Simultaneous Treatment with Tebuconazole and Abscisic Acid

Article pubs.acs.org/jpr

Simultaneous Treatment with Tebuconazole and Abscisic Acid Induces Drought and Salinity Stress Tolerance in Arabidopsis thaliana by Maintaining Key Plastid Protein Levels Ruth Horn,† Ivana Chudobova,† Ulrike Han̈ sel,‡ Denise Herwartz,† Pascal von Koskull-Döring,‡,§ and Stefan Schillberg*,† †

Department Plant Biotechnology, Fraunhofer Institute for Molecular Biology and Applied Ecology (IME), Aachen, Germany Bayer CropScience AG, Frankfurt a.M., Germany

‡

S Supporting Information *

ABSTRACT: Arabidopsis thaliana plants were treated simultaneously with the fungicide tebuconazole and the phytohormone abscisic acid (ABA). We carried out comparative proteomic and transcriptomic analysis against untreated controls under different stress regimes. The chemicals were applied 24 h before the onset of drought stress (removal of the nutrient medium) or salinity stress (hydroponic culture using 150 mM NaCl), and samples were taken during the stress treatments and after a 24 h recovery period. The combined chemical treatment protected plants against both forms of stress. Difference in-gel electrophoresis revealed 18 and 34 unique protein markers representing induced tolerance to drought and salinity stress, respectively. Most of the markers represented plastid functions (such as CO2 fixation and photosystem II activity), and their abundance was reduced under stress conditions but maintained at near normal levels in the treated plants. The corresponding transcripts were reduced in abundance primarily under drought stress but not salinity stress, indicating that the signal transduction pathways activated by tebuconazole/ABA treatment depend on the nature of the stress stimulus. KEYWORDS: abscisic acid, Arabidopsis thaliana, difference-in-gel electrophoresis, marker, proteome, stress tolerance, tebuconazole, transcriptome

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INTRODUCTION

Chemical treatments can also induce stress tolerance by activating endogenous stress-response pathways. The phytohormone abscisic acid (ABA) plays an important role in stress signal transduction and accumulates in response to both salinity and drought stress,8 thus inducing genes required for stress acclimation.9 The exogenous application of ABA has been shown to induce cold tolerance,10 drought tolerance,11 and salinity tolerance.12,13 The impact of ABA on the transcriptome and proteome has been widely studied under normal conditions,14−17 but its role in salinity stress tolerance has been considered at the proteomic level in only one previous investigation, which attributed tolerance to a priming effect in which ABA activates the stress-response pathways before the onset of stress so that the plants are preadapted.18 As well as natural phytohormones, stress response pathways can be activated by xenobiotics such as the triazole-type fungicides uniconazole and diniconazole, which inhibit sterol biosynthesis. The application of such fungicides can induce tolerance to drought19−22 and salinity23 by inhibiting the ABA degrading enzyme ABA 8′-hydroxylase and thus promoting the accumulation of ABA.20,21 The related fungicide tebuconazole does not inhibit ABA 8′-hydroxylase20 but can nevertheless induce stress tolerance, as claimed in a recent patent

More than 70% of the world’s plants are frequently exposed to unfavorable environmental conditions such as high salinity, drought, and extreme temperatures, indicating that abiotic stress has a major impact on agriculture.1 Plants respond to stress by activating signal transduction pathways that induce stress-response markers, leading to the accumulation of proteins and metabolites that mediate stress acclimation and tolerance. Many components of these signaling cascades are common to different forms of stress.2,3 Enhancing stress tolerance in crops is an important goal in the agricultural industry, particularly in response to concerns about the potential impact of climate change. Because the genome sequences of several crops are available, large-scale transcriptomic and proteomic studies can be used to gain a more comprehensive understanding of stress tolerance mechanisms by identifying the corresponding markers. For example, extremophiles such as the moss Physcomitrella patens (salinity and desiccation tolerance), the cress Thellungiella halophilia (salinity tolerance), and the bread wheat cultivar Shanrong number 3 (drought tolerance) have been used for comparative studies.4−7 Stress tolerance markers revealed by such studies can be used to accelerate selection during conventional breeding programs or as transgenes in genetically engineered stress-tolerant crops. © 2013 American Chemical Society

Received: October 4, 2012 Published: February 5, 2013 1266

dx.doi.org/10.1021/pr300931u | J. Proteome Res. 2013, 12, 1266−1281

Journal of Proteome Research

Article

active compound equivalent to 4.9 μmol tebuconazole and 5.6 μmol ABA, supplied by Bayer CropScience, Frankfurt am Main, Germany). For the treatment compound, concentrations were chosen based on the concentrations used for field trials performed by Bayer CropScience. Control plants were treated with 30 mg of a matching formulation lacking the active compounds. After 24 h, the plants were subjected to salinity stress (transfer to fresh MS medium supplemented with 150 mM NaCl for 96 h) or drought stress (transfer to a box without medium for 48 h, sealed to reduce the drying rate). The plants were then transferred to normal MS medium for recovery. Whole rosette leaf samples were taken after 24 and 96 h of salinity stress and after 24 and 48 h of drought stress, and after 24 h of recovery in both cases. Four replicate samples were taken at each sampling stage, each comprising the rosettes pooled from four plants. Samples were also taken from untreated control plants (stressed and unstressed) at parallel time points. Figure 2 summarizes the experimental workflow,

application.24 Preliminary experiments have shown that the stress tolerance induced by tebuconazole is enhanced by the simultaneous application of ABA (unpublished data). Therefore, in order to gain insight into the mechanisms of tebuconazole-induced stress tolerance, we investigated changes in the Arabidopsis thaliana proteome following the simultaneous application of tebuconazole and ABA in the presence and absence of drought and salinity stress. Two-dimensional difference in-gel electrophoresis (2D-DIGE) revealed many protein markers representing induced drought and salinity stress tolerance, the majority of which appeared to be involved in essential plastid functions. The profiles of these markers were compared to the corresponding transcripts to gain insight into the regulation of stress tolerance induced by tebuconazole/ ABA.

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MATERIALS AND METHODS

Plant Culture and Stress Treatment

Arabidopsis thaliana plants (ecotype Col-0) were grown in hydroponic culture using seed holders (Araponics SA, Liège, Belgium) in custom 26-L boxes with black lids (Figure 1). The

Figure 2. Experimental workflow for chemical and stress treatments. Five-week-old plants were treated with tebuconazole plus ABA or a matching formulation lacking the active compounds as a control. After 24 h, treated and untreated control plants were either subjected to the stress conditions (stress, TS and S) or left at the normal growth conditions (control, T and C). After 48 h drought stress (DS) and 96 h salinity stress (SS) the plants were transferred back to conditions without stress (Rec). Samples were taken at the first and the last day of stress and after 24 h of recovery. Samples from the unstressed plants (T, C) were taken at corresponding time points. C = untreated and unstressed, S = untreated and stressed, T = treated and unstressed, TS = treated and stressed.

Figure 1. Hydroponic cultivation of Arabidopsis thaliana plants. (A) Box with 168 plants grown under short-day conditions. (B) Box with 50 plants after transfer to long-day conditions and selection of homogeneous plants. (C) View of the seedholders below the lid with protruding roots.

seeds were incubated in darkness at 4 °C for 72 h in 0.1% (w/ v) agar and then placed on seed holders containing 0.65% (w/ v) agar plugs. The boxes were filled with 1% (w/v) Murashige− Skoog (MS) medium (Duchefa Biochemie BV, Haarlem, Netherlands), and 168 plants per box (Figure 1A) were grown under short-day conditions (8-h photoperiod, 110 μE, 21/18 °C, 70% humidity) for 4 weeks. A BioFiltre (General Hydroponics Europe, GHE, Fleurance, France) was used to prevent the growth of root pathogens. The plants were then transferred to long-day conditions (16-h photoperiod, other conditions as above) and fresh MS medium, at a reduced density of 50 plants (matched for developmental stage) per box (Figure 1B). Oxygen was supplied to the medium every 2 h for 15 min using aquarium pumps and air stones. The plants were cultivated under long-day conditions for 1 week prior to treatment. Compounds were sprayed onto the rosettes using an air pressure spray gun (SATA Dekor 2000, SATA, Kornwestheim, Germany). Each box of 50 plants was treated with equal amounts of tebuconazole and ABA (1.5 mg of each

which resulted in four different experimental groups representing each form of stress. Leaves were frozen immediately in liquid nitrogen and stored at −80 °C prior to proteomic and transcriptomic analysis. Microarray data were collected from plants grown and treated using the same procedures at Bayer CropScience. Protein Extraction

