One dry summer: A leaf proteome study on the response of oak to drought exposure

J O U R NA L OF PR O TE O MI CS 7 4 ( 2 01 1 ) 1 3 8 5–1 3 9 5

available at www.sciencedirect.com

www.elsevier.com/locate/jprot

One dry summer: A leaf proteome study on the response of oak to drought exposure Kjell Sergeant a,⁎,1 , Nadine Spieß b,1 , Jenny Renaut a , Eva Wilhelm b , Jean François Hausman a a

Centre de Recherche Public-Gabriel Lippmann, Department of Environment and Agrobiotechnologies, Rue du Brill 41, L-4422 Belvaux, Luxembourg Austrian Institute of Technology, Health and Environment Department, 2444 Seibersdorf, Austria

b

AR TIC LE I N FO

ABS TR ACT

Article history:

One of the most prominent hallmarks of the expected climate change in Europe is the higher

Received 14 January 2011

prevalence of longer and more intense periods of summer drought. To preserve European oak

Accepted 14 March 2011

forests, of considerable importance for European economical and ecological development,

Available online 23 March 2011

under these conditions knowledge on the mechanisms by which broad-leaved trees cope with drought is needed. In this study the effect of one season of drought stress, corresponding in

Keywords:

length and soil water content to a dry summer, on young pedunculate oak trees (Quercus robur L.)

Oak

was investigated by monitoring phenotypical parameters, the analysis of carbohydrate

Drought

accumulation and a 2D-DIGE-based proteome study of leaves.

Climate change

In our experimental system, mimicking the conditions of a dry summer, the plants displayed

Proteomics

reduced growth, moreover the transition through the developmental stages was affected. The

2D-DIGE

data obtained during this study, supported by a separately published gene expression analysis

Forest

study, indicated that the oak tried to adapt its metabolism in order to maintain its full molecular functionality. Initially the plants seemed to be able to cope with the imposed stress. However prolonged drought exposure overwhelmed the adaptive mechanisms and at the last sampling point of this study the molecular machinery succumbed. © 2011 Elsevier B.V. All rights reserved.

1.

Introduction

Because sessile plants cannot apply the classic Fight-or-Flight stress response of animals; therefore they utterly depend on their ability to fine-tune their metabolism in order to cope with changes in the environment in which they are rooted. The biotic and abiotic factors of the environment that determine the wellbeing of plants are numerous and furthermore in continuous interaction in determining the fitness of a plant. So not only the metabolism needs to be shifted to an alert state once an environmental factor becomes limiting, but the opposite, restoration of optimal productivity under better conditions, is just as essential [1]. By favouring the survival and the chance of

propagation of the best adapted individuals, Darwinistic natural selection will allow annual plant species to adapt relatively fast to changing conditions. However the short- to mid-term survival of most forest tree species almost exclusively depends on the molecular flexibility encrypted in the genetic resources of the species. Different types of abiotic constraints, notably drought (i.e. water availability insufficient to support normal physiology), high temperatures, salinity and freezing, can cause water stress in plants. For the future the different climate models agree, although with a range of magnitude, that the predicted increase of the Earth's surface temperature together with precipitation shifts will result in more intense and more frequent drought

⁎ Corresponding author. E-mail address: [email protected] (K. Sergeant). 1 Contributed equally to this work. 1874-3919/$ – see front matter © 2011 Elsevier B.V. All rights reserved. doi:10.1016/j.jprot.2011.03.011

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events in certain areas [2–4]. This will exacerbate the current situation wherein drought already has detrimental effects on plant productivity in large geographical areas [5]. Negative effects of drought on our forest trees are not new phenomena. In the past long episodes of drought have been associated with large-scale forest mortality and decreased forest-productivity. However, there remain large hiatuses in our knowledge of drought tolerance, acclimation and molecular adaptations [6,7], and on the effects of drought on forest ecosystems [8]. . The molecular responses of plants to drought include the accumulation of osmoprotectants, the expression of protective proteins to retain or restore the activity of the cellular machinery and decreasing growth. So far not many studies, using transcriptomics and proteomics, have been done to characterize the responses of forest trees exposed to drought. Existing studies mainly concentrate on the tree model species poplar [9–11]. However, the effect of drought on the leaf proteome of Quercus species was studied [12,13]. Results obtained on short-living herbaceous plants, such as rice or Arabidopsis, cannot always be extrapolated to trees and furthermore the outcome of a drought experiment is utterly dependent on the exact experimental setup used. For instance molecular adaptation after applying drought instantly is different from the application of a more natural gradual decrease of water availability [14]. Although the way plants perceive drought is not fully understood, the involvement of a membrane-bound histidine kinase, ATHK1 in Arabidopsis [15], as osmosensor is known. The increase in the osmolarity triggers this kinase. The functional analysis of different ATHK1-mutants furthermore indicated that this protein is involved as upstream regulator in both ABAdependent and independent signalling pathways [16]. Downstream signalling involves phospholipase C, Ca2+ influxes and the activation of protein kinases and phosphatases [17]. Drought-regulated genes are characterized by the DRE (Dehydration-Responsive Element) as cis-acting element and different transcription factors binding to these elements have been identified and characterized [18–20]. Pedunculate oak (Quercus robur L.) also known as common, English or truffle oak is the dominant tree in many natural West-European forests excluding large parts of Spain, Portugal and North Scandinavia [21]. Natural oak stands are particularly rich in biodiversity, adding ecological attributes to their economical importance as sources of wood for construction or heating. However due to the weak natural regeneration of oak forests and the appearance of new infections, the area covered and the survival of current oak forests in the next century are uncertain. The variable efficiency of reforestation efforts, by natural regeneration, direct seeding or planting, and the potential for economical losses associated with this, were discussed in recent publications [22,23]. Using dendrochronology it was furthermore established that secondary growth and thus productivity of Q. robur, one of the less drought-resistant species among the members of the oak family [24,25], is negatively affected by low precipitation rates both in the same and in the previous years [26]. Furthermore, the different oak species are under attack by different emerging diseases such as Sudden Oak Death and acute oak decline. It is now known that one of the main factors that triggers the development of the symptoms of Sudden Oak Death is drought and irregular precipitation [27]. The changes induced by drought allow the

