J Korean Soc Appl Biol Chem (2014) 57(4), 491−502 Online ISSN 2234-344X DOI 10.1007/s13765-014-4166-1 Print ISSN 1738-2203

ARTICLE

Metabolic Phenotyping of Berries in Different Six Grape (Vitis vinifera) Cultivars

Hong-Seok Son · Kwang-Sei Lim · Hyun-Jung Chung · Soo-Jin Yang · Young-Shick Hong

Received: 17 May 2014 / Accepted: 12 June 2014 / Published Online: 31 August 2014 © The Korean Society for Applied Biological Chemistry and Springer 2014

Abstract Metabolic behaviors of different grapevine cultivars, Introduction grown in the same greenhouse, were characterized through a 1H NMR-based metabolomic approach. Pattern recognition, including Grapevine (Vitis vinifera) is the most widely cultivated and used the principal component analysis, revealed clear dependence of fruit for wine and juice production in the world. It is well known the grape metabolome on the grape cultivar. Interestingly, high that grape chemical composition depends on the grape variety, accumulations of proline in the purebred grape cultivars of Cabernet which influences wine quality. Furthermore, environmental Sauvignon, Merlot, and Chardonnay were found, whereas the conditions of climate, soil, and cultural practices in viticulture also proline levels were depleted in the crossbred grape cultivars of affect the grape chemical compositions and metabolites. Recently, Steuben, Campbell Early (C. E.), and Seibel. Intrinsic levels of these metabolic variations in grapes and wines have been well alanine, glutamine, and trans-feruloyl derivative were highest in established and characterized through metabolomics approaches C. E. cultivar, which grows easily in a wild vineyard, particularly with global metabolic profiling (Pereira et al., 2005; 2006; 2007; in Korea, suggesting that their levels play important roles in the Son et al., 2008; 2009a; 2009b; 2009c; Lee et al., 2009a). In improvement of resistance or adaptation of the plant to environ- addition, metabolomics provided better physiological understanding mental stress, such as freezing stress during the winter season in of plant resistance mechanisms to biotic and abiotic stresses (Choi Korea. The present study highlights that metabolomics is a powerful et al., 2004; Figueiredo et al., 2008; Leiss et al., 2009; Simoh et approach for better understanding the differences of intrinsic al., 2009). For example, the metabolic profile revealed an metabolic variables of grape berries among various grape cultivars accumulation of inositol and caffeic acid in a fungi-resistant Vitis and their associations with the plant physiological mechanisms. vinifera cultivar (Figueiredo et al., 2008) and of chlorogenic acid in thrips-resistant chrysanthemums (Leiss et al., 2009), which also Keywords cold stress · grape · metabolites · metabolomics · confers resistance to fungi. Metabolomics or metabonomics is NMR generally defined as both qualitative and quantitative measurements of the metabolic response of living systems to physiological stimuli or genetic modification (Nicholson et al., 1999). In metabolomics, pattern recognition and related multivariate statistical approaches can be used to discern significant patterns in complex data sets obtained from global profiling of metabolites, H.-S. Son and K.-S. Lim contributed equally. with the aim of classifying objects by identifying the inherent patterns in a set of indirect measurements. Pattern recognition H.-S. Son methods, such as the unsupervised principal component analysis Department of Food and Nutrition, Dongshin University, Naju, Jeonnam 520-714, Republic of Korea (PCA) and supervised methods, such as partial least squares (PLS) and orthogonal PLS (OPLS) discriminant analysis (DA), can K.-S. Lim reduce the dimensionality of complex data sets, thereby facilitating Dairy Food R&D Center, Maeil Dairies Co., Ltd, Jinwiseo-ro, Jinwi- Myeon, Pyeongtaek-si, Gyeonggi-do 451-861, Republic of Korea the visualization of the inherent patterns and the identification of the metabolites responsible for the classification among objects. In H.-J. Chung · S.-J. Yang · Y.-S. Hong (
) addition, metabolomics provides the possibility of integrating data Division of Food and Nutrition, Chonnam National University, Yongbong- ro, Buk-gu, Gwangju 500-757, Republic of Korea sets from transcriptomics and proteomics to provide information E-mail: [email protected] on the fundamental systems biology to give a more holistic 492 J Korean Soc Appl Biol Chem (2014) 57(4), 491−502 overview of living systems (Deluc et al., 2007; Figueirdo et al., a field frequency lock and chemical shift reference (1H, δ 0.00), 2008). respectively. 