Physiological and comparative proteomic analyses of Thai jasmine rice and two check cultivars in response to drought stress

Abstract

To understand the molecular responses of Thai jasmine rice (Oryza sativa L. ssp. indica cv. KDML105) under drought, proteomes of KDML105 cultivars and two check cultivars, namely NSG19 (tolerant) and IR20 (sensitive) were investigated. The effect of PEG6000 and mannitol for creating osmotic stress (−0.1 MPa to − 2.1 MPa) was tested in KDML105 before applied to the three cultivars. The leaf proteomes were analyzed by GeLC-MS/MS shotgun proteomics. Among 623 proteins identified, 53 proteins showed significant difference in protein expressions, which could be hierarchically classified into three clusters of proteins up-regulated in IR20, NSG19, or in KDML105. Coronatine-insensitive 1 protein that thought to be involved in stomatal closing was prominently observed in NSG19, regarding to its physiology of rapid stomatal closing and highest stability of photosystem II. WD-40 repeat protein which involves in cell death was induced in IR20. On the other hand, H-protein promoter binding factor-2a remarkably increased in KDML105.

Keywords:

• shotgun proteomics
• drought stress
• PEG6000
• KDML105
• NSG19
• IR20

1. Introduction

Drought is a major constraint to the yield of rice (Oryza sativa L.). More than half of all rice production is often lost due to drought stress, leading to increased poverty and reducing world food security (Pandey et al. 2007). Drought stress response mechanisms in plants are a result of complex molecular and physiological mechanisms (Chaves et al. 2003). These molecular responses are initiated by signal transduction resulting in transcriptional and translational control of stress-responsive genes. Several stress-responsive mechanisms are activated to reestablish cellular homeostasis as well as to protect cells during water deficit stress and include regulation of macromolecules involved in cell wall metabolism, accumulation of compatible solutes, and altered transportation of water and micromolecules (Wang et al. 2003). Drought stress enhances accumulation of reactive oxygen species, activates programmed cell death, and induces male sterility in rice anthers (Nguyen et al. 2010). Photosynthesis capacity is directly related to growth and rice yields and is one of the major systems affected by drought. Under drought stress, levels of abscisic acid are increased, resulting in closing of stomata, which is accompanied by reduced concentrations of CO₂ in photosynthetic cells. In addition, photosynthetic apparatuses, especially in photosystem II (PSII), are damaged by drought stress (Giardi et al. 1996) altering the electron transport chain and ultimately leading to a reduction in photosynthetic ability, growth and yield of rice (Giardi et al. 1996; Yamane et al. 2003; Pieters & Souki 2005).

Thai jasmine rice (Oryza sativa L. ssp. indica cv. KDML105) originated in Chonburi province of Thailand and it was named as KDML105 by Sunthorn Seehanern, an official of the ministry of agriculture in Chachoengsao province of Thailand in 1954. It is also known as Thai fragrance rice caused by 2-acetyl-1-pyrroline (http://en.wikipedia.org/wiki/Jasmine_rice). NSG19 or Nam Sagui 19, a breeding line originated in Thailand, has seedling vigor and drought-adapted traits while IR20, indica rice from Philippines, has semidwarf and drought-sensitive characters (Cabuslay et al. 2002). Among the exported indica rice, Thai Jasmine rice has been classified as high-end trait and it was reported as the moderate drought tolerance (Sarkarung 2003). Whereas NSG19 and IR20 have been used as check cultivars in the rainfed lowland rice breeding program for drought tolerance and have been previously used as drought stress tolerant and sensitive cultivars, respectively (Sarkarung 2003). Although many studies have investigated the effects of drought stress in rice (Cabuslay et al. 2002; Khan & Komatsu 2004), the molecular responses of these indica cultivars to water deficit have not been clearly reported. In order to improve tolerance of indica rice to drought conditions, a better understanding of the molecular mechanisms that enable adaptation to conditions of drought stress is needed.

Proteomics is a powerful and direct approach for functional gene analysis and is widely used to understand the response of plants under abiotic stresses (Hashiguchi et al. 2010). Several protocols for proteome analysis are available that are based on protein separation, detection, and identification methods. Two major techniques used for protein separation in proteomic analysis are two-dimensional polyacrylamide gel electrophoresis (2D-PAGE)-based proteomics and liquid chromatography (LC)-based shotgun proteomics. Currently, the 2D-PAGE-based technique has been utilized almost exclusively for rice proteomics analysis (Khan & Komatsu 2004; Hashiguchi et al. 2010). However, this technique is known to be less suitable for the analysis of very high and low molecular weight proteins, as well as hydrophobic, highly acidic, and alkaline proteins (Santoni et al. 2000). In order to overcome these limitations, shotgun proteomics has been developed where the total mixture of digested proteins is separated on LC columns, analyzed with tandem mass spectrometry (MS/MS) followed by mapping of the peptide sequences onto the corresponding proteins using computer algorithms (Yates 1998; Matros et al. 2011). Shotgun proteomics can identify more than 1000 peptides within one analysis and also enables the detection of proteins rarely found using 2D-PAGE-based proteomics (Washburn et al. 2001). Moreover, shotgun proteomics has proven to be more reproducible in terms of peptide identification and protein quantitation than other techniques (Delmotte et al. 2009; Piersma et al. 2010; Tabb et al. 2010). While the complexity of the peptide mixtures limits the use of shotgun proteomics (Malmstrom et al. 2007), this problem can be reduced through the use of gel-based liquid chromatography-tandem mass spectrometry (GeLC-MS/MS) in which proteins are first separated by sodium dodecyl sulfate-PAGE (SDS-PAGE) then the entire gel is sliced into pieces according to protein marker sizes before digestion and analysis by MS/MS (Rezaul et al. 2005). Thus, the GeLC-MS/MS approach can substantially reduce the number of proteins present in each sample making it suitable for quantitative proteomic studies in complex organisms.

