Shotgun Proteomic Analysis of the Mexican Lime Tree Infected with

Article pubs.acs.org/jpr

Shotgun Proteomic Analysis of the Mexican Lime Tree Infected with “Candidatus Phytoplasma aurantifolia” Aboozar Monavarfeshani,† Mehdi Mirzaei,‡,§ Elham Sarhadi,∥ Ardeshir Amirkhani,⊥ Mojtaba Khayam Nekouei,† Paul A. Haynes,‡ Mohsen Mardi,†,* and Ghasem Hosseini Salekdeh*,†,∥,# †

Department of Genomics, Agricultural Biotechnology Research Institute of Iran, Karaj, Tehran, Iran Department of Chemistry and Biomolecular Sciences, Macquarie University, North Ryde, NSW, Australia § The Australian School of Advanced Medicine, Faculty of Human Sciences, Macquarie University, Sydney, NSW, 2109, Australia ∥ Department of Systems Biology, Agricultural Biotechnology Research Institute of Iran, Karaj, Tehran, Iran ⊥ Australian Proteome Analysis Facility (APAF), Department of Chemistry and Biomolecular Sciences, Macquarie University, Sydney, Australia # Department of Molecular Systems Biology, Cell Science Research Center, Royan Institute for Stem Cell Biology and Technology, ACECR, Tehran, Iran ‡

S Supporting Information *

ABSTRACT: Infection of Mexican lime trees (Citrus aurantifolia L.) with the specialized bacterium “Candidatus Phytoplasma aurantifolia” causes witches’ broom disease. Witches’ broom disease has the potential to cause significant economic losses throughout western Asia and North Africa. We used label-free quantitative shotgun proteomics to study changes in the proteome of Mexican lime trees in response to infection by “Ca. Phytoplasma aurantifolia”. Of 990 proteins present in five replicates of healthy and infected plants, the abundances of 448 proteins changed significantly in response to phytoplasma infection. Of these, 274 proteins were less abundant in infected plants than in healthy plants, and 174 proteins were more abundant in infected plants than in healthy plants. These 448 proteins were involved in stress response, metabolism, growth and development, signal transduction, photosynthesis, cell cycle, and cell wall organization. Our results suggest that proteomic changes in response to infection by phytoplasmas might support phytoplasma nutrition by promoting alterations in the host’s sugar metabolism, cell wall biosynthesis, and expression of defense-related proteins. Regulation of defense-related pathways suggests that defense compounds are induced in interactions with susceptible as well as resistant hosts, with the main differences between the two interactions being the speed and intensity of the response. KEYWORDS: Candidatus Phytoplasma aurantifolia, shotgun proteomics, label-free, Mexican lime tree, witches’ broom disease, biotic stress

1. INTRODUCTION

discovery of the broad spectrum of genes and molecular mechanisms that are involved in plant responses to pathogens. Platforms that have been used to study the interactions between plants and pathogens at the molecular level include high-throughput sequencing,3,4 DNA microarrays,5 and cDNAAFLP,6,7 all of which have been used to investigate pathogeninduced changes in the plant transcriptome.8,9 Proteomics has also been shown to be a powerful approach to study plant responses to biotic stresses.10−13 Traditional two-dimensional electrophoresis (2-DE) techniques have been the primary method to quantify changes in the abundances of proteins in many plant species after exposure to biotic stresses.14,15 Use of 2-DE to analyze the response of

Infection of Mexican lime (Citrus aurantifolia L.) by “Candidatus Phytoplasma aurantifolia”, which causes witches’ broom disease, has the potential to cause significant economic losses throughout parts of western Asia and North Africa.1 In Oman, the area under cultivation with lime trees is currently only 50% of that used for lime cultivation in 1990. In Iran, 30% of the Mexican lime trees (over half a million trees/7000 ha) have been destroyed by witches’ broom disease of lime (WBDL) since 2000.2 Identification of the causal agent of WBDL has not yet led to practical solutions to thwart the disease. A key challenge for enhancing plant tolerance to biotic stresses is to identify the genes and molecular mechanisms that are involved in the interactions between plants and pathogens. The advent of “omics” technologies has paved the way for the © 2012 American Chemical Society