1H NMR spectra were recorded on a Varian Inova- In the present study, we selected the Cabernet Sauvignon (C. S.) 600 MHz NMR spectrometer, operating at 599.84 MHz 1H and Merlot grape cultivars, as well as the Chardonnay cultivar, frequency and 298 K, using a triple-resonance 5 mm HCN salt- which are the most popular grapes for producing red and white tolerant cold probe. The Noesypresat pulse sequence was applied wines. The Campbell Early (C. E.), Steuben, and Seibel cultivars to suppress the residual water signal. For each sample, 16 were also selected, as they are the most widely cultivated grapes transients were collected into 32 K data points using a spectral in Korea for consuming as table grapes. We characterized their width of 9615.4 Hz with a relaxation delay of 1.5 s, an acquisition metabolic behaviors to provide the potential effects of the grape time of 4.00 s, and a mixing time of 400 ms. A line-broadening metabolome on the grape wine produced and to gain a better function of 0.3 Hz was applied to all spectra prior to Fourier physiological understanding of grape variety through 1H NMR- transformation. Signal assignment for representative samples was based metabolomics. All grape cultivars were grown in a green- facilitated via acquisition of two-dimensional (2D) total correlation house to avoid environmental effects on the grape metabolome. spectroscopy (TOCSY), heteronuclear multiple bond correlation (HMBC), heteronuclear single quantum correlation (HSQC), and spiking experiments. Materials and Methods Spectral preprocessing and multivariate data analysis. After manual phasing and baseline correction of each NMR spectrum, Origin of grapes. All grape cultivars (C. S., Vitis vinifera; Merlot, all NMR spectra with full resolution were imported into MATLAB V. vinifera; Steuben, hybrid of V. vinifera and V. labrusca; C. E., (R2007a, Mathworks, Inc., 2007, USA). The regions corresponding hybrid of V. vinifera and V. labrusca; Chardonnay, V. vinifera; to residual water (4.7–4.9 ppm), residual ethanol (1.15–1.20 and Seibel, hybrid of V. vinifera, V. rupestris, V. lincecumii and V. 3.59–3.72 ppm), and TSP (−0.1–0.5 ppm) were removed from all berlandieri) were cultivated in a greenhouse at the same vineyard spectra prior to spectral alignment and normalization. The NMR in Korea and harvested in October, at which time all berries were spectral data were aligned by a recursive segment-wise peak fully ripe. The green-house was not specifically designed for scientific alignment algorithm (Veselkov et al., 2009), and normalized using research, but for normal production of grape berries, thus all the probabilistic quotient method (Dieterle et al., 2006) together grapes were grown not in pots but in a vineyard under natural with normalization to the total spectral area. The resultant data sunlight. Temperature in the green-house was maintained at 10oC sets were then imported into SIMCA-P version 12.0 (Umetrics, during winter to prevent freezing of the grapevines. Five grape Sweden) for multivariate statistical analysis. The mean center was bunches were obtained from each different plant to avoid intra- applied for all multivariate analysis by SIMCA-P software. At variations in metabolites. The genera and species of all grapes first, principal component analysis (PCA), an unsupervised pattern were determined by phylogenetic analysis at Jeollabuk-Do recognition method, was performed to examine the intrinsic Agricultural Research & Extension Services, Korea. variation and identify outliers in the data set. To maximize the Extraction of pulp and skin. Extraction procedures were separation between samples through removal of non-correlated performed according to the methods of Pereira et al. (2005). Pulps variation in X variables (metabolites in NMR spectra) to Y and skins from 10 g of berries from 5 different bunches of each variables (predefined classes such as grape cultivars under study) grape cultivar were separated manually. The pulps were directly and to identify the metabolites responsible for the separation extracted with 95% ethanol for 15 min. The skins were ground in through pair-wise comparisons, orthogonal projections to the a blender for 2 min and extracted with 95% ethanol for 1 h. All latent structures discriminant analysis (OPLS-DA) model, a extractions were conducted with agitation at 4oC, and 80 mL of supervised pattern recognition method, was applied. Hotelling’s ethanol was used to extract each 10 g sample of berries. After T2 region, shown as an ellipse in the score plots of the models, drying each extract under vacuum, the pellets were dissolved in defines the 95% confidence interval of the modeled variation oxalate buffer (400 mM, pH 4.0), and dried again under vacuum. (Hotelling, 1931). The quality of the models was described by R2 Each extract was freeze-dried to reduce the residual water and (R2X and R2Y) and Q2 (Q2Y) values. R2 is defined as the ethanol signals in the NMR spectra. proportion of variance in the data explained by the models, 1H NMR spectroscopic analysis of skin and pulp extracts. indicating the goodness of fit, whereas Q2 is dined as the Freeze-dried samples (extracts of skins and pulps) were dissolved proportion of variance in the data predictable by the model, in 2 mL of oxalate buffer (400 mM, pH 4.0). A 400 µL aliquot indicating predictability (Trygg and Wold, 2002; Holmes et al., was then mixed with oxalate buffer (140 µL, 400 mM, pH 4.0) 2008). and trimethylsilylpropionic-2,2,3,3-d4 acid (60 µL, TSP, 5 mM), Chemicals. All chemical reagents were of analytical grade. D2O then centrifuged at 13,000 rpm for 10 min. Supernatants (550 µL) (99.9%) and TSP (97%) were purchased from Sigma (USA). were transferred into 5 mm NMR tubes. D2O and TSP provided J Korean Soc Appl Biol Chem (2014) 57(4), 491−502 493