The identification of proteins with altered abundance in response to drought stress has been reported for many crop plants (Hashiguchi et al. 2010). However, studies of rice proteomes under drought stress have been limited to only a few cultivars with most studies being conducted with japonica rice (Oryza sativa L. ssp. japonica) using conventional 2D-PAGE-based proteomic analysis (Salekdeh et al. 2002; Ali & Komatsu 2006; Zang and Komatsu 2007). Moreover, there is no convincing evidence on the suitability of osmotic agents for generating osmotic stress in rice seedlings to simulate drought conditions. Hence, the effects of polyethylene glycol (PEG) 6000- and mannitol-inducing water stress on rice seedling were investigated in KDML105 and leaf proteomes were analyzed using the GeLC-MS/MS technique in the three different indica cultivars, KDML105, NSG19, and IR20. Comparative proteomic analyses of these cultivars in response to drought stress may lead to a better understanding of the drought stress response mechanisms and could identify proteins that may suggest strategies for generating rice capable of thriving in drought-prone environments.

2. Materials and methods

2.1. Plant materials, growth conditions, and stress treatments

Seeds of rice (Oryza sativa L. ssp. indica cv. KDML105, NSG19, and IR20) were obtained from the Pathumthani Rice Research Center, Rice Research Institute, Department of Agriculture, Ministry of Agriculture and Cooperative, Pathumthani, Thailand. The accession numbers of KDML105, NSG19, and IR20 are 27748, 11462, and 11355, respectively. Seedlings were hydroponically grown in Yoshida nutrient solution (Yoshida 1981) under controlled conditions (25±2 °C, 60±5% relative humidity, and 80±5 µmol m⁻² s⁻¹ photosynthetic photon flux with a 16 h d⁻¹ photoperiod). The susceptibility of KDML105 rice seedling to drought was tested by transferring the two-week-old seedlings into media solutions containing 0, 20, 30, and 40% (w/v) PEG6000 or 0-, 200-, 400-, and 800-mM mannitol, which created osmotic stress of approximately − 0.1 to − 2.1 MPa. The period of exposure to drought stress was varied between 0, 24, 48, 96, and 192 h. The three-week-old seedlings of all cultivars were grown under PEG-created water stress at approximately − 0.5 MPa for 48 and 96 h. The osmolality of the media was measured using the VAPRO®® Vapor Pressure Deficit Osmometer (WESCOR Inc., USA) and osmotic pressure of the media was calculated following the method of Money (1989). Leaf samples were harvested for GeLC-MS/MS shotgun analysis.

2.2. Morphological and physiological measurements

Seedling fresh weight (FW), dry weight (DW), shoot length, and the maximum quantum efficiency of PSII photochemistry (Fv/Fm) were measured from 20 seedlings in each treatment group. The Fv/Fm was measured using a portable fluorometer Handy PEA (Hansatech Instruments Ltd., King's Lynn, UK) following the manufacturer's instructions. Eight seedlings were randomly sampled to calculate the shoot:root ratio (division of shoot DW to root DW) and percentage of relative water content (RWC) calculated using the equation: RWC=([FW − DW]/FW)×100. Concentrations of chlorophyll a (chl a) and chlorophyll b (chl b) were analyzed as described by Cha-um et al. (2006) with the modifications from Lichtenthaler (1987) and Shabala et al. (1998). To evaluate the rate of stomata closing, the three-leaf stage seedlings from tissue culture were used to evaluate guard cells under water stress of approximately − 0.5 MPa for 0, 1, 3, 5, and 10 min. Guard cells were sampled from the fully expanded leaf of two seedlings (two regions per seedling) and evaluated within an area of 66.57 mm² by observation with 40× microscopic magnification. The percentage of stomatal closing calculated using the equation: stomatal closing (%)=(numbers of close stomata/numbers of total stomata)×100. Statistical analysis of all data was conducted using the SPSS software (SPSS for Windows; SPSS Inc., Chicago, IL) and mean values were compared by employing Duncan's multiple range test with a P value ≤ 0.01 defined as statistically significant. The bars in all figures represent the standard deviation of the mean.

