Proteomic analysis of grapevine resistance induced by Trichoderma harzianum T39 reveals specific defence pathways activated against downy mildew.

Downy mildew is caused by the oomycete Plasmopara viticola and is one of the most serious diseases of grapevine. The beneficial microorganism Trichoderma harzianum T39 (T39) has previously been shown to induce plant-mediated resistance and to reduce the severity of downy mildew in susceptible grapevines. In order to better understand the cellular processes associated with T39-induced resistance, the proteomic and histochemical changes activated by T39 in grapevine were investigated before and 1 day after P. viticola inoculation. A comprehensive proteomic analysis of T39-induced resistance in grapevine was performed using an eight-plex iTRAQ protocol, resulting in the identification and quantification of a total of 800 proteins. Most of the proteins directly affected by T39 were found to be involved in signal transduction, indicating activation of a complete microbial recognition machinery. Moreover, T39-induced resistance was associated with rapid accumulation of reactive oxygen species and callose at infection sites, as well as changes in abundance of proteins involved in response to stress and redox balance, indicating an active defence response to downy mildew. On the other hand, proteins affected by P. viticola in control plants mainly decreased in abundance, possibly reflecting the establishment of a compatible interaction. Finally, the high-throughput iTRAQ protocol allowed de novo peptide sequencing, which will be used to improve annotation of the Vitis vinifera cv. Pinot Noir proteome.

Introduction

The oomycete Plasmopara viticola (Berk. & Curt.) Berl. & de Toni is the causal agent of downy mildew, one of the most damaging diseases of grapevine. P. viticola is an obligate biotroph, which infects leaves and clusters of young berries. To acquire nutrients, it penetrates into the substomatal cavity where the primary hyphae develop and then expand to form complex intercellular mycelia with haustoria within host mesophyll cells (Unger ; Díez-Navajas ).

Resistant Vitis species exhibit varying levels of resistance and P. viticola infection may be obstructed by an array of plant responses (Gessler ). Aside from constitutive physical and chemical barriers, downy mildew resistance is mainly based on post-infection processes (Díez-Navajas ; Polesani ). Microscopic observations have revealed that the first stages of infection are essentially the same in both susceptible and resistant grapevines, and development of the disease is restricted after the first haustoria have established contact with the mesophyll cells (Unger ; Diez-Navajas ; Polesani ). Post-infection mechanisms include fortification of plant cell walls through localized callose deposition (Diez-Navajas ; Jürges ), coupled with generation of reactive oxygen species (ROS), increase in peroxidase activity and hypersensitive response activation (Kortekamp, 2006; Diez-Navajas ).

The susceptibility of Vitis vinifera to downy mildew suggests that this species lacks a P. viticola-specific recognition system (Di Gaspero ). However, transcriptional (Hamiduzzaman ; Kortekamp, 2006; Trouvelot ; Polesani ) and proteomic (Milli ) changes associated with the early stages of P. viticola infection indicate the presence of a weak, but insufficient, defence response in susceptible grapevines.

Several substances with the ability to activate plant-mediated defence mechanisms and increase grapevine resistance to downy mildew have been identified (Hamiduzzaman ; Trouvelot ). For example, benzothiadiazole-7-carbothioic acid S-methyl ester (BTH) has been found to significantly reduce downy mildew symptoms in susceptible grapevines (Perazzolli ) by activating salicylic acid (SA)-dependent pathways, with a high energy cost for the plant (Perazzolli ). In addition to chemical inducers, some beneficial soil-borne microorganisms have been shown to promote plant growth and activate induced systemic resistance against a broad spectrum of pathogens and insects (Van Hulten ). In particular, Trichoderma spp. are ubiquitous filamentous fungi that colonize the rhizosphere and phyllosphere, promote plant growth, and antagonize numerous foliar and root pathogens (Vinale ; Shoresh ). Trichoderma spp. have various antagonistic mechanisms, including competition for nutrients and space, production of antifungal compounds, direct parasitism, and induction of plant resistance (Vinale ; Shoresh ) by reprogramming the plant transcriptome (Bailey ; Alfano ; Brotman ; Morán-Diez ) and proteome (Segarra ; Shoresh and Harman, 2008). Treatments with Trichoderma harzianum T39 (T39) has been found to activate grapevine resistance to downy mildew (Perazzolli ) without negative effects on plant growth (Perazzolli ). Although T39 appears to be a promising alternative for controlling downy mildew in the vineyard, the key components of the defence mechanism need to be identified in order to better understand how this method of biocontrol functions and how to maximize its efficacy.

This study analysed proteomic changes occurring in grapevine leaves in response to T39 treatment and P. viticola inoculation using the high-throughput eight-plex iTRAQ protocol in order to identify proteins and pathways affected by resistance activation. A histological analysis of cellular responses to P. viticola inoculation was carried out in order to clarify cellular processes of T39-induced resistance.

Materials and methods

Resistance induction and assessment of disease in grapevine plants

Susceptible grapevine V. vinifera cv. Pinot Noir plants and the P. viticola inoculum were grown and propagated as previously described in Perazzolli . A commercial product based on T. harzianum T39 (Trichodex, Makhteshim, Israel) was applied at a concentration of 8g l–1 in water, corresponding to a conidia suspension of approximately 105 colony-forming units ml–1. In addition, plants were either treated with water (control) or with the chemical inducer BTH (Bion 50WG, Syngenta Crop Protection, Switzerland) diluted in water at a concentration of 0.5g l–1. The abaxial and adaxial surfaces of all the leaves of the grapevine plants were sprayed three times with T39, BTH, or water (20–30ml per plant, depending on the number of leaves) using a compressed air hand sprayer, avoiding any spilling or dripping. Treatments were carried out at 3, 2, and 1 days before pathogen inoculation. One day after the final treatment, a fresh suspension of P. viticola sporangia (105 sporangia ml–1) was sprayed onto the abaxial leaf surfaces of all grapevine leaves (20–30ml per plant, depending on the number of leaves). Inoculated plants were incubated overnight in the dark at 25 °C and 99–100% relative humidity and then kept under controlled greenhouse conditions. Ten days after inoculation, plants were incubated overnight in the dark at 25 °C and 99–100% relative humidity. Disease severity was visually assessed as the percentage of the abaxial leaf surface area covered by sporulation, and disease incidence was calculated as the percentage of leaves showing sporulation (EPPO, 2001). Twelve plants (replicates) were analysed for each treatment in a randomized complete block, and the experiment was carried out three times.

