Changes in the proteome of pad2-1, a glutathione depleted Arabidopsis mutant, during Pseudomonas syringae infection

Journal of Proteomics 126 (2015) 82–93

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Changes in the proteome of pad2-1, a glutathione depleted Arabidopsis mutant, during Pseudomonas syringae infection Riddhi Datta, Sharmila Chattopadhyay ⁎ Plant Biology Laboratory, Drug Development/Diagnostics & Biotechnology Division, CSIR — Indian Institute of Chemical Biology, 4, Raja S.C.Mullick Road, Kolkata 700 032, India

a r t i c l e

i n f o

Article history: Received 13 February 2015 Received in revised form 20 April 2015 Accepted 28 April 2015 Available online 30 May 2015 Keywords: Infection GSH pad2-1 Pseudomonas syringae Proteomics

a b s t r a c t The involvement of glutathione (GSH) in plant defense against pathogen invasion is an established fact. However, the molecular mechanism conferring this tolerance remains to be explored. Here, proteomic analysis of pad2-1, an Arabidopsis thaliana GSH-depleted mutant, in response to Pseudomonas syringae infection has been performed to explore the intricate position of GSH in defense against biotrophic pathogens. The pad2-1 mutant displayed severe susceptibility to P. syringae infection compared to the wild-type (Col-0) thus re-establishing a fundamental role of GSH in defense. Apart from general up-accumulation of energy metabolism-related protein-species in both infected Col-0 and pad2-1, several crucial defense-related protein-species were identified to be differentially accumulated. Leucine-rich repeat-receptor kinase (LRR-RK) and nucleotide-binding site–leucine-rich repeat resistance protein (NBS–LRR), known to play a pioneering role against pathogen attack, were only weakly upaccumulated in pad2-1 after infection. Transcriptional and post-transcriptional regulators like MYB-P1 and glycine-rich repeat RNA-binding protein (GRP) and several other stress-related protein-species like heat shock protein 17 (HSP17) and glutathione-S-transferase (GST) were also identified to be differentially regulated in pad2-1 and Col-0 in response to infection. Together, the present investigation reveals that the optimum GSHlevel is essential for the efficient activation of plant defense signaling cascades thus conferring resistance to pathogen invasion. © 2015 Published by Elsevier B.V.

1. Introduction Pathogen-mediated yield loss of economically important plant products and food crops is one of the most serious concerns in today's world. According to the Food and Agricultural Organization of the United Nations (2015), plant pests and diseases affect food crops, causing significant losses to farmers and threatening food security [1]. Although pest and disease management has helped in considerably increasing food production in the last 40 years, pathogens still claim 10–16% of the global harvest [2]. Abbreviations: 2-DE, two dimensional gel electrophoresis; Avr, avirulence; BSA, bovine serum albumin; CC, control Col-0; Col-0, Arabidopsis thaliana plants of Columbia ecotype; CP, control pad2-1; DAMP, damage-associated molecular pattern; dpi, days post-infection; EDTA, ethylene diamino tetraacetic acid; GAPDH, glyceraldehyde-3-phosphate dehydrogenase; GPX, glutathione peroxidase; GRP, glycine rich repeat RNA binding protein; GSH, glutathione; GSSG, glutathione disulfide; GST, glutathione-S-transferases; Hsfs, heat stress transcription factors; HSP, heat shock protein; IC, infected Col-0; IEF, isoelectric focusing; IP, infected pad2-1; IPG, immobilized pH gradient; LRR-RK, leucine-rich repeat receptor kinase; MALDI–TOF–MS/MS, matrix assisted laser desorption ionization–time of flight– tandem mass spectrometry; MAMP, microbe-associated molecular patterns; NBS–LRR, nucleotide-binding site–leucine-rich repeat resistance protein; pad2-1, phytoalexin deficient mutant; PCA, principal component analysis; qRT-PCR, quantitative real time-polymerase chain reaction; R, resistance; ROS, reactive oxygen species; SA, salicylic acid. ⁎ Corresponding author. E-mail address: [email protected] (S. Chattopadhyay).

http://dx.doi.org/10.1016/j.jprot.2015.04.036 1874-3919/© 2015 Published by Elsevier B.V.

Plant defense responses are regulated through a network of signaling pathways which cross-communicate among themselves and with other signal molecules to fine-tune the plant's response to different pests and pathogens encountered. In fact, plant defense is initiated as soon as a microbe generates signals that can be perceived by the plant. Initial perception of pathogen invasion is thought to occur by recognition of microbe-associated molecular patterns (MAMPs or PAMPs) and damage-associated molecular patterns (DAMPs) by the host plant pattern recognition receptors (PRRs) which trigger PAMP-triggered immunity (PTI) in plants [3]. In non-host defense, the plant can recognize general MAMPs [4], or molecules released as a result of the initial invasion [5]. Compatible pathogens on their hosts may surmount MAMP triggered immunity, and release effectors for progression of their attack. The plant can recognize these effectors, leading to a more precise immune response [6–8]. This second stage of the immune response is defined by specific gene-for-gene interactions in which a pathogen molecule (Avr, avirulence) is detected by the product of a corresponding plant R (resistance) gene [9]. This constitutes a part of effectortriggered immunity (ETI). Successful recognition limits the spread of the infection, resulting in an incompatible interaction. Glutathione (GSH) is a nearly ubiquitous molecule, which plays a critical role in diverse physiological processes in almost all organisms. In plant tissues GSH is considered as a redox buffer and is present in its reduced form at 2 to 3 mM concentrations compared to its oxidized