2.3. GeLC-MS/MS shotgun proteomics

Leaf samples were taken from the pooled samples of leaves from 20 independent rice seedlings grown under PEG-created water stress at − 0.5 MPa for 0, 48, and 96 h. Total proteins were extracted from 200 mg of leaf samples after being ground in liquid nitrogen and precipitated with trichloroacetic acid/acetone according to Granier (1988) with slight modifications. The protein concentrations were determined as described by Lowry et al. (1951). Fifteen nanogram of the protein from each sample was pre-fractionated by 12.5% SDS-PAGE followed by silver staining (Blum et al. 1987). The size of polypeptides was estimated by comparison with a low molecular weight protein standard marker (Amersham Biosciences, Buckinghamshire, UK). Gels were cut into five pieces according to their sizes (less than 20, 20–30, 30–45, 45–66, and over 66 kDa). The 45 gel pieces from nine treatments were obtained and each gel piece was cut into small pieces (about 1×1 mm²) before aliquot into a 96-well plate for in-gel digestion. In brief, the gel plugs were dehydrated with 100% acetonitrile (ACN), reduced with 10-mM dithiothreitol in 10-mM ammonium bicarbonate, and alkylated (in the dark) with 100-mM iodoacetamide in 10-mM ammonium bicarbonate, respectively. Each step was performed at room temperature for 1 h and gel pieces were dehydrated twice with 100% ACN for 5 min before digestion with 10 µL of trypsin solution (10-ng/µL trypsin in 50% ACN/10-mM ammonium bicarbonate) at room temperature for 20 min. Gels were immersed in 20 µL of 30% ACN and incubated at 37 ° C overnight, and digested peptide products were extracted from gel pieces by adding 30 µL of 50% ACN in 0.1% formic acid and shaking at room temperature for 10 min. Extracted peptides that came from the same treatment and the same size interval on the 96-well plate were pooled together in a new tube. Finally, the 45 peptide samples (nine treatments×five intervals) were prepared for further mass spectrometric analysis and the analysis was performed in triplicate. Peptide samples were injected into an Ultimate 3000 LC System (Dionex, Sunnyvale, CA) coupled to an ESI-Ion Trap MS, HCT Ultra PTM Discovery System (Bruker, Rheinstetten, Germany) with electrospray at a flow rate of 300 nL/min to a nanocolumn (Acclaim PepMap 100 C18, 3 µm, 100Å, 75 µm id×150 mm). The solvent gradient utilized in this analysis was solvent A (0.1% formic acid in water) and 80% of solvent B (0.1% formic acid in 80% ACN) with the grading running in 40 min.

2.4. Protein quantitation and identification

Proteins were quantified by using the DeCyder MS 2.0 Differential Analysis software (GE Healthcare Europe GmbH, Freiburg, Germany) (Johansson et al. 2006; Thorsell et al. 2007). The PepDetect module was used for peptide detection, charge state assignments, and quantitation. Protein identifications were assigned using the Mascot search engine (Matrix Science, London, UK) (Perkins et al. 1999). Only significant hits, as defined by the MASCOT algorithm at P ≤ 0.05, were accepted. The subcellular and functional annotation of the identified proteins was screened and assigned according to the biological function using the Gene Ontology Categorizer (http://eagl.unige.ch/GOCat). The protein expression patterns from each cultivar was statistically compared among the treatments by one-way ANOVA algorithm using the Multi Experiment Viewer (MEV) software version 4.6.2 (The Institute for Genomic Research) with P ≤0.05 and 0.10. Proteins which displayed significant differences in their expression patterns were further analyzed using hierarchical and K-mean clustering and their biological functions were defined using the InterPro database (http://www.ebi.ac.uk/interpro).

3. Results

3.1. Effect of PEG6000- and mannitol-inducing drought stress on rice seedlings

To compare the effect of PEG6000 and mannitol on rice seedlings, 2-week-old seedlings of KDML105 were transferred into media containing PEG6000 or mannitol, both of which mimic the effect of drought stress. The reduction of shoot or root FW, shoot length, and Fv/Fm were more readily observed upon treatment with PEG6000 compared with mannitol (Figure 1). These changes could be observed at an osmotic stress of approximately − 0.4 to − 0.6 MPa with more severe effects observed with an increase in stress. At an osmotic stress of approximately − 0.9 to − 1.1 MPa, shoot FW, shoot length, and Fv/Fm were decreased by 57.6, 14.9, and 93.0%, respectively, with PEG6000 treatment, whereas these parameters were decreased by 42.7, 1.1, and 55.1% with mannitol treatment.

Figure 1. Effect of PEG6000- or mannitol-induced drought stress on growth and photooxidative damage to photosystem II (PSII). Shoot fresh weight (a), root fresh weight (b), shoot length (c), and the maximum quantum efficiency of PSII photochemistry (Fv/Fm) (d) were measured from 2-week-old seedlings of KDML105 hydroponically grown in nutrient solution containing PEG6000 or mannitol, which generated various osmotic stresses for 48 h. Data represent means±SD of 20 replicates. Asterisks indicate a significant difference (P ≤ 0.01) between PEG6000 and mannitol treatments at each concentration point.