Resistance induction and assessment of disease in grapevine leaf discs

The third and fourth leaves from the top of the plants were collected and washed in tubes containing a 1% hypochlorite solution for 10min (Sánchez Márquez ). After treatment they were rinsed three times in sterile water. Leaf discs with a diameter of 1cm were then cut and transferred (lower surface uppermost) onto moist filter paper (three foils) in Petri dishes (9cm diameter). A total of 45 discs were prepared: 15 discs per treatment placed in each of three Petri dishes. A Potter Precision Spray Tower (Burkard Scientific, UK) was used to spray 1.5ml water suspension onto each Petri dish at a pressure of 48.2MPa. Water, T39, and BTH were applied at 3, 2, and 1 days before pathogen inoculation. After each treatment the discs were left to dry and then kept at 25 °C under greenhouse conditions. One day after the final treatment, leaf discs were spayed (1.5ml per dish) with a fresh suspension of P. viticola (105 sporangia ml–1), then immediately covered and kept at 25 °C under greenhouse conditions. Seven days after inoculation, the percentage of disc area covered by downy mildew sporulation was visually assessed.

For treatment with the inhibitor of callose synthesis, leaf discs were floated on a solution of 2mM 2-deoxy-d-glucose (2-DDG, Sigma-Aldrich, MO) in water for 24h in the dark (Bayles ), while control leaf discs were floated on water for 24h in the dark. The leaf discs were then washed twice in water and transferred to Petri dishes (lower surface uppermost) for subsequent treatments with water, T39, or BTH followed by P. viticola inoculation, as described above.

Aniline blue staining

In order to observe P. viticola structures and callose deposition in plant cells, leaf discs were collected before inoculation (uninoculated) and at 1, 5, and 7 days post inoculation (dpi) with P. viticola then stained with aniline blue (Sigma-Aldrich) according to Díez-Navajas . The discs were then incubated in 0.05% aniline blue in 0.067M K2HPO4 pH 8. Microscope observations were carried out with a Leica LMD7000 microscope (Germany) using two different excitation filters: an A4 filter (BP 320–400nm excitation, 400nm dichroic mirror, and BP 470nm emission) for blue-based images and an H3 filter (BP 420–490nm excitation, 510nm dichroic mirror, and LP 515nm emission) for green-based images. With the A4 filter, callose deposits were recognized by a turquoise fluorescence and encysted zoospores by an intense, bright-blue fluorescence. With the H3 filter, plant and P. viticola structures were recognized by green fluorescence. The various stages of the development and structure of P. viticola were classified and described according to Unger and Godard .

Staining reactive oxygen species

Leaf discs were collected before (uninoculated) and after (1 and 7 dpi) P. viticola inoculation and incubated in 10 µM 5-(and-6)-carboxy-2’,7’-dichlorodihydrofluorescein diacetate (Invitrogen, CA, USA) for 20min in order to stain the ROS. The leaf discs were then washed with water and microscope observations were carried out using an LMD7000 microscope mounted with an H3 filter (Leica).

Sample collection and protein extraction

For the proteomic study, leaf samples from Pinot Noir plants were collected immediately before (uninoculated) and at 1 dpi of P. viticola: these samples comprised T39-treated leaves (T39-uninoculated and T39-1 dpi), and controls (control-uninoculated and control-1 dpi). All the leaves from each plant were collected, then frozen in liquid nitrogen and stored at –80 °C. For each treatment, leaf samples from three replicates (plants) were collected at each time point. A total of six plants were used for each treatment, three sampled before inoculation and three at 1 dpi, in order to avoid wounding stress.

Frozen samples were ground in a mixer-mill disruptor (MM 400, Retsch, Germany) at 20 Hz for 40 s. Finely ground leaf samples were immediately suspended in 10 volumes of TCA/acetone solution (10% w/v TCA and 0.07% w/v 2-mercaptoethanol in acetone) for overnight protein precipitation at –20 °C to remove secondary metabolites. Samples were centrifuged at 17,000 g for 20min at 4 °C and precipitated proteins were washed twice with three volumes of cold (–20 °C) 100% acetone, incubated at –20 °C for 2h, and then centrifuged. To solubilize precipitated proteins, the air-dried powder was suspended in two volumes of reducing solubilization buffer (6M urea, 2M thiourea, 1% CHAPS, 2mM DTT) in the presence of a protease inhibitor cocktail (1 × Complete Tablet, Roche Molecular Biochemicals, Germany) and incubated at 4 °C for 1h with continuous shaking. The homogenate was centrifuged at 17,000 g for 20min at 4 °C and solubilized proteins were stored at –80 °C. Protein concentration was determined with a protein assay (Bio-Rad, CA, USA).

Protein digestion and iTRAQ labelling

Total protein extracts (100 µg at 1mg/ml) were denatured and reduced, and the cysteines blocked using iTRAQ reagents (8plex, AB Sciex, CA, USA), according to the manufacturer’s instructions. Proteins were then diluted with five volumes of 50mM TEAB to reduce urea concentration to 1.4M, and twice digested with trypsin (Applied Biosystems, CA, USA) at a trypsin/protein ratio of 1:300 (37 °C, overnight. and then for 3h). The resulting peptide solution was concentrated in a centrifugal vacuum concentrator and diluted with six volumes of 100% isopropanol.

Digested peptides were labelled with iTRAQ reagents. Samples were then mixed in equal ratios and dried in a centrifugal vacuum concentrator to remove isopropanol. Two 8-plex iTRAQ-labelled peptide mixtures were prepared. The first mixture (iTRAQ1) contained proteins extracted from control-uninoculated and control-1 dpi plants, including two technical replicates of isotopic labelling, while the second mixture (iTRAQ2) contained proteins extracted from control-uninoculated, T39-uninoculated, and T39-1 dpi plants.

Peptide clean-up and chromatography

The iTRAQ-labelled peptide mixture was cleaned using a column of ReproSil-Pur C18 AQ beads (3µm, 20Å, Dr Maisch, Germany) prepared in a 4-mm diameter syringe filter (0.2 µm, PVDF membrane, Whatman, USA). The column was equilibrated with buffer A (50% acetonitrile and 5% formic acid) and then with buffer B (5% formic acid). The iTRAQ-labelled peptide mixture was acidified with a final concentration of 5% formic acid, loaded onto the column and eluted with 100 µl buffer C (80% acetonitrile and 5% formic acid). Acetonitrile and formic acid were removed by vacuum centrifugation and cleaned samples were subject to iTRAQ-compatible OFFGEL electrophoresis (Chenau ) using an 3100 OFFGEL fractionator (Agilent Technologies, CA, USA). The iTRAQ-labelled peptide mixture was focused according to its isoelectric point on a 24-cm immobilized non-linear pH 3–10 gradient (IPG strip, Agilent Technologies), according to the manufacturer’s instructions, and the default peptide-focusing programme (50kV/h). To prevent the trapping column clogging, the 24 fractions were clarified with C18 StageTips (Thermo Scientific, Germany), as described above for the C18 AQ columns. The eluted iTRAQ-labelled peptide mixture was then vacuum-concentrated and reconstituted with 18 µl 5% formic acid.