Frozen leaves were ground to a fine powder under liquid nitrogen, and the proteins were precipitated by adding 1.8 mL of ice-cold acetone containing 0.7% (v/v) 2-mercaptoethanol. The samples were incubated at −20 °C for at least 1 h and then centrifuged at 13,000g, 4 °C for 20 min. The pellets were resuspended in ice-cold acetone with 0.7% (v/v) 2mercaptoethanol and treated as above. After the second centrifugation step, the pellets were washed twice in ice-cold 1267



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Table 1. CyDye Labeling Scheme for the Samples of the First Day of Stressa Gel 1 2 3 4 5 6 7 8 a

Cy2 internal internal internal internal internal internal internal internal

standard standard standard standard standard standard standard standard

Cy3 Day Day Day Day Day Day Day Day

11111111-

Cy5

control − 1 control − 2 treated − 1 treated − 2 stressed − 1 stressed − 2 treated and stressed − 1 treated and stressed − 2

Day Day Day Day Day Day Day Day

11111111-

treated and stressed − 3 treated and stressed − 4 control − 3 control − 4 treated − 3 treated − 4 stressed − 3 stressed − 4

Samples from the last day of stress and the first day of recovery were labeled accordingly.

sample is expressed as a ratio relative to the internal standard. As a second step, the software performs an intergel matching of internal standard samples across all gels within the experiment. Preparative gels were poststained with 1 mM ruthenium(II)tris(bathophenantroline disulfonate (RuBPS) fluorescent stain (RubiLAB, Burgdorf, Switzerland) in fixation solution (40% (v/ v) ethanol, 10% (v/v) acetic acid) for 2 h. After destaining in fixation solution overnight and washing in water for 20 min, gels were scanned with the EDI using the Cy2 filter. Reference markers were attached to the glass plates in three corners prior to scanning, and 2D images were imported into the DeCyder BVA (Biological Variation Analysis) workspace and matched to the master image. Spots of interest were marked on the preparative gel and picked by blind picking. The proteins in gel spots were alkylated and digested with trypsin (Promega, Mannheim, Germany) as previously described.26

acetone without 2-mercaptoethanol, dried at room temperature, and stored at −20 °C. The proteins were solubilized overnight at room temperature in IEF buffer (7 M urea, 2 M thiourea, 2% (w/v) CHAPS, 30 mM Tris-HCl pH 8.8), and the solution was centrifuged as above to remove debris. The protein content of the supernatant was quantified using the 2D quant kit (GE Healthcare, Munich, Germany), and 50-μg aliquots (5 μg/μL) were prepared for each sample. Equal amounts of all protein samples from the different treatment regimens were pooled, and 50-μg aliquots (5 μg/μL) were prepared and used as the DIGE internal standard. In the same way 300-μg aliquots were prepared for preparative gels. Two Dimensional DIGE and Image Analysis

Each 50-μg protein aliquot was labeled with 200 pmol CyDyes (GE Healthcare) according to the manufacturer’s instructions. The internal standard was labeled with Cy2, whereas the comparative samples were labeled with Cy3 and Cy5 according to Table 1 before pooling. The internal standard was run on every single gel along with each individual sample, so that every protein from all samples was represented in the internal standard, which is present on all gels. For analytical gels, immobilized pH gradient (IPG) strips (24 cm, pH 4−7, GE Healthcare) were passively rehydrated for 10 h with 150 μg of labeled protein in 450 μL of IEF buffer (7 M urea, 2 M thiourea, 2% (w/v) CHAPS, 40 mM DTT, 0.5% (v/v) pharmalytes pH 3−10 (GE Healthcare)). For preparative gels, IPG strips were rehydrated with 450 μL of IEF buffer containing 300 μg of unlabeled protein. The strips were focused using the IPGphor 3 (GE Healthcare) overnight at 20 °C running the following program: (1) step, 500 V for 1 h; (2) gradient, 1000 V for 8 h; (3) gradient, 8000 V for 3 h; (4) hold at 8000 V until 52 kVh total. After focusing, the proteins were reduced in 1% (w/v) DTT for 15 min followed by alkylation in 2.5% (w/v) iodacetamide for 15 min in 4 mL of equilibration buffer (6 M urea, 30% (v/v) glycerol, 50 mM Tris-HCl pH 8.8, 2% (w/v) SDS). Second-dimension separation was carried out by 12.5% (w/v) SDS polyacrylamide gel electrophoresis25 using the Ettan DALTtwelve gel system (GE Healthcare) and applying a constant current at 20 °C overnight (1 h at 10 mA/gel, 13 h at 13 mA/gel and 2 h at 16 mA/gel). For preparative gels, the glass plates were silanized on one side prior to casting. After protein separation, gels were scanned using the Ettan DIGE Imager (EDI, GE Healthcare) equipped with appropriate filters for Cy2, Cy3, and Cy5. The images were processed with DeCyder software v7.0 (GE Healthcare). The software performs first co-detection of sample and internal standard protein spots on each gel. The spot volume for each experimental sample is compared directly to the internal standard so that the protein abundance for each spot in each

NanoLC−MS/MS Analysis

Peptides were separated on a nanoHPLC System (Switchos and Ultimate, LC PAckings, Dionex, Idstein, Germany) coupled via a Nanomate ESI source (Advion BioSciences, Harlow, UK) to a Q-TOF-2 mass spectrometer (Waters, Eschborn, Germany). The nanoHPLC system was controlled by Chromeleon software v6.70, the Nanomate system was controlled using ChipSoftManager v8.2.5.126, and the Q-TOF2 was controlled using MassLynx v4.0. The dried peptide sample was dissolved in 15 μL of solution A (5% (v/v) acetonitrile, 0.05% (v/v) formic acid), and 10 μL was injected using a Famos autosampler (LC PAckings, Dionex) into the liquid chromatography system supplied with the reversed phase precolumn (LC PAckings, Dionex) and the 15-cm PepMap 100 reversed-phase C18 column, 75 μm i.d. (LC PAckings, Dionex). The peptides were trapped on the precolumn for desalting and then separated on the PepMap column using a 30-min linear gradient from 95% (v/v) solution A and 5% (v/ v) solution B (85% (v/v) acetonitrile, 0.05% (v/v) formic acid) to 50% (v/v) solution A and 50% (v/v) solution B at a flow rate 250 nL/min. The Nanomate was operated in positive ion mode with 1500−2000 V spray voltage and 0.3−0.5 psi gas pressure. The cone voltage was set to 40 V, and the MS survey was switched to MS/MS mode when the intensity of individual ions exceeded seven counts per second. The maximum number of ions selected for MS/MS from a single MS survey scan was set to three. The normalized collision energy was set to 30.0. MS Data Analysis

Raw data files were evaluated using Matrix Science Software with the client application Mascot Daemon 2.2.2, the data browser Mascot Distiller 2.3.2.0 and the search engine Mascot Search 2.3.01 (Matrix Science Ltd., London, U.K.). The spectra were searched against the NCBInr 20100716 database 1268



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(11493268 sequences; 3921624007 residues) using the following parameters: enzyme trypsin, up to one missed cleavage permitted, no fixed modifications and variable modifications carbamidomethyl (C), oxidation (M), and propionamide (C) allowed, mass tolerance for precursor ion ±0.4 Da and fragment ion ±0.3 Da, species restriction Arabidopsis thaliana. Only significant hits were accepted, as defined by the MASCOT probability analysis (p < 0.05), and only the most significant hit for each spot was used for further analysis. Where multiple proteins with similar significance were found in one spot or the protein was identified with only one single peptide, the spots were excluded from the analysis. If peptide identifications led to an indistinguishable group of similar proteins, one representative protein with the most informative name was used for subsequent analysis.