3.2. Effect of PEG6000-induced drought stress on growth, RWC, photosynthesis, and stomatal closing

PEG6000-induced drought stress (−0.5 MPa) significantly affected the growth, RWC, and photosynthetic ability of rice seedlings (Figure 2). The RWC and shoot:root ratio were dramatically reduced after 48 and 96 h of stress. After 96 h of drought stress, the RWC of seedlings was reduced by 7.3% and the shoot:root ratio was decreased by 25.0%. Moreover, the levels of chl a and chl b were decreased by 10.6 and 11.5%, respectively. To determine the best osmotic stress agent, the effects of mannitol and PEG6000 were evaluated with KDML105 rice. Once it was established that PEG6000 was more effective for inducing drought stress in KDML105 seedlings, its effect on Fv/Fm and stomatal closing was evaluated in KDML105, NSG19, and IR20. The Fv/Fm was observed at 48 and 96 h after treatment with PEG6000 (Figure 2e) and after 96 h of stress, the reduction in Fv/Fm was larger in IR20 (9.4%) than in KDML105 (4.2%) with no significant change was observed in NSG19. In addition, NSG19 closed their stomata faster than KDML105 and IR20 (Figure 4). After 1 min of water deficit, 80% of stomata in NSG19 was closed, whereas only about 20% in KDML105 and IR20 was closed, even though eventually 100% of the stomata was closed after 3, 5, and 10 min, respectively.

Figure 2. Effect of PEG6000 (approximately − 0.5 MPa) on 2-week-old seedlings of KDML105. The effect of water stress on water status, growth, and chlorophyll contents (a–e). Relative water content (a), shoot: root ratio (b), chlorophyll a (c), and chlorophyll b (d) were observed in a time-dependent manner under water stress. The maximum quantum efficiency of PSII photochemistry (Fv/Fm) was measured from leaves of 3-week-old seedlings of NSG19, KDML105, and IR20 rice after 48 and 96 h of water stress (e). Data represent means±SD and the different letters indicate highly significant difference (P ≤ 0.01) analyzed by Duncan's multiple range tests.

3.3. Comparative proteomic analyses of three indica rice cultivars under drought stress

In the present study, 623 proteins, with significantly differing expression patterns under drought stress were detected using GeLC–MS/MS shotgun proteomics and identified using the Mascot software (Perkins et al. 1999). Among these proteins, 369 could be assigned to known proteins, while the others were hypothetical, unknown, or unnamed proteins. The expression patterns of all 623 proteins were compared among the three indica cultivars, IR20, KDML105, and NSG19, by using the MEV software. Fifty-three proteins showed significantly different protein expression patterns among the three cultivars (P ≤ 0.10), whereas 44 proteins showed similar expression patterns (P ≤ 0.05). Other proteins do not meet the statistical criteria analyzed by MEV software. The 53 differentially expressed proteins were further analyzed using hierarchical clustering (Figure 3a) and K-mean clustering (Figure 3b). Hierarchical clustering showed expression patterns of each protein and K-mean sorted these proteins into three groups: Group I, up-regulated in IR20; Group II, up-regulated in KDML105; and Group III, up-regulated in NSG19. The biological functions of the proteins present in each group were defined using the InterPro database (http://www.ebi.ac.uk/interpro) (Figure 3c). Proteins that exhibited a significant difference in expression patterns under water stress (>1.5-fold change) among the rice cultivars are given in Table 1.

Figure 3. Hierarchical and K-mean clustering of protein expression patterns of 53 differentially expressed proteins in three indica rice cultivars under water stress. Protein expression patterns of rice cultivar KDML105 (KDML), NSG19 (NSG), and IR20 (IR) under control conditions (C0) and conditions of water stress (D) for 48 and 96 h were clustered using the Multi Experiment Viewer (MEV) software. Hierarchical clustering input data were the fold-induction/reduction of proteins, the values of which were calculated by dividing the peak area of each protein at the defined stress condition by the peak area of the same protein at the control condition (a). Each row represents an individual sample and each column represents an individual protein. Red indicates up-regulation; green, down-regulation; and black, no change in the relative protein expression. Protein patterns were clustered by K-mean gene clustering into three groups: Group I: up-regulated in IR20; Group II: up-regulated in KDML105; Group III: up-regulated in NSG19 (b). The biological function of the significantly differentially expressed proteins in each group was defined by searching throught the InterPro database (http://www.ebi.ac.uk/interpro) (c).

Table 1. Proteins that exhibited different expression patterns (P ≤ 0.10) among rice cultivars and was inducted more than 1.5 folds under water stress.