Received: September 11, 2012 Published: December 17, 2012 785

dx.doi.org/10.1021/pr300865t | J. Proteome Res. 2013, 12, 785−795

Journal of Proteome Research

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Mexican lime trees to infection by “Ca. Phytoplasma aurantifolia” identified 39 differentially accumulated proteins that were involved in oxidative stress defense, photosynthesis, metabolism, and stress responses.14 Nevertheless, 2-DE is limited by the under-representation of low-abundance proteins, which is caused by an upper restriction on the amount of protein that can be loaded on gels and difficulties in detecting proteins with high molecular weights, extreme pIs, or poor solubility (e.g., proteins that span hydrophobic membranes). Quantitative proteomics techniques that are label-free or involve labeling with stable isotopes address these issues. Among these techniques, label-free quantitation has become a popular and efficient approach to study plant responses to biotic and abiotic stresses.16,17 In the present work, we used a label-free shotgun proteomics approach to gain comprehensive insight into changes in the proteomes of Mexican lime trees in response to infection by Ca. Phytoplasma aurantifolia. Expression of several proteins was altered significantly during the interaction between Mexican lime trees and Ca. Phytoplasma aurantifolia. The biological processes and pathways that were affected most during phytoplasma infection included the metabolism of ribosomes, starch sucrose, and cyanoamino acids, photosynthetic carbon fixation, and phenylpropanoid biosynthesis.

quantified using the Bradford assay. Equal amounts of protein (120 μg per well) from five biological replicates each of healthy and infected plants were separated on 10% bis-Tris polyacrylamide gels at 150 V for 1 h. After electrophoresis, the proteins were visualized using colloidal Coomassie Blue. The gels were then washed twice in water (10 min per wash) before the individual lanes were cut into 16 slices of equal size from top to bottom. 2.4. In-Gel Tryptic Digestion

Each of the 16 equally sized pieces of gel from each SDS-PAGE lane was placed into a well of a 96-well plate. In-gel digestion was performed on each slice as described previously.22 The extracted peptides were dried in a speed vacuum centrifuge, resuspended in 10 μL of 2% formic acid with 30 μL of ACN (50%)/formic acid (2%), dried, vacuum centrifuged, and reconstituted to 10 μL with 2% formic acid. 2.5. Nanoflow Liquid Chromatography

Tryptic peptides were separated by reverse-phase chromatography and analyzed using an LTQ-XL ion-trap mass spectrometer (Thermo Scientific, CA, USA).23 Peptides from each fraction were injected onto the C18 column using a Surveyor Autosampler (Thermo Scientific). The methods of nanoLC−MS/MS, previously followed by Chick et al.24 were adopted. In a fused silica capillary with an integrated electrospray tip, reversed-phase columns were packed inhouse to approximately 8 cm (100 μm i.d.) using 100 Å, 5 μm Zorbax C18 resin (Agilent Technologies, CA, USA). An electrospray voltage of 1.8 kV was applied via a liquid junction upstream of the C18 column. Loaded samples on the C18 column were subjected initially to a washing step with buffer A [5% (v/v) ACN, 0.1% (v/v) formic acid] for 10 min at 1 μL/ min. Subsequently, peptides were eluted from the C18 column with 0%−50% Buffer B [95% (v/v) ACN, 0.1% (v/v) formic acid] over 58 min at 500 nL/min, followed by 50%−95% Buffer B over 5 min at 500 nL/min. The eluted peptides were directed into the nanospray ionization source of the LTQ-XL with a capillary voltage of 1.8 kV. The collected spectra were scanned over the mass/charge (m/z) range of 400−1500 atomic mass units using Xcalibur software (version 2.06, Thermo Scientific). Data-dependent scan settings were defined to choose the six most intense ions with a dynamic exclusion list size of 90 s. The MS/MS spectra were generated by collision-induced dissociation of the peptide ions at a normalized collision energy of 35%.

2. MATERIALS AND METHODS 2.1. Plant Material

Ten healthy Mexican lime seedlings were grown in a greenhouse at a relative humidity of 50% and at a temperature of 25−28 °C. One-year-old trees were used for all experiments. Specimens from Mexican lime trees infected with Ca. Phytoplasma aurantifolia were grafted to five healthy trees, and specimens from healthy Mexican lime trees that were not infected with Ca. Phytoplasma aurantifolia were grafted to other healthy trees. To increase the humidity, the grafted plants were covered with plastic bags for 1 month, and they were arranged randomly throughout the greenhouse. Approximately 20 weeks after diseased branches were grafted onto healthy trees, Mexican lime trees developed typical symptoms of witches’ broom disease, and the leaves of five healthy and five infected plants were harvested, frozen immediately in liquid nitrogen, and stored at −80 °C for further use. 2.2. Detection of Phytoplasma Infection by Nested Polymerase Chain Reaction (PCR)

2.6. Protein Identification

Total DNA was extracted from leaf vascular tissues in accordance with the method developed by Daire et al.18 with some modifications.7 A region within the phytoplasma 16S rRNA gene was amplified by PCR in a total reaction volume of 25 μL using an Applied Biosystems thermal cycler. The first set of PCR primers used was P1 (5′-AAGAGTTTGATCCTGGCTCAGGATT-3′)19 and P7 (5′CGTCCTTCATCGGCTCTT-3′).20 The resulting P1−P7 amplicons were then used as the template DNA for nested PCR amplification using the universal primer pair for phytoplasmas r16r2/r16F2n.21

