JXB Advance Access published online on July 22, 2008
Journal of Experimental Botany, doi:10.1093/jxb/ern187
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RESEARCH PAPER |
Transcriptional and metabolic profiling of grape (Vitis vinifera L.) leaves unravel possible innate resistance against pathogenic fungi
1Unit of Molecular Biology and Plant Biotechnology, ICAT, FCUL, Campo Grande, 1749-016 Lisboa, Portugal
2Division of Pharmacognosy, Section Metabolomics, Institute of Biology Leiden University, Leiden, The Netherlands
3Department of Statistics and Operational Research, FCUL, CEAUL, Lisboa, Portugal
* To whom correspondence should be addressed. E-mail: figueiredo.andreia{at}gmail.com or aafigueiredo{at}fc.ul.pt
Received 24 March 2008; Revised 20 June 2008 Accepted 24 June 2008
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Abstract |
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Grapevine species (Vitis sp.) are prone to several diseases, fungi being the major pathogens compromising its cultivation and economic profit around the world. Knowledge of the complexity of mechanisms responsible for resistance to fungus infection of cultivars, such as Regent, is necessary for strategies to be defined which will improve resistance in highly susceptible crop species. Transcript and metabolic profiles of the Vitis vinifera cultivars Regent and Trincadeira (resistant and susceptible to fungi, respectively) were analysed by cDNA microarray, quantitative real-time PCR, and nuclear magnetic resonance spectroscopy. The integration of datasets obtained through transcriptome and metabolome analysis revealed differences in transcripts and metabolites between both cultivars. These differences are probably associated with the innate resistance of Regent towards the mildews. Several transcripts related to stress and defence, namely a subtilisin-like protease, phenylalanine ammonia lyase, S-adenosylmethionine synthase, WD-repeat protein like, and J2P, were up-regulated in Regent suggesting an intrinsic resistance capability of this cultivar. A metabolic profile revealed an accumulation of compounds such as inositol and caffeic acid, which are known to confer resistance to fungi. The differences in transcripts and metabolites detected are discussed in terms of the metabolic pathways and their possible role in plant defence against pathogen attack, as well as their potential interest to discriminate among resistant and susceptible grapevine cultivars.
Key words: cDNA microarrays, NMR-based metabolomics, Regent, Vitis vinifera
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Introduction |
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Grapevine (Vitis vinifera L.) is the most widely cultivated and economically important fruit crop in the world. It is prone to several diseases mainly caused by fungal pathogens. During the 19th century, pathogens such as powdery mildew [Uncinula necator (Schw.) Burr] and downy mildew [Plasmopara viticola (Berk. et Curt.) Berl. et de Toni] were introduced in Europe from North America and became major risk factors for viticulture. Since Vitis vinifera does not carry any resistance to mildew fungi, multiple fungicide applications per growing season became indispensable for traditional cultivars. The best way to overcome these problems is to develop resistant cultivars producing high quality fruit.
The grapevine variety Regent was bred at the Institute for Grapevine Breeding Geilweilerhof and combines high wine quality and resistance to the two most important pathogens (downy and powdery mildew agents) worldwide (Anonymous, 2000). Its resistance traits were achieved by multiple crosses involving wild species such as Vitis rupestris, V. riparia, and V. lincecumii, that are known sources of resistance to both pathogens, and susceptible traditional V. vinifera cultivars like Diana and Chambourcin (Eibach and Toepfer, 2003). Resistance QTLs were detected for both powdery and downy mildew (Fisher et al., 2004; Welter et al., 2007).