otherwise weak fungal pathogens to overcome the plant defences [28,29]. Although the cause of the damage is not always clear, the decrease in undamaged oak crowns described by La Porta et al. and references therein [30], provides another argument for the study of drought-induced metabolic changes in oak. In the current study the proteome changes induced by longterm water deficit in Q. robur were studied, by using clone P28, vegetatively propagated and characterized at the biochemical level for some years [31]. To approach naturally occurring drought as much as possible the stress was imposed gradually by initially withholding water and maintaining the soil water content at a low level afterwards [9]. Leaves were collected and used to accomplish a non-targeted proteome study. The observed differences in protein abundance were correlated with acquired morphological and biochemical data. Samples from the same experiment were furthermore used in a transcriptome study that is published separately [32].

2.

Materials and methods

2.1.

Plant material and morphological parameters

Five-year-old plants, oak clone P28 (Q. robur L.), of Austrian origin and vegetatively propagated via somatic embryogenesis were used [31]. The plants were grown in peat moss (Einheitserde, Frux ED 63) in 20 L plastic containers in a greenhouse where air temperature (ambient) and humidity are continuously monitored. A schematic of the experimental setup and pictures of the experiment are added as Supplemental Figure. Fourteen 5-year-old oak plants were used for the control treatment and 30 plants for the drought stress treatment. At the beginning of the growing season (April 2006) all plants were watered up to the saturation point of the soil (55–60% soil water content). The plants were manually watered and the soil water content maintained between 35 and 50% SWC for control plants. Starting from the 8th of May 2006 water was withheld from the stressed plants and, after reaching 15% SWC, the plants were rewatered to stabilize the volumetric water content. The volumetric soil moisture content was measured with a ThetaProbe ML2x FD-Probe (Delta-T Devices, Cambridge, UK) usually three times a week. Detailed description of the conditions, soil water content, days after initiation treatment and developmental stage of the control and drought-treated plants is given in Supplemental Table 1A.

2.2.

Plant development and sampling

A scheme for describing the leaf developmental stages was developed based on the BBCH [33] and scored on a scale of 1–10 for the first flush and six stages each for subsequent flushes (11– 16, 17–22, see Supplemental Table 1 for a description of the developmental stages). Four samplings were done, the first two (three and eight days after the start of the drought treatment) to allow the recognition of early events when the plants are exposed to mild to very mild drought conditions. For the second flush samples were taken when the development of the leaves, not the flush, was estimated the same as the samplings of flush 1. Four leaves from the branch tips were sampled from each

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plant, pooled and immediately frozen in liquid nitrogen and stored at −80 °C prior to use. The methods used for the extraction and the analysis of carbohydrates from plant leaves have previously been published [34,35].

2.3.

Protein extraction

Approximately 400 mg of ground, lyophilized plant material was used for the extraction of proteins. Although during preliminary tests a TCA/acetone-based extraction protocol (extraction 1 detailed later) was found to be optimal, seasonal changes in the chemical composition of the leaves were not sufficiently considered during these tests. Using the TCA/acetone procedure initially gave good results. However, gels loaded with samples from leaves harvested later in season, control and drought exposed plants, resulted in a very poor separation, possibly due to quantitative and qualitative changes in the phenolic content of oak leaves as was previously observed [36,37]. The limited availability of samples excluded the option of using a new extraction protocol on a set of samples representative for the experiment. Furthermore, the application of different methods (3 kDa cut off spin columns, precipitation using different protocols, application of PVPP on the protein extracts) failed to remove the contaminants completely. Therefore a second, hot SDS-based extraction protocol was applied on the pellet of the first extraction. Extraction 1: the ground, lyophilized sample was suspended in lysis buffer (7 M urea, 2 M thiourea, 4% w/v CHAPS). After onehour incubation on a rotary shaker at 4 °C the samples were centrifuged (5 min, 10,000 g), supernatants and pellet separated and both stored at −80 °C. Proteins in the supernatants were precipitated using a TCA/acetone protocol as previously described [38,39] and the protein fraction resolubilized in labeling buffer (7 M urea, 2 M thiourea, 4% w/v CHAPS, 30 mM Tris HCl, pH 8.5). Extraction 2: To the pellet of the first extraction 200 mg polyvinylpolypyrrolidone (PVPP, Sigma, Bornem, Belgium) and 8 ml of SDS-buffer (5% sucrose, 4% SDS, 20 mM sodium phosphate pH 7 and 0.3% DTT) preheated at 65 °C were added [40]. This mixture was incubated in the oven at 65 °C for 30 min with a brief vortex step every 5 min and subsequently put on ice for 15 min. After centrifugation at 19,000 g for 15 min the supernatant was recovered and centrifuged again (same conditions) to eliminate all traces of PVPP. Proteins were further purified by TCA/acetone precipitation and finally solubilized in labelling buffer. The protein content in all the extracts was quantified using the 2D-Quant Kit (GE Healthcare, Little Chalfont, UK) with BSA as standard. The protein content of these extracts and the average number of spots detected on the gel images from the different treatments*sampling are given in Supplemental Table 1B.