			Fold induction/reduction^a
			KDML105		NSG19		IR20
Protein identification	Acc. no. NCBInr	Biological function	48 h	96 h	48 h	96 h	48 h	96 h	Adjust P value	Peptide
Group I Up-regulated in IR20
Os02g0511600	gi|115446315	Protein glycosylation	0.49	0.47	0.32	0.24	2.21	2.62	0.041	GAPPVR
WD-40 repeat protein-like	gi|23617056	Cell death	0.37	0.39	1.24	0.42	2.63	2.45	0.059	VLVTGSMNGIVR
Putative potassium transporter	gi|34395199	Transport	0.61	1.17	0.40	0.39	1.86	2.75	0.077	MAVIHTEDTTGTGLVMR
OSIGBa0147J19.11 [O. sativa Indica Group]	gi|116309657	Transcription	1.00^b	1.07^b	0.88	1.03	1.87	1.66	0.089	LAELYITHVVCR
P0582D05.123	gi|115474645	Transport	0.00	0.33	0.54	0.00	2.25	1.80	0.090	APSPVVGK
Curculin-like (mannose-binding) lectin domain containing protein	gi|115456469	Unknown	0.40	1.12	1.00^b	0.93^b	1.93	1.87	0.098	EKLGAGGFGAVYR
Group II Up-regulated in KDML105
Similar to Hypersensitive reaction associated Ca2 + -binding protein	gi|115452635	Transport	2.14	2.15	0.51	0.51	0.39	0.78	0.043	AAAGAGCRPAMEDVLR
Putative H-protein promoter binding factor-2a	gi|15451553	Transcription	3.56	3.33	0.53	0.54	0.95	1.11	0.060	ESEESLGEMGECRGGGGGGDGLIK
MtN3 and saliva-related transmembrane protein family protein	gi|115445683	Development	1.93	2.44	0.26	0.84	0.83	0.95	0.069	MVIQTK
Putative DEAD/DEAH box RNA helicase protein	gi|24476045	Cell and DNA repair	1.60	1.73	0.00	1.00^c	0.74	0.98	0.070	GHPLHDVPCV
Transposon protein, putative, unclassified	gi|77556325	Transposable elements	1.89	1.88	0.68	0.60	0.53	1.20	0.074	REPLEPGTFLDILTKPSVEK
Retrotransposon protein, putative, unclassified	gi|108862435	Transposable elements	4.57	4.06	1.00^b	0.78^b	1.60	0.85	0.084	NDGTAR
Retrotransposon protein, putative, unclassified	gi|77548423	Transposable elements	2.94	3.36	0.50	1.12	1.21	0.87	0.092	NNIVFNEVR
Group III Up-regulated in NSG19
H0525C06.10 [Oryza sativa (indica cultivar-group)]	gi|116309808	Cell and DNA repair	0.00	0.00	1.79	1.60	0.29	0.29	0.039	LPPAIAQQAVVEQVDARFGGR
Tetratricopeptide repeat protein-like	gi|47848590	Cell and DNA repair	0.29	0.76	1.52	2.36	0.35	0.38	0.076	GLSKSK
Similar to Coronatine-insensitive 1	gi|115452097	Photosynthesis	1.08	0.00	1.84	1.74	0.00	0.59	0.079	LDLQFTFLSTEDHCQLVQR
Zinc finger, RING-type domain containing protein	gi|297597672	Cell death	0.61	1.07	2.90	3.01	1.00^b	1.30^b	0.085	LVPLYGR
O-diphenol-O-methyl transferase-like	gi|57899239	Transcription	0.00	1.67	3.46	3.45	0.80	0.76	0.091	MRNQWQR
Transposon protein, putative, unclassified	gi|62734076	Transposable elements	0.00	0.79	3.12	4.27	0.00	1.00^c	0.092	GWSSRGGTGGSAAGMGGGGATAGGSR
Putative lateral root primordia (LRP1)	gi|42407540	Development	0.00	1.07	1.88	1.83	1.13	0.74	0.095	GFDCATHVK
Magnesium transporter CorA-like family protein, putative, expressed	gi|108710450	Transport	0.52	0.75	2.45	1.71	1.20	1.21	0.096	EEDKPGDDGSR

^aThe fold induction or reduction of proteins was calculated by dividing the peak area of each protein at the defined stress condition by the peak area of the same protein in the control condition (0 h). The values represent the following: 0 = absent or cannot be detected, 1 = not changed compared to the control at 0 h, >1 = induction, and <1 = reduction.

^bThe fold induction or reduction of proteins at 48 h and 96 h compared to the same protein at 48 h, since the protein could not be detected at 0 h.

^cThe fold induction was normalized to 1 since the protein was detected only at 96 h.

4. Discussion

4.1. PEG6000 is a better osmoticum for water stress studies in rice seedlings compared to mannitol

Many studies on drought stress responses in rice have been conducted by transferring rice seedlings into a nutrient solution containing osmotic agents such as mannitol (Zang & Komatsu 2007) or PEG6000 (Xiong et al. 2010) to create osmotic stress. However, examination as to which agent is more suitable for induction of water stress in rice seedlings has rarely been reported. In the present study, PEG6000 affected the growth and photosynthetic ability of rice seedlings and resulted in a remarkable reduction in shoot FW, shoot length, and Fv/Fm. The effect of PEG6000 on these parameters was greater than upon treatment with mannitol, and these effects became more severe as the stress increased (Figure 1). There are some comparative studies on the effect of PEG and mannitol as osmotic agents on plants (Hohl & Schopfer 1991; Pandey et al. 2004; Slama et al. 2007). PEG6000 affects on changes of stress-related enzymes and compounds in 5-day-old rice seedlings more than mannitol (Pandey et al. 2004). In contrast to PEG6000, mannitol can be transported into root cells lowering its ability to produce water stress in plants (Hohl & Schopfer 1991). Thus, in order to avoid the adaptation of rice seedlings under conditions of mild stress, PEG6000 is preferred for use as an osmoticum.