The raw files from each nanoLC−MS/MS run were converted to mzXML format using the ReAdW program and processed through the global proteome machine (GPM) software using version 2.1.1 of the X!Tandem algorithm, which is freely available at http://www.thegpm.org.25,26 For each experiment, the five replicates were processed sequentially with output files for each individual replicate, and a merged, nonredundant output file was generated for protein identifications with log (e) values less than −1. Tandem mass spectra were subjected to a search against the in-house transcriptome database of the Mexican lime tree. The database also incorporated common human and trypsin peptide contaminants, and additional searching was performed against a reversed sequence database to evaluate the false discovery rate (FDR). Search parameters included MS and MS/MS tolerances of ±2 Da and ±0.2 Da, tolerance of two missed tryptic cleavages, and K/R-P cleavages. Fixed modifications were set for carbamidomethylation of

2.3. Protein Extraction and Separation by SDS-PAGE

Leaf samples (1 g) that had been collected from five independent replicates were pulverized to a fine powder in liquid nitrogen, using a mortar and pestle. Protein was extracted using the methods described previously.16 The extracted proteins in sodium dodecyl sulfate (SDS) sample buffer were 786



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Figure 1. (A) Mexican lime trees developed typical symptoms of witches’ broom disease approximately five months after diseased branches were grafted onto healthy trees. (B) A color map of the abundances of 990 reproducibly identified proteins in five replicates of healthy samples (H) and five replicates of infected (I) samples. Darker colors represent higher abundances. For both the healthy and infected samples, all five replicates clustered together, which suggested high reproducibility of the preparation and analysis of samples. (C) Expression pattern of the 990 proteins that were identified in all five replicates of healthy plants and all five replicates of infected plants. Of these, 274 proteins were down-accumulated (green spots), and 174 proteins were up-accumulated in infected plants compared to in healthy plants (red spots). (D) Functional classification of differentially accumulated proteins.

2.7. Differentially Accumulated Proteins

cysteine, and variable modifications were set for oxidation of methionine. The five individual GPM result files for each condition were combined to create a single merged result file for each condition. Only proteins that were identified in all five replicates were considered to be a valid hit in the final data set for each condition, and protein-level false discovery rates (FDRs) in the merged result files were calculated at less than 1% for each condition. Reversed database hits and contaminants were excluded. An additional requirement imposed was the necessity for a total spectral count of at least six for at least one condition.27,28

Data on protein abundance were calculated on the basis of normalized spectral abundance factors (NSAFs) as described previously.16,29 A spectral fraction of 0.5 was added to all spectral counts initially, to compensate for null values and to allow for log transformation of the NSAF data before statistical analysis.30 Proteins that were accumulated differentially between infected and healthy plants were identified by performing a t test comparison. Only proteins that were present in all replicates for at least one condition were included in the data set. Two-sample unpaired t tests were carried out on log transformed NSAF data, and proteins with a p-value less 787



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Figure 2. MapMan Visualization of 144 differentially accumulated proteins related to biotic stress. These included proteins involved in redox regulation, signaling, cell wall biosynthesis and degradation, secondary metabolism, protein degradation, hormone metabolism, and responses to abiotic stress, as well as heat shock proteins (HSPs) and pathogenesis-related proteins. Each square and the color indicate the Log2FC value of proteins that were accumulated differentially in response to infection by phytoplasma. Information related to each gene is included in Table S3 (Supporting Information). Green and red colors represent proteins that were down- and up-accumulated in response to phytoplasma infection, respectively. Categories with no differentially accumulated proteins were shown by a gray circle.

than 0.05 on the t test were considered to be accumulated differentially. The resulting sets of differentially abundant proteins were then annotated functionally.

replicates in total) in each of healthy and infected plants. The qRT-PCR data were analyzed using the comparative CT method (ΔΔCT method).33 The 18s rRNA gene was used as a reference gene, and the statistical significance of differences in transcript abundances was determined using one-way analysis of variance followed by Least Significant Difference post hoc tests. The statistical analyses were performed using SPSS 16.0 (SPSS Inc., IL, USA).

2.8. Functional Classification

For the differentially accumulated proteins, functional assignment was performed using the Goanna tool (http://agbase. msstate.edu/cgi-bin/tools/GOanna.cgi).31 Gene expression patterns were visualized using the MapMan software.32 The software and mapping files were downloaded from the MapMan Web site (http://mapman.gabipd.org/web/guest). The log2-transformed fold-change in differentially accumulated proteins was subjected to MapMan analysis.