2.4.

2DE, image capture and analysis

All protein extracts and a pooled internal standard were labeled with CyDyes™ (GE Healthcare) prior to electrophoresis. Ninety micrograms of proteins (two samples of 30 μg each and 30 μg of internal standard), the volume adjusted to 450 μl with lysis buffer and 2.7 μl of Destreak Reagent (GE Healthcare), 9 μl of IPG buffer 3–10 NL (GE Healthcare) added, were loaded by passive

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rehydration on 24 cm 3–10 NL ReadyStrip IPG strips (Biorad, Hercules, California, U.S.) [41]. Isoelectric focusing was performed using an Ettan IPGphor Manifold (GE Healthcare) in an IPGphor IEF unit (GE Healthcare) with the following protocol: 300 V for 2 h, gradient to 1000 V for 2 h, 1000 V for 2 h, gradient to 2000 V over 2 h, 2000 V for 2 h, gradient to 4000 V over 4 h, 4000 V for 2 h, gradient to 8000 V over 4 h, 8000 V until approximately 75,000 Vh were reached at 20 °C with a maximum current setting of 50 μA per strip. After equilibration, reduction and alkylation of cysteines using respectively DTT and iodoacetamide, the strips were applied on precast gels (GelCompany, Tübingen, Germany) using the buffer kits according to the manufacturer's instructions. Images for the different CyDyes were acquired using a 9400 Typhoon Variable Mode Imager (GE Healthcare) at a resolution of 100 μm using the excitation and detection wavelengths specified by the manufacturer. Images were analyzed using the Decyder v6.5.14.1 software (GE Healthcare). A Two-Way ANOVA with time point of sampling as one factor and treatment as second factor was performed. All spots with a significant score for one of the factors or for the interaction between the two factors (p-value < 0.05), a total of 113 spots hereafter designated as spots of interest, were considered for more detailed statistical analysis and submitted to MS-based identification. A Three-Way ANOVA was performed on this dataset of 113 spots. After a Log10 transformation on the relative abundance data (normalized and standardized) extracted from the Decyder, a linear mixed model statistical analysis was done using PASW (version 18.0.0, SPSS Inc., Somers, NY). Since we were interested in differences in protein abundance between flush 1 and flush 2 the first and the third sampling were defined as physiological state 1 (fully expanded young leaves) and sampling two and four as physiological state 2 (fully expanded mature leaves). The data were reorganized defining the Log10 (Rel Abundance) as dependent variable and the flush, the physiological state and the stress treatment as fixed factors.

2.5.

Mass spectrometry and protein identification

The 113 spots of interest, selected as described earlier, were excised from a non-charged gel containing 90 μg of protein sample (30 μg Internal Standard, 30 μg Cy3 labelled and 30 μg Cy5 labelled sample), and digested using the fully automated Ettan Spot Handling Workstation (GE Healthcare) as described previously [42]. All MS and MS/MS analyses were performed using a 4800 MALDI TOF/TOF (Applied Biosystems, Foster City, CA, USA). An Applied Biosystems GPS-server was used for database searches with an in-house MASCOT platform (Matrix Science, www.matrixscience.com, London, UK). All proteins from the taxonomy viridiplantae were downloaded from the NCBI server and used as protein database, likewise the used EST database contained all viridiplantae ESTs downloaded on 18/02/ 2010. All searches (combined MS and 8 MS/MS spectra) were carried out using a mass window of 100 ppm for the precursor and 0.75 Da for the fragments. During the different searches the following parameters were defined: two missed cleavages, fixed carbamidomethylation of cysteine, variable oxidation of methionine and tryptophan to kynurenine or double oxidation to Nformylkynurenine. When a protein is identified as “hypothetical”, “unknown” or based on an EST-sequence the sequence

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3.