LC-MS/MS analysis

Each fraction (5 µl) was injected into an EasyLC capillary chromatographic system (Thermo Scientific), as described in Matafora . Peptide separation was carried out using a home-made 10-cm fused silica capillary (75 µm inner diameter, 360 µm outer diameter; Thermo Scientific) filled with Reprosil-Pur C18 3 µm resin (Dr Maisch, Germany). Peptides were eluted with a 60-min gradient of eluent A (2% acetonitrile and 0.1% formic acid in distilled water) and eluent B (98% acetonitrile and 0.1% formic acid in distilled water), starting with 8% eluent B (flow rate 0.2 µl min–1) and finishing with 50% (flow rate 0.2ml min–1). The EasyLC system was connected to an LTQ-Orbitrap mass spectrometer equipped with a nanoelectrospray ion source (Thermo Scientific). Full-scan mass spectra were acquired in an LTQ-Orbitrap mass spectrometer in the mass range m/z 350–1600Da with the resolution set to 60000. The lock-mass option was used to obtain accurate mass measurements. The four most intense doubly and triply charged ions were automatically selected and fragmented in the iontrap. Target ions already selected for the MS/MS were dynamically excluded for 60 s. Target values were 1,000,000 for the survey scan and 100,000 for the MS/MS scan. Pulsed Q dissociation parameters were set at an isolation width of 3 m/z, normalized collision energy 30%, activation Q 0.55, and activation time 0.4ms; the threshold for MS/MS acquisition was set to 200 counts. Each OFF-gel fraction was injected twice onto the LC-MS/MS (runs MS1 and MS2) to increase the number of proteins identified: a total of 96 LC-MS/MS were run.

Raw data obtained from the LC-MS/MS runs were grouped and processed using the Proteome Discoverer Software version 1.1.0 (Thermo Scientific), which uses data from the Mascot 2.2.07 search and performs relative quantification of the eight reporter ions derived from the iTRAQ reagents. The database search parameters included the following settings: number of permitted missed tryptic cleavage sites set to 2; and iTRAQ peptide labelling and confidence cut off >95%. Cysteine carbamidomethylation was searched as a fixed modification, while N-acetyl protein and oxidized methionine were searched as variable modifications. Peptide mass tolerance was set to 10 ppm and fragment mass tolerance to 0.8Da. The criterion for evaluating the quality of the MS/MS data was an ion score cut off greater than 20. Peptides were accepted with a false discovery rate of 0.5%, estimated on the basis of the number of accepted reverse hits. Where proteins were characterized by only one peptide, this peptide had to contain at least two spectral counts and had to be classified as unique. Proteins were identified by integrating the results from two protein database searches (Supplementary Fig. S1, available at JXB online): the predicted Pinot Noir grapevine proteome (Release 3, http://genomics.research.iasma.it; Velasco ), and a non-redundant Viridiplantae Uniprot protein database (http://www.uniprot.org/taxonomy/33090, downloaded from July 2011). Due to the size of the Viridiplantae Uniprot protein database, replicated LC-MS/MS runs (MS1 and MS2) were processed separately using the Proteome Discoverer Software. In order to properly quantify identified proteins, only unique peptides were subjected to protein quantification, which was calculated as the median of all peptide ratios belonging to the protein group.

Protein annotation

Additional alignments were performed to convert proteins obtained from Mascot search of the Viridiplantae Uniprot database into the V. vinifera homologue and to identify proteins not predicted from the Pinot Noir grapevine genome (Supplementary Fig. S1). An initial protein homology search was carried out against the Pinot Noir grapevine proteome with an E-value 60%; a second search was carried out against all protein predictions of the Pinot Noir grapevine genome with an E-value 60%; a third was carried out against the Pinot Noir grapevine genome with an E-value 60% (Supplementary Fig. S1). Identified protein sequences were then aligned against the 12× PN40024 grapevine genome with an E-value T. atroviride v2 (http://genome.jgi-psf.org; E-value 60%) and Hyaloperonospora arabidopsidis 7.0.1 databases (http://genome.wustl.edu/genomes/view/hyaloperonospora_arabidopsidis; E-value 60%).

Gene ontology (GO, Ashburner ) annotation was carried out by aligning the Pinot Noir grapevine proteome (Release 3) against the UniProt databases (downloaded from July 2011) using blastp (Altschul ). blast results were analysed using the ARGOT2 software (Falda ) and the associated GO biological process terms, grouped into 19 functional categories, and were used for protein annotation. The results of the automatic annotation of proteins with significant changes in abundance were manually inspected and integrated with GO biological process terms supported by evidence from the literature. GO frequencies were evaluated for proteins with significant changes in abundance compared with the Pinot Noir grapevine proteome annotations using a chi-squared test (P < 0.05).

For the MapMan ontology analysis (Thimm ), proteins were aligned against the Arabidopsis thaliana proteome TAIR9 (www.arabidopsis.org) using blastx with an E-value 60%. Searches were also carried out using the Plant Protein Annotation Program (PPAP; http://www.uniprot.org/program/Plants; Schneider ) and InterProScan (http://www.ebi.ac.uk/interpro). The results of all searches were compiled and classified into MapMan BINs (http://mapman.gabipd.org). Assigned categories were then manually checked against literature searches. Arabidopsis MapMan pathways of overview of metabolism and photosynthesis-primary metabolisms were adopted, and a specific pathway for biotic and abiotic stresses was developed on the basis of literature searches. Proteins that could not be associated to any biological process category were assigned to the GO root (biological process) and to BIN 35.

Statistical analysis

In order to compare disease severity under the various conditions, an analysis of variance (ANOVA) was performed with the Statistica 9 software (StatSoft, OK, USA) using Tukey’s test to detect significant differences between treatments (P < 0.05), and an F-test to identify non-significant treatment–experiment interactions (P > 0.05).

For the proteomic study, normalized iTRAQ ratios were log2 transformed to generate a normally distributed set of data, and processed using the Multi Experiment Viewer (MeV; http://www.tm4.org/mev/software; Saeed ). A t test (Welch approximation, p-values based on t-distribution, P < 0.05) coupled with a fold-change threshold of the log-transformed ratios was performed (volcano plot visualization). Four pairwise comparisons were performed, and the fold-change threshold was calculated as the standard deviation (δ) of log-transformed ratios, which corresponded to 0.73 for the control-1 dpi/control-uninoculated ratios, 0.74 for T39-uninoculated/control-uninoculated ratios, 0.76 for T39-1 dpi/control-uninoculated ratios, and 0.64 for T39-1 dpi/T39-uninoculated ratios.

Results

Mechanisms of disease reduction after Trichoderma harzianum T39 treatments

As previously reported (Perazzolli ), the severity of downy mildew is significantly reduced in T39-treated grapevines (Supplementary Fig. S2A). Disease reduction was also observed in T39-treated leaf discs at 7 dpi (Supplementary Fig. S2B). Specifically, sporadic sporulation (2% of incidence) was observed on T39-treated leaf discs, in contrast to intense sporulation (100% incidence) on control discs at 7 dpi. BTH was applied to grapevine plants and leaf discs, resulting in a considerable reduction of disease severity (Supplementary Fig. S2A, B).