3. RESULTS

2.9. Quantitative Real-Time PCR (qRT-PCR) Analysis

3.1. Infection of Mexican Lime Seedlings with Candidatus Phytoplasma aurantifolia

Total RNA was isolated from leaves of the same five healthy trees and five infected trees that were used for the proteomic analysis, by using TRIzol reagent (Invitrogen, CA, USA) in accordance with the manufacturer’s instructions. The quality and quantity of isolated RNA were measured using a Nanodrop ND-1000 instrument (Nanodrop Technologies, DE, USA). cDNA was synthesized from each RNA sample using an iScript cDNA Synthesis Kit (Bio-Rad, CA, USA) in accordance with the manufacturer’s instructions. Real-time PCR analysis of the mRNAs that encoded some of the identified proteins was performed using the MyiQ Single-Color System (Bio-Rad) and iQ SYBR Green Supermix Kit (Bio-Rad) in accordance with the manufacturer’s instructions. We analyzed five biological replicates of cDNA, each with three technical replicates (15

Five months after specimens from trees infected with Ca. Phytoplasma aurantifolia had been grafted to healthy Mexican lime trees, the previously healthy trees developed the typical symptoms of witches’ broom disease (Figure 1A). Infection of the grafted plants with phytoplasma was confirmed by nested PCR. Sequence analysis of the resulting PCR fragment of phytoplasma 16S rDNA using iPhyClassifier indicated that it was most similar to the reference pattern of the 16Sr group II, subgroup B phytoplasma (GenBank accession: U15442), with a coefficient of pattern similarity of 0.99, which confirmed that it was a variant of 16SrII-B and related to Ca. Phytoplasma aurantifolia. 788



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Figure 3. Mapman analysis of differentially accumulated proteins that were categorized as participating in metabolism. Each square and the color indicate the Log2FC value of a protein that was accumulated differentially in response to phytoplasma infection. Information on each gene is included in Table S3 (Supporting Information). Green and red colors represent proteins that were differentially abundant in response to the abiotic stress, respectively.

3.2. Annotation of Identified Proteins Using Transcriptome Data

3.3. Differentially Accumulated Proteins

Quantitative analysis of the data that were generated by labelfree LC−MS/MS analysis of five replicate samples of healthy and infected Mexican lime leaves revealed significant changes in protein synthesis caused by phytoplasma infection. Whereas expression of 542 of the 990 proteins identified did not differ significantly between healthy and infected plants, the abundance of 448 proteins changed significantly in response to the pathogen. Of these 448 proteins, 274 were increased, and 174 were decreased in abundance in response to biotic stress (Figure 1C and Table S2, Supporting Information). Of the 448 differentially accumulated proteins, 360 could be assigned to a major functional group (Figure 1D). The functional groups included response to stress, protein metabolism, carbohydrate metabolism, generation of precursor metabolites, lipid metabolism, growth and development, signal transduction, photosynthesis, cell cycle, and cell wall organization. These results suggest that the down-accumulation of nearly 61% of the differentially accumulated proteins reflects the exploitation of cellular resources and/or the suppression of defense responses.34

To identify proteins, tandem mass spectra from five healthy and five infected samples were subjected to a search against an inhouse Transcriptome database of the Citrus aurantifolia. A total of 990 nonredundant proteins were identified in the infected and healthy samples. The details of all 990 reproducibly identified proteins are provided in Table S1 (Supporting Information). The calculated peptide FDR varied from 0.06% to 0.24%, whereas the protein FDR was less than 1%, which indicated that the data set was sufficiently stringent to not require further filtering. As a first level of quality control of the shotgun proteomic analysis, the correlation of the expression profiles of all the replicate samples was assessed with respect to biological reproducibility. We observed that, for both the healthy and infected samples, all five replicates clustered together, which suggested high reproducibility of the preparation and analysis of samples (Figure 1B). The naturallog-transformed NSAF data that were generated by overlapping kernel density plots for all samples showed that the data for each sample were distributed normally. 789



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Figure 4. mRNA expression ratios for seven genes that showed changes in abundance at the protein level. The ratio is expressed as log2 (expression level in infected plants/expression level in healthy plants). The fold changes in down-accumulated mRNAs and proteins are shown as negative values.