Fig. 1 Representative 600 MHz 1H NMR spectra of skin extracts of various grape cultivars. C.S. and C. E. represent C. S. and C. E., respectively. 494 J Korean Soc Appl Biol Chem (2014) 57(4), 491−502

Results

Identification of grape metabolome using 1H NMR spectroscopy. Representative 600 MHz 1H NMR spectra of skin extracts obtained from each grape variety are shown in Fig. 1A. Wide range of metabolites can be assigned in each 1H NMR spectrum, providing complementary information for global differences in metabolites among the grape cultivars. The assignments of the metabolites were carried out based on the analysis of 2D NMR and comparison with information published elsewhere (Pereira et al., 2005; Figueiredo et al, 2008; Son et al., 2009a). Seventeen metabolites were identified, and their assignments were verified through 2D TOCSY experiment (Supplementary Fig. 1). Visual inspection of the 1H NMR spectra revealed marked intrinsic differences in several metabolites according to grape variety. Of the red grape cultivars, proline and sucrose levels were the highest in C. S. and Steuben cultivars, respectively. In the visual comparison of white grape cultivars, proline and malate levels in the Chardonnay cultivar were higher than those in the Seibel cultivar. To visualize the global variance of the data sets and provide comparative interpretations for metabolic differences in each grape cultivar, pattern recognition methods, including PCA and OPLS-DA, were employed. Metabolic differentiation of red grape cultivars. PCA score plot revealed clear differentiation among the skin and pulp extracts from C. S., Merlot, Steuben, Chardonnay, and Seibel cultivars (data not shown). For further analysis, the OPLS-DA model was applied to give a more discernible differentiation between groups through removal of metabolic information 1 (metabolites in NMR spectra) that was not correlated to the Fig. 2 OPLS-DA score plot derived from the H NMR spectra of skin (A) and pulp (B) extracts of grapes. C. S., Merlot, and Steuben represent predefined classes (grape cultivars). The OPLS-DA score plot red grape cultivars, whereas Chardonnay and Seibel denote white grape also showed clear differentiation among the skin and pulp extracts cultivars. One sample of the skin extracts from each Steuben and from each grape cultivar (Fig. 2). Prior to the pair-wise comparisons Chardonnay variety was excluded from the model due to failed spectral between grapes in the OPLS-DA model, a permutation test was alignment. Moreover, skin and pulp extracts from the C. E. variety were excluded in this model due to large variations in NMR chemical shifts, performed with 200 random permutations in a PLS-DA model and were compared with those of C. S. in a separate model. with the same number of components of the OPLS-DA models (Supplementary Fig. 2). In typical validation of the OPLS-DA model between the skin extracts of C. S. and Merlot cultivars, all names in the loading plots (Lee et al., 2009a). randomly permutated R2Y and Q2 values were lower than the Carbernet Sauvignon vs Merlot vs Steuben. Skin extracts of C. original R2Y and Q2 values, verifying that the original OPLS-DA S. were characterized by higher levels of proline, α- and β- model was reliable, not over-fitting. Other OPLS-DA models glucose, fructose, sucrose, citrate, malate, and lactate, and by were also validated in the permutation (data not shown). lower levels of valine, isoleucine, leucine, threonine, alanine, OPLS-DA score plots showed