Article

RESULTS AND DISCUSSION

Stress Phenotypes in Treated and Untreated Plants

Arabidopsis thaliana plants were grown in hydroponic cultures (Figure 1) to provide homogeneous starting material and thus minimize biological variation unrelated to the experimentally induced stress responses and to ensure that the stress conditions were equivalent for each plant. The experimental workflow (Figure 2) resulted in four different treatment conditions for each type of stress: TS (treated and stressed), T (treated but not stressed), S (stressed but not treated), and C (untreated and unstressed control). The applied stress conditions were designed to cause severe (sublethal) phenotypes and thus clearly show differences between the treated and untreated groups. Preliminary experiments (data not shown) revealed that the combination of tebuconazole and ABA induces stress tolerance more effectively than either chemical alone, so additional groups treated with single chemicals were not included in further experiments. Salinity stress, induced by exposing the roots to 150 mM NaCl for 96 h, inhibited leaf development and resulted in chlorosis (Figure 3A). Similar effects have been reported in

Preparation of cRNA

Frozen leaves were ground to a fine powder under liquid nitrogen, and total RNA was prepared using the RNeasy MidiKit (QIAGEN, Hilden, Germany). After precipitation, 10 μg samples of total RNA were quality tested by denaturing agarose gel electrophoresis. Double-stranded cDNA was then prepared from high-quality 10 μg samples of total RNA using the Microarray cDNA Synthesis Kit (Roche Diagnostics, Mannheim, Germany) and purified by phenol extraction and ethanol precipitation. The air-dried cDNA pellet was resuspended in ultrapure water and transcribed in vitro in the presence of biotin-labeled ribonucleotides using the T7 BioArray High Yield RNA transcript labeling kit (Enzo Life Science, Inc., Farmingdale, USA). The biotinylated cRNA was purified using the RNeasy Mini Kit (Qiagen). Microarray Hybridization

Biotin-labeled cRNA samples (20 μg) were incubated for 20 min at 94 °C in fragmentation buffer (Affymetrix Inc., Santa Clara, USA) to produce 35−200 bp fragments. The fragmented cRNA was then added to the hybridization mixture, containing control oligonucleotide B2, control cRNA, 0.1 mg/mL herring sperm DNA, and 0.5 mg/mL acetylated BSA in hybridization buffer (100 mM MES, 1 M NaCl, 20 mM EDTA, 0.01% Tween-20, pH 6.5). The samples were applied to A. thaliana ATH1 genome arrays (Affymetrix) for 16−20 h at 45 °C and 60 rpm. The arrays were then washed in an Affymetrix Fluidics Station 450/250 using the Arabidopsis ATH1 Chip EukGEWS2v4_450 protocol. Arrays were scanned using a Gene Chip Scanner 3000 (Affymetrix), and CEL files were generated using the Affymetrix GCOS software. Data Analysis

The Genedata Expressionist software package (Genedata, Basel, Switzerland) was used for data analysis. Raw chip data (CEL files) were loaded into the Genedata Expressionist Refiner module. Data were condensed, and expression levels for individual chips were calculated after background correction and normalization using the RMA algorithm.27 The resulting expression values were further analyzed using the Analyst module of Genedata Expressionist. All chips were normalized to a median expression value of 200, with a quality criteria pvalue set at 0.04. Fold induction levels were obtained as ratios of medians. Student’s t test was performed to calculate p-values, and the false discovery rate was controlled according to Benjamini and Hochberg.28

Figure 3. Phenotypes of A. thaliana plants subjected to salinity or drought stress conditions. (A) Plants treated with tebuconazole plus ABA (TS) and untreated control plants (S) were exposed to 150 mM NaCl for 96 h to induce salinity stress. Samples for proteome profiling were taken at the first (24 h) and the last day of stress (96 h) and after 24 h of recovery. (B) Plants treated with tebuconazole plus ABA (TS) and untreated control plants (S) were grown without liquid medium for 48 h to induce drought stress. Samples for proteome profiling were taken at the first (24 h) and the last day of stress (48 h) and after 24 h of recovery. Applied were 4.9 μmol tebuconazole and 5.6 μmol ABA each per 50 plants. 1269



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Figure 4. 2D maps of the A. thaliana leaf proteome. Samples were separated in the first dimension on a pH 4−7 IPG strip and in the second dimension by 12.5% (v/v) SDS-PAGE. (A) CyDye image of the internal standard. (B) CyDye image of a sample from the last day of salinity stress. (C) CyDye image of a sample from the last day of drought stress.

stress experiment that matched on more than 70% of the gel images (72 images in each stress group). Student’s t test was used to determine which protein spots differed significantly in abundance in response to the stress and chemical treatments by comparing treated versus control (T/C), stressed versus control (S/C) and treated plus stressed versus stressed (TS/ S) under each stress regimen. Only proteins that were present on more than 70% of the images/spot maps and met the p < 0.05 threshold in the statistical test were considered for further analysis. We focused on protein spots showing a greater than 1.5-fold change in abundance following the application of tebuconazole/ABA under stress at any of the three sampling points, as these represented potential stress tolerance markers. These protein spots were excised from the preparative gels and identified by mass spectrometry. We found 46 unique proteins in 82 spots that showed differential expression in the stress experiments, indicating that some proteins were present in multiple spots perhaps reflecting different forms of posttranslational modification or alternative forms of mRNA splicing. These 46 proteins included 18 putative markers of increased drought stress tolerance and 34 putative markers of increased salinity stress tolerance, with five proteins featuring in both groups. Almost 50% of these putative markers corresponded to photosynthesis-related functions (15 in the salinity stress group and 8 in the drought stress group). The remaining salinity tolerance markers corresponded to protein folding and degradation functions (eight proteins), protein synthesis (three proteins), metabolism (two proteins), and transport (one protein), with the functions of five additional proteins unknown. The remaining drought tolerance markers corresponded to stress and defense functions (three proteins), metabolism (three proteins), protein synthesis (two proteins), and protein folding (one protein), with one additional protein lacking a known function. We identified ribulose bisphosphate carboxylase/oxygenase (RubisCO) large subunit fragments (RLSFs) with the highest score in 12 spots from the drought stress experiment. RLSFs were also identified in six of the spots in the salt stress experiment but their abundance was not affected by treatment under stress and therefore they were excluded from the final list of stress tolerance marker proteins.

previous studies using the same conditions, and both studies also found evidence of increasing electrolyte leakage (indicating membrane damage) as the salinity stress became more severe.4,29 In our tests, the 24-h recovery period was insufficient to reverse the physical deterioration of the stressed plants, which showed further chlorosis and loss of turgor (Figure 3A, left panel). Treatment with tebuconazole and ABA 24 h before the onset of stress resulted in a less severe phenotype after 96 h and better recovery (Figure 3A, right panel). These data showed that the combined treatment increased salinity tolerance under our test conditions. Drought stress was induced by withdrawing the nutrient medium and allowing the roots to dry slowly in air for 48 h. This resulted in the loss of turgor and wilting of the rosette (Figure 3B). The 24-h recovery period was insufficient to reverse the impact of the drought stress (Figure 3B, left panel). Treatment with tebuconazole and ABA 24 h before the onset of stress resulted in a less severe phenotype and allowed near full recovery after 24 h in normal medium (Figure 3B, right panel). These data showed that the combined treatment increased drought tolerance under our test conditions. The application of tebuconazole plus ABA had no apparent effect on unstressed plants (data not shown), suggesting that the only effect of the combined treatment was to increase salinity and drought stress tolerance. Identification of Marker Proteins by 2D-DIGE and MS/MS

We carried out a quantitative comparative proteomic analysis of plants from the four treatment groups in each stress category to identify changes in the proteome corresponding to stress treatments with and without the application of tebuconazole and ABA. Protein samples extracted and labeled as shown in Table 1 were separated by 2D-DIGE. Figure 4A shows a representative Cy2 image of the internal standard, comprising a pool of all samples from each treatment group, whereas Figure 4B and C show dye images of samples from salinity-stressed plants after 96 h and drought-stressed plants after 48 h, respectively. In all cases, the proteins were reproducibly separated with high resolution within the pH range 4−7 in the first dimension and the size range 10−120 kDa in the second dimension. Image analysis using DeCyder software resolved 1156 spots in the salinity stress experiment and 1423 spots in the drought 1270