3.4. Functional Classification of Differentially Accumulated Proteins

analyze the expression of seven proteins at the RNA level, using five biological replicates of infected plants and five biological replicates of healthy plants (Figure 4). Our results showed similar trends in the changes in mRNA and protein levels for seed lectin (P81371), superoxide dismutase (O65174), pectinesterase 3 (P83948), and ATP-dependent Clp protease (Q8LB10). However, the levels of mRNAs for a GDSL esterase/lipase (Q9SVU5), a germin-like protein (Q9FMA8), and a small heat shock protein (P30222) changed in the opposite direction to those of the proteins they encode. The observed discrepancies between the expression levels of proteins, and their corresponding mRNAs confirm that protein levels do not necessarily correlate with mRNA levels, as reported elsewhere (e.g., refs 35 and 36). This lack of correspondence between mRNA and protein levels may reflect the fact that the mRNA level usually peaks before an increase in protein level. Post-transcriptional and post-translational modifications as well as different rates of degradation of mRNA and protein could also contribute to these discrepancies.

To gain insight into the biological context of relevant differences in protein accumulation, the identified differentially accumulated proteins in response to phytoplasma were analyzed using MapMan (Table S3, Supporting Information). Our analysis revealed that 144 of the differentially accumulated proteins could be categorized as being related to biotic stress (Figure 2). These included proteins involved in signaling, redox regulation, hormone metabolism, cell wall biosynthesis and degradation, protein degradation, secondary metabolism, and responses to abiotic stress, as well as heat shock proteins (HSPs) and pathogenesis-related proteins. Furthermore, Mapman analysis identified 141 differentially accumulated proteins as metabolism-related proteins (Figure 3). These included proteins involved in cell wall, starch, lipid, and amino acid metabolism, as well as secondary metabolism, photosynthesis, and the tricarboxylic acid (TCA) cycle. 3.5. Correlation between mRNA and Protein Levels

4. DISCUSSION We studied the proteome of healthy and infected Mexican lime trees using a shotgun proteomics approach. We used leaf samples from healthy controls and infected plants at the symptomatic stage because the plant/pathogen interaction is well established, but the plant cells are still active and can maintain pathogen survival. We observed differential accumulation of 448 proteins in response to pathogen infection. To

Protein levels can be regulated at different stages, for example during the transcription of the mRNA or the translation of the protein itself. The extent to which changes in mRNA levels are reflected in changes in the levels of the proteins that they encode is an important consideration when evaluating the physiological relevance of stimulus-induced changes in transcript abundances. To compare changes in gene expression at the mRNA and protein levels further, we used qRT-PCR to 790



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production of expansins is one of the mechanisms used by phytoplasma to enable the infection of Mexican lime trees. 4.1.3. Protein Degradation. Proteolytic enzymes are essential for the degradation of harmful, damaged, and misfolded proteins and thus are involved in all aspects of the plant life cycle.47 We identified 33 proteins involved in protein degradation that were differentially abundant in response to phytoplasma infection. Of these, the abundance of 21 proteins decreased, and 12 proteins increased after phytoplasma infection. Proteins with lower abundance in infected plants included a putative zinc metalloprotease, an aminopeptidase M1, a serine-type peptidase, a cucumisin-like serine protease, a subtilisin-like serine protease, a member of the aspartyl protease family, three ATP-dependent Clp protease subunits, a casein lytic proteinase/heat shock protein 100, a serine-type peptidase, two serine-type carboxypeptidases, the ethylene-dependent gravitropism-deficient and yellow-green-like 3 protein (a S2Plike putative metalloprotease), a member of the AAA-type ATPase family, a ubiquitin activating enzyme 2, a ubiquitin carboxyl-terminal hydrolase, a proteasome subunit alpha type-3, a proteasome alpha subunit-like protein, and the 26S proteasome subunit P45. Proteins with higher abundance in infected plants comprised a member of the peptidase M1 family, two xaa-Pro aminopeptidase 1 proteins, a cysteine proteinase, a cystatin-like protein, a xyloglucan-specific endoglucanase inhibitor protein, a serine carboxypeptidaselike 42 precursor, a NEDD8-activating enzyme E1 catalytic subunit, a member of the family of F-box proteins, the proteasome subunit PAB1, a 20S proteasome alpha 6 subunit, and the proteasome subunit alpha type-6. Some cysteine proteases play important roles in plant immunity during the interaction between pathogens and plants.48,49 Pearce et al.50 showed that a member of the subtilisin-like protease (subtilase) family can induce the expression of known defense-related genes by producing a peptide defense signal in soybean. Other types of protease, such as aspartic proteases and ATP-dependent Clp proteases, contribute to plant responses to different stresses.51−53 Increasing evidence indicates the essential role of the proteasome pathway in plant defense responses to pathogens during plant−pathogen interactions.54 However, this pathway plays different roles during the interactions of plants with pathogenic viruses, bacteria, and fungi.54 Although proteasome activity could be important for the plant host as a defense mechanism that targets and degrades phytoplasma proteins, it might also benefit phytoplasmas by increasing the pool of free amino acids available for phytoplasma nutrition. 4.1.4. Signaling. Approximately 28 of the proteins that were modulated by infection were found to function in signal transduction and hormone signaling. Whereas the abundance of six receptor kinases, including three leucine-rich repeat (LRR) receptors, increased in response to phytoplasma infection, the abundance of an epidermis-specific secreted glycoprotein EP1, a lysM domain-containing GPI-anchored protein 1, and a chloroplast sensor kinase decreased after phytoplasma infection. The LRR-containing proteins that are encoded by many plant disease resistance genes transduce pathogen signals, which lead to plant resistance to diseases, or are involved in developmental signaling pathways. The disease-resistance LRR-containing proteins are effective against pathogens that can grow only on living host tissue (obligate biotrophs) or pathogens that are hemibiotrophic.55