clear differentiations between tartarate, arginine, glutamine, and polyphenols, compared to those the skin and pulp extracts of grape cultivars in the pair-wise of Merlot (Fig. 3A). Although malate and tartarate levels showed comparison, demonstrating dependence of the grape metabolome no difference between the skins of C. S. and Steuben, the levels on the grape variety (data not shown). To identify the metabolites of proline, γ-aminobutyric acid (GABA), fructose, α- and β- that contributed to the differentiation in the OPLS-DA score plots, glucose, trigonelline, citrate, and lactate were higher in the skins corresponding loading plots were generated from the score plots of C. S. than in those of Steuben (Fig. 3B). In addition, valine, (Figs. 3 and 4). In the loading plot illustrated in Fig. 3A, the upper isoleucine, leucine, threonine, alanine, arginine, sucrose, and section represents the metabolites that were higher in skin extract polyphenol levels were higher in the skins of Steuben than those of C. S., whereas the lower section denotes the metabolites that of C. S. Compared to the skin extracts of Steuben, the skin were lower. Metabolites with statistically significant differences at extracts of Merlot showed higher levels of proline, GABA, citrate, the 95% confidence levels were indicated by their corresponding fructose, tartarate, α- and β-glucose, trigonelline, and lower levels J Korean Soc Appl Biol Chem (2014) 57(4), 491−502 495

Fig. 3 OPLS-DA loading plots derived from the 1H NMR skin extract spectra, leading to identification of metabolites responsible for the differentiation between grape cultivars. Upper sections of the loading plot represent metabolites that were higher, whereas the lower sections reveal metabolites that were lower in C. S. than in Merlot (A), in C. S. than in Steuben (B), and in Merlot than in Steuben (C). All OPLS-DA models were generated with one predictive component and one orthogonal component, resulting in high R2X values ranging from 80.5 to 95.6%, R2Y values from 99.1 to 99.7%, and Q2 values from 83.9 to 98.4%. The R2 values represent the goodness of fit of the models, and the Q2 value represents the predictability of the models. Stars denote protons of sugar and amino acid CH groups. Val, valine; Ile, isoleucine; Leu, leucine; Thr, threonine; Lac, lactate; Ala, alanine; Arg, arginine; Pro, proline; Gln, glutamine; Mal, malate; Tar, tartrate; Cit, citrate; GABA, γ-aminobutyrate; Glc, glucose; Frc, fructose; Suc, sucrose. of alanine, malate, and sucrose (Fig. 3C). in pulp than in the skin. GABA levels in pulp extracts of C. S. Differences in most metabolites of the pulp extracts between were also higher than in those of Steuben (Fig. 4B). In contrast, the grape cultivars were similar to those observed in the skin GABA levels between pulp extracts of Merlot and Steuben were extracts, except for GABA, malate and some other amino acids. not significantly different, but they were higher in the skin extract For example, GABA levels were higher in pulp extracts of C. S. of Merlot (Fig. 4C). Therefore, GABA content in grape cultivar than in those of Merlot, but the levels in skin extracts were not may not be dependent on the skin and pulp of the grape, but rather significantly different between the two (Fig. 4A). This may on the grape variety. indicate that for C. S., GABA is more accumulated or synthesized C. S. vs C. E. To characterize the variance of the metabolome in 496 J Korean Soc Appl Biol Chem (2014) 57(4), 491−502