The origin of the resistance traits encountered in Regent is not yet known, but cultivars that were bred as Regent, Orion and Phoenix, present a defence response that seems to be based on biochemical reactions rather than on hypersensitive reactions (Kortekamp and Zyprian, 2003). A better understanding of the complexity of the resistance capability of Vitis vinifera cultivars, such as Regent, is necessary for an overall understanding of the resistance mechanism and for the definition of improvement strategies for highly susceptible crop species like Trincadeira (a traditional Portuguese cultivar, widely used for wine manufacture and highly susceptible to both downy and powdery mildew). To understand the possible innate resistance capabilities of the cultivar Regent, both transcript and metabolome profiling were accessed. Microarray technology allows monitoring the expression level of thousands of genes simultaneously in one single experiment (Shena et al., 2002; Meyers et al., 2004). It has been used to study the grapevine transcriptome related to berry development (Terrier et al., 2005; Waters et al., 2005; Ageorges et al., 2006), modulation of endogenous grapevine gene expression (Franks et al., 2006), water and salinity stress (Cramer et al., 2007), and virus infection (Espinoza et al., 2007) responses.
Metabolome analyses describe the chemical profile or fingerprint of the metabolites in whole tissues (Choi et al., 2004; Krishnan et al., 2005; Ward et al., 2007). Nuclear magnetic resonance, in particular 1H NMR, yields a comprehensive fingerprint of all hydrogen-bearing metabolites. It can also provide direct structural information regarding individual metabolites in the mixture, with the help of two-dimensional techniques (Defernez and Colquhoun, 2003; Kikuchi et al., 2004). The data obtained by NMR can be analysed by multivariate or pattern recognition techniques such as principal component analysis (PCA) (Choi et al., 2004). Hence, metabolic profiling, especially when integrated with transcript profiling, has a high potential to elucidate gene function and to establish data networks.
As far as is known, the only published data regarding the comparison of resistant and susceptible grapevine plants involves plant–pathogen interaction. The aim of the present work was to discriminate between grape cultivars, which vary in resistance to mildews, using transcript and metabolic profiles, and to discuss these profiles with possible known resistance mechanisms.
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Materials and methods |
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Plant material
Young leaves (second to fourth insertion from the apex) of Vitis vinifera L. cvs Regent and Trincadeira were randomly collected from 15 plants with no evidence of disease or stress symptoms, prior to flowering (PLANSEL Vineyards, Montemor, Portugal), late in the morning in two years (2004 and 2006), and stored after shock freezing with liquid nitrogen at –80 °C. Regent was bred at the Institute for Grapevine Breeding Geilweilerhof and shows resistance to both powdery (Uncinula necator) and downy mildew (Plasmopara viticola) (Anonymous, 2000). Trincadeira is a traditional fungus-susceptible Vitis vinifera cultivar.
RNA extraction
Total RNA was isolated from young leaves of Regent and Trincadeira cultivars essentially as described by Wan and Wilkins (1994). Briefly, 2 g of ground plant material were added to 20 ml of extraction buffer (0.2 M sodium borate, 30 mM EDTA, 1% SDS, 1% sodium deoxicolate, 10 mM DTT, 1% Nonidet P-40, 2% PVP-40) previously heated to 90 °C. Proteinase K (1.2 mg ml–1) (Gibco BRL, Life Technologies) was added and samples were kept for 90 min at 42 °C with agitation. Proteins were selectively precipitated with 160 mM KCl by incubating at 4 °C for 30 min. Supernatant was recovered by centrifugation at 9500 g (Biofuge Stratos, Heraeus Instruments) for 30 min and nucleic acids were precipitated overnight with 2 M LiCl. The pellet was washed three times with cooled 2 M LiCl and resuspended in 10 mM TRIS–HCl, pH 7.5. After 10 min centrifugation at 12 000 g, supernatant was collected and polysaccharides were precipitated with 0.2 M KAc, pH 5.5, by incubating at 4 °C for 30 min. Supernatant was collected and RNA was precipitated by addition of 2.5 volumes of 100% ethanol. The pellet was washed with 70% ethanol. RNA purification was carried out with DNase (Invitrogen, San Diego, CA, USA) and with the RNeasy Plant Mini kit according to the supplier's instructions (Qiagen, Valencia, CA, USA). All solutions used were treated with diethyl pyrocarbonate (Sigma).