Microscope observation of aniline blue-stained leaf discs revealed no differences between control and T39-treated plants before P. viticola inoculation (Fig. 1A–C). At 1 dpi, the pathogen had already penetrated the stomata of control leaf discs and encysted zoospores (Fig. 1D) and primary haustoria were visible (Fig. 1G). The number of zoospores that had successfully penetrated stomata at 1 dpi was drastically reduced in leaf discs treated with T39 (Fig. 1E, 1H) and almost completely absent in those treated with BTH (Fig. 1F, 1I). Where present, spore germination on T39-treated leaf discs was similar to controls (Fig. 1H). Strong turquoise fluorescence was observed in the stomata of BTH-treated leaf discs, indicating intense callose deposition at infection sites (Fig. 1F). A reaction in the epidermal cells surrounding P. viticola zoospores was also observed (Fig. 1I), indicating localized necrosis (Godard ). Turquoise fluorescence of stomatal guard cells (but not the surrounding epidermal cells) was also observed in T39-treated leaf discs (Fig. 1E, 1H), indicating early accumulation of callose. In T39-treated leaf discs, callose deposition was more localized than in BTH-treated leaf discs and there were no necrotic areas.

Fig. 1.

Time course of intercellular colonization by Plasmopara viticola during Trichoderma harzianum T39-induced resistance. Susceptible Vitis vinifera cv. Pinot Noir leaf discs were treated with water (control), T. harzianum T39, or benzothiadiazole (BTH). Pathogen development and callose deposition were monitored before inoculation (uninoculated), and 1, 5, and 7 days post inoculation (1, 5, 7 dpi) with P. viticola using epifluorescence microscopy after aniline blue straining. (A–C) Uninoculated leaf discs showing basal fluorescence of stomata. (D–I) Leaf discs at 1 dpi. (D, G) Encysted zoospores on stomata and intracellular infection structures, such as a septum, between encysted zoospores, and the substomatal vesicle, were visible on control leaves at 1 dpi. (E, F, H, I) Callose deposition in the stomata together with zoospores penetrating the stomata were visible on T39-treated discs (E, H), whereas only prominent callose depositions were detected on BTH-treated discs (F, I) at 1 dpi. (J–L) Leaf discs at 5 dpi. Long, branched hyphae of P. viticola were visible on the control (J). Intense fluorescence of epidermal cells around stomata guard cells (St) in T39-treated discs (K) and BTH-treated discs (L) indicated callose deposition. (M–R) Grapevine leaf discs after P. viticola sporulation (7 dpi). Sporulation was visible on the control (M), but was greatly inhibited on T39-treated discs (N) and absent from BTH-treated discs (O). Treatment with the callose synthesis inhibitor (2-DDG) reduced both T39-induced protection (Q) and BTH-induced protection (R) against P. viticola, sporulation being comparable to the control (P). Images were acquired by epifluorescence microscopy (Leica LMD7000) using an H3 filter for green fluorescence pictures (J–L) and an A4 filter for blue-based fluorescence pictures (A–I, M–R). A representative leaf disc of ten per treatment is shown. The experiment was repeated three times with similar results. Cal, callose deposition; eZ, encysted zoospores; H, hyphae; pHA, primary haustoria; se, septum, sV, substomatal vesicle; ve, vesicle.

At 5 dpi, P. viticola mycelium had spread to the parenchyma of control leaves (Fig. 1J) and had produced sporangiophores at 7 dpi (Fig. 1M). In contrast, T39-treated leaf discs exhibited extensive fluorescence around the stomata at 5 dpi (Fig. 1K), indicating intense callose deposition, as in the BTH-treated leaf discs (Fig. 1L). Moreover, P. viticola sporulation was greatly reduced in T39-treated leaf discs (Fig. 1N) compared to control discs (Fig. 1M). Interestingly, stomata with callose deposition did not show fungal infection (Fig. 1K), suggesting that callose plays a role in T39-induced resistance to P. viticola. As further evidence, inhibition of callose synthesis by 2-DDG treatment negatively affected disease attenuation in T39-treated leaf discs (Fig. 1P–R). 2-DDG has been found to block callose deposition, presumably not by acting directly on the synthase but rather by affecting metabolized product (Schreiner ). Specifically, 2-DDG reduced turquoise fluorescence at 1 dpi (Supplementary Fig. S2C) and considerably increased development of P. viticola mycelia at 5 and 7 dpi in T39-treated leaf discs (Fig. 1Q and Supplementary Fig. S2C). A similar effect was observed in leaf discs treated with 2-DDG and BTH (Fig. 1R), confirming the role of callose deposition in the resistance to downy mildew.

No ROS were produced in control leaf discs before P. viticola inoculation and at 1 dpi (Fig. 2A, 2D) and only weak fluorescence was visible at 7 dpi around infection sites (Fig. 2G). ROS were not produced in plants treated with T39 before pathogen inoculation (Fig. 2B), but they were accumulated in guard cells at 1 dpi (Fig. 2E). The intensity of ROS fluorescence in T39-treated leaf discs increased in the days following inoculation (data not shown) and was localized in cells near the stomata (Fig. 2H). In BTH-treated leaf discs, ROS accumulation started before P. viticola inoculation (Fig. 2C) and it was greater than in T39-treated leaf discs after inoculation (Fig. 2F, 2I).

Fig. 2.

Accumulation of reactive oxygen species (ROS) after Plasmopara viticola inoculation. Leaf discs of susceptible Vitis vinifera cv. Pinot Noir were treated with water (control), Trichoderma harzianum T39, or benzothiadiazole (BTH). ROS accumulation was monitored before inoculation (uninoculated), and 1 and 7 days post inoculation (1 and 7 dpi) with P. viticola using epifluorescence microscopy (Leica LMD7000, H3 filter) and 5-(and-6)-carboxy-2’,7’-dichlorodihydrofluorescein diacetate fluorescent dye. (A, D, G) No fluorescence staining was visible on control leaf discs before inoculation or at 1 dpi, and only weak ROS accumulation was visible at 7 dpi in the infected area. (B, E, H) ROS were not detected on T39-treated leaf discs before inoculation; ROS accumulated in stomata cells at 1 dpi and in epidermal cells around infected stomata at 7 dpi. (C, F, I) ROS accumulation was rare on BTH-treated leaf discs before P. viticola inoculation but increased in the epidermal cells around the stomata between 1 and 7 dpi. Ten representative leaf discs per treatment are shown. The experiment was repeated three times with similar results.

Optimization of the grapevine iTRAQ procedure and protein identification

In order to analyse proteomic changes associated with T39-induced resistance, leaf samples were collected from T39-treated and control plants immediately before inoculation and at 1 dpi of P. viticola. The latter time point was chosen in order to study early plant response, when substomatal vesicles and primary haustoria have already developed (Fig. 1D, 1G). Defence mechanisms (Fig. 1E, 1H and Fig. 2E) and defence gene induction (Perazzolli ) were also observed at 1 dpi in T39-treated grapevines. The grapevine proteome was analysed using the high-throughput eight-plex iTRAQ technique combined with a high resolution LC-MS/MS Orbitrap mass spectrometer.