Results and discussion

The drought treatment started when the trees (both control and stressed) were in developmental stage 8 (08/05/2006) and although after 3 and 8 days of drought exposure effects can already be observed in the quantity of the different carbohydrates, the effects at the morphological and proteomic level are scarce. Few differences in the proteome pattern of control versus stressed were detected in samples from sampling I (sampled on 11/05/2006) and II (sampled on 16/05/2006). These differences become observable only during the second flush. Drought-exposed plants passed through the developmental stages slower than control ones (Fig. 1). In order to compare leaves from plants in the same developmental stage sampling III and IV (second flush) for the stressed trees had to be delayed by 3 weeks relative to the control trees (Fig. 1). In a study on the impact of temperature on the development of Q. robur effects on the appearance of the first leaf and on the length of the growing season were observed [46]. In this study by Morin et al. the effects of low soil water content on these parameters were also monitored and contrary to our observations drought conditions did not affect growth under the applied conditions. Nonetheless, developmental delay and a decrease in primary production, at the individual and the forest level, is one of the classic symptoms of drought stress in trees. The effects of the drought on shoot growth and stem diameter observed in the current experiment are described in detail by Spieß et al. A more than 10-fold decrease in shoot growth and a significant decreased increment in stem diameter, 2 cm increase in stem diameter in control trees and less than 1.5 cm in drought-treated trees, illustrate the stress to which the plants were exposed [32]. The concentration of the monosaccharides glucose, fructose and galactose is higher in treated versus control trees from the first sampling point after only three days of drought exposure (Fig. 2). At the last sampling point these concentrations are approximately 4 times higher for fructose and more than 2 times higher for glucose and galactose. The disaccharide sucrose only accumulates during the second flush in the stressed plants. Although a previous study indicates that a shift in leaf–root biomass ratio is the main response of Q. robur to severe drought stress and that osmotic adjustment is only important under mild drought conditions [47], the observed accumulation of

20

* *

Development Stage

18

*

16

*

*

*

14 12 10 8 6 4 -10

3

17

31

45

59

Days after the start of the drought treatment Fig. 1 – The impact of drought exposure on the transition of oak through the developmental stages (described in Supplemental Table 1A). Control indicated in red, drought-exposed plants indicated in blue. The green arrow indicates the moment drought treatment was initiated; different sampling dates are indicated with red and blue arrows for respectively control and stressed trees; *: indicates significant differences between the developmental stage of control and drought-treated trees. (Adapted from [32]).

sugars and the time points are in agreement with previous studies on the accumulation of osmoprotectants in trees [48– 50]. In a study on oak the accumulation of both glucose and fructose was observed [51], however the nearly 2-fold decrease in sucrose content after three weeks of drought exposure was not observed in our experiment possibly due to the timing of sampling. Contrary to this, in a study on Quercus prinus L. the

4

Control Glucose

3,5

Relative concentration

was used for a BLAST analysis and the protein with the highest homology (when significant) added in Supplemental Table 2. All identifications were manually validated and extra precursors were selected for fragmentation if the obtained data were judged as insufficient. The extra information coming from these MS/MS spectra are inserted in red in the supplemental table. When high quality spectra were not matched to sequences, a sequence was determined manually and in the current data set could be linked to the identified protein by allowing for more missed cleavages or semitryptic peptides. This resulted in the identification of signal cleavage sites that could be confirmed either by homology to known signal cleavage sites or by using SignalP [43]. When the same protein was identified in different spots, the corresponding MS peak lists were extracted and used to find unique peaks for each spot applying SPECLUST [44] as was recently done for banana [45].

Fructose Galactose

3

Sucrose

2,5 2 1,5 1 0,5 I

II

Flush 1

Sampling

III

IV

Flush 2

Fig. 2 – Relative concentration (expressed with the analyzed concentration in the control as 1) of the different sugars analyzed in oak leaves during drought exposure (adapted from [32]).

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Fig. 3 – 2D-electrophoresis gel of oak leaf samples labelled with CyDye5, indicated on the image are the spots with a p-value below 0.05 in the Two-Way ANOVA. The spot numbering corresponds to the numbering used in Table 2 and in Supplemental Tables 2 and 3.

accumulation of sucrose was much more pronounced than the accumulation of the monosaccharides [52], but similar to our results sucrose accumulation only started later in the season. Although a role of osmoprotectants is also attributed to galactose, data on its accumulation during drought exposure is scarce nonetheless a significant accumulation was measured in Populus euphratica [9]. A Two-Way ANOVA analysis on the gel images from the different samples resulted in the detection of 113 spots that were submitted to MS-based identification (Fig. 3). A protein could be significantly identified in 106 of these spots (94%), with

more than one protein identified in 11 spots (10%), excluding them from biological interpretation. The experimental evidence obtained to delineate transit peptides for several proteins is also given in Supplemental Table 2. We were interested in highlighting protein abundance changes directly related to drought (only 18 with a significant score for the factor treatment in the TwoWay ANOVA, Table 1) and not to normal leaf development. Therefore we focused on the spots that changed significantly (intensity ratio treated/control below 0.66 or above 1.5 and T-test score p < 0.05) in a pairwise comparison of control and stressed samples taken at each time point (Table 1). Thirty-three of the

Table 1 – Summary of all statistically significant spots using the tools described in the Materials and Methods section. Two-Way ANOVA

a

Sampling time

Treatment

Interaction

109

18

22

Pairwise comparison stressed/control samples

Increase Decrease

Sampling I

Sampling II

Sampling III

Sampling IV

3 2

0 5

4 1

5 17

Three-Way ANOVA Flush

50 a

Physiological state

83

b

Treatment

21

Flush * Physiological state

33

c

Physiological state * Treatment

26

Flush * Treatment

15

Flush * Physiological state * Treatment 25

Two-Way ANOVA using the Decyder software, indicated is the number of spots with a p-value < 0.05 for the different fixed factors and the interaction between them. b Pairwise comparison control/stressed of the spot intensity at each sampling date, average ratios of the protein abundance (stressed/control). For those time points that both the ratio in relative abundance is below 0.66 or above 1.5 and T-test < 0.05. c Three-Way ANOVA using PASW software package indicated is the number of spots with a p-value < 0.05 for the different fixed factors and the interaction between them.