Construction of grapevine cDNA microarray
A cDNA library representative of the two Vitis vinifera cultivars was constructed with leaves collected in 2004 according to Figueiredo et al. (2007). Randomly chosen clones were PCR amplified using M13fw (5'-TGCAACATTTTGCTGCC-3') and M13rev (5'-CAGGAAACAGCTATGAC-3') primers, and inserts longer than 400 bp were selected (2712 cDNAs).
Amplified cDNA inserts were purified with MultiScreen-PCR plate (Millipore, Bedford, MA, USA), transferred to printing plates (Microtiter V plates, Sigma), resuspended in 50% dimethylsulphoxide and 0.2% SSC, and spotted in duplicate onto poly-L-lysine-treated glass slides using VersArray ChipWriter Compact® (Bio-Rad Lab., Hercules, CA, USA). ESTs (expression sequence tags) coding for a chitinase class III (DQ406693 [GenBank] ), actin (AY847627 [GenBank] ), stilbene synthase (DQ406692 [GenBank] ), allene oxide cyclase (DQ406694 [GenBank] ), 26S ribosomal RNA (AY847628 [GenBank] ), and AUX like protein (AY849386 [GenBank] ) were included as controls.
Technical details of spotting are provided as MIAME (Brazma et al., 2001). Briefly cDNA clones were printed on poly-L-lysine-coated slides, using a 12-pin configured ChipWriter Compact System (Biorad). The general design of the microarray was 12 subgrids, each subgrid with 24 columns and 23 rows, with each clone replicated within the same subgrid. After printing, cross-linking was performed with heat and UV according to the protocol from Vodkin Laboratory (http://soybeangenomics.cropsci.uiuc.edu). Microarray quality was accessed by GelStar nucleic acid stain (FMC, Rockland, ME, USA).
Cy-labelling and hybridization
RNA used for hybridizations was obtained from three sets of leaves from 15 different plants collected in 2004, yielding three biological replicates. Four hybridizations were carried out corresponding to three biological replicates and one dye-swap. Self–self hybridizations were also carried out to evaluate dye bias. Seventy micrograms of RNA from each biological replicate were used for cDNA synthesis with an RT primer (oligo dT) and labeled with either Cy3 or Cy5 dye molecules (Amersham, Bucks, UK). Following cDNA synthesis, RNA was removed with RNase (Fermentas) and labelled targets were purified using QIAquick PCR purification kit (Qiagen, Valencia, CA, USA). Prior to hybridization, slides were treated with 1% BSA, 5x SSC, and 0.1% SDS (w/v) for 30 min at 50 °C. Briefly, the hybridization mix consisted of 30 µl of labelled cDNAs, 7.5 µg Cot-1 DNA (Invitrogen), 15 µg poly(A)+, 7.5 µg of salmon sperm DNA, 1.5 µl of 50x Denhardt's solution, 7.9 µl 20x SSC, and 1.5 µl 10% SDS (w/v). The mixture was denatured at 97 °C for 2 min, kept on ice, and then 1 µl of 10xDIG blocking buffer was added (Roche Diagnostic GmbH, Mannheim, Germany). Hybridization was carried out for 17 h at 65 °C in ArrayIt chambers (Telechem International, Sunnyvale, CA, USA) placed in a water bath. Slides were then washed once in 0.5x SSC and 0.1% SDS (w/v), then in 0.5x SSC and 0.01% SDS (w/v) and twice in 0.06x SSC.
Microarray analysis
Slides were scanned using a VersArray ChipReader®. Spot and background intensities were quantified using VersArray Analyser software (Bio-Rad Lab.). Background was calculated as the trimmed mean of pixel intensity on local corners of spots. Low intensity signal spots (trimmed mean of raw intensity/trimmed mean of background <1.5), uneven background (trimmed mean of raw intensity/standard deviation of background <2.5), uneven spots (trimmed mean of raw intensity of background/standard deviation of raw intensity <1), and not validated spots (flags) were removed from analysis before normalization. Data files were imported into GEPAS (http://gepas.bioinfo.cipf.es) and log2-transformed prior to normalization (DNMAD). Print-tip Lowess (Yang et al., 2002) was used for within-slide normalization considering background subtraction. Statistical analysis was performed using the Rank Products (RP) method (Breitling et al., 2004). The RP values for each gene were compared with the RPs of 1000 random permutations, with the same number of replicates and genes as the real experiment. ESTs with a false discovery rate <0.05 and a fold change of 1.6 were considered differentially expressed. This rank-based test statistic is a non-parametric method shown to generate accurate results with biological datasets, particularly with small numbers of replicates (Breitling and Herzyk, 2005). This method has already been used to analyse transcriptional profiling in plants (Armengaud et al., 2004).