4.2. Drought affects chlorophyll and photooxidative damage of PSII in rice seedlings

Drought stresses have been reported to damage the photosynthetic system by causing severe disruption of the chloroplast envelope (Yamane et al. 2003; Vassileva et al. 2012) and the PSII complex. A consequence of this damage is a reduction in chlorophyll content, chl a fluorescence parameters, and D1 protein levels (Giardi et al. 1996; Pieters & Souki 2005). In this study, we observed that the contents of chl a and chl b were progressively reduced after 24 h of water stress (Figures 2c and 2d). This suggests that the degradation of chlorophylls is occurring under drought stress. Remarkably, the contents of chl b were slightly higher than the contents of chl a. This might due to the low light intensity during the growing and experimental condition. Chlorophyll fluorescence is widely used as a tool to assess the response of plants to environmental stress (Sayed 2003) and the decrease in Fv/Fm, one of the chlorophyll fluorescence parameters, refers to increases in photo-inactivation and oxidative damage of PSII reaction centers (Baker 2008). Our results indicated that drought stress may indirectly affect PSII activity of rice seedlings as shown by a reduction of Fv/Fm (Figures 1d and 2e). The reduction in Fv/Fm under drought stress has been observed in many plant species such as rice (Garg et al. 2002), barley (Li et al. 2006), and maize (Efeoglu et al. 2009). The smaller changes in the chlorophyll fluorescence yield can indicate the ability to tolerate water stress. The data in Figure 2e indicate that high photooxidative damage to the PSII reaction center occurred in the IR20 and that this cultivar was more susceptible to such damage than the KDML105 and NSG19 cultivars. Consistent with our results, Cha-um et al. (2010) observed that Fv/Fm ratios were significantly reduced after water deficit (7% soil water content) in IR20, KDML105, and NSG19 by 25.9, 6.5, and 2.1%, respectively. In addition, a reduction in chl a, chl b, RWC, and yield traits were more readily observed in IR20 compared to NSG19 and KDML105 (Cha-um et al. 2010). Taken together, these observations suggest that under drought stress reduced levels of water in rice leaves may increase oxidative stress leading to damage of chlorophyll pigments and PSII ultimately resulting in reduced photosynthetic efficiency. Finally, these changes are likely to affect overall photosynthesis and yield, especially in the IR20 cultivar.

4.3. GeLC-MS/MS shotgun proteomics as a powerful tool for analysis of leaf proteins in rice

Proteomics has been proposed to be an effective tool for studying plant stress responses (Hashiguchi et al. 2010; Kim et al. 2011). With the increased interest in analyzing plant proteomes, shotgun proteomics has been developed for rapid, highly accurate, and effective detection of protein expression under specific conditions (Yates 1998; Delmotte et al. 2009; Piersma et al. 2010; Tabb et al. 2010; Matros et al. 2011). However, shotgun proteomics is limited by the complexity of the peptide mixtures (Malmstrom et al. 2007). From our preliminary data, the subcellular and functional categorization of the 369 known proteins was performed based on their biological function using the Gene Ontology Categorizer (http://eagl.unige.ch/GOCat). These results indicate that the response to water stress mainly altered proteins localized in the nucleus (58%), cytoplasm (13%), and membranes (12%). However, proteins localized to organelles that play important roles in energy and metabolism, i.e. mitochondria (7%), chloroplast (3%), endoplasmic reticulum (ER), and Golgi apparatus (3%), were also affected. Proteins whose functions correspond to stress response (17%) and metabolism (17%) were detected as well as those involved in transcription (11%), transport (9%), signaling (7%), and cell death (7%). The wide variety of proteins identified indicates the complexity of leaf proteins and the complicated network of drought stress response in rice leaves. GeLC-MS/MS is a recent technique for shotgun proteomics and is designed to overcome problems caused by sample complexity (Rezaul et al. 2005; Gilmore and Washburn 2010). Since shotgun proteomics produces very large data-sets due to the extreme redundancy in shotgun MS/MS data-sets, it may be difficult to interpret the results (Malmstrom et al. 2007). In this study, the complex protein mixtures were pre-fractionated by SDS-PAGE then physically separated by cutting each lane in the gel into five pieces prior to digestion and analysis by MS, resulting in reduced protein complexity in each analysis. Several thousand peptide sequences were detected from each gel piece using the DeCyder MS 2.0 Differential Analysis Software and the redundant data were filtered out and only the MS/MS data showing significant differences among treatments (P ≤ 0.05) were exported to the Mascot search engine. The 623 nonredundant proteins were then obtained from the Mascot search at P ≤ 0.05 and the patterns of protein expression were statistically compared and clustered by using the MEV software, as shown in Figures 3a and 3b. Analysis with the MEV software package has the potential to increase the reliability of the results and reveal new insights from large data-sets. Thus, even with the high level of complexity, proteome analysis of rice samples can be studied effectively by the GeLC-MS/MS technique, together with the appropriate computer algorithms and databases.