R. Datta, S. Chattopadhyay / Journal of Proteomics 126 (2015) 82–93

form (glutathione disulfide; GSSG) whose concentration is around 10– 200 μM [10–12]. It is an important metabolite with a broad spectrum of functions, and its homeostasis is essential to maintain cellular redox potential and effective stress responses in plants [13]. The role of GSH in plant defense has long been known [14,15]. The Arabidopsis pad2-1 mutant belongs to a series of non-allelic camalexindeficient mutants and exhibits a strong susceptibility to virulent strains of Pseudomonas syringae and hyper-susceptibility to oomycete pathogen Phytophthora brassicae. This mutant has been reported to contain only about 22% of wild-type amounts of GSH [16]. Recent reviews have suggested a strong involvement of GSH in plant–microbe interactions and that GSH deficiency in pad2-1 affects defense-related signaling events conferring susceptibility to pathogens [17,18]. Recently, the pad2-1 mutant has also been reported to exhibit vulnerability to combined osmotic and cold stress. Transcript analysis has revealed that GSH provides resistance against this combined stress by modulating phenylpropanoid, lignin and ethylene biosynthetic pathways [19]. The enzymes of GSH synthesis and metabolism are reported to be induced in response to stress [20]. This implies a considerable overlap in the signal transduction cascades that persuade GSH synthesis and those involved in defense functions in a GSH-dependent manner, such as glutathione-Stransferases (GST) and glutathione peroxidase (GPX) [21]. Previously we have shown that GSH confers biotic stress tolerance in plants likely through the NOREXPRESSOR OF PR GENES (NPR1)dependent salicylic acid (SA)-mediated pathway [22,23]. GSH has also been shown to act in NPR1-independent pathway to allow increased intracellular H2O2 to activate SA signaling, a key defense response in plants [24]. Recently we have shown that GSH is related to ethylene biosynthetic pathway, in addition to SA pathway, to combat biotic stress [25]. The main goal of proteomics is to understand how the proteins interact with one another and with other molecules to construct the cellular building, how they can be modified in order to fit in with programmed growth and development, and to interact with their biotic and abiotic environment [26]. Proteomic approach has been undertaken to explore the plant's response to various biotic stresses [27,28]. Analysis of proteomic changes in sugarcane during sugarcane mosaic virus infection and kiwi shoot during infection with a pandemic pathogen P. syringae pv. actinidiae has revealed valuable information [29]. P. syringae is a Gram-negative bacterium and its different strains are known for their diverse interactions with plants [30]. Among these strains, P. syringae pv. tabaci is a non-host pathogen of Arabidopsis thaliana. Understanding the molecular basis of the high level of host specificity has been a driving force in using P. syringae as a model for studying host–pathogen interactions. Alterations in the Arabidopsis proteome have been compared between basal and gene-for-gene defense against P. syringae [31,32]. There has, however, been no complete proteomics study of the non-host resistance during Arabidopsis–P. syringae interaction under GSH depleted condition. To explore the involvement of GSH in stress tolerance and non-host pathogen defense, here, we have investigated the proteome profiles of pad2-1 mutant and wildtype A. thaliana plants in response to P. syringae pv. tabaci infection, which may provide novel strategies for stress management and minimize pathogen-mediated damages in plants.

2.2. P. syringae infection P. syringae pv. tabaci was grown up to an optical density of 0.7, centrifuged and the cells were resuspended in 25 mL of 10 mM MgCl2 [34]. This bacterial culture was found to contain 2.46 × 107 CFU mL−1. Approximately 5 μL of this suspension was inoculated on the upper side of intact leaves using a 1 mL plastic syringe. Plants were maintained at 22 °C with a 16 h light/8 h dark cycles and under 70% relative humidity. The infection was monitored up to 5 days post-inoculation (dpi). The leaves were harvested from control and infected plants at 1, 2, 3, 4 and 5 dpi, photographed and used for further experiments. Three independent batches (3 biological replicates) of Col-0 and pad2-1 were used for infection and further experiments. Each batch consisted of 40 plants. In this study, P. syringae pv. tabaci has been used to explore the non-host resistance in plants under GSH-depleted condition. 2.3. Callose deposition estimation Estimation of callose deposition was performed according to Pozo et al. [35]. Briefly, control and infected Col-0 and pad2-1 leaves, collected at 3 dpi, were treated with 96% ethanol overnight to remove chlorophyll. The decolorized leaves were washed in 0.07 M phosphate buffer (pH 9) and subsequently incubated with the same buffer containing 0.01% aniline blue (Sigma, USA). Samples were placed in the dark for a period of 2 h at room temperature and observed under a fluorescence microscope. One plant for each sample (control and infected Col-0 and pad2-1) was selected from 3 independent batches of infection and 3 leaves from each plant were analyzed. 2.4. Measurement of chlorophyll contents Estimation of total chlorophyll was done from control and infected Col-0 and pad2-1 leaves at 3 dpi according to Lichtenthaler [36].

Table 1 List of selected marker genes along with the sequences of primer pairs used. Gene

Forward primer (5′–3′)

Reverse primer (5′–3′)

LRR-RK

ATGGAAGGTCGTCGTCAA CG ATGTCTGAAGTTGAGTAC CGG ATGTTCAGATCCGAACGC AAGA ATGTCTCTAATTCCAAGCAT CTT ATGGCAGGAATGAAAGTT TTC ATGAATTTTACTGGCTATTC TCG ATGTCTGAATCAAGGAGC TTAGC ATGGAGAATAATCCAAGA AG ATGGCTTCTATTTCAACCCC TTT ATGGCTAAGTTTGCTTCCAT CATC ATGATTACTCGGCTGTTC GCC ATGTCACCACAAACAGAG ACTAA ATGGCGTCTCTGCAACTC TG TGAGGAAGACTGTTGCCA AG ATGGCTGACAAGAAGATC AGAAT ATGGCTGATGGTGAAGAT ATTCAA

TTATCAAATAGTTGATGCCT TGCT CGCCGCTACCTCTCGACT

GRP NBS–LRR HSP17 GST PR1 PR2 SDRLP AOS PDF1.2

2. Materials and methods

83

ATP synthase

2.1. Plant materials and growth condition

Rubisco

Wild-type A. thaliana plants of Columbia ecotype (Col-0; Nottingham Arabidopsis Stock Centre, N1093) and phytoalexin deficient mutant (pad2-1; Nottingham Arabidopsis Stock Centre, N3804) plants were grown in Murashige and Skoog (MS) medium and maintained in a growth chamber at 22 °C under a 16 h light/8 h dark cycles as standardized before [33]. A pictorial depiction of the experimental design is shown in Supplemental Fig. 1.

Photosystem II OEC Chlorophyll a/b binding protein GAPDH Actin

CTAGATGACTTGTTGACT GAAA CTCTCTCCACTTATCTGA AGT CGACTCAATTTCAATGCCCA TGG TACGTGTGTATGCATGAT CAC ATTTGCGTCGAATAGGTT TTGGTA ATTAGACAACAAAGCCTC CAT TAAAACCCATTTAGTGAT CAAACC TTAACATGGGACGTAACA GATACA CCCCTGCCATGAAGAAGC TAT TTCTCCTGGAACGGGCTC GAT ATCGATTGGGAGAAACAC TCT CGAACTTGACTCCGTTCCTA AACAATGCCAAACCTGTC ATTAAT CATTGTAGAAAGTATGAT GCCAGA

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Fig. 1. Comparison of P. syringae pv. tabaci infection of A. thaliana Col-0 and pad2-1 leaves. The leaves were collected from control and infected plants at different time points and photographed. Figure shows the representative leaves images out of three biological replicates.

Fig. 2. Leaves of pad2-1 mutant exhibit lower level of callose deposition in response to P. syringae infection. The leaves were collected from control and infected plants at 3 dpi and stained with aniline blue to visualize callose deposition. Arrows indicate callose deposition. Figure shows the representative leaves images out of three biological replicates.

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Fig. 3. Transcript analysis of selected marker genes from control and infected Col-0 and pad2-1 leaves. Infected leaves were collected at 1, 2, 3, 4 and 4 dpi. Error bars are standard error of the mean of the relative expression derived from three biological replicates.