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Changes in Protein Abundance in Response to Stress and Chemical Treatments

symbols to the right of the dotted line). Graphs A−D represent protein markers whose abundance declines steadily over time in the S group (from the first to the last day of stress including the 24 h recovery period) but which appear to be partly or fully “protected” in the TS group thus remaining at or returning to near normal levels during the stress and recovery period. The differences between the four graphs reflect the timing and robustness of the recovery. Pattern A represents markers that behave similarly in the S and TS groups on the first day of stress but which remain more abundant in the TS group thereafter. Pattern B represents markers that behave similarly in the S and TS groups during the entire stress period but which plateau during the recovery period in the TS group. Pattern C is similar to pattern B during the stress period, but here the abundance increases at least 1.5-fold during the recovery period in the TS group. Pattern D represents those markers that do not decline in abundance at all in the TS group, i.e., they appear to be completely protected by the chemical treatments. Finally, pattern E represents a unique set of markers that appear unaffected by stress and also increase in abundance in the TS groups. Because the number of stressed plants available for testing was limited, we did not take samples immediately before the onset of stress but used plants from the C group instead, since these were equivalent up to the point of stress treatment. They are shown to the left of the dotted line (Figure 5, “first C”). A black square shows that the abundance of a protein marker was ≥1.5-fold higher in the C group than in the TS and S groups on the first sampling day, meaning that the abundance of the protein declines due to stress and treatment plus stress. A gray square shows that the protein abundance of a protein marker was ≥1.5-fold lower in the C group than in the TS and S groups on the first sampling day, meaning that the abundance of the protein increases due to stress and treatment plus stress. A white square indicates no change. The number of marker proteins assigned to each pattern for each form of stress is shown by the blocks in Figure 5 (DS = drought stress and SS = salinity stress). In addition to the pattern assignment, the color of the blocks indicates whether the marker protein abundance in the C group on the first sampling day was ≥1.5-fold higher (black), ≥1.5-fold lower (gray), or the same (white) than in the TS and S groups. Some proteins were found in more than one spot, and their variants were in some cases assigned to different patterns, resulting in a greater number of marker profiles than the number of unique proteins stated above. We found that 35 (92%) of the salinity tolerance markers and 12 (63%) of the drought tolerance markers could be assigned to patterns A, B, or D, i.e., their abundance was partially or fully protected by the chemical treatment prior to stress. Most of these were assigned to pattern D, which represented full protection from day 1 of stress, i.e., 23 (60%) of the salinity tolerance markers and 6 (32%) of the drought tolerance markers. The remaining 12 salinity tolerance and 6 drought tolerance markers (32% each) showed attenuation of the stress effect (patterns A and B). We identified three drought tolerance markers (but no salinity tolerance markers) whose abundance increased during recovery (pattern C). We identified three salinity tolerance markers and two drought tolerance markers that were unaffected by stress but whose abundance increased following chemical treatment. A comprehensive list of these markers with their biological functions is provided in Tables 2 and 3, along with the corresponding patterns as shown in Figure 5. The average

We compared the relative abundance of the 82 putative markers over time in the C, S, T, and TS groups to investigate the response to stress before and after recovery in the presence and absence of ABA and tebuconazole. As no significant changes occurred on the level of protein abundance in response to the treatment alone, the results for this condition will not be discussed here. Each marker was assigned a profile based on its temporal profile in the S and TS treatment categories (Figure 5,

Figure 5. Patterns observed for the changes in protein abundance for salinity stress (SS) and drought stress (DS). Standardized abundance (SA) is the protein abundance relative to the internal standard image displayed as a ratio. Log10 SA values from the four replicate images are displayed for stress alone (S, gray circles) and for treatment plus stress (TS, black circles). Lines connect the mean values (stress, solid line; treatment plus stress, broken line). The squares indicate the protein abundance value in the control at the first sampling day (first C). Black: 1.5-fold or more higher than S and/or TS. White: same level as S and/or TS. Gray: 1.5-fold or more lower than S and/or TS. Number of proteins belonging to each pattern is written in the squares next to each graph. 1271


1272

Chloroplastic heat shock 70 kDa protein 1 (cpHsp70-1) Chloroplastic heat shock 70 kDa protein 2 (cpHsp70-2) Chloroplastic heat shock 90 kDa protein 5 (cpHsp90-5)

436

349

426

409 420 650

1053

1052

1047

896

2779

2844

2065

1253 1855

1221

1213

1100

Glutamine synthetase Cysteine synthase Carbonic anhydrase 1 Carbonic anhydrase 1 Carbonic anhydrase 1 Carbonic anhydrase 2 Carbonic anhydrase 2 Ferredoxin−NADP reductase, leaf isozyme 1 Ferredoxin−NADP reductase, leaf isozyme 2 Fructose-bisphosphate aldolase 1, probable Fructose-bisphosphate aldolase 2, probable Glutamate-1-semialdehyde 2,1-aminotransferase 1 Glutamate-1-semialdehyde 2,1-aminotransferase 1 Glyceraldehyde-3-phosphate dehydrogenase B Glyceraldehyde-3-phosphate dehydrogenase B Phosphoribulokinase PSI type II chlorophyll a/b-binding protein (Lhca2) PSI type III chlorophyll a/b-binding protein (Lhca3) Ribulose bisphosphate carboxylase small chain 1A Ribulose bisphosphate carboxylase small chain 1B Ribulose bisphosphate carboxylase/oxygenase activase (RCA) Ribulose bisphosphate carboxylase/oxygenase activase (RCA) Ribulose bisphosphate carboxylase/oxygenase activase (RCA) Ribulose bisphosphate carboxylase/oxygenase activase (RCA) Transketolase, putative Transketolase, putative Chaperonin 60 alpha (Cpn60α)

protein name

1012 1513 1941 1955 1995 1779 1782 1471 1508 1462 1439 1088

spot no.a

263483_at

248582_at

254148_at

251396_at 251396_at 264069_at

245061_at

245061_at

245061_at

245061_at

264474_at

264474_at

265033_at

255720_at 251325_s_at

259625_at

259625_at

247392_at

249710_at 260566_at 259161_at 259161_at 259161_at 246596_at 246596_at 247131_at 261218_at 263761_at 252929_at 247392_at

chip IDb

At2g04030

At5g49910

AT4g24280

At3g60750 At3g60750 At2g28000

At2g39730

At2g39730

At2g39730

At2g39730

At5g38430

At1g67090

At1g61520

At1g32060 At3g61470

At1g42970

At1g42970

At5g63570

At5g35630 At2g43750 At3g01500 At3g01500 At3g01500 At5g14740 At5g14740 At5g66190 At1g20020 At2g21330 At4g38970 At5g63570

AGIc

Table 2. List of Marker Proteins for Increased Salt Stress Tolerance

Q9SIF2

Q9M637

Q9STW6

Q8RWV0 Q8RWV0 P21238

P10896

P10896

P10896

P10896

P10796

P10795

Q9SY97

P25697 Q9M320

P25857

P25857

P42799

Q43127 P47999 P27140 P27140 P27140 P42737 P42737 Q9FKW6 Q8W493 Q9SJU4 Q944G9 P42799

Uniprot IDd

2.5

1.6

1.7

1.8 1.8 1.7

2.1

1.7

2.0

2.1

2.5

1.6

1.8

1.5 1.9

2.7

1.9

1.7

2.4 2.3 2.3 2.8 1.9 2.5 2.5 1.7 1.7 1.9 2.2 1.9

av ratioe

0.003

0.003

0.003

0.006 0.005 0.001

0.001

0.031

0.000

0.000

0.001

0.016

0.004

0.008 0.009

0.000

0.001

0.004

0.000 0.014 0.001 0.000 0.045 0.001 0.000 0.003 0.000 0.001 0.000 0.005

p-value

D+

D

D

D D A

A-

A-

A-

A-

D

D

A

A D

D-

D-

D

D D D D A D D A D D D D

PP timef

F

F

F

G G F

G

G

G

G

F

F

G

GG

G

G

F

G G Xj X X G-k GG GF G F

TP time

P

P

P

P P P

P

P

P

P

P

P

P

P P

P

P

P

P/M P P P P C C P P P P P

Lg

photosynthesis photosynthesis protein folding, degradation protein folding, degradation protein folding, degradation protein folding, degradation