further examine the differentially accumulated proteins, the identified proteins were grouped into functional categories using the MapMan ontology as shown in Figures 2 and 3. 4.1. Proteins Involved in Stress Responses and Defense

4.1.1. Redox Proteins. The production of reactive oxygen species (ROS), via the consumption of oxygen during a socalled oxidative burst, is one of the earliest cellular responses that follows successful pathogen recognition. Numerous ROSscavenging systems, which include ascorbate peroxidases, superoxide dismutases, catalases, and glutathione, maintain ROS homeostasis in the plant cell.37 These enzymes can finely tune ROS-dependent signal transduction and restrict ROSdependent damage. In the present study, of the 10 differentially accumulated proteins that were involved in the detoxification of ROS, six proteins showed lower abundance, and four proteins showed higher abundnace in response to phytoplasma infection (Figure 2). Whereas the abundance of two protein disulfide isomerases, glutathione reductase, and Cu/Zn superoxide dismutase was higher in infected plants than in healthy plants, the protein levels of ascorbate peroxidase (cytosolic), ascorbate peroxidase (chloroplastic), dehydroascorbate reductase 1, glutathione peroxidase, manganese superoxide dismutase, and catalase were lower in infected plants than in healthy plants. A decrease in the levels of ROS scavenging enzymes has also been observed in resistant rice plants during infection with bacterial leaf blight38 and during the response of cucumber plants to infection by Pseudoperonospora cubensis.39 It is possible that a decrease in these enzymes in response to phytoplasma contributes to the accumulation of ROS, which in turn induces a hypersensitive response in Mexican lime trees.38 4.1.2. Greater Abundance of Cell-Wall-Related Proteins in Infected Plants. Modifications of the cell wall are often associated with plant defense responses owing to its role as a physical barrier between the environment and the internal contents of plant cells.40 Of the 13 differentially accumulated cell-wall-related proteins that were identified in the present study, 12 showed higher abundance in response to pathogen infection. We observed an increase in abundance of two other proteins that were related to the cell wall component. In addition to increased expression of β-1,4-endoglucanase, a cellulase that catalyzes the hydrolysis of cellulose in the cell wall,41 we observed an increase in abundance of five pectin methylesterases in response to disease. Pectin methylesterases catalyze the specific deesterification of pectin within plant cell walls, releasing methanol and pectate in the process.42 Pectins are major components of the middle lamellae and primary plant cell walls in dicotyledonous species, in which they comprise 30%−35% of the cell wall dry weight.43 Changes in the expression and activities of pectin methylesterase, and associated changes in the degree of methylesterification of cell wall pectins, have been correlated with changes in the susceptibility of plants to pathogens and abiotic stresses.44 Furthermore, targeting of demethylesterified homogalacturonan by pectin-degrading enzymes, such as polygalacturonases (PGs), affects the texture and rigidity of the cell wall. The levels of two expansins were also higher in infected plants than in healthy plants. Expansins promote cell growth by loosening the wall.45 Infection with Xanthomonas oryzae pv. oryzicola and Magnaporthe grisea also induces the expression of expansins, and the activation of expansins increases the susceptibility of rice to these pathogens.46 It is likely that phytoplasma-induced 791