Sequencing and sequence analysis
Differentially expressed ESTs were sequenced using the BigDye Terminator Cycle sequencing kit (Applied Biosystems, Inc.) with an ABI Prism 310 genetic analyser (Applied Biosystems, Inc.) according to the manufacturer's instructions. Homology searches were performed at NCBI databases, using the blastx and megablast algorithms (Altschul et al., 1997). Sequences with an e-value 
10–4 were considered to identify known genes or have partial similarity to known genes. Assembly of the individual ESTs into groups of tentative consensus sequences, representing unique transcripts, was performed using the SeqManII program (DNAStar) (90% similarity over 40 nt length).
Quantitative real-time (qRT)-PCR
To evaluate the accumulation pattern of ESTs considered to be directly or indirectly related to Regent's innate resistance capabilities, six differentially expressed ESTs were analysed by qRT-PCR.
Five micrograms of total RNA from each biological replicate (leaves collected in 2006) were used to synthesize cDNA. Complementary DNAs were quantified using a spectrofluorimeter (Anthos Zenyth 3100, Anthos Labtec Instruments, Austria) and brought to equal concentration. Quantitative RT-PCR reactions were performed with the LightCycler Fast Start ReactionMix MasterPLUS SYBR Green I (Roche, Mannheim, Germany) on a LightCycler® detection system (Roche) according to the manufacturer's instructions. Gene-specific oligonucleotide sequences are shown in Table 1. The transcript concentration for each sample was calculated based on a standard calibration curve obtained from serial dilutions of plasmid DNA containing the insert to be analysed. A negative control reaction without a template was always included for each primer combination. Two to three biological replicates and one technical replicate were performed for each time point. The average of three or four qRT-PCR reactions is presented for each time point.
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Metabolic profiling using 1H NMR, J-resolved, COSY, and multivariate analysis
Plant material collected in 2006 was frozen, ground in liquid nitrogen, and lyophilized for at least 72 h at –40 °C. Twenty-five milligrams of material were used from each sample extraction according to Choi et al. (2006) and Abdel-Farid et al. (2007). Potassium phosphate (KH2PO4) was added to D2O (99.9%; Cambridge Isotope Laboratories, Miami, FL, USA) as a buffering agent. The pH of the D2O for NMR measurements was adjusted to 6.0 with 1 N NaOD solution (Cortec, Paris, France). Samples were dissolved in 750 µl of KH2PO4 with 0.1% trimethyl silane propionic acid sodium salt (standard purchased from Merck, Darmstadt, Germany), and 750 µl of methanol-d4 (99.9%, Cambridge Isotope Laboratories). Then, samples were briefly vortexed, sonicated for 10–20 min, and centrifuged for 10 min at 17 000 g.