Using the Pinot Noir grapevine protein database, this study identified and quantified 459 grapevine proteins in the iTRAQ1 experiment and 211 in iTRAQ2 (Table 1 and Supplementary Table S1). An additional 230 proteins were identified in iTRAQ1 and 159 in iTRAQ2 using the Viridiplantae Uniprot protein database, which was used to improve protein identification (Supplementary Fig. S1 and Table 1). After removing common proteins, 601 proteins were quantified in the iTRAQ1 experiment and 302 proteins were quantified in the iTRAQ2 experiment, corresponding to a total of 800 unique proteins (Table 1, Supplementary Table S1, and Supplementary Fig. S3A). Of these unique proteins, 206 were identified using the Viridiplantae Uniprot database (Supplementary Fig. S3B), of which 155 were homologous with one or more predicted proteins for grapevine, eight matched into the Pinot Noir grapevine genome, and 43 had no match with the Pinot Noir genome (Supplementary Fig. S1). These 43 proteins had no match even when aligned to T. atroviride and H. arabidopsidis proteomes.

Table 1.

Proteins quantified by LC-MS/MS

Experiment	Database	Quantified proteins				Total quantified proteins
		MS1	MS2	MS1 + MS2	Total
iTRAQ1	Pinot Noir grapevine			459	601	800
	Viridiplantae Uniprot	149	131	230
iTRAQ2	Pinot Noir grapevine			211	302
	Viridiplantae Uniprot	105	107	159

In each iTRAQ experiment, two different MS runs (MS1 and MS2) and two database searches (Pinot Noir grapevine proteome and Viridiplantae Uniprot database) were used to maximize the number of identified proteins. After removing redundancies by a blast search, 601 unique proteins in iTRAQ1 and 302 in iTRAQ2 were identified. A total of 800 unique proteins were identified and quantified by Mascot search in the iTRAQ experiments.

Separate analyses of MS1 and MS2 were carried out with the Proteome Discoverer Software using the Viridiplantae Uniprot database in order to evaluate reproducibility in terms of the number of proteins commonly quantified in both MS runs: this turned out to be 22% for iTRAQ1 and 33% for iTRAQ2 (Table 1 and Supplementary Fig. S3C, D), which was similar to the rates described by other groups (Jones ; Lucker ). Having two technical replicates of isotopic labelling allowed the evaluation of the labelling efficiency. The resulting median (log transformed) of 0 and σ of 0.21 indicated the absence of a significant labelling bias between technical replicates (data not shown). The high degree of accuracy obtained with the Orbitrap mass spectrometer not only boosts confidence in protein database search results, it also shows the potential of this tool for de novo sequencing. Indeed, at least one de novo amino acid assignment was found in 52% of the total peptides quantified in iTRAQ1 and in 45% of those quantified iTRAQ2 (Table 2), leading to an improvement in grapevine proteome annotation.

Table 2.

De novo peptide sequencing for improvement of the grapevine Pinot Noir proteome

Peptide sequence	Protein ID	Modifications	Peptide ion score	m/z (Da)	MH+ (Da)
XDXVLDFARGKGGDLIK	glimmer.VV78X173528.16_1	X1(111.0), N-Term(Acetyl), X3(111.0), K17(iTRAQ8plex)	24	724.71	2172.12
HLSTSPAGXSIDALSPSASSPSVR	fgenesh.VV78X255168.8_2	N-Term(iTRAQ8plex), X9(S)	24	872.46	2615.38
EDLEISSAXSQRTLEMEVVELRR	fgenesh.VV78X213438.7_2	N-Term(iTRAQ8plex), X9(Q), M16(Oxidation)	15	1013.50	3038.55
DFLWGGGVXEK	fgenesh.VV78X086249.3_3	N-Term(iTRAQ8plex), X9(P)	16	754.91	1508.82
WLPDRQMPXMKSLR	fgenesh.VV78X217256.7_1	N-Term(iTRAQ8plex), X9(A)	16	678.37	2033.10
FIIQMLEX	glimmer.VV78X175476.41_1	N-Term(iTRAQ8plex), X8(W)	16	692.38	1383.76
INGMSGLXR	glimmer.VV78X219975.6_1	N-Term(iTRAQ8plex), X8(T)	15	626.85	1252.69
MVGIAFDXITSPSSVNSSQVPSVSPFSVYR	glimmer.VV78X147076.10_6	N-Term(iTRAQ8plex), X8(Q)	29	873.45	3490.79
DASKNWRXAGSDGSSQLTIK	glimmer.VV78X028231.22_4	N-Term(iTRAQ8plex), X8(N)	17	813.75	2439.25
LLVTAGXR	sim4.VV78X013999.4_1	N-Term(iTRAQ8plex), X7(Y)	15	399.58	1196.73
VPVSMEXLGGIGEFFRFAIPSAVMICTR	glimmer.VV78X110157.9_11	N-Term(iTRAQ8plex), X7(W), M24(Oxidation)	16	873.45	3490.79
RREAPAXALTFAEHPQMK	fgenesh.VV78X075745.12_7	N-Term(iTRAQ8plex), X7(S), M17(Oxidation)	17	787.42	2360.24
EAISLAXMKDEQLQR	fgenesh.VV78X252996.9_1	N-Term(iTRAQ8plex), X7(P)	15	678.37	2033.11
EVIIISXR	fgenesh.VV78X017221.8_1	N-Term(iTRAQ8plex), X7(M)	29	632.88	1264.75
KDKDDLXSELK	twinscan.VV78X136286.3_2	N-Term(iTRAQ8plex), X7(L), K11(iTRAQ8plex)	26	638.05	1912.13
ILVIEGXHPMYDAR	glimmer.VV78X175632.6_4	N-Term(iTRAQ8plex), X7(L)	33	966.04	1931.07

Examples of peptides containing de novo amino acid assignment. In total, 1357 peptides out of 2591 in iTRAQ1 and 594 out of 1330 in iTRAQ2 contained at least one amino acid assignment. Protein ID, Accession number in the predicted Pinot Noir grapevine proteome database; Modifications, modifications identified by Mascot 2.2.07 search; m/z, peptide mass-to-charge ratio; MH+, singly protonated peptide mass.