4.4. Protein profiling reveals different drought stress responses of the rice cultivars

Different rice cultivars may have different response mechanisms to drought and in this study we observed that each indica rice cultivar altered expression of a different set of proteins in response to drought stress (Table 1). According to the results from hierarchical and K-mean clustering, protein expression can be classified into three groups according to their specific high accumulation in each cultivar (Figure 3). Possible functions of the proteins in each group were reviewed and discussed in order to gain an insight for adaptation to water stress in these three indica rice.

4.4.1. Proteins highly up-regulated in IR20

Cell death is one of the commonly defensive mechanisms of plant under abiotic stresses. The WD-40 repeat proteins (WD40) were increased about 2.5 times under water stress in IR20 (Table 1). These data are in accordance with those found in leaves and roots of maize seedlings after treatment with 20% PEG for 6 h (3.77- and 1.44-fold increase, respectively; Zheng et al. 2004). In addition, BnSWD1, a WD40 repeat-containing protein, is involved in salt stress in Brassica napus (Lee et al., 2010). The WD-40 repeats are short ~40 amino acid motifs and are usually found in E3 ubiquitin ligase enzymes which functions in cell cycle control and apoptosis (Yee & Goring 2009). Thus, IR20 may have a higher rate of cell death due to sensitivity to drought stress compared to KDML105 and NSG19. According to Liu et al. 2007, root cells of IR20 display and increased rate of cell death in response to drought stress (15% PEG treatment for 2, 4, and 8 days) than those found in the drought-adapted line, IRAT109 (O. sativa ssp. japonica). Moreover, it is in fact that PSII cannot function in the death cells thus the increase expression of WD40 might indirectly involve with a reduction of Fv/Fm in IR20. It was found that Os02g0511600 protein showed 2.21–2.62 times induction in IR20 under water stress (Table 1). This protein was annotated by the Rice Annotation Project (http://rapdb.lab.nig.ac.jp/) as a glycosyltransferase (GT) family 31 protein (GT31) and its function is protein glycosylation (GO:0006486). GTs were found to be induced by drought stress in Macrotyloma uniflorum (Lam.) Verdc. (Reddy et al. 2008). Glycosylation by GTs has a key function in the modification of plant secondary metabolites and maintenance of cell and hormone homeostasis (Wang & Hou 2009). The GT31 enzyme was found to be widely distributed in several plants but was most abundant in rice (Cao et al. 2008) suggesting a vital role of GT31 in rice. The function of GT31 in rice is unclear but it has been hypothesized to be involved in cell wall biosynthesis (Cao et al. 2008; Bosch et al. 2011). However, this protein consists of short amino acid sequence (132 amino acids) so it would not be involved in modifications related to the cell wall. In addition, 15% of the up-regulated proteins were thought to be involved in transportation (Figure 3c). For example, a putative potassium transporter was up-regulated 2.75 times in IR20 (Table 1). This may implicate the activity of K⁺ uptake to balance the cellular electro-neutrality under water stress (Banuelos et al. 2002). Therefore, drought stress appears to mainly induces changes in expressions of proteins involve in cell death, protein glycosylation, and transportation.

4.4.2. Proteins highly up-regulated in KDML105

H-protein promoter binding factor-2a (Hp2a) was found to be increased about 3.5-fold in KDML105 (Table 1). This protein is homologous to the Dof zinc finger protein, a plant-specific transcription factor, which functions as a transcriptional activator or repressor and is involved in diverse plant-specific biological processes (Yanagisawa 2004). Rdd1 is a homolog of Hp2a in rice. It has been reported to be involved in phytochrome A signaling (Tepperman et al. 2001), growth, grain size, and flowering time in rice (Iwamoto et al. 2009). Another protein of interest is the DEAD/DEAH box RNA helicase protein (Table 1). This protein plays a role in DNA repair and was identified as a new candidate for engineering plant stress tolerance (Vashisht & Tuteja 2006). Moreover, it is of note that 28% of the up-regulated proteins in KDML105 were assigned as transposable elements (TEs) e.g. transposon or retrotransposon-encoded proteins (Figure 3c and Table 1). Of these, retrotransposon protein (gi|108862435) was increased about 4.06–4.57 times after drought stress treatment in KDML105 while slightly changed in NSG19 and IR20. TEs contribute to the organization and evolution of plant genome (Sabot et al. 2004). It may assist in adaptation to extreme environment by insertion causing null alleles, genome restructuring, sequence reorganization, coding sequences generation, increase in genome size, chromosomal breaks and translocations, splicing, and intron creation (Sabot et al. 2004). The finding that much higher percentage of TE-related proteins was found in KDML105 than NSG19 and hardly any found in IR20 might implicate to the ability of cellular adaptation to drought of these cultivars. KDML105 and NSG19 should have better adaptation to drought than IR20. However, at the cellular level, NSG19 may experience less water stress due to longer root structure (data not shown) and rapid stomatal closing (Figure 4) and these may consequently result to less stimuli for enhancing the expression of TE-related proteins in NSG19. TEs also play important role in rice epigenetic regulation (Zhao & Zhou 2012). Generally, DNA methylation is maintained for repressing the rice TE expression but the expression can be triggered by the environmental stresses coincide with the loss of DNA methylation (Zhao & Zhou 2012). Therefore, the major responses in KDML105 might be involved with phytochrome A signaling, DNA repair and alteration of the epigenetic regulation resulting in higher water stress tolerance.