The supernatant (800 µl) was then used for analysis. 1H NMR spectra were recorded at 25 °C on a 500 MHz Bruker DMX-500 spectrometer operating at a proton NMR frequency of 500.13 MHz. Each 1H NMR spectrum consisted of 128 scans requiring 10.26 min measuring time with the following parameters: 0.16 Hz/point, pulse width 30°, acquisition time 3.17 s, and relaxation delay 1.5 s. A presaturation sequence was used to suppress the residual water signal at 
4.91 with low power selective irradiation at the water frequency during the recycle delay. Free induction decays were Fourier transformed with LB = 0.3 Hz. The resulting spectra were manually phased and baseline corrected and calibrated to TSP at 0.0, all using XWIN NMR (version 3.5, Bruker). The 1H NMR spectra were automatically reduced to ASCII files using AMIX (version 3.8, Bruker Biospin). Spectral intensities were scaled to TSP and to reference and reduced to integrated regions of equal width (0.04 ppm) corresponding to the region 0.40–10.00. The region 3.28–3.34 was excluded from the analysis due to the residual signal of water and methanol-d4. PCA analysis was carried out with the SIMCA-P software (version 11.0; Umetrics, Umea, Sweden). PCA is an unsupervised pattern recognition method requiring no knowledge of the data set and acts to reduce the dimensionality of multivariate data while preserving most of the variance within it (Goodacre et al., 2000). The Pareto scaling method was used as it gives each variable a variance numerically equal to its standard deviation.
Two dimensional (2D) J-resolved 1H NMR spectra were acquired using eight scans per 128 increments that were collected into 8 k and 128 data points for the F2 and F1 axes, respectively, using spectral widths of 5 kHz in F2 (chemical shift axis) and 66 Hz in F1 (spin-spin coupling constant axis). A 1.49 s relaxation delay was employed, giving a total acquisition time of 56.7 min. Both dimensions were multiplied by sine-bell functions prior to double complex FT. J-resolved spectra were tilted by 45°, symmetrized about F1, and then calibrated, all using XWIN NMR (version 3.5, Bruker). 1H–1H-correlated spectroscopy (COSY) spectra were acquired with 1.0 s relaxation delay, 5 kHz spectral width in both dimensions. Microsoft® Excel files containing spectral intensities reduced to integrated regions of equal width (0.04 ppm) were used for Wilcoxon rank sum test to evaluate metabolites present in samples in significantly different amounts (P <0.05). The Wilcoxon rank sum test is the classical non-parametric alternative to the t-test when comparing two samples, being more powerful in the case of non-normal data distribution.
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Genes differentially expressed in Vitis vinifera Regent and Trincadeira cultivars
The differential transcript expression of both cultivars was analysed by cDNA microarrays, allowing the identification of 121 transcripts as being differentially expressed, representing 29 unique gene sequences. The identified unigenes were classified into eight primary functional categories, according to the putative function of their homologous genes in databases (National Center for Biotechnology Information) as follows: stress and defence, transcription regulation, regulation of chloroplast gene expression, photosynthesis, energy, signal transduction, protein modulation, and unknown function. The most redundant contigs included: sequences homologous to genes coding for a transcript that presented no homology with the database (59 clones); a cDNA coding for a subtilisin-like protease (ES880786 [GenBank] ) (13 clones); a cDNA coding for a chlorophyll a/b binding protein (EC907687 [GenBank] ) (15 clones); a cDNA coding for a phenylalanine ammonia lyase (PAL; EC907689 [GenBank] ) (four clones); and cDNAs coding for Lil3 protein (EC907663 [GenBank] ), catalase (EC907667 [GenBank] ), and S-adenosyl methionine synthase (EC907686 [GenBank] ) (two clones each). The remaining differentially expressed cDNA clones were unique sequences.
The ratios (fold change of normalized signals) of the clones found within the same contig were averaged and counted as one gene. Unigenes are shown in Table 2.
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Complementary DNA encoding a subtilisin-like protease, PAL, WD-repeat protein like, S-adenosylmethionine synthase, cinnamyl alcohol dehydrogenase (CAD), and J2P were independently quantified by qRT-PCR in both cultivars presenting similar expression patterns as the ones obtained by microarray analysis (Fig. 1).