One plant for each sample was selected from 3 independent batches of infection and 6 leaves from each sample were pooled and analyzed. 2.5. Protein extraction and 2-DE analyses Total protein was isolated using the phenol extraction method from control and infected Col-0 and pad2-1 plants collected at 3 dpi. The entire experiment was repeated thrice. Briefly, about 4.5 g of leaf tissue from 30 plants for each sample was ground in liquid nitrogen and suspended in extraction buffer (700 mM sucrose, 500 mM Tris–HCl, pH 7.5, 50 mM EDTA, 100 mM KCl, 2% (w/v) βmercaptoethanol, 1 mM phenyl methyl sulfonyl fluoride) and protein extraction was done following standard protocol. The resulting

protein was resuspended in isoelectric focusing (IEF) buffer consisting of 7 M urea, 2 M thiourea, 4% 3-[(3-cholamido propyl)dimethylammonio]-1-propane sulfonate (CHAPS), 20 mM DTT and 1% (w/v) Bio-Lyte (3/10) ampholyte (BioRad Laboratories, Hercules, CA, USA) as standardized before [37,38]. The protein concentration was determined by Bradford's method using bovine serum albumin (BSA) as standard [39]. 500 μg of protein (equivalents of BSA) was used to passively re-hydrate immobilized pH gradient strip (18 cm; pH 4–7; Bio-Rad, USA) for 12 h. IEF was performed as follows: 250 V linear 30 min, 10,000 V linear 4 h, 10,000 V rapid 43,000 V-h, 500 V linear 1 h on Bio-Rad PROTEAN IEF Cell system. Focused strips were then equilibrated in equilibration buffers I & II (Bio-Rad, USA) for 15 min each. For running gels in the second dimension, 12% SDS polyacrylamide gels

Fig. 4. Representative 2-DE gel image of leaf proteome in Col-0 and pad2-1 mutant under control and infected conditions (CC, IC, CP and IP). Proteins were extracted from respective samples harvested at 3 dpi, electro-focused on IPG strip (pH-4–7) and resolved on 12% (w/v) SDS-PAGE. Gel images were analyzed using PDQuest version 8.0.1. Molecular marker is indicated as kDa.

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Table 2 Summary of the detected spots in CC, IC, CP and IP gels used for proteome analysis. Set

CC vs. IC CP vs. IP a b c d e

Sample

CC IC CP IP

Protein yield (μg/g fresh tissue)

375 ± 21 326 ± 19 363 ± 26 342 ± 23

No. of spots detected

339 ± 11 352 ± 23 371 ± 16 356 ± 7

No. of spots showing differential abundancea

No. of spots identifiedb Total

Spots showing quantitative difference

Spots showing qualitative difference

Up-accumulatedc

Down-accumulatedd

Newly appearede

Mean coefficient of variance (CV)

65 ± 5

65

49

14

2

30.96%

56 ± 3

53

27

23

3

37.32%

Spots showing differential abundance with respect to control (CC in CC vs. IC; CP in CP vs. IP). Spots reproducibly identified in 3 biological replicates. Up-accumulated: above 2-fold. Down-accumulated: below 2-fold. Newly appeared: detected only in infected sample.

were used and stained with colloidal Coomasie Brilliant Blue (CBB) G250 [40]. 2.6. Image and data analysis The gel images were acquired using Versa Doc imaging system (BioRad, USA) and image analysis was performed with PDQuest software version 8.0.1. Detection of spots was performed by matching the gels automatically, followed by manual verification. Protein spots were annotated only if detected in all gels after normalization of the spot densities against the whole gel densities; the percentage volume of each spot was averaged for three replicate gels for each set and statistical analysis was performed to find out significant fold changes. For handling the missing spots master gel was created containing spots from all the gels, which was then used to match all the spots. Principal component analysis (PCA) was performed on the entire dataset using The Unscrambler X10.2 (CAMO A/S, Norway). 2.7. Protein identification using matrix assisted laser desorption/ionization– time of flight–tandem mass spectrometry (MALDI–TOF–MS/MS) Selected spots were excised from 2-DE gels and subjected to in-gel digestion with trypsin following the manufacturer's instructions (Pierce, USA). Digested proteins were further desalted with Zip-Tip C18 (Millipore, USA) and analyzed using a 4800 MALDI–TOF–MS/MS analyzer (Applied Biosystems). Peptides were evaporated with a Nd:YAG laser at 355 nm, using a delayed extraction approach. They were accelerated with 25 kV injection pulse for TOF analysis. Each spectrum was the cumulative average of 1000 laser shots. The MS/MS spectrum was collected in MS/MS 1 kV positive reflectron mode with fragments generated by post-source decay. The MS/MS mass tolerance was set to ±20 ppm. After processing, ten MS/MS precursors were selected (minimum signal to noise ratio 50). Before each analysis, the instrument was calibrated with the 4700 Proteomics Analyzer Calibration Mixture (Applied Biosystems). Data interpretation was carried out using the GPS Explorer Software (Applied Biosystems) and an automated database. A search was carried out using the MASCOT program (MatrixScience Ltd., UK). The MS/MS data was used to perform protein identification by searching in a non-redundant protein sequence database (NCBI nr — 20070216; 4,626,804 sequences, 1,596,079,197 residues) using a MOWSE algorithm as implemented in the MASCOT search engine version 3.5 (http://www.matrixscience.com). The following parameters were used for database searches: taxonomy: Viridiplantae (green plants; 186,963 sequences); cleavage specificity: trypsin with one missed cleavages allowed; mass tolerance of 100 ppm for precursor ions and a tolerance of 0.2 Da for the fragment ions; allowed modifications: carbamidomethyl (fixed), oxidation of methionine (variable); cleavage by trypsin: cuts C-term side of KR unless the next residue is P. According to MASCOT probability analysis, only significant hits

(p b 0.05) were considered. To evaluate the functional categories and hierarchies of identified proteins, KEGG (Kyoto Encyclopedia of Genes and Genomes, http://www.genome.jp/kegg/) was used [41]. The theoretical peptide mass and pI of the polypeptides were evaluated at EXPASy (http://www.expasy.org/tools/pi tool.html) [42,43] for final confirmation according to their positions in the 2-DE gel map. 2.8. RNA extraction and quantitative RT PCR (qRT-PCR) Total RNA was extracted from control and infected leaf samples, collected at 1, 2, 3, 4 and 5 dpi, using TRIzol method. One plant for each sample was selected from each of the 3 independent batches of infection (3 biological replicates) and 3 leaves from each sample were pooled for RNA extraction. cDNA was synthesized using the Revert Aid H Minus cDNA Synthesis kit (Fermentas, USA) using 1 μg RNA sample. The qRT-PCR analysis was performed using Light Cycler 96 System (Roche Applied Science, USA) with FastStart Essential DNA Green Master (Roche Applied Science, USA) following manufacturer's instructions. qRT-PCR was performed for selected marker genes presented in Table 1. The constitutively expressed actin gene was used as the reference gene. 2.9. Western blot analysis Proteins were extracted after homogenizing leaves in 50 mM potassium phosphate buffer, pH 7.8, containing 0.15% (v/v) Triton X-100. Protein samples were quantified by Bradford assay, using BSA as standard, 25 μg of protein (equivalents of BSA) was resolved in 12% SDS-PAGE gel and transferred onto polyvinylidenedifluoride membrane (Millipore, USA), blocked with 5% skimmed milk and GST protein bands were detected by using a rabbit polyclonal antibody raised against Arabidopsis GST (Agrisera, Sweden) as the primary antibody and an anti-rabbit

Fig. 5. Venn diagram representation showing the overlap of the identified protein-species showing differential abundance in Col-0 and pad2-1 mutant in response to infection (CC vs. IC and CP vs. IP).