Figure 4. Percentages of stomata closing in rice seedlings under water stress. Seedlings at the three-leaf stage from tissue culture were used to evaluate guard cells under water stress of approximately − 0.5 MPa. Guard cells were sampled from the fully expanded leaf of two seedlings (two regions per seedling) and evaluated within an area of 66.57 mm² by observation with 40×microscopic magnification. Data represent means±SD.

4.4.3. Proteins highly up-regulated in NSG19

Our results showed that 14% of proteins highly expressed in NSG19 are involved in cell and DNA repair (Figure 3c). Among those proteins, the tetratricopeptide repeat (TPR) protein was induced 2.36-fold in NSG19, while it was reduced in KDML105 and IR20 (Table 1). Proteins containing TPR motifs can act as molecular chaperones and regulate the cell cycle (D'Andrea & Regan 2003). The TPR protein also plays an important role in hormone signaling under stress (Rosado et al. 2006) and is also induced in perennial rye grass under drought stress (Liu & Jiang 2010). In addition, a protein similar to coronatine-insensitive 1 (COI1), which thought to be involved in stomatal closing, was increased about 1.8-fold, after treatment (Table 1). The COI1 protein is a part of the SCF^COI1-ubiquitin ligase complex that catalyzes ubiquitination of the JAZ repressor. Degradation of JAZ repressor triggers the induction of jasmonate response genes and subsequently results in an increase in the innate immune response and stomatal closure (Munemasa et al. 2007; Melotto et al. 2008). Our results also demonstrate that stomata closure in NSG19 occurred faster than in KDML105 and IR20 (Figure 4). Thus, an increase in COI1 proteins might involve with rapid stomata closure to limit water loss via guard cells to help maintain water balance in photosynthetic cells. This may result in protection of photosynthetic activity, as shown by the stability of Fv/Fm in NSG19 under drought stress (Figure 2e).

4.5. Unknown and common stress-inducible proteins

We were able to identify not only proteins known to play important roles in stress responses but also hypothetical or unknown proteins that may have critical functions. Results from our preliminary analysis indicate that among the 623 detected proteins, 254 proteins were hypothetical or unknown proteins and 23 of those proteins have significant differential expression patterns in the three rice cultivars. Those proteins were induced more than three to four times in a specific cultivar, indicating cultivar-specific functions under water stress. Although many stress-responsive proteins with unknown functions have been reported, only a few proteins have been further characterized. Overexpression of a novel protein of unknown function may result in increased tolerance to environmental stresses, as shown for overexpression of AtSAP10 (a novel stress associated protein) and OsCEST (a novel chloroplast protein in rice; chloroplast protein-enhancing stress tolerance) in Arabidopsis (Dixit & Dhankher 2011; Yokotani et al. 2011). Moreover, we also observed proteins that are expressed in a similar pattern in all three rice cultivars under water stress, indicating that in addition to cultivar-specific response there is also common response to water stress. Therefore, studies of these proteins may provide new candidate genes for future rice improvement.

5. Conclusion

In summary, this study suggests that PEG6000 is a better osmoticum for drought stress studies in rice seedlings than mannitol and that a complex mixture of protein samples from rice leaves can be effectively analyzed using GeLC-MS/MS shotgun proteomics together with an appropriate bioinformatics. We also report for the first time the proteomes under water stress of Thai jasmine rice (KDML105) and the two indica cultivars (NSG19 and IR20) that have been used as check cultivars for breeding drought-tolerant rice. The different responses to drought stress in these three contrasting cultivars were revealed by changes in Fv/Fm, stomatal closure, and protein expression patterns. For future perspective, proteins that exhibited specific highly increase under drought stress in the tolerant cultivars would have potential for developing to a drought-tolerant marker. This study suggests avenues for further research that may lead to a comprehensive understanding of the highly-complex drought stress responses in each of these rice cultivars.

Acknowledgements

This research was supported by Mahidol University and the program Strategic Scholarships for Frontier Research Network for the Joint Ph.D. Program Thai Doctoral degree. We are also thankful to the Office of the Higher Education Commission, Thailand and BIOTEC, NSTDA.