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to import ATP from the environment.63 This highly specialized nutritional strategy, which typifies biotrophic plant pathogens, probably involves the diversion of host-cell metabolism to maintain pathogen survival and host-pathogen compatibility.63 4.2.1. Reduced Abundance of Photosynthesis Proteins in Response to PhytoplasmaCause or Consequence? Plants harvest light energy to produce ATP and reducing power in the form of NADPH, which can be used to generate photosynthetic assimilates. Given that the pool size of a range of metabolic intermediates is altered during plant defense responses, it is likely that photosynthetic metabolism is influenced as it adjusts to meet the demands of the cell. The number of nonessential cellular activities is reduced during the resistance response.64 Many studies have suggested that rates of photosynthesis are reduced locally after treatment with virulent or avirulent pathogens, after herbivore attack or wounding, or after hormone treatment (for review see ref 65). This might reflect the fact that the production of defense-related compounds becomes a top priority or that rates of plant photosynthesis are reduced until growth of the pathogen has terminated. Furthermore, to free up resources that can be used for defense responses,64 a decrease in the photosynthetic rate might protect the photosynthetic apparatus against oxidative damage66 or be a consequence of oxidative damage67{Bazargani, 2011 #129}. Consistent with previous studies, we observed that infection decreased the expression of 14 proteins involved in photosynthesis. Changes in the levels of photosynthetic proteins have also been shown by proteomic analyses of the responses of the mulberry tree68 and Mexican lime tree14 to phytoplasma.69 4.2.2. Energy Production by the TCA Cycle. The TCA cycle, or Krebs cycle, is a central metabolic pathway in all aerobic organisms and is responsible for the production of energy through the oxidation of carbohydrates, fatty acids, and amino acids.70 Previous studies showed that energy metabolism might decrease in response to phytoplasma infection68 and oxidative stress,71 to reduce the excess production of ROS. Reduced levels of proteins such as malate dehydrogenase,71 a key enzyme in the TCA cycle that appears to be particularly susceptible to oxidative damage, might protect against phytoplasma infection.68 Other proteins decreased in abundance were carbonic anhydrase, 2-oxoglutarate dehydrogenase, and succinate semialdehyde dehydrogenase. In addition, several proteins, including citrate synthase, NADP-dependent malic enzyme, pyruvate dehydrogenase, and fumarate hydratase 1, were accumulated in response to infection. Increased levels of citrate synthase and pyruvate dehydrogenase, both of which are considered to be rate-limiting enzymes for the TCA cycle,72 suggest that flux through the TCA cycle is increased following infection. 4.2.3. Carbohydrate Metabolism. The reduction in photosynthetic metabolism and increased cellular demands during the resistance response cause infected tissue to transition from source status to sink status. The levels of 18 proteins involved in carbohydrate metabolism changed in response to pathogen infection. Of these, 10 proteins were decreased in abundance, whereas only eight proteins were accumulated. This demonstrated that phytoplasma infection had systemic effects on carbohydrate metabolism. Interestingly, all eight proteins involved in starch metabolism were decreased in response to phytoplasma infection. These include two starch synthase enzymes (granule-bound starch synthase 1 and starch synthase 3), two

The abundance of Ras-related proteins Rab11B and RABH1B decreased and increased, respectively, following phytoplasma infection. Rab is the largest family of small GTPases involved in vesicle trafficking. A pea Rab11 protein (PRA2) modulates the light-mediated biosynthesis of brassinosteroids.56 The direct binding of GTP-bound PRA2 to the CPC cytochrome p450 suggests a direct role for PRA2 in signaling. The abundance of three proteins with WD40 repeat domains changed in response to infection by phytoplasma. These were the TGF-β receptor-interacting protein 1, a guanine nucleotidebinding protein subunit beta-like protein, and a member of the transducin protein family. The WD40 repeat domain is found in several eukaryotic proteins with a wide variety of functions, which include roles as adaptor and regulatory modules in signal transduction, cytoskeleton assembly, and pre-mRNA processing. The accumulation of the TGF-β receptor interacting protein in response to abiotic stress has been reported previously,57 and the protein plays a critical role in developmental events and signal transduction in response to external stimuli.58 The observation that several other signaling components responded to infection by phytoplasma suggests that the regulation of endogenous signaling pathways is necessary to establish compatible interactions. 4.1.5. Heat Shock Proteins. We identified 18 HSPs that were accumulated differentially in the present study. Interestingly, the abundance of these proteins decreased in response to infection by phytoplasma. The decrease in abundance of HSPs in response to WBDL has been reported previously.14 Given the role of HSPs in maintaining the structural and functional integrity of proteins, the decrease in abundance of HSPs could result in the accumulation of misfolded proteins following phytoplasma infection.59 4.1.6. Crosstalk between Biotic and Abiotic Stress Responses. Plants are exposed continuously to various abiotic and biotic stresses in their natural environment and have evolved sophisticated mechanisms to perceive and respond to environmental fluctuations. The generation of reactive oxygen species (ROS) is a key process that is common to the response to both biotic and abiotic stresses.60 Phytohormones such as abscisic acid (ABA), jasmonic acid (JA), ethylene (ET), and salicylic acid (SA) are endogenous molecules that primarily modulate the protective responses of plants to both biotic and abiotic stresses via synergistic and antagonistic actions that are referred to as signaling crosstalk.61,62 Our results support the notion that plant responses to disease involve extensive crosstalk within the network of signals that coordinate responses to abiotic stress. The levels of several proteins that are involved in abiotic stresses, the detoxification of ROS, HSPs, and hormone signaling changed significantly in response to disease (Figure 2). 4.2. Metabolism