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IgG conjugated to horseradish peroxidase (Agrisera, Sweden) as the secondary antibody. Immunoreactive proteins were visualized using the SuperSignal West Pico chemiluminescent substrate (Pierce, USA). Two plants for each sample were selected from each of the 3 independent batches of infection (3 biological replicates) and 10 leaves from each sample were pooled for protein extraction. 2.10. Statistical analysis Data collected from three independent biological experiments were compared to control and infected leaves of Col-0 and pad2-1 for all experiments performed. Means and standard deviations were calculated from three independent sets of biological replicates. For proteomic analysis, the differences in spot abundance were statistically evaluated using the t-test function implemented in the PDQuest software. The spot quantitation employing version 8.0.1 of PDQuest was followed by manual verification and then reported to give the best results [44]. The PDQuest data was subjected to PCA using The Unscrambler X10.2 (CAMO A/S, Norway). Spots showing differences between control and infected leaves with a p-value of b0.05 were chosen for further analysis. The p-values were adjusted for false discovery rate correction using Benjamini–Hochberg method. 2.11. In silico protein–protein interaction analysis A protein–protein interaction network analysis with the identified proteins was done by STRING software through inputting sequences of the identified proteins at http://string-db.oorg. 3. Results 3.1. Morphological analysis, estimation of callose deposition and chlorophyll content after P. syringae infection Four weeks old Col-0 and pad2-1 plants were infected with P. syringae and infection was scored for 5 dpi. Infection development was evident as yellowing of the infected parts of the leaves. The pad21 mutant exhibited more susceptibility to P. syringae infection compared to Col-0 plants and exhibited strong infection at 3 dpi (Fig. 1). Recognition of MAMPs by host receptors leads to callose deposition in the cell wall which is a part of induced defense in plants [4]. So, callose deposition in response to P. syringae infection was investigated here at 3 dpi. Results revealed a greater deposition of callose in Col-0 leaves compared to pad2-1 leaves in response to infection, which can be corroborated with the morphological data (Fig. 2). Chlorophyll content estimation also revealed a 40.1% drop in chlorophyll levels in pad2-1 leaves after infection compared to 18.6% in Col-0 (Supplementary Fig. 2). 3.2. Transcript analysis of selected marker genes To study the response of Col-0 and pad2-1 mutant to P. syringae infection we studied the expression of several marker genes of SA and jasmonic acid (JA) pathways at 1, 2, 3, 4 and 5 dpi and compared with uninfected control. We studied the expression patterns of selected SA pathway marker genes, viz. pathogenesis related gene 1 (PR1), PR2 and short-chain dehydrogenase/reductase family protein (SDRLP). We observed that the expression of PR1 and PR2 genes were induced at 2 dpi in Col-0 and their expression levels were highest at 3 dpi. SDRLP displayed a similar expression pattern. However, in case of pad2-1, PR1 was only weakly induced at 3 dpi and its expression did not increase up to 5 dpi. PR2 and SDRLP also exhibited a similar expression pattern. The expression of plant defensing gene 1.2 (PDF1.2), a marker gene for JA signaling pathway, and allene oxidase (AOS), a JA biosynthetic pathway gene, were also studied. PDF1.2 expression was repressed at

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3 dpi in both Col-0 and pad2-1 while AOS expression was unaltered (Fig. 3). Hence, we performed proteomic analysis at 3 dpi. 3.3. Proteomic analysis To investigate the role of GSH in plant defense, we compared the differential protein abundance in Col-0 and pad2-1 leaves in response to P. syringae infection using 2-DE coupled to protein identification by MALDI–TOF–MS/MS. The result showed significant difference in global expression patterns. Fig. 4 shows the representative gel images of control Col-0 (CC), infected Col-0 (IC), control pad2-1 (CP) and infected pad2-1 (IP) leaves. The numbers of resolved spots were 339, 352, 371 and 356 in CC, IC, CP and IP gels respectively. Among these spots 65 and 56 spots were found to be reproducibly altered in intensity in response to infection in Col-0 (CC vs. IC) and pad2-1 (CP vs. IP) respectively. The overall mean coefficient of variation for the intensity of the spots matched was determined to be 30.96% and 37.32% respectively. The spot detection data has been summarized in Table 2. This data was subjected to multivariate statistical analysis, and PCA results showed that PC1 and PC2 explained 84% and 15% of total variance in Col-0 (CC vs. IC) and 97% and 3% of total variance in pad2-1 (CP vs. IP) (Supplementary Fig. 3). Correlations between PCs are indicated in Supplementary Table 1. The spots showing statistically significant differential abundance between the samples were manually excised from the gel. This was followed by in-gel tryptic digestion and protein-species identification through MALDI–TOF–MS/MS analysis. The number of spots successfully identified was 65 and 53 in CC vs. IC and CP vs. IP respectively (Fig. 5, Supplementary Fig. 4). These protein-species were further categorized as up-accumulated (above 2-fold), down-accumulated (below 2-fold) and newly appeared (detected only in infected sample) spots using PDQuest software. Table 3 and Fig. 6 show the list of identified protein-species; the average fold change represents the ratio of change of spot intensity in comparison to the control. The peptide sequences of the identified protein-species are presented in Supplementary Table 2. Functional categorization of the identified protein-species from CC vs. IC gels revealed that about 28% of the identified proteinspecies belonged to Carbon and energy metabolism, 44% to Stress and defense, 3% to Signaling and gene regulation and 25% to Others. In case of CP vs. IP gels, 36% of the identified protein-species belonged to Carbon and energy metabolism, 38% to Stress and defense, 4% to Signaling and gene regulation and 22% to Others. The distribution of the identified protein-species according to their functional categories is shown in Fig. 7. A protein–protein interaction network among the identified stress and defense-related protein-species was generated in silico using STRING software (Supplementary Fig. 5). 3.4. Validation of selected protein-species by qRT-PCR analysis To validate the differential abundance of several primary metabolism as well as stress-related protein-species identified, we have performed qRT-PCR analysis of the corresponding genes. We have examined the expression levels of ATPase alpha subunit, glyceraldehyde-3-phosphate dehydrogenase (GAPDH), ribulose bisphosphate carboxylase/oxygenase large subunit, photosystem II stability/assembly factor HCF132, chlorophyll a/b binding protein, leucine-rich repeat receptor kinase (LRR-RK), GST, heat shock protein 17 (HSP17), glycine rich repeat RNA binding protein (GRP) and nucleotide-binding site–leucine-rich repeat resistance protein (NBS– LRR) genes in CC, IC, CP and IP leaves. ATPase alpha subunit and GAPDH are energy metabolism-related genes and were found to be upregulated after infection in both Col-0 and pad2-1. The carbon metabolism-related genes, ribulose bisphosphate carboxylase/oxygenase large subunit and photosystem II stability/assembly factor HCF132 were also up-regulated in both Col-0 and pad2-1 in response to infection while chlorophyll a/b binding protein was down-regulated. NBS–LRR is

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Table 3 Differentially expressed proteins identified from the leaf proteome of Col-0 and pad2-1 in response to P. syringae infection. U: up-accumulated, D: down-accumulated, I: newly appeared, nd: not detected. Spot Accession no. no.