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Log10(Rel. Abund.)

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Flush 1 I

II

Flush 2 III

IV

Flush 1 I

II

Sampling

Flush 2 III

Sampling

IV

Flush 2

Flush 1 I

II

III

IV

Sampling

Fig. 4 – Illustration of the changes in spot intensity that were detected in the Three-Way ANOVA. Control data is indicated in red and data from stressed plants in blue. From the left to the right the spots 105, 112 and 30, respectively with a Three-Way ANOVA p-value for the interaction of the Log10(Rel Abundance) with the three fixed factors of 0.815; 0.314 and 0.043. For the spots 105 and 112 the change in direction of the relative spot intensity during the two flushes are more (spot 105) or less (spot 112) similar between control and treated samples. This results in a non-significant p-value for the Three-Way ANOVA. The direction of change in relative intensity of spot 30 for treated samples is completely different from that of control samples and furthermore completely different between the first (control and treated change in the same direction) and the second flush. For spot 30 the Three-Way ANOVA as described in the Materials and methods section resulted in a significant p-value, hence the spot was retaken in Table 2 and the biological analysis.

113 spots changed significantly at one of the time points in this comparison. By looking at the expression data it became apparent that some proteins did not change significantly in the pairwise comparison but nonetheless showed a distinct behaviour in a comparison of the first and the second flushes of stressed versus control plants. To identify the significant changes illustrated in Fig. 4, a Three-Way ANOVA analysis was applied on the dataset of 113 spots that changed significantly in the Two-Way ANOVA. We performed a vector comparison of the changes in the relative abundance of a spot (i.e. dependent factor) in relation to the flush, the physiological state and the treatment (i.e. fixed factors). The data were reorganized, grouping sampling I and III (fully expanded young leaves) as Physiological State 1 and data from sampling II and IV as Physiological State 2. There was a significant change in Log10(Rel Abundance) in the interaction with the three fixed factors for 25 spots (Supplemental Table 3), the majority of which was already identified as changing significantly in the pairwise comparison. The highest number of significant p-values was recorded for the fixed factor “Physiological State” indicating that the time of sampling is the cause of most of the observed variation, as already observed in the Two-Way ANOVA (Table 1). The 41 proteins that were found to change significantly in the pairwise comparison and/or in the vector comparison (Flush * Physiological State *Treatment) are given in Table 2, complete identification data are given in Supplemental Table 2 and all statistical data in Supplemental Table 3. Twenty-two out of 95 identified proteins, excluding those spots wherein two proteins are identified, are involved in carbon fixation. Another 25 out of 95 include proteins involved in photosynthesis (13 out of 95) or other metabolic processes involving carbohydrates (12 out of 95). Twenty of the identified proteins are directly involved in the folding (7) or metabolism of proteins (6) or amino acids (7). The two other functional groups that have more than 1 representative are proteins involved in stress responses (7 in total; 1 involved in the response to heat; 2

general stress; and 4 related to oxidative stress) and with a nutrient reservoir function (5). When the analysis is limited to the 41 spots that changed significantly according to the criteria discussed above (Table 2), it is remarkable that one third of the group of 33 proteins (excluding spots wherein more than 1 protein was identified) is involved in carbon fixation (10). The group of proteins involved in photosynthesis (3) or carbohydrate metabolic processes (2) is limited. Groups of proteins relatively overrepresented among these 33 spots are proteins with a nutrient reservoir function (3) or involved in stress responses (3). Given the limited number of significant changes, most likely due to the high biological variation between the different samples, it is difficult to extract biologic knowledge on the response of oak when exposed to drought from the current data. There are furthermore no pathways that are highlighted in our data by analogous changes in the abundance of several functionally linked proteins. Nonetheless, some significant observations can be made. A first remarkable observation is that some spots containing RuBisCo large chain are more abundant in the samples from leaves of stressed trees (Table 2; spots 810 and 816), contrary to the decrease in photosynthetic proteins that is generally observed in drought stressed plants. Looking at the gel image it is however apparent that the polypeptides in spots 810 and 816 are only fragments of RuBisCo large chain (Fig. 3). At the molecular weight where the RuBisCo large chain is expected to be it has been identified in several spots (spots 336 and 355), spots that decrease slightly, but not significantly, in intensity (Supplemental Tables 2 and 3). Apart from the two spots that change significantly, several other spots of increasing intensity contained degradation products of the RuBisCo large chain. These spots include the spots 1172 and 1188 with an expression pattern very similar to the one of spots 810 and 816 (Supplemental Tables 2 and 3). A similar degradation of RuBisCo during exposure to abiotic stress is for instance previously described for rice exposed to chilling stress [53]. RuBisCo degradation is important in different physiological states of a plant and is particularly well studied in senescence [54].