Fig. 4 OPLS-DA loading plots derived from the 1H NMR spectra of pulp extract, leading to identification of metabolites responsible for the differentiation between grape cultivars. A, R2X=99.6% R2Y=99.7% Q2=77.9%; B, R2X =97.7% R2Y =99.3% Q2=98.2%; C, R2X =98.3% R2Y =99.8% Q2=97.8%. Stars denote protons of sugar and amino acid CH groups. Val, valine; Ile, isoleucine; Leu, leucine; Thr, threonine; Lac, lactate; Ala, alanine; Arg, arginine; Pro, proline; Gln, glutamine; Mal, malate; Tar, tartrate; Cit, citrate; GABA, g-aminobutyrate; Glc, glucose; Frc, fructose; Suc, sucrose. n.s. denotes no significant differences in the levels.

C. E. planted widely throughout Korea, we compared the tartarate were excluded from the model due to failed spectral metabolites in skin extracts of C. E. and C. S. The OPLS-DA alignment during the preprocessing of multivariate data sets. score plot revealed clear differentiation between skin extracts of Therefore, their levels were compared by the NMR integral areas C. E. and C. S., with high goodness of fit (R2X=0.86, R2Y=0.91) of the individuals after normalization. As a result, malate and and predictability (Q2=0.79) of the model (Fig. 5A). Most citrate levels were found to be significantly higher in the skin metabolites in the skin extracts of C. E. cultivar were lower than extracts of C. S. compared to those of C. E., whereas tartarate those of C. S., except for alanine, glutamine, and polyphenols levels showed no significant difference: malate (0.0071 in C.S. vs. (Fig. 5B). In particular, proline levels were markedly higher in C. 0.0029 in C. E., p <0.001), citrate (0.0005 in C. S., vs. 0.0003 in S., occurring in negligible amounts in the skin extract of C. E. C. E., p <0.05) and tartarate (0.0053 in C. S. vs. 0.0098 in C. E., (Fig. 1). In the loading plot between C. E. and C. S. illustrated in p =0.0605). Fig. 5B, NMR spectra corresponding to malate, citrate and Metabolic variation between white grapes cultivars. In the J Korean Soc Appl Biol Chem (2014) 57(4), 491−502 497