Protein-species (Organism)

SC Th. pI/Mwc (%)

Exp. pI/Mwd

Average fold change CC vs. IC

Average fold change CP vs. IP

0.243 0.255

59 109

18 49

5 9

Chloroplast Chloroplast

25 31

5.19/55.33 5.96/50.91

5.29/63.88 6.65/56.43

3.98 (U) 5.62 (U)

2.4 (U) 3.7 (U)

0.269 0.435 0. 276 0.433 0.381 0.280 0. 490

193 77 49 48 48 154 66

91 21 18 18 26 78 29

17 7 7 9 8 19 9

Chloroplast Chloroplast Plasma membrane Extracellular region Mitochondrion Chloroplast Chloroplast

36 11 21 19 16 49 36

6.13/47.63 6.79/44.10 5.82/119.88 8.84/43.91 6.55/27.34 6.13/52.85 6.04/51.65

6.03/60.46 5.48/39.57 4.37/35.88 5.47/23.19 8.01/49.30 6.95/58.95 6.11/59.53

3.6 (U) 323.91 (I) nd 2.5 (U) 2.11 (U) 3.19 (U) 4.42 (U)

2.97 (U) 2.99 (U) 2.53 (U) 2.76 (U) 2.22 (U) 2.76 (U) 2.31 (U)

0. 410

67

19

11

Chloroplast

34

6.30/51.50

5.04/61.96

4.91 (U)

144.08 (I)

0.160

331

91

17

Chloroplast

28

5.87/52.95

5.29/60.93

6.32 (U)

179.65 (I)

0.496

416

180

19

Chloroplast

26

5.87/52.95

5.58/58.8

193.59 (I)

386.98 (I)

0.336 0.42 4 0.122 0.269 0.150 0.156

78 88 44 170 61 420

27 29 19 76 27 304

9 11 5 8 6 13

Chloroplast Chloroplast Chloroplast Cytosol Chloroplast Cytosol

19 9 53 22 26 21

5.12/24.99 6.17/42.41 9.18/35.28 5.39/27.16 5.91/35.02 6.34/36.98

4.54/26.63 4.95/42.1 8.00/48.08 6.09/27.18 5.14/32.51 8.46/46.04

0.31 (D) nd nd nd nd 2.32 (U)

0.48 (D) 0.32 (D) 0.19 (D) 0.58 (D) 0.38 (D) 2.56 (U)

0.310 0. 233

178 91

86 43

11 9

Mitochondrion Chloroplast

11 32

4.97/17.18 7.58/20.35

4.8/19.1 8.13/21.67

nd 3.42 (U)

2.99 (U) nd

0.320 0.025 0.060 0.120

48 59 59 91

22 18 26 43

5 5 7 11

Mitochondrion Chloroplast Chloroplast Chloroplast

46 42 26 25

6.55/27.34 5.19/55.33 5.19/55.33 7.58/20.35

5.99/25.06 5.33/52.16 5.39/53.97 8.01/21.64

5.19 (U) 2.96 (U) 2.58 (U) 3.21 (U)

nd nd nd nd

0.393

246

156

17

Chloroplast

31

7.59/20.22

7.62/21.19

2.92 (U)

nd

5 9

Plasma membrane Plasma membrane Nucleus/vacuolar membrane Nucleus Plasma membrane Nucleus/vacuolar membrane Plasma membrane/cytosol Chloroplast/plasma membrane/ cytosol/Golgi apparatus Chloroplast/plasma membrane/ cytosol/Golgi apparatus Chloroplast/plasma membrane/ cytosol/Golgi apparatus Extracellular region Cytosol/nucleus/mitochondrion/ peroxisome/chloroplast Nucleus/chloroplast

19 16 9 31 26 17 22 39

6.18/99.35 5.98/98.96 9.63/9.37 7.07/16.77 6.18/99.34 9.63/9.37 7.96/53.08 5.78/25.65

5.05/65.11 5.89/91.45 5.85/39.3 6.76/20.73 5.85/99.3 4.46/22.29 6.87/49.65 5.23/29.95

3.87 (U) 4.31 (U) 5.63 (U) 4.71 (U) 6.11 (U) 2.1 (U) 2.91 (U) 2.76 (U)

2.18 (U) 2.91 (U) 3.62 (U) 2.58 (U) 3.11 (U) 3.17 (U) 2.28 (U) 0.28 (D)

42

5.78/25.65

4.99/20.16

2.19 (U)

0.12 (D)

26

5.72/27.56

5.56/25.62

0.46 (D)

0.39 (D)

52 29

6.26/135.29 6.72/42.21 7.83/16.71 7.06/20.35

3.51 (U) 2.96 (U)

0.41 (D) 0.5 (D)

13

5.45/34.95

0.21 (D)

0.48 (D)

Stress and defense 26 gi|15238489 27 gi|42569427 28 gi|50909973 29 gi|4519204 30 gi|15238489 31 gi|50909973 32 gi|10185114 33 gi|4006934

Leucine-rich repeat protein kinase, putative [A. thaliana] Leucine-rich repeat protein kinase, putative [A. thaliana] Unknown protein [O. sativa (japonica cultivar-group)] MYB-P1 [Perilla frutescens] Leucine-rich repeat protein kinase, putative [A. thaliana] Unknown protein [O. sativa (japonica cultivar-group)] Wound-induced GSK-3-like protein [Medicago sativa] Glutathione transferase [A. thaliana]

0.266 0.292 0.336 0.404 0.226 0.036 0.011 0.496

45 40 53 47 49 48 52 206

26 25 22 29 31 36 19 164

7 11 5 6 19

34

gi|4006934

Glutathione transferase [A. thaliana]

0.022

69

211

11

35

gi|21554322

L-ascorbate

0.257

119

18

16

36 37

gi|44921729 Disease resistance protein [Glycine max] gi|6911144 Putative glycine-rich RNA binding protein [Catharanthus roseus]

0.021 0.262

50 50

26 18

7 5

38

gi|21539543 Putative fibrillin [A. thaliana]

0.325

195

107

9

peroxidase [A. thaliana]