photosynthesis

photosynthesis

photosynthesis

photosynthesis

photosynthesis

photosynthesis

photosynthesis

photosynthesis photosynthesis

photosynthesis

photosynthesis

photosynthesis

metabolism metabolism photosynthesis photosynthesis photosynthesis photosynthesis photosynthesis photosynthesis photosynthesis photosynthesis photosynthesis photosynthesis

function

1163/13

298/6

1034/14

571/7 616/15 781/9

1794/12

1056/10

2417/13

1995/13

380/5

248/3

190/3

755/11 119/3

527/7

533/6

266/3

714/8 347/7 647/7 647/7 380/5 444/6 385/5 300/4 335/5 351/5 943/9 243/4

score/SPh

23%

12%

24%

20% 30% 20%

50%

39%

52%

56%

36%

36%

36%

46% 20%

25%

25%

22%

44% 51% 44% 44% 37% 51% 38% 18% 34% 29% 46% 19%

SCi

88608/69478

77059/67048

76461/66962

79918/67304 79918/67304 57132/60485

42261/49476

42261/49539

42261/49476

42261/53216

14797/14466

14699/13749

29163/27530

39196/44690 27768/31906

39304/45783

39304/45842

46545/48418

42475/50365 35166/38415 25573/30016 25573/29826 25573/29188 28287/33315 28287/33273 35161/39354 35076/38611 41909/39404 37957/39908 46545/48789

Mr expt/obsd

5.43/5.36

5.13/5.36

5.07/5.3

5.94/6.18 5.94/6.12 4.8/5.41

5.09/6.04

5.09/5.92

5.09/6.18

5.09/5.72

5.7/6.79

5.69/6.81

8.61/6.23

5.16/5.92 6.9/6.76

5.71/6.75

5.71/6.55

5.65/6.59

5.28/5.88 5.54/6.45 6.13/6.36 6.13/6.7 6.13/6.67 5.64/6.03 5.64/6.19 5.54/6.22 6.19/7.01 5.95/6.06 5.36/6.26 5.65/6.42

pI expt/obsd

Journal of Proteome Research Article


1273

Zinc metalloprotease FTSH2

Zinc metalloprotease FTSH2

Elongation factor G Elongation factor Tu 30S ribosomal protein S6 alpha Vacuolar H+-ATPase, subunit A Cystine lyase CORI3 Cystine lyase CORI3 Major latex protein, putative Myrosinase 1/Beta-glucosidase 38 Myrosinase 1/Beta-glucosidase 38 Myrosinase 2/Beta-glucosidase 37 Quinone oxidoreductase-like protein Quinone oxidoreductase-like protein

342

490

527

332 952 1895 493 1027 1029 2642 449 444 611 1494 1497

262645_at 254480_at 261954_at 264302_at 254232_at 254232_at 254225_at 246880_at 246880_at 246880_at 265182_at 265182_at

267196_at

267196_at

251840_at

251305_at

250995_at

chip IDb

At1g62750 At4g20360 At1g64510 At1g78900 At4g23600 At4g23600 At4g23670 At5g26000 At5g26000 At5g25980 At1g23740 At1g23740

At2g30950

At2g30950

At3g54960

At3g62030

At5g02500

AGIc

Q9SI75 P17745 Q8VY91 O23654 Q9SUR6 Q9SUR6 Q9SUR0 P37702 P37702 Q9C5C2 Q9ZUC1 Q9ZUC1

O80860

O80860

Q8VX13

P34791

P22953

Uniprot IDd

1.8 1.7 1.6 2.2 2.2 2.2 2.1 2.4 1.7 2.1 1.7 1.8

2.1

2.1

2.3

3.0

2.2

av ratioe

0.037 0.000 0.035 0.000 0.001 0.000 0.001 0.001 0.032 0.005 0.036 0.000

0.000

0.000

0.001

0.007

0.000

p-value

A D A E+ E+ E+ E+ D B A B A

D

D+

D+

D

D+

PP timef

GG X F F F G F F F F F

F

F

G+

G

F

TP time

P P P V C C V ? ? ? P P

P

P

?

P

C,N

Lg protein folding, degradation protein folding, degradation protein folding, degradation protein folding, degradation protein folding, degradation protein synthesis protein synthesis protein synthesis transport unknown unknown unknown unknown unknown unknown unknown unknown

function

1560/16 595/7 123/2 522/12 518/7 337/4 195/4 554/9 773/13 385/6 200/2 448/6

847/11

635/8

341/6

400/6

635/13

score/SPh

33% 36% 15% 28% 39% 26% 25% 27% 31% 21% 19% 42%

21%

16%

13%

37%

32%

SCi

86003/69921 44722/52058 16438/31065 68769/64538 47009/49982 47009/49918 17507/16386 59067/66623 59067/66792 59595/61415 32755/38956 32755/38906

65626/63885

65626/64867

61560/69566

19915/20038

71327/65948

Mr expt/obsd

5.43/5.84 5.31/6.05 4.74/6.7 5.11/5.84 5.89/6.68 5.89/6.87 5.91/6.93 5.43/6.1 5.43/6 6.87/7.3 5.65/6.15 5.65/6.35

5.11/5.81

5.11/5.78

4.72/5.28

5.47/6.34

5.03/5.62

pI expt/obsd

e

Spot numbers as assigned in Supplemental Data 3. bAffimetrix chip identification number. cArabidopsis Genomics Initiative locus name. dDatabase accession number from UniProt (www.uniprot.org). Ratio abundance values TS/S after 24 h of recovery and t test p-values. fPatterns of protein abundance (PP)/transcript abundance (TP) according to Figures 5 and 6. gLocation (L) and function: inferred from annotations in UniProt, NCBI (www.ncbi.nlm.nih.gov/gene), TAIR (www.arabidopsis.org), and literature. P: chloroplast, C: cytoplasm, M: mitochondrion, N: nucleus, V: vacuole, ?: no information found. hProtein scores are derived from ions scores as a nonprobabilistic basis for ranking protein hits. Ions score is −10 log P, where P is the probability that the observed match is a random event. SP: number of identified significant peptides. iPercentage of the protein amino acid sequence covered by all peptides found for the listed protein. jNo pattern obtained due to high variation of the data. kRatio abundance values TS/C at the first day of stress: proteins: ≥ 1.5-fold (+), ≥ −1.5-fold (−); transcripts: ≥ 2.0-fold (+), ≥ −2.0-fold (−).

a

Protein disulfide-isomerase-like protein

2369

protein name

Heat shock cognate 70 kDa protein 1 (Hsc701) Peptidyl-prolyl cis−trans isomerase CYP20-3

465

spot no.a

Table 2. continued



1274

2317

2308

2258

1839

1808

1736

1689

1685

1669

1658

1656

1210 1223 1649

2043

1691

1683

1616

1606

1086

1481 978 1030

1704

1513 1012 1587

spot no.a

Cysteine synthase Glutamine synthetase Hydroxyethyl thiazole phosphate (HET-P) synthase Hydroxyethyl thiazole phosphate (HET-P) synthase Ferredoxin–NADP reductase, leaf isozyme 1 Fructose-1,6-bisphosphatase Glutamate-1-semialdehyde 2,1aminotransferase 2 Glutamate-1-semialdehyde 2,1-aminotransferase 2 Oxygen-evolving enhancer protein 1−1 (PsbO1) Oxygen-evolving enhancer protein 1-1 (PsbO1) Oxygen-evolving enhancer protein 1-2 (PsbO2) Oxygen-evolving enhancer protein 1-2 (PsbO2) PSI type III chlorophyll a/b-binding protein (Lhca3) Sedoheptulose-1,7-bisphosphatase Sedoheptulose-1,7-bisphosphatase Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment) Ribulose bisphosphate carboxylase large chain (fragment)

protein name

N.E.

N.E.

N.E.

N.E.

N.E.

N.E.

N.E.

N.E.

N.E.

N.E.

N.E.