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Table 2 – Identification of the proteins in the spots that change significantly during the exposure of oak to a long drought period. Spota

Proteinb

Accessionb

GO annotation

Fold change in the different samplingsc I

30 38 101 165 192 378 379 380 382 408 416 418 445 472 516 572 653 671

679 686 756 758 760 810 816 857

952 960 968 1008 1026 1084 1121 1151 1194 1228 1244 1261 1320 1418 1420

Precursor of carboxylase p-prot 1

224088838

GO:0006519

heat shock protein, putative Protein disulfide-isomerase: hypothetical protein calreticulin-1 RuBisCO activase RuBisCO activase RuBisCO activase RuBisCO activase RuBisCo activase 1 RuBisCO activase Plastid lipid-associated protein 3, precursor, putative Actin RuBisCO activase Phosphoribulokinase conserved hypothetical protein Sedoheptulose-1,7-bisphosphatase Ribonucleoprotein chloroplast put. ATP synthase gamma chain, chloroplast Malate dehydrogenase Allergic isoflavone reductase-like protein

255570990 11133818 225452887 117165712 266893 266893 3914605 3914605 223527225 266893 255555879

GO:0019538 GO:0006457

32186900 266893 1885326 223547704 242055003 255540443

GO:0015031 GO:0015977 GO:0005975

5708095 3193222 10764491

GO:0019538 GO:0015977 GO:0015977 GO:0015977 GO:0015977 GO:0015977 GO:0015977

GO:0005975 GO:0032388 GO:0015986 GO:0005975 GO:0006808

OEE1 OEE1 OEE1 RuBisCo large subunit RuBisCo large subunit Put. nascent polypeptide associated complex α chain triosephosphate isomerase,putative no hom. protein, ABA-resp domain no hom. protein, ABA-resp domain putative auxin-binding protein 2 Carbonic anhydrase putative auxin-binding protein 2 putative auxin-binding protein 2 Glycine-rich protein 2

12644171 12644171 12644171 20257338 224382632 46575976

GO:0006808 GO:0006808 GO:0006808 GO:0015977 GO:0015977

223531284 EST 262510259 EST 262515733 167857053 1354517 167857053 167857053 121631

GO:0005975 GO:0006950 GO:0006950 GO:0045735 GO:0006730 GO:0045735 GO:0045735 GO:0006255

Put. peptidyl-pro cis-trans isomer. RuBisCo LSU

223545637 290770929

GO:0006457 GO:0015977

PS1 reaction center subunit III peroxiredoxin, putative RuBisCo small subunit rbcS3 Plastocyanin

157678948 223531975 10946379 130277

GO:0015979 GO:0006979 GO:0015977 GO:0015979

aNumbering

II

III

IV

Direction of changed Flush I

Flush II

C

S

C

S

1.03 0.43 0.90 0.98 0.98 0.81 0.65 0.77 0.70 0.87 0.68 0.97

1.26 0.70 1.03 1.24 1.24 0.93 0.93 0.71 0.85 0.61 1.19 0.89

0.45 0.33 1.15 1.21 1.21 1.06 1.61 1.08 1.57 0.86 1.96 1.28

1.71 0.63 0.57 0.50 0.50 0.29 0.71 0.49 0.65 0.34 0.75 0.51

↓ ↓ ↓ ↓ ↓ = = = = = = ↑

↓ ↓ ↓ = = = ↑ ↑ ↑ ↓ ↑ =

↓ ↓ = = = = ↑ ↑ ↑ = ↑ =

↑ = ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓

0.77 1.06 0.75 1.16 1.16 1.08 0.84

1.27 0.81 1.29 1.14 1.14 0.61 1.07

1.48 0.56 2.51 0.90 0.90 1.24 1.25

0.65 0.61 1.13 0.59 0.59 0.55 0.51

↑ = ↑ ↓ ↓ = =

↑ ↓ ↑ ↓ ↓ ↓ =

↑ ↓ ↑ = = = =

↓ ↓ ↑ ↓ ↓ ↓ ↓

0.78 0.89

0.63 0.84

0.77 1.81

0.88 0.48

= =

↓ =

↓ ↑

↓ ↓

0.89 1.16 0.69 0.71 0.94 0.89 1.11

0.84 0.70 1.92 1.68 1.09 1.05 0.86

1.81 0.93 4.29 5.71 3.06 0.65 0.69

0.48 0.30 1.20 0.86 0.71 2.60 2.05

= ↓

= ↓

↑ ↓

↓ ↓

↑ ↑ ↑ = ↑

↑ ↑ ↑ = ↑

↑ ↑ ↑ ↓ =

= = = ↑ ↑

0.82 1.03 2.15 1.62 1.38 1.07 0.93 1.04 1.10 0.69 1.09 1.09 1.49 4.55 0.73 0.59 1.41

1.02 0.46 2.82 2.39 0.59 0.64 1.23 1.07 1.06 1.34 0.92 0.92 1.83 7.43 1.16 1.37 1.03

1.28 0.94 1.02 0.90 1.40 0.74 1.73 2.17 1.39 2.10 2.32 2.32 1.68 2.23 1.23 1.97 1.52