The genomes of phytoplasmas are minimal, and many genes that code for proteins involved in metabolic pathways that are essential in other organisms are missing. It is unlikely that phytoplasmas can synthesize nucleotides (the components of DNA and RNA), hence they probably need to import them from the host plant. Important genes that encode enzymes that participate in the biosynthesis of amino acids and fatty acids are also missing. In addition, given that phytoplasmas are the only organisms that lack an ATP synthase, they probably also have 792



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spectral abundance factor (NSAF). Supplementary Table S2. List of differentially accumulated proteins (up-, downaccumulated) obtained from t test analysis of healthy versus infected plants, including normalized spectral abundance factor (NSAF) and p-values. Supplementary Table S3. Functional classification of differentially accumulated proteins using the MapMan software. This material is available free of charge via the Internet at http://pubs.acs.org.

starch branching and debranching enzymes (starch branching enzyme II, isoamylase-type starch-debranching enzyme 2), two starch phosphorylases (alpha-1,4 glucan phosphorylase L-1 isozyme and starch phosphorylase type H), and two starch ADP/ATP carrier proteins. In addition, the levels of three sucrose synthases and two fructokinases increased in response to infection by phytoplasma. Given that phytoplasmas lack enzymes for the metabolism of sucrose, they can use glucose or fructose as a source of energy.73 We observed that the levels of sucrose synthase and fructokinase proteins were greater in the leaves of infected plants than in healthy plants. This confirms the hypothesis that the increase in demand for simple sugars is probably compensated for by the accumulation of plant sucrose synthase in infected tissue. In addition, the reduction in the expression of genes involved in starch synthesis suggests a possible shift to increase the content of simple sugars in the host plant. 4.2.4. Lignin Biosynthesis. Lignin is a major component of plant cell walls and the second most prevalent biopolymer on earth, after cellulose. We observed increased levels of cinnamyl alcohol dehydrogenase, a key enzyme involved in lignin biosynthesis, after infection with phytoplasma. It has been shown that the lignin pathway is activated in response to pathogens because lignification and reinforcement of cell walls are important processes in the responses of plants to infection.74 A tomato line that was resistant to infection by Verticillium dahliae showed higher levels of lignin synthesis than sensitive lines.75 The observed increase in levels of cinnamyl alcohol dehydrogenase in Mexican lime trees after phytoplasma infection might enhance the capacity for lignification and thereby offer a strategy to restrict pathogen invasion.

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Corresponding Author

*Ghasem Hosseini Salekdeh. E-mail: [email protected]. Fax: +98-26-32704539. Tel.: +98-26-32700845. Mohsen Mardi. E-mail: [email protected]. Fax: +98-26-32704539. Tel.: +98-2632708282. Notes

The authors declare no competing financial interest.

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ACKNOWLEDGMENTS This work was supported by grants from the Iranian Witches’ Broom Disease of Lime Network (IWBDLN) and Agricultural Biotechnology Research Institute of Iran. P.A.H. acknowledges support from the Australian Research Council.

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REFERENCES

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5. CONCLUSION The results of the present label-free shotgun proteomics study revealed complex interactions between the Mexican lime tree and phytoplasma and thus contribute substantially to our understanding of the still largely unknown mechanisms that underlie the pathogenicity of Ca. Phytoplasma aurantifolia. The results obtained indicate that phytoplasma infection induced both the reprogramming of the primary and secondary metabolic pathways and the activation of proteins related to defense mechanisms. Proteomic changes in response to infection by phytoplasmas might support phytoplasma nutrition by promoting alterations in the host’s sugar metabolism, cell wall biosynthesis, and expression of defense-related proteins. The regulation of defense-related pathways supports the hypothesis that defense compounds are induced in interactions with susceptible as well as resistant hosts, with the main difference being in the speed and intensity of the response.76 It is likely that the results of the study will not only broaden the understanding of the fundamental aspects of lime-tree interactions with phytoplasma but also provide insight into the roles of these proteins in the susceptibility and resistance of the Mexican lime tree to Ca. Phytoplasma aurantifolia. This might eventually pave the way to develop strategies that incorporate the manipulation of these proteins into molecular breeding programs.

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AUTHOR INFORMATION

ASSOCIATED CONTENT

S Supporting Information *

Supplementary Table S1. List of proteins identified reproducibly in healthy and infected plants, including the normalized 793



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Shotgun Proteomic Analysis of the Mexican Lime Tree Infected with

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