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Metabolic profiling
One-dimensional and 2D NMR techniques enabled the recognition of a broad metabolome allowing the identification of several compounds such as amino acids, carbohydrates, organic acids, and phenolic compounds. 2D NMR spectroscopy, such as J-resolved and COSY techniques, was applied to identify metabolites from the congested 1H NMR signals (Choi et al., 2004). Amino acids were identified at
2.46 (m) and 2.14 (m) as glutamine; at 2.39 (m) and 2.04 (m) as glutamate; at 2.18 (s) as methionine; 1.48 (d, J=7.5 Hz) as alanine; 1.34 (d, J=6.5 Hz) as threonine and at 1.06 (d, J=6.6 Hz), and 1.01 (d, J=7 Hz) as valine. At 2.35 (m) trace amounts of proline were detected. Anomeric protons of sucrose were detected at 5.40 (d, J = 3.5 Hz), 
-glucose at 5.18 (d, J=3.8 Hz), β-glucose at 4.58 (d, J=7.8 Hz), and fructofuran moiety of sucrose at 4.17 (d, J=8.5 Hz). In addition to these compounds, inositol 4.03 (t, J=2.8 Hz), succinic acid 2.54 (s), and trace amounts of -linolenic acid 0.95 (t) were identified (Fig. 2). Signals at 5.7–9.0 correspond to the phenolic compound region (Fig. 3).
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The 2D techniques, J-resolved and COSY (Fig. 4), were used to identify metabolites in the phenolic compound region. The combination of the 2D techniques and the comparison with libraries of spectra corresponding to pure compounds enabled the identification of caffeic acid, quercetin-3-O-glucoside, and shikimic acid (Table 3.)
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Two principal components (PC1 and PC2) were considered as they explain 76.2% of the variation in the entire data set: PC1 accounts for 41.2% of the variation while PC2 accounts for 35.0%. PC2 discriminates both Regent and Trincadeira cultivars as it is shown by the score scatter plot (Fig. 5). The Wilcoxon Rank Sum test (non-parametric test) was performed in order to identify the metabolites that discriminate between both cultivars using spectral intensities at different chemical shifts (
0.4–10.0). The metabolites that were found to be statistically different between both cultivars (P <0.05) are shown in Table 4. Regent was characterized by a higher amount of inositol, alanine, glutamine, glutamate, and caffeic acid and Trincadeira by a higher amount of -glucose, succinic acid, and shikimic acid.
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Discussion |
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TopAbstractIntroductionMaterials and methodsResultsDiscussionConclusionReferences |
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With the progress of genomic initiatives to profile genome and gene expression in the context of plant–pathogen interaction, metabolic profiling is increasingly needed for a better understanding of plant defence mechanisms against various environmental stresses, including pathogen stress. In this context, knowledge of the defence capabilities of resistant cultivars such as Regent is gaining higher importance.
The combined metabolomic and transcriptomic approaches made it possible to identify transcripts and metabolites which could discriminate between Regent and Trincadeira (resistant and susceptible to mildews, respectively) without inoculation with a pathogen.
The rank products statistic (RP) was used in order to determine which transcripts are statistically relevant. Absolute fold change does not provide a significant estimate for the observed changes and the normal cut-off values (2-fold) are essentially arbitrary. According to Breitling et al. (2004), the difference between RP and fold change criteria is very important because RP is more robust against outliers. At the same time it provides a simple and straightforward procedure to determine the significance of an observed change. This way, transcripts statistically relevant by the rank product method and presenting a fold change lower that 2-fold were also considered.
Regent presents several up-regulated transcripts that are known to be associated with defence capabilities against stress, such as subtilisin-like protease, S-adenosyl methionine synthase, PAL, WD-repeat protein like, and J2P protein (Table 2).
Subtilisin-like proteases can play important roles in the regulation of biological processes such as the recognition of pathogens with consequent induction of defence responses (van der Hoorn and Jones, 2004). These proteases have been shown to accumulate after a pathogen attack (Tornero et al., 1996, 1997; Jorda et al., 1999; Golldack et al., 2003; Coffeen and Wolpert, 2004). As the plant material used in this study was not infected, we hypothesize that accumulation of subtilisin-like protease transcripts in Regent could participate in pathogen recognition upon infection and subsequent induction of the defence responses.