Grapevine proteins with significant changes in abundance during T39-induced resistance

The global normalization protocol (summed intensities) was used to correct intensities between iTRAQ labels, and the resulting histogram of log-transformed ratios had a normal distribution (data not shown), suggesting the absence of a significant bias between samples. Statistical analysis revealed that 128 and 118 proteins had significant changes in abundance in the iTRAQ1 and iTRAQ2 experiment, respectively. These proteins were grouped into three clusters (CL) based on the different expression profiles (Fig. 3A). CL1 comprised 128 proteins affected by P. viticola at 1 dpi in control plants (Supplementary Table S2), CL2 comprised the 58 proteins directly affected by T39 treatment (Supplementary Table S3), and CL3 comprised the 60 proteins affected by P. viticola in T39-treated plants and not by T39 before pathogen inoculation (Supplementary Table S4). Interestingly, 34 proteins of CL2 were exclusively affected by T39, whereas 24 proteins were significantly affected by T39 treatment and after P. viticola inoculation of T39-treated plants (Supplementary Table S3). Most of the proteins in CL1 (79%) showed a decrease in abundance, whereas this was the case for about half of the proteins in the other two clusters (45% in CL2, 46% in CL3).

Fig. 3.

Clustering and gene ontology (GO) annotation of proteins with significant changes in abundance during T39-induced resistance. (A) Proteins were grouped in clusters (CLs) according to their expression profiles: CL1, proteins significantly affected by Plasmopara viticola at 1 dpi in control plants (n = 128); CL2, proteins significantly affected by Trichoderma harzianum T39 treatment (n = 58); CL3, proteins significantly affected by P. viticola at 1 dpi in T39 plants (n = 60), according to comparisons control-uninoculated vs. T39-1dpi, and T39-uninoculated vs. T39-1dpi. Proteins of CL2 were either exclusively affected by T39 (34 proteins) or affected by T39 and by P. viticola in T39-treated plants (24 proteins). (B) Distribution of significantly affected proteins across 19 GO functional categories. GO frequencies were calculated as percentages of the total number of biological process terms (195 in CL1, 107 in CL2, and 131 in CL3, and 39,399 in the total Pinot Noir grapevine proteome). For each CL, categories marked by an asterisk were differentially represented to the total Pinot Noir grapevine proteome, according to a chi-squared test (P < 0.05).

Proteins were annotated and grouped into 19 selected GO biological process categories, and proteins with unknown function were assigned to the GO root (biological process; Fig. 3B). Although proteins with unknown function predominated (about 10–15%), the functional category of generation of precursor of metabolites and energy was well represented in all CLs (about 10%) compared with the grapevine proteome (2%). Moreover, in all three CLs, large groups of proteins were assigned to the functional categories of signal transduction, response to stress, and response to stimulus (more than 5, 8, and 7%, respectively). The categories of biological regulation, carbohydrate metabolic process, and nucleic acid metabolic processes (4, 8, and 4%, respectively) were significantly overrepresented in CL3 compared with the grapevine proteome.

Out of proteins with significant changes in abundance, 191 were assigned to at least one MapMan (Thimm ) functional category and the proteins with unknown function were assigned to the MapMan BIN 35. MapMan overview of metabolism and photosynthesis-primary metabolisms were used to visualize the metabolic processes affected by compatible interaction and T39-induced resistance (Supplementary Fig. S4). Visualization of proteins affected by P. viticola in control plants revealed global negative regulation of amino acid biosynthesis, secondary metabolism, and photosynthetic processes (Supplementary Fig. S4A). In particular, proteins involved in photosynthesis (i.e. PSII and PSI subunits and ferredoxin-NADP-oxidoreductase) and the pentose phosphate cycle (i.e. phosphoglycerate kinase and fructose-bisphosphate aldolase) had decreased abundance (Supplementary Fig. S4B). Repression of metabolic processes by P. viticola was attenuated during T39-induced resistance (Supplementary Fig. S4C, E). Proteins involved in amino acid metabolism (i.e. fumarylacetoacetase), cell-wall metabolism (i.e. rhamnogalacturonate lyase) and photosynthesis (i.e. photosystem I subunit D-1, a rubisco activase) had increased abundance after T39 treatment (Supplementary Fig. S4C, D). Increased abundance of proteins related to photosynthesis (i.e. chlorophyll-binding proteins, PSI and PSII subunits, ATP synthase), pentose phosphate cycle (a glyceraldehyde-3-phosphate dehydrogenase and a fructose-bisphosphate aldolase), and secondary metabolic processes (i.e. an O-acetylserine lyase, a thymidylate synthase, and a UDP-d-glucuronate 4-epimerase) was also observed after P. viticola inoculation in T39-treated plants (Supplementary Fig. S4E, F).

An in-house pathway of biotic and abiotic stress responses was developed in MapMan using the Arabidopsis biotic stress pathway as template and manually integrating it with other correlated MapMan pathways, according to information found in the literature (Fig. 4). In control plants, proteins related to receptor classes (two LRR receptor-like proteins, cysteine-rich receptor-like protein kinase, and a G-type lectin S-receptor-like serine/threonine-protein kinase) had mostly decreased abundance, whereas proteins related to signal cascade [two recognition of Peronospora parasitica (RPP) proteins and three nucleotide-binding site-encoding resistance (NBS-R) proteins] had increased abundance at 1 dpi (Fig. 4A), indicating weak recognition of P. viticola. Interestingly, P. viticola inoculation mostly decreased the abundance of proteins involved in transport, transcription regulation, and signal transduction pathways, such as ethylene (ET)-, jasmanic acid (JA)- and SA-mediated signalling in control plants. On the other hand, proteins involved in biotic and abiotic stress response had increased abundance in T39-treated plants (Fig. 4B). T39 treatment caused an increase in abundance of two receptors (a leucine-rich repeat receptor-like protein kinase and a receptor-like protein kinase precursor), a guanine nucleotide-binding protein (GTPase-activating protein), three resistance proteins (an RPP protein and two TMV resistance proteins N), proteins involved in hormone signalling (abscisic acid and auxin signalling and metabolism), and redox balance (a thioredoxin and a ferredoxin-thioredoxin reductase). In addition, other proteins associated with signal transduction (a Pseudomonas syringae resistant protein, a Rabgap/TBC domain-containing protein, and two disease resistance proteins) and redox balance (a glutaredoxin, a copper/zinc superoxide dismutase, and a glutathione reductase) were induced in T39-treated plants after P. viticola inoculation, showing that control and T39-treated plants react differently to pathogen infection.

Fig. 4.

Biotic and abiotic stress responses. MapMan overview of proteins with significant changes in abundance during Trichoderma harzianum T39-induced resistance. The pathway was originally generated from information in the literature and pre-existing MapMan pathways. Annotation of proteins was manually checked and the assigned BINs were loaded onto MapMan software. Each square represents a significantly affected protein (green, decreased in abundance; red, increased in abundance) compared with control-uninoculated plants. (A) Proteins significantly affected by Plasmopara viticola inoculation in control plants (CL1). (B) Proteins significantly affected in T39-treated plants. Expression levels before (squares on the left of each BIN) and after (squares on the right) P. viticola inoculation in T39-treated plants are shown. Asterisks indicate the pairwise comparisons with significant change in protein abundance.