251762_at 251762_at N.E.

265033_at

252130_at

252130_at

247073_at

247073_at

252318_at

247131_at 251885_at 252318_at

248128_at

260566_at 249710_at 248128_at

chip IDb

AtCg00490

AtCg00490

AtCg00490

AtCg00490

AtCg00490

AtCg00490

AtCg00490

AtCg00490

AtCg00490

AtCg00490

AtCg00489

At3g55800 At3g55800 AtCg00490

At1g61520

At3g50820

At3g50820

At5g66570

At5g66570

At3g48730

At5g66190 At3g54050 At3g48730

At5g54770

At2g43750 At5g35630 At5g54770

AGIc

O03042

O03042

O03043

O03042

O03042

O03042

O03042

O03042

O03042

O03042

O03042

P46283 P46283 O03042

Q9SY97

Q9S841

Q9S841

P23321

P23321

Q42522

Q9FKW6 P25851 Q42522

Q38814

P47999 Q43127 Q38814

Uniprot IDd

Table 3. List of Marker Proteins for Increased Drought Stress Tolerance

4.2

2.3

5.7

2.2

1.8

3.8

4.0

5.0

4.5

3.2

3.2

1.6 1.5 1.5

1.6

1.8

2.0

1.9

1.8

2.1

1.7 1.5 2.0

3.6

1.7 1.5 2.4

av ratioe

0.000

0.000

0.000

0.000

0.004

0.000

0.000

0.000

0.000

0.000

0.000

0.000 0.000 0.001

0.040

0.000

0.000

0.000

0.001

0.000

0.000 0.004 0.000

0.000

0.000 0.002 0.000

p-value

B

B

B

B

B

B

B

B

B

B

B

D D B

B

D

D

D

D

B

D B B

C

A B C

PP timef

X

X

X

X

X

X

X

X

X

X

X

C.1 C.1 Xk

B

C

C

C

P

P

P

P

P

P

P

P

P

P

P

P P P

P

P

P

P

P

P

C.1-j C

P P P

P

P P/M P

Lg

C C.1 C.1

B

G B B

TP time

photosynthesis photosynthesis photosynthesisfragment photosynthesisfragment photosynthesisfragment photosynthesisfragment photosynthesisfragment photosynthesisfragment photosynthesisfragment photosynthesisfragment photosynthesisfragment photosynthesisfragment photosynthesisfragment photosynthesisfragment

photosynthesis

photosynthesis

photosynthesis

photosynthesis

photosynthesis

photosynthesis

photosynthesis photosynthesis photosynthesis

metabolism

metabolism metabolism metabolism

function

177/3

194/3

321/6

534/9

320/5

752/11

479/6

107/2

383/6

686/9

452/7

709/10 664/7 265/4

232/4

287/3

753/9

568/8

799/8

174/2

672/12 956/11 175/3

410/4

553/7 595/8 182/2

score/ SPh

7%

10%

22%

21%

26%

25%

18%

4%

18%

28%

21%

41% 36% 13%

23%

21%

40%

42%

43%

8%

37% 37% 18%

16%

27% 35% 13%

SCi

52736/21105

52736/21186

52736/22319

52736/32028

52736/32727

52736/34217

52736/35009

52736/35233

52736/35322

52736/35683

52736/35774

36092/45783 36092/45551 52736/35820

29085/28132

26572/35143

26572/35233

26566/36324

26566/36371

46446/48851

35161/39105 38993/51269 46446/49982

36641/34920

35166/38415 42475/50365 36641/37165

Mr expt/obsd

5.88/5.85

5.88/5.66

5.88/6.42

5.88/6.67

5.88/6.93

5.88/7.15

5.88/6.87

5.88/7

5.88/6.73

5.88/7.04

5.88/7.12

5.21/5.52 5.21/5.6 5.88/7.19

8.61/5.91

5.02/5.46

5.02/5.54

4.93/5.46

4.93/5.55

5.7/6.64

5.54/6.4 4.72/5.34 5.7/6.45

5.82/6.39

5.54/6.45 5.28/5.88 5.82/5.89

pI expt/obsd



Spot numbers as assigned in Supplemental Data 3. bAffimetrix chip identification number (N.E.: chloroplast encoded protein, no element on the chip). cArabidopsis Genomics Initiative locus name. Database accession number from UniProt (www.uniprot.org). eRatio abundance values TS/S after 24 h of recovery and t test p-values. fPatterns of protein abundance (PP)/transcript abundance (TP) according to Figures 5 and 6. gLocation (L) and function: inferred from annotations in UniProt, NCBI (www.ncbi.nlm.nih.gov/gene), TAIR (www.arabidopsis.org) and literature. P: chloroplast, C: cytoplasm, M: mitochondrion, N: nucleus, V: vacuole, ?: no information found. hProtein scores are derived from ions scores as a nonprobabilistic basis for ranking protein hits. Ions score is −10 log P, where P is the probability that the observed match is a random event. SP: number of identified significant peptides. iPercentage of the protein amino acid sequence covered by all peptides found for the listed protein. jRatio abundance values TS/C at the first day of stress: proteins: ≥ 1.5-fold (+), ≥ −1.5-fold (−); transcripts: ≥ 2.0-fold (+), ≥ −2.0-fold (−). kNo pattern obtained due to high variation of the data.

ratios TS/S and their p-values are given in both tables for the first day of recovery. The patterns for each spot and the average ratios for all sampling days are shown in Supplemental Data 1. Changes in Transcript Abundance in Response to Stress and Chemical Treatments

Parallel transcriptomic analysis using the Affymetrix ATH1 Genome Array, which contains more than 22,500 probe sets representing approximately 24,000 A. thaliana genes, allowed us to compare the expression profiles of transcripts corresponding to the marker proteins identified above. No transcript data were available for the gene encoding the RubisCO large subunit (drought stress, fragments in several spots) as this is a plastidencoded protein and only nucleus genes are present on the chip. It was also not possible to assign unique transcriptomic data to β-glycosidases 37 and 38, and RubisCO small subunits 1A and 1B, because each of these pairs was represented by a single probe on the microarray. The transcript data were analyzed and categorized in the same manner as the proteomics data, and corresponding patterns representing changes in transcript abundance are shown in Figure 6. We found that some transcripts followed patterns similar to those observed for the proteins (patterns B, C, D, and E), and these are represented as boxes at the top of Figure 6 with no further explanation. We also found variants of patterns B and C (described as B.1 and C.1) in which the transcript abundance was more than 2-fold higher in the TS group compared to the S group on the first day of stress but there was no difference on the last day of stress, followed by the typical attenuated decline during recovery (B/B.1) or the significant increase during recovery (C/C.1). There were also two new patterns: F, in which the transcripts were affected neither by stress nor by stress plus treatment, and G, in which the transcript levels declined throughout stress and recovery without any sign of attenuation. As for the protein data, we included the transcript values for the C samples on the first sampling day (Figure 6, first C) to indicate changes that occurred during the first 24 h of stress. We only recorded differences in transcript levels between the C group and the TS or S groups if they were ≥2.0-fold in magnitude. In patterns B.1 and C.1, the abundance was already ≥2.0-fold lower in the S group compared to the TS group, and the squares relate to comparisons between the C and TS groups only. When we assigned individual transcripts to their corresponding expression patterns we found a striking difference between the transcript and protein data. Most of the transcripts representing drought tolerance marker proteins (78%) were affected by the chemical treatment and were therefore assigned to patterns B, B.1, C, C.1, D, or E. In contrast, we found that 100% of the transcripts representing salinity tolerance marker proteins were unaffected by the chemicals and were therefore assigned to patterns F (no change in abundance in the S or TS groups, 43%) or G (similar decline in abundance in the S and TS groups, 57%). The assignment of the transcripts is shown in Tables 2 and 3, and the transcript patterns are shown in Supplemental Data 1. Treatment-Induced Salinity Stress Tolerance: Key Targets