Fig. 5 OPLS-DA score (A and C) and loading (B and D) plots derived from the 1H NMR spectra of skin extracts of C. S. and C. E. cultivars (A and B), and Chardonnay and Seibel cultivars (C and D), demonstrating clear metabolic differences between two red grape cultivars, and between a red and white grape variety. The OPLS-DA models were generated with one predictive and one orthogonal component, resulting in high goodness of fit (R2X=0.86, R2Y=0.91 in panel A; R2X=0.68, R2Y=0.85 in panel C) and predictability (Q2=0.79 in panel A; Q2=0.50 in panel C) of the models. NMR peaks corresponding to malate, citrate, and tartrate were excluded from the model, because the spectral alignment failed due to large variations in chemical shift; hence, their levels were compared with spectral integral values in the text. Val, valine; Ile, isoleucine; Leu, leucine; Thr, threonine; Lac, lactate; Ala, alanine; Arg, arginine; Pro, proline; Gln, glutamine; Mal, malate; Tar, tartrate; Cit, citrate; GABA, g-aminobutyrate; Glc, glucose; Frc, fructose; Suc, sucrose. n.s. denotes no significant differences in the levels. comparison of the white grape cultivars, Chardonnay and Seibel, Fig. 6. The quantifications were calculated as the sum of the higher levels of malate, proline, threonine, and trigonelline along individual integral areas corresponding to each 1H NMR peak with lower levels of alanine, isoleucine, leucine, valine, and obtained from both skin and pulp extracts to avoid subtle variation polyphenols were observed in the skin extracts of Chardonnay, between the extracts, which could result from incomplete separation compared to those of Seibel (Fig. 5D); these were responsible for between the two during sample preparation. Interestingly, large the clear differentiation between these grapes shown in the OPLS- amounts of alanine and glutamine were found in the C. E. grapes DA score plot (Fig. 5C). used in this study (Fig. 6). Furthermore, the amounts of trans- Metabolic variation between white and red grape cultivars. feruloyl derivative in the skin extracts of the C. E. cultivar were Interestingly, the skin and pulp extracts of Chardonnay and C. S., 2.45- and 12.8-fold higher than those of Steuben and C. S. grape cultivars widely used to make white and red wines, cultivars, respectively (Fig. 7). respectively, were clearly discriminated, revealing clear metabolic differences (Supplementary Fig. 3A and C). Higher levels of α- and β-glucose, fructose, proline, and trigonelline were detected in Discussion the skin and pulp extracts of C. S., whereas higher levels of alanine, threonine, valine, isoleucine, leucine, arginine glutamine, Identification of a phenylpropanoid compound. Two major GABA, citrate, and malate were found in the skin and pulp doublets (H15, J =16.0 Hz, and H16, J =16.0 Hz) resolved at δ extracts of Chardonnay. These differences were responsible for 6.43 and δ 7.67 were significantly responsible for the differentiation the differentiation between the two grapes (Supplementary Fig. of the C. E, cultivar from the C. S. cultivar (Fig. 5B). The 3B and D). The subtle differences of polyphenol levels may be chemical structure of trans-5-O-feruloyl quinic acid as a typical due to the different polyphenol compositions of Chardonnay and phenylpropanoid is shown in Fig. 7A. The two major doublets C. S. correlated with protons resolved at d 6.94 (H21, d, J =8.3 Hz), d The quantitative differences in major metabolites are shown in 7.15 (H22, dd, J =8.3 Hz, J =2.0 Hz), and δ 7.22 (H18, d, J =2.0 498 J Korean Soc Appl Biol Chem (2014) 57(4), 491−502

Fig. 6 Metabolic pathway in grape berry (A) and quantitative dependence of major metabolites on grape variety (B-I). Letters in black in the metabolic pathway are metabolites observed in 1H NMR spectroscopy. Major metabolites were quantified by the sum of the integral areas corresponding to their 1H NMR peaks obtained both from skin and pulp extracts. Arrows in red indicate up-regulation for photosynthesis. Values on the Y-axis of the quantitative graphs (B-I) represent integral area values of normalized 1H NMR spectra. aDifferent letters in the graphs indicate significant differences in the levels by Duncan’s multiple range test at p <0.05. C.S. and Campbell represents C. S. and C. E.; P5C denotes pyrolline-5-carboxylate. E.C. 6.3.1.2, glutamine synthetase.

Hz) in 2D TOCSY spectrum (Fig. 7B). The major doublets of stresses. Metabolome of a grapevine, especially, could contribute H15 and H16 were typical for trans olefinic protons of a to wine quality. Our results reveal the metabolic dependence on phenylpropanoid, thus they were assigned to a trans-feruloyl grape variety and the possible mechanisms of plant resistance or derivative, consistent with the assignment in Chrysanthemum and adaptation to environmental stresses through metabolomics approach, grapevine (Ali et al., 2009; Leiss et al., 2009). The largest amounts demonstrating that grape metabolome could be influenced by of trans-feruloyl derivative were observed in the C. E. cultivar genetic and environmental factors. (Fig. 7C). Sugars in the grapes. As expected, glucose contents varied by Plant metabolites act as signaling/regulatory agents, compatible grape variety, with the highest levels detected in the C. S. cultivar, solutes, and antioxidants in defense against biotic or abiotic and the lowest in the Steuben cultivar (Fig. 6B). Moreover, J Korean Soc Appl Biol Chem (2014) 57(4), 491−502 499