Discussion

Trichoderma species have been recognized as biocontrol agents for a number of pathogens (Perazzolli ; Vinale ; Shoresh ). The biocontrol ability of Trichoderma species is based on different mechanisms, such as antibiosis, mycoparasitism, competition, and induction of plant resistance (Shoresh ). In grapevine, the T39 strain reduces downy mildew severity by activating plant-mediated resistance mechanisms, without any direct toxic effect on P. viticola sporangia germination (Perazzolli ). T39-induced resistance is mediated by direct modulation of defence-related genes and by their enhanced expression after pathogen inoculation (Perazzolli ). Whereas direct antagonistic effects of T39 could occur at the early stages of zoospores germination, pronounced accumulation of callose in stomata guard cells was observed at 1 dpi in T39-treated plants. The present histological analysis indicates that biocontrol mechanisms of T39-induced resistance are related to the early activation of plant defence processes, which begin as soon as the zoospore germ tubes have penetrated the stomata and mainly involve plugging and closure of stomata. Moreover, high amounts of ROS were produced in stomata guard cells in T39-treated plants at 1 dpi of P. viticola. Interestingly, P. viticola was never observed in the areas of ROS accumulation, suggesting a prominent role of ROS in the T39-induced resistance to downy mildew. As reported for other inducers (Trouvelot ; Allegre ), the biochemical changes in T39-treated grapevines mainly occurred subsequent to downy mildew inoculation. This response indicates that T39 primes grapevine defences, as suggested by the absence of apparent energy costs in T39-treated plants (Perazzolli ).

In order to better understand the cellular processes associated with the early stages of T39-induced resistance, proteomic changes activated by T39 were analysed before and at 1 dpi of P. viticola. The high-throughput eight-plex iTRAQ protocol combined with an integrated approach to protein identification, resulted in the quantification of 800 unique proteins. Of these, 43 proteins did not match with Pinot Noir, or the T. atroviride and H. arabidopsidis proteomes, indicating that the analysis had identified grapevine proteins not yet predicted by bioinformatic approaches or proteins belonging to natural microorganisms of the leaf phyllosphere. Interestingly, about half the total quantified peptides contained at least one de novo amino acid assignment in their sequence, which will be used to improve annotation of the Pinot Noir protein sequences.

Annotation of the proteins with significant changes in abundance highlighted considerable differences in the responses of T39-treated and control plants to P. viticola inoculation. Interestingly, 58 proteins were directly affected by T39 treatment (CL2) and 60 proteins were affected after pathogen inoculation in T39-treated plants (CL3), confirming the dual effect of T39 previously suggested by gene expression analyses (Perazzolli ).

Grapevine proteins affected by Plasmopara viticola in control plants

Analysis of the grapevine proteome in response to P. viticola infection revealed weak pathogen recognition coupled with an ineffective attempt to activate a resistance response at 1 dpi (CL1). Proteins responsible for microbial recognition and signal transduction components (for example, five NBS-R proteins) increased in abundance at 1 dpi. In particular, changes in abundance of two proteins similar to the Arabidopsis recognition of Peronospora parasitica (RPP8), known to be induced by oomycete infection (McDowell ), suggested recognition of P. viticola. However, the increase in abundance of these proteins did not correspond to an effective activation of resistance response in grapevine. The lack of a downstream defence response could be interpreted as being part of a pathogen defence suppression strategy (Milli ). Indeed, suppression of endogenous signalling pathways by pathogenic effectors is probably required to establish compatible interactions (Milli ) and is followed by metabolic reprogramming associated with compatibility (Polesani , 2010). Suppression of defence responses is corroborated by the repression of grapevine proteins involved in signal transduction processes at 1 dpi (i.e. a RAF-like MAP3Ks involved in ET-mediated signalling and the homologue of resistance-inducing protein PBS1). Similarly, proteins associated with hormone metabolism and hormone signalling were found to be less abundant after P. viticola inoculation: for example, a 1-aminocyclopropane-1-carboxylate synthase, a lipoxygenase (LOX), a RRM-containing protein, polyubiquitin 10, and a PKN/PRK1 effector-like domain protein. Among the proteins whose abundance was decreased, two NADPH-oxidases, a peroxiredoxin 2B and a peroxiredoxin Q (Prx Q), involved in controlling redox balance. In particular, PrxQ has been reported to be repressed by P. viticola at the oil spot stage (Polesani ).

Proteins associated with defence (a defence response-induced protein), responses to abiotic stresses (a heat shock protein DNAJ homologue), and secondary metabolism (two polyphenol oxidase, a flavonoid 3’-monooxygenase, and a momilactone A synthase-like) had decreased abundance in control plants at 1 dpi.

Among the proteins related to transport, a vacuolar ATP synthase and a voltage-dependent anion channel (VDAC) had decreased abundance. VDAC proteins are porin-type β-barrel diffusion pores and are involved in the formation of permeability transition pores (PTP; Shimizu ), which can contribute to cell shrinkage during the hypersensitive response (Kusano ). Since obligate biotrophic fungi require living host cells to complete their infection cycle, the decrease in abundance of VDCA could be part of the attempt by P. viticola to keep the host alive.

P. viticola inoculation mainly caused a decrease in abundance of proteins involved in photosynthesis (PSII D2 proteins, PSI subunit F and D1, ferredoxin-NADP-oxidoreductase 1 and 2), pentose phosphate cycle (aldolase, GAP, FBPase, phosphoglycerate kinase, and PRK) and photorespiration (glycine decarboxylase P-protein 1 and 2). A decrease in abundance of proteins related to photosynthetic processes has been previously observed during P. viticola infection (Milli ) and linked to source-to-sink transition of infected tissues (Gamm ).

Grapevine proteins directly affected by T39

T39 treatment directly affected proteins (CL2) associated with signal transduction, response to stresses, response to stimuli and energy metabolism. As part of signal transduction, ten proteins were directly affected by T39 and eight of them maintained similar expression levels after P. viticola inoculation, indicating that the microbial recognition machinery is active prior to pathogen arrival and may create the conditions for a rapid response. Among the receptor-recognition proteins, a probable LRR-kinase, a receptor-like protein kinase, and three NBS-R proteins (two TMV resistance proteins N and RPP8) increased in abundance, in agreement with Trichoderma-treated maize and bean (Marra ; Shoresh and Harman, 2008). Moreover, three proteins of the G protein family were affected by T39. In particular, a GTPase-activating protein, AGD3-like, a key regulator of vesicular trafficking of auxin efflux in Arabidopsis (Sieburth ), increased in abundance, suggesting that vesicle trafficking processes are involved in the Trichoderma-induced response.

Among the stress related proteins, T39 increased the abundance of a member of the non-specific lipid transfer protein (nsLTP) family. Genes encoding LTPs were induced by Trichoderma spp. in cacao and Arabidopsis plants (Bailey ; Morán-Diez ) and several LTPs showed antimicrobial properties (Blein ). Interestingly, a nsLTP (glimmer.VV78X270940.4_1) increased in abundance after T39 treatment (CL2), and another nsLTP isoform (glimmer.VV78X102158.67_1) increased in abundance upon P. viticola inoculation of T39-treated plants (CL3), indicating specific involvement of LTP isoforms in plant responses.