0.58 1.14 0.46 0.30 0.59 0.54 1.14 1.09 0.43 1.04 0.98 0.98 0.53 12.02 1.92 1.25 0.60

↑ = = = ↑ = ↓ ↓ ↑ = ↑ ↑ ↑ ↑ = = ↑

↑ ↓ = = = ↓ = ↓ ↑ ↑ ↑ ↑ ↑ ↑ = ↑ ↑

↑ ↓ ↓ = ↑ = = = ↑ = ↑ ↑ ↑ = ↑ ↑ ↑

↓ = ↓ ↓ ↓ ↓ ↓ ↓ ↓ ↓ = = ↓ ↑ ↑ ↑ ↓

according to Fig. 3, detailed identification data Supplemental Table 2.

b

NCBI protein database; either the name of the highest scoring protein is given or in the case a protein was identified based on the EST database, the EST sequence was blasted and the protein with highest, significant homology given. c Average ratios of the protein abundance (stressed/control). For those time points that both the ratio in relative abundance is below 0.66 or above 1.5 and the T-test score p < 0.05, the value of the ratio is given in bold face. d Direction of change of the intensity of spots during flush I (from sampling I to II) and flush 2 (from sampling III to IV), “C” indicates control plants, “S” drought-exposed plants; spots that have a significant change in the direction from flush 1 to flush 2 (a score of <0.05 in the Three-Way ANOVA) are shaded.

In our data different arguments against the hypothesis that the trees in this study are subject to a massive net-degradation of RuBisCo, as seen during senescence, can be found. Firstly, the significant increase of the intensity of the spot containing RuBisCo small chain (spot 1418) suggests that the plants are trying to cope with the stress. The same observation was made during the gene expression analysis [32]. Secondly, spots containing detoxifying enzymes (spot 1320 in Table 2 and spots 975, 1070, 1280 and 1343 in Supplemental Tables 2 and 3), expected to be

down regulated during induced senescence [54], increase or do not decrease significantly. In literature there are two other possibilities for increased RuBisCo degradation described, direct ROS-induced degradation by reactive oxygen species generated in the active site of RuBisCo or the degradation and replacement of deactivated RuBisCo. Direct evidence for neither mechanism can be obtained from our data but both are possible. Direct degradation of RuBisCo by ROS generated in the active site is proven in vitro and in vivo RuBisCo [55] and in wheat the cleavage

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site is a Gly-residue situated at the entrance of the active site, a residue and surrounding sequence that is conserved in the protein from Quercus phillyraeoides (gi:16326287) [56]. Exposure to drought is known to induce oxidative stress in oak trees [57,58], and the higher abundance of the protective enzyme peroxiredoxin (spot 1320) [59], previously identified as being more abundant in drought-exposed rice and sugar beet [60,61], indicates that ROS levels are high in the chloroplasts and that RuBisCo oxidation might occur. One protease involved in oxidative stress responses [62] and in the degradation of oxidized proteins was identified in the current study, Clp protease. In the proteomic data a non-significant decrease of spot 58 is observed (Supplemental Tables 2 and 3) however during the gene expression analysis a significant increase of the activation of a Clp protease gene was observed at sampling IV [32]. Since of the proteolytic enzymes studied during the transcriptome study only the Clp protease is upregulated, the link with the increased appearance of RuBisCo degradation products in the proteome study and the timing of these two events is striking. Several other proteins from Table 2 can be related to carbon fixation and photosynthesis. During the first flush spots containing RuBisCo activase (spots 378, 379, 380, 382, 408, 416 and 472) in general have a lower abundance in treated versus control plants. However, at the beginning of the second flush (sampling III) they are of higher abundance followed by a significantly lower abundance at the end of the second flush (sampling IV). This same trend is observed for the 3 spots containing OEE1 (spots 756, 758 and 760) but also for plastocyanin (spot 1420) and a glycine rich protein (spot 1151). The glycine-rich protein is interesting since it contains a “cold shock domain” (NCBI CD04458), a domain found in eukaryotic and prokaryotic proteins that is known to interact with single stranded RNA and DNA and is implicated in the regulation of translation under suboptimal growth conditions. The identified oak EST is more homologous to Arabidopsis glycine rich protein 2, an isoform previously found to be unregulated in Arabidopsis during drought, this contrary to the glycine rich protein 1 [63]. The intensity of most of the spots that changed significantly according to the different criteria has a similar trend. At sampling III the abundance is higher in droughttreated versus control plants, a ratio that changes at sampling IV with generally a significantly lower abundance in treated versus control plants. This general trend might indicate that during the flush transition (sampling II to sampling III) the molecular machinery needed for primary production is reinforced in plants exposed to drought, hence the higher abundance of these proteins in treated plants at sampling III. However, given the prolongation of the stress exposure and the negative effects of having fully equipped cells in suboptimal conditions, such as the production of ROS, the plants are unable to maintain this alert state further on. The spots with the most striking change in relative abundance between flush 1 and flush 2 are the spots 960 and 968, wherein ESTs were identified that share a stretch of sequence homologous to abscisic acid stress ripening proteins (ASR) [64]. These spots are more abundant in exposed trees during the first flush but their relative abundance diminishes strongly during the second flush. Although the exact function of ASRs remains elusive, they are implicated in stress responses and bind directly to the promoter region of genes involved in glucose transport [65,66]. The protein was found to accumulate