We can conclude from our data that the application of tebuconazole plus ABA increases salinity stress tolerance primarily by attenuating or preventing the loss of certain key proteins when the plants are exposed to salinity. The loss of these proteins in response to salinity stress has been reported in a number of other proteomic studies using Arabidopsis thaliana,

d

P P P C C P F F C.1 E+ F D C CE E D D 0.000 0.000 0.009 0.009 0.000 0.000 2.7 2.4 1.4 2.2 2.7 2.0 P17745 Q9FNA8 Q9C5R8 P46422 Q05431 Q9ZUC1 254480_at 250256_at 250733_at 266746_at 261412_at 265182_at Elongation factor Tu TypA translation elongation factor, putative 2-Cys peroxiredoxin (2-cys Prx) Glutathione S-transferase (GST) L-Ascorbate peroxidase 1 Quinone oxidoreductase-like protein 1261 382 2235 1965 1855 1497

At4g20360 At5g13650 At5g06290 At4g02520 At1g07890 At1g23740

P B.1 B 0.000 2.1 O65282 At5g20720 246003_at 20 kDa chaperonin 2019

Article

a

5.31/5.63 5.95/7.01 4.79/5.3 5.93/6.95 5.72/6.76 5.65/6.35 44722/44577 67492/68252 22466/22807 23997/29600 27544/31906 32755 27% 18% 30% 60% 40% 44% 645/7 511/6 427/5 572/9 448/7 742/9

5.23/6.07 21398/28528 58% 709/10

5.23/5.87 21398/28673 54% 592/9

protein folding, degradation protein folding, degradation protein synthesis protein synthesis stress and defense stress and defense stress and defense unknown P B.1 C 0.000 2.3 O65282 At5g20720 246003_at 20 kDa chaperonin 2015

spot no.a

Table 3. continued

protein name

chip IDb

AGIc

Uniprot IDd

av ratioe

p-value

PP timef

TP time

Lg

function

score/ SPh

SCi

Mr expt/obsd

pI expt/obsd


1275



Article

response to high salinity in Arabidopsis thaliana suspension cells highlighted dynamic changes in translation as an important cause for differences in protein abundance.33 Our data indicate that treatment with tebuconazole plus ABA under salinity stress induces changes in protein abundance by positively influencing translation and/or protein stability. We found that 38% of the markers combined transcript pattern F with protein patterns A, B, or D, showing that neither stress nor the chemical treatment had an impact on transcript abundance and both acted at the protein level, whereas 47% combined transcript pattern G with protein patterns A or D, showing that stress had an impact on transcription and/or mRNA stability but the adaptive response induced by chemical treatment was mediated at the protein level. We found that 44% of the salinity tolerance marker proteins were related to photosynthesis functions, indicating that the maintenance of photosynthesis is a key target of the chemical treatment. Several of these markers are involved in the protection and repair of photosystem II (PSII), which is vulnerable to photoinhibition in turn often resulting from salinity stress.34−36 The markers included both isoforms of ferredoxin-NADP+ reductase, which is required for both linear and cyclic electron transport.37−40 Cyclic electron transport prevents photoinhibition by protecting PSII from photodamage and promoting its repair.41 We also identified the zinc metalloprotease FTSH2, an enzyme responsible for the turnover of the damaged central core proteins of PSII, D1 and D2,42,43 and elongation factor G (EF-G), which is involved in the de novo synthesis of D1 during PSII repair.44 We identified a second group of salinity tolerance markers involved in CO2 assimilation, a process that is limited by salinity and drought stress because these inhibit CO2 diffusion in the leaves.45 The markers included β-carbonic anhydrases 1 and 2, which are thought to improve CO2 delivery to RubisCO,46 RubisCO activase (RCA), which is required for RubisCO catalytic activity,47 and Calvin cycle enzymes such as fructose-bisphosphate aldolase and transketolase, which are necessary for the control of carbon flux.48−52 Additional markers were found to be involved in the assimilation of ammonia and sulfur, e.g., glutamine synthetase (GS2)53 and cysteine synthase (OASTL), which is known to induce oxidative stress tolerance when overexpressed in tobacco by facilitating glutathione synthesis.54 We identified molecular chaperones as another large group of salinity tolerance markers, and these are known to play a major role in abiotic stress responses.55,56 These included members of Hsp90, Hsp70 and chaperonin/Hsp60 families. The Hsp70 family plays an important role in the salinity tolerance of the halophytic moss Physcomitrella patens6 and the overexpression of heat shock cognate 70 kDa protein 1 (Hsc70−1) in A. thaliana also resulted in greater salt tolerance.57 We found that subunit A of the vacuolar H+-ATPase was induced by stress with or without chemical treatment but that further increases during the stress and recovery period were depending on the ABA plus tebuconazole. The ability to sequester Na+ into the vacuole is an important mechanism to maintain ion homeostasis under salinity stress58 which depends on the formation of a protein gradient by a H+-ATPase and a H+-pyrophosphatase (H+-PPase).59 Upregulation of H+-ATPase may explain salinity tolerance in the halophyte Suaeda salsa.60 Only two other salinity markers, the enzyme cystine lyase (CORI3) and the putative major latex protein, showed the same increase in protein abundance due to treatment plus

Figure 6. Patterns observed for the changes in transcript abundance for salinity stress (SS) and drought stress (DS). Patterns like those in Figure 5 named B, C, D, and E are shown by letter coding at the top. Normalized expression values from the four replicate arrays are displayed for stress alone (S, gray circles) and for treatment plus stress (TS, black circles). Lines connect the mean values (stress, solid line; treatment plus stress, broken line). The squares indicate the transcript abundance value in the control at the first sampling day (first C). Black: 2.0-fold or more higher than TS. White: same level as TS. Gray: 2.0-fold or more lower than TS. Number of transcripts belonging to each pattern is written in the squares next to each graph or below the letter coding.

rice, and other species.30 The processes that influence protein abundance include transcription, mRNA stability, translation and protein stability, and the matching transcriptomic data we acquired enabled us to determine which of the above processes are involved in the adaptive process. Remarkably, when we compared the transcript and protein expression patterns, we found that the chemical treatment had no impact on transcript levels (identical transcript profiles in the S and ST groups) and therefore must act directly on the proteins by stimulating translation and/or increasing stability. A poor correlation between transcriptomic and proteomic data has previously been observed in bread wheat responding to drought stress and potato responding to cold and salinity stress.31,32 A detailed comparison of early transcriptional and translational changes in 1276



Article

stress as H+-ATPase subunit A. No specific function has been described for either protein thus far and our data provide the first evidence that they may be involved in salinity tolerance.

cys Prx, chloroplast), ascorbate peroxidase 1 (APX1, cytosol), and glutathione S-transferase (GST, cytosol) from the ROS scavenging system,65,66,68,69 and hydroxyethyl thiazole phosphate (HET-P) synthase, which is required to synthesize the antioxidant thiamin.70,71 GST, APX1, and 2-cys Prx have been overexpressed in transgenic plants, inducing tolerance to chilling, salinity, and oxidative stress.72−74 GST and 2-cys Prx were the only two proteins that did not change in response to the stress alone, but whose abundance increased in response to the treatment (pattern E). Several protein spots identified as drought tolerance markers contained RubisCO large subunit fragments (RLSFs), which are induced in rice and wheat plants under drought and salinity stress, during development and in response to ABA.32,75−78 The accumulation of RLSFs under drought stress following treatment suggests that the fragments have an active stress response role, e.g., acting as signaling peptides to coordinate nuclear and plastid gene transcription, as suggested by Møller and Sweetlove.79

