the field. Further, the response to abiotic stress is also developmentally regulated (Vinocur and Altman 2005; Nouri et al. 2011). It has been observed, in plant species such as rice, wheat, tomato, barley and corn, salt tolerance increases with an increase in plant age. Also it has been studied that QTLs (quantitative trait loci) associated with salt tolerance in the germination stage in barley, tomato and Arabidopsis are different from QTLs associated with the early stage of growth. In transgenic studies on crop plants such as rice, the majority have not evaluated the effect of stress on grain yield. This aspect becomes especially important in plants such as rice, in which stresses such as salinity do not affect the vegetative growth as much as the grain yields (Yamaguchi and Blumwald 2005). It is apparent that an understanding of the abiotic stress responsive network will require a considerable amount of time and resources, but a systematic and concerted effort will ensure that only the most suitable genes are identified for crop improvement. The task can be shortened by integrating the information already available and by avoiding the repetition of effort or branching away from the main focus. The work performed by already existing list of candidate genes and their alleles identified through this approach with a handful of positive results, but there is still a long way to go.

5

Stress-Associated Genes and Proteins Expression

With the availability of the Arabidopsis and rice genome sequences, together with improvements in the methods used to detect DNA polymorphisms,

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has made map-based cloning a viable option for global functional genomics (Friso et al. 2004). There are 37,344 single nucleotide polymorphisms (SNPs), 18,579 insertions/ deletions (InDels) and 747 large InDels available at TAIR website (The Arabidopsis Information Resource website). A large number of markers are also available for rice, including 3,267 markers released in 2000, 332 PCR-based genetic markers released in 2002 (rgp.dna.affrc.go.jp) and over 18,000 simple sequence repeats (SSRs) (International Rice Genome Sequencing Project 2005). A large number of genes in the abiotic stress response pathway have been identified using the map-based cloning approach, including SOS1, SOS2, SOS3, SOS4, SOS5, HOS1, SpI7, STT3, FRO1, LOS5/ABA3 and AtCesA8/IRX1 (Liu and Zhu 1998; Liu et al. 2000; Shi et al. 2000, 2002, 2003; Lee et al. 2001, 2002; Xiong et al. 2001; Yamanouchi et al. 2002; Koiwa et al. 2003; Chen et al. 2005). Targeting induced local lesions in genomes (TILLING) is another high-throughput technology helpful in identifying mutations in a selected gene or a variant allele (Henikoff and Comai 2003). TILLING projects are underway for plant species such as Arabidopsis, lotus, maize, wheat and Brassica (Gilchrist and Haughn 2005). In rice, mutants generated in the IR64 background using EMS and diepoxybutane have been used to generate DNA pools for TILLING, and were screened for mutations in selected genes (Wu et al. 2005). With the advantage of amenability to high throughput, together with the capability of generating an allelic series at a given locus, TILLING will also find application in the identification of useful alleles for abiotic stress tolerance. QTLs are specific genetic loci in the genome associated with a particular trait. Stress tolerance is a complex trait, and dissection of its QTLs would be of immense value in understanding the stress response and would be useful for plant breeders (Gorantla et al. 2005). Several QTLs involved in the stress response have been reported recently (Xu et al. 2005; Tuberosa and Salvi 2006). Most of the plant QTLs cloned to date have been obtained using a map-based cloning strategy. Lin et al. (2004) mapped eight QTLs responsible for variation in K+ or Na+ content from an F2 population derived from a cross