S-Adenosyl-L methionine synthase (AdoMet synthetase, EC 2.5.1.6) plays an important role in the production of S-adenosyl methionine which acts as precursor for the biosynthesis of several compounds involved in defence responses namely phenylpropanoids and polyamines (Roje, 2006). Phenylpropanoid compounds derived from the shikimic acid pathway are precursors of several phenolics, such as lignan, flavonoids, isoflavonoids, coumarins, and stilbenes. The deamination of phenylalanine by PAL (EC 4.3.1.5) is the branch point between primary metabolism (shikimate pathway; Herrmann, 1995) and secondary metabolism (phenylpropanoid synthesis) (Rohde et al., 2004). Vitis vinifera cv. Regent presents up-regulation of the transcripts coding for PAL while the transcripts coding for CAD are down-regulated.
CAD (EC 1.1.1.19 [EC] 5) is a key enzyme in lignin biosynthesis (Blanco-Portales et al., 2002). Metabolome analysis revealed a significant (P <0.05) increased content of caffeic acid in Regent. All together, these data suggest that secondary metabolite production in Regent may be more activated than in Trincadeira (Fig. 6). Moreover, the down-regulation of CAD in Regent suggests that phenolic compounds are not being polymerized to lignin, thus, the general phenylpropanoid pathway leading to the accumulation of phenolic compounds may be more active than the monolignol-specific pathway (Fig. 6).
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Compounds occurring constitutively in healthy plants are likely to represent chemical barriers to infection and may protect plants against attack by a wide range of potential pathogens (Creasy, 1999; Morrissey and Osbourn, 1999). Constitutive phenols are known to directly or indirectly confer resistance through activation of post-infection responses in the host (Dixon, 2001; Lavania et al., 2006). Analysis of transgenic tobacco plants with reduced levels of constitutive phenolic compounds resulting from sense inhibition of PAL expression revealed an important role of preformed phenolic compounds in limiting the extent of infection by a fungal pathogen (Maher et al., 1994). Tobacco plants overexpressing L-phenylalanine ammonia-lyase (PAL+) were shown to produce high levels of chlorogenic acid and to exhibit reduced susceptibility to infection with a fungal pathogen (Shadle et al., 2003). PAL activity has also been related to the accumulation of caffeic acid derivatives in Echinacea purpurea hairy root cultures (Abbasi et al., 2007). Also, the accumulation in Regent of caffeic acid, a compound presenting antimicrobial activity (Dixon, 2001; Iwaki et al., 2006) which inhibits the germination of zoospores of the pathogenic fungi Phytophthora spp. (Widmer and Laurent, 2006), could be related to the inherent resistance of this cultivar to Plasmopara viticola. The constitutive up-regulation of PAL in Regent, together with the accumulation of caffeic acid, may be responsible for the higher protection of this grapevine cultivar to a possible pathogen attack.
WD repeat protein-like and J2P transcripts overexpressed in Regent may also be involved in the resistance of this cultivar. According to Park et al. (2004), the WD repeat protein-like may be involved in the regulation of secondary metabolism. J2P (a member of the J-protein family) is a molecular chaperone that regulates the activity of Hsp70 proteins (Walsh et al., 2004). J2P has been suggested to be related to the intrinsic resistance of a Beta vulgaris genotype (Puthoff and Smigocki, 2007).
Regent presents a constitutive accumulation of alanine. Although the precise function of stress-induced alanine in the cell is still unknown, it has been shown that added alanine stimulates a gene encoding for stress-protein synthesis in mammalian kidney, protecting cells against injury damage (Monselise et al., 2003). The constitutive accumulation of alanine in Regent may allow a faster accumulation of this compound upon fungus attack, contributing to an increased resistance to the pathogen. Moreover, the constitutive accumulation of inositol in resistant cultivars may be related to a higher signal perception and transduction capacity, enabling a rapid response to pathogen attack as suggested by Hamzehzarghani et al. (2005). Indeed, plants with higher levels of resistance to diseases present overproduction of inositol (Smart and Flores, 1997; Pritsch et al., 2000).