5.46/40.91

R. Datta, S. Chattopadhyay / Journal of Proteomics 126 (2015) 82–93

Carbon and energy metabolism 1 gi|5881679 ATPase alpha subunit [A. thaliana] 2 gi|2654333 Ribulose-bisphosphate carboxylase large subunit [Helianthemum grandiflorum] 3 gi|1944432 Ribulose-bisphosphate carboxylase [A. thaliana] 4 gi|9759370 Photosystem II stability/assembly factor HCF136 [A. thaliana] 5 gi|48995370 Cellulose synthase catalytic subunit [Mesotaenium] 6 gi|20197929 Putative polygalacturonase [A. thaliana] 7 gi|50905037 Putative ATP synthase [Oryza sativa (japonica cultivar-group)] 8 gi|1054917 Ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit 9 gi|2342970 Ribulose 1,5-bisphosphate carboxylase-oxygenase large subunit [Lotus corniculatus] 10 gi|21634161 Ribulose-1,5-bisphosphate carboxylase/oxygenase large subunit [Cuscuta japonica] 11 gi|5881702 Large subunit of ribulose-1,5-bisphosphate carboxylase/oxygenase [A. thaliana] 12 gi|5881702 Large subunit of ribulose-1,5-bisphosphate carboxylase/oxygenase [A. thaliana] 13 gi|16374 Chlorophyll a/b binding protein (LHCP AB 180) [A. thaliana] 14 gi|7263568 Sedoheptulose-bisphosphatase precursor [A. thaliana] 15 gi|29469708 Cytochrome f [Pinus koraiensis] 16 gi|7076787 Cytosolic triose phosphate isomerase [A. thaliana] 17 gi|4835233 Putative protein 1 photosystem II oxygen-evolving complex [A. thaliana] 18 gi|81622 Glyceraldehyde-3-phosphate dehydrogenase (phosphorylating) (EC 1.2.1.12), cytosolic — [A. thaliana] 19 gi|17939851 Mitochondrial F0 ATP synthase D chain [A. thaliana] 20 gi|9758820 Ribulose bisphosphate carboxylase small chain 2b precursor (RuBisCO small subunit 2b) [A. thaliana] 21 gi|50905037 Putative ATP synthase [O. sativa (japonica cultivar-group)] 22 gi|5881679 ATPase alpha subunit [A. thaliana] 23 gi|5881679 ATPase alpha subunit [A. thaliana] 24 gi|9758820 Ribulose bisphosphate carboxylase small chain 2b precursor (RuBisCO small subunit 2b) [A. thaliana] 25 gi|23198308 Ribulose bisphosphate carboxylase, small subunit [A. thaliana]

Protein Peptide No. of Cellular p-Value peptides localization (corrected)a scoreb score

gi|3201613

Glutathione S-transferase [A. thaliana]

0.317

299

138

21

40

gi|9279652

Lectin-like protein [A. thaliana]

0.316

55

13

5

41 42

gi|6016707 gi|16301

Putative thylakoid lumen rotamase [A. thaliana] Glycine rich protein [A. thaliana]

0.322 0.212

168 170

37 57

11 16

43 44 45 46

gi|7799011 gi|2326354 gi|21555637 gi|34911758

0.276 0.256 0.281 0.116

194 139 124 54

107 75 86 36

14 11 9 5

0.191 0.115

40 81

16 45

6 9

49

Heat shock protein 17 [A. thaliana] LMW heat shock protein [A. thaliana] Heat shock protein, putative [A. thaliana] Putative AKT1-like potassium channel [O. sativa (japonica cultivar-group)] gi|42569427 Leucine-rich repeat protein kinase, putative [A. thaliana] gi|21593796 Glutamate-ammonia ligase (EC 6.3.1.2) precursor, chloroplast [A. thaliana] gi|21554322 L-ascorbate peroxidase [A. thaliana]

0.313

119

18

11

50 51 52 53

gi|15487902 gi|44921729 gi|53828647 gi|2267593

0.236 0.169 0.490 0.363

39 50 66 50

13 49 15 19

5 3 6 10

0.289 0.105

53 46

29 16

0.221 0.252 0.239

50 49 49

0.291 0.270 0.366 0.475 0.252 0.600 0.224 0.426 0.471 0.163 0.297 0.156 0.219 0.303 0.203 0.136 0.269 1.3000 0.102 0.325 0.110 0.296

48 48 48 54 39 55 52 61 76 48 48 51 76 48 48 55 83 52 61 48 76 76

47 48

NBS/LRR resistance protein-like protein [Theobroma cacao] Disease resistance protein [Glycine max] At4g38970 [A. thaliana] Glycine-rich RNA-binding protein [O. sativa]

Signaling and gene regulation 54 gi|16755428 Maturase K [Jacksonia horrida] 55 gi|4761301 Maturase K [Nuphar polysepala] Others 56 gi|48727602 APETALA3-like protein [Akebia trifoliata] 57 gi|42529350 Ribonuclease H [Phaseolus vulgaris] 58 gi|50933501 Putative phospholipid/glycerol acyltransferase [O. sativa (japonica cultivar-group)] 59 gi|9665115 Similar to copia-type reverse transcriptase proteins [A. thaliana] 60 gi|2246380 Peptidyl-prolyl cis-trans isomerase [A. thaliana] 61 gi|32479732 OJ991214_12.8 [O. sativa (japonica cultivar-group)] 62 gi|2246380 Peptidyl-prolyl cis-trans isomerase [A. thaliana] 63 gi|31433565 Putative zinc finger protein [O. sativa (japonica cultivar-group)] 64 gi|21280841 Unknown protein [A. thaliana] 65 gi|51091875 Hypothetical protein [O. sativa (japonica cultivar-group)] 66 gi|34895450 P0037C04.19 [O. sativa (japonica cultivar-group)] 67 gi|54291186 Hypothetical protein [O. sativa (japonica cultivar-group)] 68 gi|50909343 Hypothetical protein [O. sativa (japonica cultivar-group)] 69 gi|55296709 Hypothetical protein [O. sativa (japonica cultivar-group)] 70 gi|39545916 TAF15b [A. thaliana] 71 gi|54291186 Hypothetical protein [O. sativa (japonica cultivar-group)] 72 gi|50909343 Hypothetical protein [O. sativa (japonica cultivar-group)] 73 gi|55296709 Hypothetical protein [O. sativa (japonica cultivar-group)] 74 gi|21280841 Unknown protein [A. thaliana] 75 gi|1755188 Germin-like protein [A. thaliana] 76 gi|51091875 Hypothetical protein [O. sativa (japonica cultivar-group)] 77 gi|34895450 P0037C04.19 [O. sativa (japonica cultivar-group)] 78 gi|32479732 OJ991214_12.8 [O. sativa (japonica cultivar-group)] 79 gi|54291186 Hypothetical protein [O. sativa (japonica cultivar-group)] 80 gi|54291186 Hypothetical protein [O. sativa (japonica cultivar-group)] a b c d

Chloroplast/plasma membrane/ cytosol/Golgi apparatus Cell wall/nucleus/plasma membrane Chloroplast Cytosol/nucleus/mitochondrion/ peroxisome/chloroplast Cytosol Cytosol Cytosol Plasma membrane

29

6.17/24.14

5.97/20.36

2.19 (U)

0.49 (D)

33

6.98/30.51

6.11/35.26

0.48 (D)

0.37 (D)