T39 affected two proteins involved in the thioredoxin system: a chloroplast thioredoxin (TRX) M-type and a ferrodoxin-dependent TRX reductase (FTR). TRXs play a role in redox regulation of enzyme activities (Gelhaye ), suggesting that they could be components of the signalling pathways in the T39-induced plant antioxidant network.

Regarding hormone metabolism and signalling, a marker for the JA pathway (LOX protein) displayed a decrease in abundance upon T39 treatment. JA has been implicated in T39-induced resistance in Arabidopsis (Korolev ) and resistance to P. viticola in resistant (Polesani ) or elicited (Hamiduzzaman ; Trouvelot ; Perazzolli ) grapevines. Induction by T39 followed by enhanced expression after P. viticola inoculation of T39-treated plants has been observed in LOX9 at the transcriptional level (Perazzolli ), indicating specific involvement of LOX isoforms in grapevine response.

A direct correlation between the ability of Trichoderma spp. to promote plant growth and its ability to induce proteins associated with carbohydrate metabolism has been reported for T. harzianum T22 in maize (Shoresh and Harman, 2008) and for T. hamatum 382 in tomato (Alfano ). This is backed up by the correlation between absence of an increase in abundance of proteins associated with carbohydrate metabolism and the lack of growth benefits observed in T39-treated grapevines (Perazzolli ).

Grapevine proteins affected by Plasmopara viticola in T39-treated plants

When Trichoderma-treated plants were challenged with a pathogen, defence gene expression and protective enzyme activity were enhanced compared with inoculated control plants (Perazzolli ; Brotman ). The current results show that proteins affected by P. viticola in T39-treated plants (CL3) are mainly associated with response to stress, photosynthesis, redox signalling, and energy metabolism. Proteins associated with photosynthesis and energy metabolism mostly increased in abundance in T39-treated plants in response to P. viticola, highlighting a specific reaction of plants treated with the resistance inducer. A correlation between increased level of proteins involved in photosynthesis and respiration and increased level of enzymes associated with cell-wall expansion has been observed in a maize–Trichoderma interaction (Shoresh and Harman, 2008). An UDP-d-glucuronate 4-epimerase responsible for pectin biosynthesis (Usadel ) showed an increase in abundance in T39 at 1 dpi, indicating that cell-wall synthesis is activated after P. viticola inoculation. Defence-related reinforcement of cell walls in T39-treated plants was also evidenced by callose deposition around stomata at 1 and 5 dpi and by the restoration of P. viticola development following 2-DDG treatment. Further support for the role of callose deposition and ROS accumulation in T39-treated plants was the induction of a Rab-GAP/TBC domain-containing protein. Rab-GAP proteins are key regulators of intracellular vesicular trafficking for the apposition of papillae components at the site of oomycete penetration (Novick and Zerial, 1997; Collinge, 2009).

Interestingly, no receptor kinases were found in CL3 but they were directly affected by T39 treatment (CL2), suggesting that the microbial recognition machinery is pre-activated by T39 in preparation for a rapid response upon pathogen arrival. Among the proteins associated with signal transduction, this study identified three resistance proteins that increased in abundance upon P. viticola inoculation in T39 treated plants. Of these, the homologous protein resistant to Pseudomonas syringae 5 has been associated with resistance to Peronospora parasitica and Pseudomonas syringae in Arabidopsis, suggesting the existence of a common protein that may be required for multiple resistance protein signal cascades (Warren ).

Among the proteins associated with oxidative stress, a glutathione reductase, a copper/zinc superoxide dismutase (SOD), and a glutaredoxin had increased abundance in T39-treated plants after P. viticola inoculation. Alteration of oxidative stress metabolism is consistent with ROS accumulation observed in T39 treated plants at 1 and 7 dpi of P. viticola. SODs are responsible for converting superoxide anion to hydrogen peroxide and their activation during T. harzianum-induced redox reprogramming have been related to the resistance in sunflower (Singh ). These results suggest that the concentration of oxidizing species induced upon P. viticola inoculation is kept under control by an array of enzymes in T39-treated plants in order to avoid biological damage.

Proteins involved in JA/ET signalling were identified in CL3, consistent with the finding that these pathways are involved in T39-induced resistance (Korolev ; Perazzolli ). In particular, P. viticola caused a decrease in abundance of an activation domain protein in T39-treated plants. Together with the protein kinases MEKK1 and WRKY53, the activation domain protein mediates negative cross-talk between defence and senescence processes, which are governed by JA and SA equilibrium (Miao and Zentgraf, 2007). Another protein involved in the ET signal cascade was a putative ubiquitin-conjugating enzyme (De Paepe ), which had increased abundance after P. viticola inoculation of T39-treated plants. The expression profiles of the activation domain protein, UBC, and the LOX of CL2 evidenced cross-communication between SA and JA/ET pathways during T39-induced resistance. As already reported, Trichoderma–plant cross-talk is dynamic and regulation of JA/ET and SA pathways may be essential for an efficient defence mechanism (Hermosa ). Thus, in the Trichoderma–grapevine interaction, cross-talk between hormone pathways could help the plant to minimize energy costs and to create a flexible signalling network to fine-tune defence response to invaders.

Conclusions

This study assessed the proteomic and histochemical changes associated with Trichoderma-induced resistance, before and after pathogen inoculation. In addition to biological information, the high-throughput proteome analysis and the integrated use of two protein databases resulted in identification of 51 grapevine proteins not yet predicted by bioinformatic approaches, and in improvement in peptide sequences by de novo sequencing. Identification of proteins with significant changes in abundance provides molecular markers of grapevine induced resistance and highlights key processes that might further studied to improve T39-induced resistance against downy mildew. Major future challenges will be to validate the roles of individual proteins and explore their function in regulatory mechanisms responsible for T39-induced resistance.

Supplementary material

Supplementary data are available at JXB online.

Supplementary Table S1. Proteins identified and quantified in control-uninoculated and control-1dpi plants in the iTRAQ1 experiment and in control-uninoculated, Trichoderma harzianum T39-uninoculated, and T. harzianum T39-1 dpi plants in the iTRAQ2.

Supplementary Table S2. Proteins affected by Plasmopara viticola inoculation in control plants (CL1).

Supplementary Table S3. Proteins affected by Trichoderma harzianum T39 treatment (CL2).

Supplementary Table S4. Proteins affected by Plasmopara viticola inoculation in Trichoderma harzianum T39-treated plants (CL3).

Supplementary Fig. S1. Annotation of quantified proteins.

Supplementary Fig. S2. Trichoderma harzianum T39-induced resistance in susceptible Vitis vinifera cv. Pinot Noir.

Supplementary Fig. S3. Proteins commonly quantified in different iTRAQ experiments.

Supplementary Fig. S4. MapMan metabolic overview and photosynthesis-primary pathways.