R. Chandna et al.

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between a salt-tolerant indica variety (Nona Bokra) and a susceptible japonica variety (Koshihikari) Of these, SKC1, a major QTL for shoot K+ content, was mapped to chromosome 1 (Ren et al. 2005). A map-based approach was also demonstrated to be useful in the identification of an important submergence tolerance QTL present on chromosome 9 of rice. Three genes belonging to the ethylene response-factor (ERF) family were identified on this locus, designated as Sub1, the variation of one of which, Sub1A, results in tolerance/susceptibility to submergence. Sub1 from the submergence-tolerant variety was introgressed in flooding susceptible local rice varieties. The new varieties showed submergence tolerance without compromising on yield or other agronomic traits, demonstrating the efficacy of this locus (Xu et al. 2006). The expression profile of genes in a QTL interval associated with the abiotic stress response is also being used to identify target genes. Gorantla et al. (2005) used information from ESTs sequenced from drought-stressed cDNA libraries to generate a transcript map of rice. Silencing of a target gene and targeted gene inactivation have also been performed by homologous recombination using a transgenic approach with respect to understanding abiotic stress response in plants (An et al. 2005; Iida and Terada 2005). Gene inactivation using anti-sense, cosuppression or RNAi strategies is largely based on a single gene approach and, at present, it is difficult to use these methods at the genome-wide level (Parinov and Sundaresan 2000). A major part of our present understanding of the plant stress responsive network comes from the functional analysis of Arabidopsis genes in transgenic systems. A number of transgenic studies have been performed in tobacco in this respect, and interestingly, the majority of such studies have aimed to decipher the function of genes encoding downstream components (effectors), such as those coding for antiporters, heat-shock proteins, SODs and LEA proteins, rather than upstream components (regulators), such as those coding for transcription factors and kinases. The functions of genes representing QTLs for abiotic stress have also been confirmed by

employing transgenics (Xu et al. 2006). Most of the stress-responsive genes have cis-acting conserved elements in their promoter region involved in regulating the stress response. A transgenic approach has also been used to dissect the role of stress-responsive promoters (YamaguchiShinozaki and Shinozaki 2005). The major cisacting elements present in several abiotic stress-responsive promoters include the ABA responsive element (ABRE) from the promoter of ABA-responsive genes (Ingram and Bartels 1996; Grover et al. 2001), dehydration responsive element (DRE) from the promoter of cold- and drought-inducible genes (Yamaguchi-Shinozaki and Shinozaki 1994; Thomashow 1999), anaerobic response element (ARE) from the promoter of genes responsive to low oxygen conditions, such as maize Adh1, LDH1 and PDC1 (Dolferus et al. 1994; Dennis et al. 2000), and heat-shock element (HSE) from the promoter of heat stress-inducible genes (Schoffl et al. 1998). With so many positive implications of the transgenic approach, still it is not useful for largescale functional analysis. Therefore, a possible way of employing transgenics for functional analysis would be the use of plant artificial chromosomes (PLACs) containing large-sized genomic fragments. This would significantly reduce the number of transgenic plants required for functional analysis (Somerville and Somerville 1999).

6

Conclusion and Future Prospects

Development of crop plants, tolerant to environmental stresses, is considered a promising approach, which may persuade the growing food demands of the developing and under-developed countries. Development of crop plants with stress tolerance, however, requires, knowledge of the physiological mechanisms and genetic controls of the contributing traits at different plant developmental stages. While, we are at a beginning to gain a skeletal understanding of the steady state changes occurring in plant proteomes during a number of environmental stresses. With proteome analysis we have been able to define the selectivity

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and functional category of these changes but we are still away from having real insight into the functional significance of many of the changes observed on plant cell stress susceptibility or tolerance. It is also important to mention that the effect of stresses on proteomes depends on the level of tolerance of the plants and thus, sensing and signalling processes can be a primary response of significance. More work will be required to demonstrate if temporal patterns in proteome stress responses exist. Further, even if temporal separation of events exists, it is still not clear how to mechanistically represent the cascade of chemicals events involving ROS, metals and lipid peroxidation with the responses in protein abundance and stability in a unified framework that allows sophisticated engineering of stress tolerance to commence. Also, there is still much work to be done on spatial resolution of stress responses and the impact on the proteome. Future proteomic studies offer the promise of new data to provide better temporal and spatial resolution of the stress response and recognition of damaged or unfolded proteins and the selectivity of metal induced oxidative modifications of proteins.

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