42 36

5.06/47.98 5.86/16.89

4.79/45.26 5.97/20.31

0.32 (D) 2.28 (U)

0.33 (D) 0.27 (D)

62 39 42 39

5.20/17.44 7.88/23.61 5.99/17.83 6.92/105.54

5.82/19.22 8.33/25.06 6.01/20.78 7.18/86.29

2.28 (U) 2.48 (U) 2.33 (D) 0.5 (D)

0.49 (D) 0.31 (D) 0.23 (D) nd

Plasma membrane Chloroplast/mitochondrion

26 16

5.98/98.96 6.43/47.41

5.69/102.23 3.67 (U) 6.26/50.13 2.11 (U)

nd nd

Chloroplast/plasma membrane/ cytosol/Golgi apparatus Cytosol Extracellular region Chloroplast Cytosol/nucleus/mitochondrion/ peroxisome/chloroplast

32

5.72/27.56

6.18/25.25

0.39 (D)

nd

42 26 9 52

9.09/19.72 6.26/135.29 6.79/42.98 7.80/16.33

8.89/20.09 5.98/112.65 7.05/40.22 7.31/14.26

2.78 (U) 0.22 (D) 0.49 (D) 2.72 (U)

nd nd nd nd

7 5

Chloroplast Chloroplast

16 11

5.05/5.74 9.78/59.61

4.92/10.12 9.18/63.02

3.88 (U) 2.98 (U)

2.98 (U) 0.42 (D)

31 26 21

7 9 7

Nucleus Nucleus Mitochondrion

31 23 19

9.73/26.35 6.22/3.21 9.24/59.78

9.02/2.36 6.86/9.32 9.36/63.25

3.25 (U) 5.23 (U) 2.71 (U)

nd nd 2.01 (U)

19 26 18 34 23 41 19 91 29 19 37 32 45 36 26 25 37 21 17 16 33 51

5 7 6 11 3 16 7 13 11 5 7 9 11 3 5 5 13 11 9 5 7 9

Nucleus Chloroplast Nucleus Chloroplast Nucleus Mitochondrion – Nucleus – Exocyst Nucleus Nucleus – Exocyst Nucleus Mitochondrion Nucleus/cell wall/apoplast – Nucleus Nucleus – –

52 36 14 65 21 42 16 12 25 22 36 39 15 17 28 36 46 17 25 29 15 31

7.37/95.63 5.10/24.74 9.27/18.87 5.10/24.74 8.52/37.56 10.75/25.81 10.75/25.81 7.50/11.38 5.18/9.10 10.88/7.72 9.62/5.68 7.93/38.89 5.18/9.10 10.88/7.72 9.62/5.67 9.69/28.82 5.86/19.22 5.86/19.22 7.50/11.38 9.27/18.87 5.18/9.10 5.18/9.10

6.09/92.11 5.36/20.95 9.33/15.29 5.86/20.66 7.99/35.03 9.32/26.02 9.03/22.69 7.22/10.03 4.96/9.22 6.23/9.01 9.63/9.33 7.29/42.31 526/9.03 9.63/10.36 9.05/9.22 9.06/32.06 6.03/20.36 4.99/18.21 7.02/10.33 8.81/23.62 4.92/10.69 5.06/9.26

0.22 (D) nd nd nd 0.39 (D) nd nd nd 2.38 (U) 0.13 (D) 3.11 (U) 2.57 (U) 2.46 (U) 2.99 (U) nd 2.13 (U) nd nd 2.06 (U) 3.11 (U) 2.37 (U) 0.21 (D)

nd 3.91 (U) 2.11 (U) 0.41 (D) 0.33 (D) 2.62 (U) 2.19 (U) 3.71 (U) nd nd nd 2.16 (U) nd nd 3.68 (U) nd 0.50 (D) 0.22 (D) nd nd nd nd

R. Datta, S. Chattopadhyay / Journal of Proteomics 126 (2015) 82–93

39

Corrected p-value (Benjamini–Hochberg FDR). Score as obtained from MASCOT search tool. Theoretical pI and Mr. Experimental pI and Mr as observed on 2-DE gel. 89

90

R. Datta, S. Chattopadhyay / Journal of Proteomics 126 (2015) 82–93

differentially accumulated in Col-0 and pad2-1 leaves after infection. To validate this we have checked protein accumulation levels of GST in CC, IC, CP and IP leaves by Western blot analysis. Results completely supported our proteomic data and GST was found to be strongly upaccumulated in IC but down-accumulated in IP (Fig. 8b). 4. Discussion

Fig. 6. Heatmap representation of the identified protein-species showing differential abundance in Col-0 and pad2-1 mutant in response to infection (CC vs. IC and CP vs. IP). Log (fold change) value for each identified protein-species has been plotted. Green color represents up-accumulation while the red color represents down-accumulation.

known to be a crucial member involved in defense signaling [45]. LRR-RKs regulate a wide variety of developmental and defense-related processes including host-specific as well as non-host-specific defense response and wounding response [46]. In the present study these genes were found to be only weakly induced in pad2-1 leaves compared to Col-0 in response to infection. HSP17 is known to be intimately involved in defense response in plants [47]. GRP is reported to play significant role in combating stress in plants [48]. GST also plays a crucial role in maintaining redox homeostasis during stress [49]. In the current study these genes were found to be up-regulated in Col-0 but down-regulated in pad2-1 after infection. These results completely validated our proteomic data (Fig. 8a). 3.5. Validation of protein accumulation of GST by Western blot analysis GST is an important GSH dependent stress-related protein that is known to play crucial role in defense, particularly in response to ROS. In the present investigation, we have identified GST as to be