during sucrose-induced stress in banana [67] and Pinus pinaster [68]. The presence of ASR in the banana B-genome but not in the A-genome is potentially linked with the higher drought tolerance of plants containing the B-genome [44]. Based on the data, we can consider that there are 2 phases in our experiment: a first one during which the plants are exposed to mild and short-term drought and the ASRs accumulate. In a second phase (flush 2), the plants are exposed to severe and long-term stress conditions and the abundance of ASR decreases. Frankel et al. studied the accumulation of carbohydrates in ASR-overexpressing and antisense potato and found an inverse relation between ASR abundance and hexose accumulation in tubers but not leaves [69]. The same inverse relationship between ASR abundance and hexose accumulation Frankel et al. found in potato tubers was observed in our leaf samples. The increased accumulation of carbohydrates is non-significant during the first flush when ASR is more abundant in stressed leaves, and only becomes significant when the abundance of ASR decreases. Another group of spots that have a similar behaviour are the 3 spots containing the putative auxin-binding protein 2 (1008, 1084 and 1121), identical to germin-like proteins containing a cupin-conserved domain with GO annotation 0045735, nutrient reservoir function. The pattern of change in relative abundance is similar to that of RuBisCo activase and other spots mentioned earlier. At the beginning of the second flush the intensity is higher in treated plants and it decreases to attain a level similar to that in control plants at the end of the second flush (Table 2). Different functions have been attributed to germin-like proteins, including nutrient storage, oxalate oxidase and superoxide dismutase functions and the group of germin-like proteins is regularly identified in studies on abiotic stress in plants [39,70]. The highest fold change was recorded for spot 1261, containing the Photosystem 1 E subunit. The semitryptic peptide at m/z 2805.23 closely matches to the predicted N-terminus of the homologue from tobacco (gi:157678948) after removal of the transit peptide. For the tobacco homologue a transit peptide of 49 amino acids is predicted (45RLVVR AAEE); in the translated oak EST the sequence around the transit peptide cleavage site is conserved but the transit peptide resulting in the peptide at m/z 2805.23 is 1 amino acid longer. Looking at the complete set of 113 MS-studied spots shows that 2 more spots, spots 1269 and 1276, contain the same protein but with a better sequence coverage, a ragged N-terminus and corresponding to much more intense spots on the gel (Fig. 3 and Supplemental Table 2). Furthermore the experimental pI of the spots 1269 and 1276, corresponds more to the theoretical pI of the protein. Since the N- and the Cterminus of the protein are identified in all three spots, drought must cause amino acid modifications, resulting in a large acidic shift in pI, in this protein. Although considered to be less sensitive to ROS-inducing stresses than PS2, degradation and oxidative modification of PS1 subunits under stress conditions has been described [71,72]. The Synechocystis homologue is involved in avoiding electron leakage and the formation of ROS around PS1, but it is non-essential [73]. In Arabidopsis, deletion of the corresponding gene did not exclude the plants from growing autotrophically but normal physiology was significantly impaired [74,75]. The accumulation of what is probably a modified, inactive form (spot 1261) and at the same time the stable abundance of what are most likely active forms (spots 1269 and

J O U R NA L OF PR O TE O MI CS 7 4 ( 2 01 1 ) 1 3 8 5–1 3 9 5

1276) of this protein again indicates that the plants are trying to preserve the molecular machinery needed for photosynthesis. In the only other quantitative study on oak and drought, young trees were used and only a short drought period of 14 days was applied [13]. Two of the 8 Q. ilex proteins that changed significantly in drought exposed plants and were identified as sole protein in a spot were also identified in the current study; OEE1, triosephosphate isomerase. However given the difference in experimental setup these results are difficult if not impossible to compare.

[3]

[4]

[5]

4.

Conclusion

In the described experiment, relatively young oak trees were exposed to conditions mimicking one dry summer by withholding water and maintaining it at a low level during an entire season. The stress resulting from this treatment slowed down the development of the trees and resulted in the accumulation of osmoactive compounds in the foliar tissue (e.g. mono- and di-saccharides). Although the described limitations do not allow the description of a final conclusion on the adaptation of oak to long-term drought exposure, our data allows an image to be drawn. It is clear that the trees are capable of adjusting their metabolism for some time, this at the cost of a reduced growth/productivity. These adaptive mechanisms are however overwhelmed at the last sampling point, when changes in protein abundances indicate that there is a general exhaustion of the capacities of the plant. The same adaptation in the first stages followed by the overwhelming of the adaptive capacities under prolonged stress conditions was observed in the gene expression analysis that was performed on the same samples. Both studies therefore hint at the existence of effective protective, adaptive systems that allow the trees to grow during dry summers only to succumb when the dry season surpasses a certain length. Supplementary materials related to this article can be found online at doi:10.1016/j.jprot.2011.03.011.

[6]

[7]

[8]

[9]

[10]

[11]

[12]

[13]

[14]

Acknowledgements [15]

The authors would like to thank Sébastien Planchon for technical assistance and Dr Torsten Bohn for helping with the statistical analyses. The present study was presented at COST Action FA0603 meetings.

[16]

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