Treatment-Induced Drought Stress Tolerance: Key Targets

As above, we can conclude from our data that the application of tebuconazole plus ABA increases drought stress tolerance primarily by attenuating or preventing the loss of certain key proteins when the plants are removed from the hydroponic medium. However, for 78% of the identified drought tolerance markers, we found that the transcript abundances were affected by the chemical treatment, which is strikingly different from the behavior of the salinity tolerance markers. Interestingly, we observed differences in transcript levels between the S and TS groups both at the start of the stress period (B.1 and C.1) and during recovery (B, C, B.1, C.1), but not at the end of the stress treatment. This suggests that there may be a biphasic response, comprising a rapid stabilization of mRNAs at the onset of stress which is overcome as the stress continues, but is then reactivated when the stress is removed and the recovery period begins. In addition to this enhanced mRNA stability, we also observed the increased transcription of genes encoding drought tolerance markers during the recovery period, in response to the chemical treatment. Although we observed higher transcript levels for several drought tolerance markers following the chemical treatment, there was no direct correlation between the changes in transcript and protein levels (Table 3). We therefore conclude that even though transcriptional regulation is an important determinant of protein expression under drought stress,61 the final protein levels reflect a combination of transcriptional regulation and the control of translation/protein stability. Analysis of the entire microarray data set indicated that the transient effect of tebuconazole plus ABA on transcript abundance observed during the early phase of drought stress for six markers was a general effect on a wider group of transcripts. We calculated the TS/S ratios for all transcripts on the first day of stress (Supplemental Data 2) and found 20-fold more transcripts with a TS/S ratio ≥2.0 in the drought stress experiment compared to the salinity stress experiment. Similarly we found 5-fold more transcripts with TS/S ratio ≥2.0 in the drought stress data set during the recovery period. Twice as many proteins were induced by chemical treatment under salinity stress compared to drought stress, and only five proteins were common to both groups. These included cysteine synthase, glutamine synthetase, and ferredoxin-NADP+ reductase 1(FNR1), indicating that PSII activity and the assimilation of ammonia and sulfur are key targets for protection under both forms of stress. Accordingly, the drought stress markers also included two isoforms of the 33 kDa membrane-extrinsic subunit of the water-oxidizing complex of PSII (PsbO1 and PsbO2), both of which are key components of the oxygenevolving complex with a proposed role in PSII repair.62−64 Drought tolerance markers in the CO2 fixation category included the Calvin cycle enzymes fructose-1,6-bisphosphatase and sedoheptulose-1,7-bisphosphatase that control carbon flux.48−52 One important process represented among the drought tolerance markers but not the salinity tolerance markers was the mitigation of reactive oxygen species (ROS), which are produced in response to abiotic stress,65,66 thus increasing oxidative damage to proteins and inducing their subsequent degradation.67 These markers included 2-cys peroxiredoxin (2-

Tebuconazole and ABA: Mode of Action

Drought and salinity stress tolerance was induced in our experiments by spraying plants simultaneously with tebuconazole and ABA. Our proteomic data show that the chemicals act by preventing the loss of certain key proteins when the plants are subjected to stress. In the salinity stress experiments, there was no difference in transcript profiles between the S and TS groups, suggesting that the chemicals affect translation or protein stability. In contrast, there were similarities but no overall correlation between the transcript and protein data in the drought stress experiments, suggesting that the chemicals affect both transcription/mRNA stability and translation/ protein stability. The fungicide tebuconazole and the phytohormone ABA are both known to induce stress tolerance when applied individually.11−13,24 The signaling factors that induce ABAresponsive genes are understood in detail,80,81 although those downstream of tebuconazole are not. The observed effects of tebuconazole and ABA on the transcriptome and the proteome could reflect the compounds acting together (synergic effect) or a combination of separate modes of action (additive effect). Some triazoles have been shown to prevent the degradation of ABA and thereby to increase stress tolerance by promoting the accumulation of the phytohormone.20,21 However, tebuconazole does not inhibit ABA-8′-hydroxylase, so another, thus far unknown mechanism must be responsible for the effect. The chemicals could affect translation by influencing the production of microRNAs (miRNAs) that are involved in ABA responses82 and can lead to both mRNA cleavage and translational repression.83 The initiation step of translation could be another target as this is inhibited under certain stress conditions.84 Finally the chemicals could affect protein stability by inducing the accumulation of compatible solutes such as glycine betaine, which has been proposed to stabilize Calvin cycle enzymes against the damaging effects of salt85 or by increasing ROSscavenging activity. The latter was observed for the drought stress where we identified enzymes from the ROS-scavenging system as tolerance markers. The chemical treatment elicited different sets of marker proteins under different stress conditions (only five markers were common to both forms of stress) and different inductive mechanisms appeared to be involved, targeting mRNA levels and protein levels in one case but only protein levels in the 1277



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other. Almost twice as many protein markers were induced by salinity stress compared to drought stress. Plants subjected to salinity and drought stress often show the same early responses which predominantly reflect the limited ability to take up water.86 However, salinity stress also induces a unique set of responses reflecting injuries that occur over time when toxic ion concentrations build up in the leaves.87 Such deleterious salinity-specific effects may have been induced by our experimental conditions, causing more target proteins to be affected and accounting for the greater number of salinity stress markers we detected as well as the minimal overlap between the two groups. The longer duration of salinity stress compared to drought stress (96 vs 48 h) may have exacerbated this difference. More than 20 times as many genes in the droughtstressed plants (compared the salinity-stressed plants) responded immediately to the application of tebuconazole plus ABA by increasing transcription and/or mRNA stability, and more than five times as many genes in the drought-stressed plants showed higher mRNA levels after the 24-h recovery period. These data suggest that even though early responses to drought and salinity are the same, specific responses were also induced depending on the combination of stress and treatment at these early stages. The differences during recovery perhaps reflect the slow decline in excess ion levels in the leaves of plants subjected to salinity stress, prolonging the inhibition of transcription. In cowpea plants exposed to 200 mM NaCl, it took 3 days for the leaf ion concentration to return to normal.88

Article

ASSOCIATED CONTENT

S Supporting Information *

Supplemental Data 1: (1) Data from Student’s t test performed in the BVA module of the DeCyder software (GE Healthcare). Protein abundance and transcript abundance patterns for salinity tolerance (2) and drought tolerance markers (3). Supplemental Data 2: Normalized expression values for all elements of the A. thaliana ATH1 genome array for the salt stress (1) and the drought stress (2) experiment. (3) TS/S ratios for the first day of stress and the first day of recovery and calculation of the frequency for TS/S ratios. Supplemental Data 3: 2D map of the A. thaliana leaf proteome with indicated spot numbers of marker proteins. This material is available free of charge via the Internet at http://pubs.acs.org.

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AUTHOR INFORMATION

Corresponding Author

*Tel: ++49 241 6085 11050. Fax: ++49 241 6085 10000. Email: [email protected]. Present Address §

Bayer AG, Corporate Development Innovation, Leverkusen, Germany. Notes

The authors declare no competing financial interest.

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Concluding Remarks

ACKNOWLEDGMENTS We thank Dr. Helga Schinkel for critically reading the manuscript. We thank Bioanalytics Division from the Institute for Molecular Biotechnology at RWTH Aachen for the support in mass spectrometric analysis. Additional thanks to Dr. Peter Eckes and Dr. Fabien Poree (both Bayer CropScience AG) for initiating the project set-up and experiments. This work was supported by the Bundesministerium fü r Bildung and Forschung (FKZ 0313989B).

We have carried out the first investigation of the combined effect of tebuconazole and ABA on salinity and drought stress tolerance in A. thaliana plants at the transcriptomic and proteomic levels, and we have provided valuable insight into the underlying mechanisms. We found that drought and salinity stress tolerance were induced in plants treated with tebuconazole and ABA before the onset of stress, as shown by the difference in phenotypes and the induction of a large number of markers. Proteomic analysis led to the identification of 18 and 34 specific markers for treatment-induced drought and salinity stress tolerance, respectively, representing important functions such as CO2 fixation, PSII activity, protein folding, and nitrogen and sulfur assimilation. Most of the markers declined in abundance under stress but were partly or fully protected by the chemical treatment. The primary protective mechanism was to prevent or attenuate the loss of these proteins by positively influencing translation and/or protein stability in the case of salinity tolerance and by influencing transcription/mRNA stability as well as translation/ protein stability in the case of drought tolerance. Although the stress tolerance markers represented the same functions, we found only five proteins that were induced under both stress conditions, revealing differences in the signal transduction pathways activated by tebuconazole plus ABA depending on the stress stimulus. The different response mechanisms are also highlighted by the much higher impact of drought stress on transcript abundance. More data are required to determine how tebuconazole plus ABA intervene primarily at the protein level in the case of salinity stress, but also at the mRNA level in the case of drought stress. Finally, we identified four proteins with unknown functions that have not previously been annotated as stress response/tolerance factors and that represent novel candidates for overexpression experiments aiming to increase stress tolerance in important crop species.

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REFERENCES

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