Plants are engaged in a continuous co-evolutionary struggle with their pathogens and the outcome of these interactions is of particular importance to human activities, as they can have intense effects on agricultural systems [50]. Because the proteome is directly related to actual function, comparative proteomic approach can offer new insights into the level where regulation, mostly occurs, and which changes are the most important [51]. In the present investigation, we aimed to study how GSH depletion in pad2-1 mutant can affect non-host resistance against P. syringae pv. tabaci infection. The pad2-1 mutant exhibited severe susceptibility to this non-host pathogen which was also evident from the transcript and proteomic analyses. These observations suggest that GSH is crucial for effective stress management in plants. The observed susceptibility in pad2-1 mutant can be due to two possible reasons. First, pathogen invasion under GSH depletion can lead to perturbed redox homeostasis and a consequent increase in oxidative stress in plant cells. This would otherwise be restored by GSH in wild-type (Col-0) plants. Second, GSH can have a direct or indirect role in regulating the genes and protein-species by thiol-mediated modulations of various transcription factors, subunits of RNA polymerase and protein kinase which could not be efficiently accomplished in pad2-1 [16,52]. In addition to its susceptibility to P. syringae, pad2-1 has also been reported to exhibit hypersusceptibility to the oomycete pathogen, P. brassicae and this susceptibility to both the pathogens is not due to camalexin deficiency, but due to GSH-depletion [16]. Very recently, we have reported that the mutant is also susceptible to combined osmotic and cold stress treatments. Microarray analysis identified that stress-mediated up-regulation of the lignin, phenylpropanoid and ethylene biosynthetic pathway genes were impaired in the pad2-1 mutant [19]. Our study has identified several protein-species which may be operative behind the susceptibility of pad2-1 and this has been summarized in Fig. 9. Recognition of MAMPs by surface-localized PRRs constitutes an important layer of innate immunity in plants. The LRR-RKs are the PRRs for the peptide MAMPs elf18 and flg22, which are derived from bacterial EF-Tu and flagellin, respectively [53]. LRR-RKs comprise the largest subfamily of transmembrane receptor-like kinases in plants, with over 200 members in Arabidopsis and regulate host-specific as well as non-hostspecific defense responses [46]. In the present investigation, LRR-RK has been identified to be strongly up-accumulated in Col-0 after infection. In pad2-1, on the other hand, LRR-RK was only up-accumulated by 3.11fold as compared to 6.11-fold in Col-0. This may be because GSH is directly or indirectly required for the complete regulation of LRR-RK during infection and as such the pad2-1 mutant fails to efficiently recognize the advent of pathogens. R genes are the key components of genetic interactions between plants and biotrophic bacteria. The most common R proteins contain a NBS–LRR domain and NBS–LRR proteins are strongly suggested to contribute to disease resistance [45,54]. In case of Col-0 plants, we observed up-accumulation of NBS–LRR protein-species while its differential abundance could not be identified in pad2-1 after infection. qRT-PCR analysis also revealed a lower basal level expression of NBS–LRR gene in pad2-1 thus supporting the role of GSH in regulating expression of NBS–LRR. This impaired regulation of R proteins under GSH-depleted condition may be one of the reason behind the susceptibility of pad2-1 mutant against infection by the non-host pathogen P. syringae pv. tabaci. Several regulators of gene expression which function during defense against infection also require GSH for their regulation. Plant MYB proteins are characterized by a highly conserved MYB DNA-binding domain

R. Datta, S. Chattopadhyay / Journal of Proteomics 126 (2015) 82–93

91

Fig. 7. Functional categorization of the identified protein-species showing differential abundance. Doughnut chart representation of the protein-species identified from CC vs. IC and CP vs. IP.

and are associated with plant development, secondary metabolism, hormone, signal transduction, disease resistance and abiotic stress tolerance [55]. Several MYB transcription factors have also been reported to be induced in response to exogenous GSH [56]. Here we have identified MYB-P1 protein to be up-accumulated by 4.71-fold in Col-0 compared to 2.58-fold in pad2-1 after infection. The RNA-binding proteins that harbor RNA-recognition motifs at the N-terminus and a glycinerich region at the C-terminus are referred to as the GRPs and regulate gene expression mainly at the post-transcriptional level. GRPs are

Fig. 8. Validation of selected identified protein-species showing differential abundance. (a) qRT-PCR analysis, (b) Western blot analysis. Error bars are standard error of the mean of the relative expression derived from three biological replicates.

reported to have different impacts on seed germination, seedling growth, and stress tolerance of Arabidopsis plants under diverse stress conditions [57]. Here, GRP has been identified to be up-accumulated in Col-0 while down-accumulated in pad2-1 in response to pathogen attack which means the optimum GSH level is perhaps essential for regulating GRP expression under stress. Plant GSTs are induced by diverse biotic and abiotic stimuli, and are important for protecting plants against oxidative damage [32]. GSTs catalyze the conjugation of GSH to an electrophilic substrate [58]. For example, they can catalyze the conversion of H2O2 at the expense of GSH, thereby producing GSSG [59]. In this study, GST has been found to be up-accumulated in Col-0 after infection while its increased abundance was not detected in pad2-1 after infection. Consequently, the biotic stress associated ROS generation and alteration of redox homeostasis in the plant cells cannot be efficiently detoxified under GSH depleted condition. It is known that HSPs and heat shock transcription factors (Hsfs) are involved in cellular response to various forms of stress besides heat [60]. HSP17 is known to exhibit chaperone activity in vitro and is induced in response to various stress conditions [47]. In our study, HSP17 was upaccumulated in Col-0 in response to infection. However, the HSP17 was not up-accumulated in the GSH depleted mutant pad2-1 after infection, thus demonstrating a critical role of GSH in modulating stress responsive proteins in plants. Apart from the stress and defense-related proteins, a general upaccumulation of energy and carbon metabolism-related proteins have been observed after infection in both Col-0 as well as pad2-1 mutant. This includes up-accumulation of photosynthesis-related protein-species like rubisco large subunit, rubisco small subunit, photosystem II stability/assembly factor HCF136 and cellulose synthase catalytic subunit and energy metabolism-related proteins like ATPase alpha subunit. Presumably up-accumulation of photosynthesis-related protein-species will lead to increased carbon fixation and generation of more substrate for cellular metabolism. The improved metabolic rate will generate additional energy which can then be utilized to synthesize stress-related metabolites. It has been suggested earlier that there is an enhanced energy requirement to meet the elevated metabolic demand at the site of infection [61]. When a plant faces pathogen invasion it switches to a series of signaling cascades ultimately leading to the production of a battery of defense-related proteins and metabolites. This increases the energy requirement and ultimately leads to up-accumulation of carbon and energy metabolism-related proteins. Our observations, together with earlier reports, signify an increased energy requirement in plants to synthesize stress-related proteins and metabolites when it switches from ‘normal mode’ to ‘defense mode’.

92

R. Datta, S. Chattopadhyay / Journal of Proteomics 126 (2015) 82–93

Fig. 9. Effect of GSH depletion on protein abundance during infection.

In the present study, several protein-species were identified where the pI and Mw values were not coincident with the heterologous sequences in the database. This may be because the apparent Mw values predicted by SDS-PAGE can have an error deviation of about ±10% compared with the theoretical values [62]. In most of the cases, this was because only a fragment of the protein sequence was included in the database. Isoforms of some proteins like LRR-RK, GST, and GRP were identified in more than one spot. This may be explained by different splicing variants, or posttranslational modified or cleaved isoforms of the same protein [63]. 5. Conclusion Collectively, this investigation reports an interesting proteomic analysis of pad2-1, a GSH depleted Arabidopsis mutant, in response to P. syringae pv. tabaci infection and its comparison with the wild-type (Col-0). In the current study, we have identified several crucial stress and defense-related proteins which are directly or indirectly regulated by GSH during infection. More specifically, the pad2-1 mutant failed to effectively regulate several protein-species involved in PTI-related first line of defense mechanism like LRR-RK. In addition, less accumulation of ETI-related R-gene products like NBS–LRR in pad2-1 during infection signifies the dynamic role of GSH in plant defense responses. Conflict of interest The authors declare no conflict of interest. Acknowledgment This work was supported by the Council of Scientific and Industrial Research (CSIR) (BSC0117) and the Department of Science & Technology (DST) (GAP302), New Delhi, India. Research activity by Riddhi Datta has been supported by fellowship from the Indian Council of Medical

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