Regent also presents higher accumulation of glutamine and glutamate. Glutamine helps the plant cell to recycle ammonia ions liberated from phenylalanine through the action of PAL. Glutamine accumulation in Regent, together with the up-regulation of PAL, may be considered as evidence for a more active PAL pathway, which is corroborated by the accumulation of caffeic acid in this cultivar. Glutamate is a precursor of stress-related molecules (Lam et al., 1995; Hothorn et al., 2006) and, therefore, a higher pool of glutamate may lead to a faster production of these molecules upon a pathogen attack.
The metabolic profile of spikelets of the wheat cultivars Roblin and Sumai3, respectively susceptible and resistant to Fusarium head blight, revealed a constitutively higher production of inositol, glutamine, and phenolic compounds in a resistant cultivar of wheat (Hamzehzarghani et al., 2005). This corroborates the results obtained for Vitis vinifera cv. Regent. Upon infection, the resistant cultivar of wheat showed a higher inositol and glutamine accumulation and increase of cinnamic acid and phenylpropanoids compared with the susceptible cultivar which did not present these compounds constitutively (Hamzehzarghani et al., 2005). It is hypothesized that the same would probably occur in Regent after pathogen attack, since this V. vinifera cultivar also presents a higher constitutive abundance of inositol, glutamine, and caffeic acid.
Regent also presents a down-regulation of a catalase and a glutathione S-transferase. RT-PCR analyses of the plant material harvested in 2006 have shown a different regulation of these transcripts (data not shown) when compared with the material harvested in 2004, so it is hypothesized that catalase and glutathione S-transferase are not related to the innate resistance capabilities of Regent against pathogenic fungi, being probably related to environmental conditions like light intensity at the harvesting times. This means that their down-regulation can be related to a new redox balance generated through photosynthesis.
The comparison between these grapevine cultivars was accomplished without plant inoculation with a pathogen, thus the transcript and metabolite differences encountered cannot be related exclusively to the innate resistance capabilities of Regent, and probably other reasons may account for these differences. Several metabolites and transcripts identified in Regent are known to be associated with defence mechanisms. This way, the hypothesis that they may be associated with the resistance mechanism of Regent cannot be discarded. It is tempting to speculate that, as the majority of the genetic background of both cultivars is the same, the transcripts and metabolites that discriminate Regent from Trincadeira could be responsible for a faster response against pathogen attack, thus contributing to the higher resistance of Regent against downy and powdery mildew.
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Conclusion |
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As far as is known, the work hereby reported on the distinction between resistant and susceptible grapevine cultivars not involving interaction between plant and pathogen, through a combination of both cDNA microarrays and 1H NMR, has no precedent. The higher transcription of genes related to defence, such as PAL, S-adenosyl methionine synthase, subtilisin-like protease, WD-repeat protein like, and J2P protein and the accumulation of inositol, glutamine, glutamate, alanine, and caffeic acid in V. vinifera cv. Regent suggest that this cultivar presents a higher capacity to resist a pathogen attack. It also suggests that susceptible varieties, such as Trincadeira, possess the genetic information required to initiate a defence response. However, the perception and transduction pathways, probably being very slow, may not be able to switch-on in time for the defence mechanisms to cope with invasion of fungi. These preliminary results allow us to hypothesize that the resistant cultivar, Regent, may be able to activate the defence response in a rapid and more intensive manner. The hypotheses presented here have to be confirmed through studies in which both cultivars are inoculated with powdery or downy mildew. These studies have already been initiated.
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Acknowledgements |
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The authors wish to acknowledge Jorge Boehm (PLANSEL) for supplying the plant material and to Dr Filipa Monteiro for the careful review of the manuscript. This work was supported by Agência de Inovacão, S.A under the project RESISTEVID and by Fundação para a Ciência e Tecnologia through fellowship SFRH/BD/12403/2003 and projects FCT/POCI 2010 and PTDC/MAT/64353/2006.
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Footnotes |
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Present address: Dep. Micologia, Univ. Federal de Pernambuco. Av. Prof. Nelson Chaves s/n, Cidade Universitária, 50670-420, Recife, PE, Brazil. 
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