Amino acids in the grape. Among amino acids, valine, isoleucine, and leucine levels did not trend toward unique differences by variety in the red grapes used in this study. The levels of all amino acids, including valine, isoleucine, leucine, alanine, threonine, and glutamine, with the exception of proline, were higher in the white grape variety of Chardonnay than in the red grape variety of C. S. (Supplementary Fig. 3). Amino acids play important roles in wine made from the grapes, because they are simultaneously synthesized by wine yeast and bacteria during wine fermentation (Lee et al., 2009a; Son et al., 2009c). Amino acids in the grape berries are also involved in the synthesis of higher alcohols during wine fermentation via the Ehrlich mechanism, hence playing a crucial role in the sensory characteristics and quality of the wines produced (Bell and Henschke, 2005). For example, valine, phenylalanine, leucine, and isoleucine are transformed into isobutyl alcohol, 2- phenylethanol, isoamyl alcohol, and active amyl alcohol, respectively, by Saccharomyces cerevisiae (Dickinson et al., 1997; 1998; 2000; 2003). Organic acids and their metabolic associations in the grape. GABA, arginine, proline, glutamine, 2-oxoglutarate, and aspartate are synthesized from glutamate occupying a central position in amino acid metabolism in plants (Forde and Lea, 2007). Proline appears to play a role in nitrogen storage, especially as a major component of the storage proteins, and is one of the compounds sensitive to abiotic stresses in the plant, such as drought, salt stress, and cold stress. Proline synthesis or accumulation increases under stress to adjust osmosis in the plant cells (Xin and Browse, 1998; Diaz et al., 2005; Huang et al., 2009; Perez-Tornero et al., 2009; Widodo et al., 2009). Moreover, it is well known that proline levels in grape berries increase with increasing sun- exposure and decreasing rainfall in the vineyard (Son et al., 2008; Lee et al., 2009b; Son et al., 2009b). Furthermore, the ratio of proline/arginine can serve as a marker of varietal differences in grapes and has been shown to range from 0.5 in Pinot Noir to 11 in C. S.. In the present study, the marked dependence of proline content on grape variety was also observed (Fig. 6). Of the red grape cultivars used in the present study, C. S. had the highest levels of proline, resulting in the highest ratio of proline/arginine (Fig. 6G and I). In addition, the proline level and proline/arginine ratio were higher in the white grape Chardonnay than in the red Fig. 7 Chemical structure of a typical phenylpropanoid derivative, trans- 1 1 grape Merlot. The amount of proline and the ratio of proline/ 5-O-feruloyl quinic acid (A), H- H TOCSY correlation of trans-feruloyl derivative in skin extracts of C. E. cultivar (B) and mean 1H NMR arginine in the grapes showed the same trends among the spectra of H15 protons of trans-feruloyl derivative obtained from skin cultivars. Recently, higher levels of proline were reported in C. S. extracts of each variety (C). C. E.; C. S. grapes and wine than in Shiraz grapes and wine (Du et al., 2003; Son et al., 2008). The present study therefore suggests that proline sucrose levels were the highest in the Steuben cultivar, compared alone could be a good marker of varietal difference, both in grape to the other cultivars (Figs. 5 and 6). These results indicated lower berries and grape wines (Son et al., 2008; 2009c), because while metabolism of sucrose to glucose or fructose in Steuben cultivar arginine is metabolized to ornithine by wine yeast and bacteria compared to those in the other grapes. These variations in glucose during wine fermentation, proline is not used by wine yeast (Lee and sucrose levels revealed intrinsically different characteristics of et al., 2009a). Proline is also recognized as an important each grape variety in terms of sugar accumulation by photosynthesis, metabolite in grape wine, because it provides the wine sensory because the grapes used in this study were all grown in the same property of “body”, viscous mouthfeel (Skogerson et al., 2009). greenhouse. 500 J Korean Soc Appl Biol Chem (2014) 57(4), 491−502

Grape metabolite dependence on cultivar and association with In addition to the increased resistance to environmental stress growing environment. Interestingly, the levels of alanine and inferred by large production of alanine and glutamine in the C. E. glutamine showed a reverse association with the levels of proline cultivar, the great accumulation of trans-feruloyl derivatives of and GABA. For example, among red grape cultivars, alanine and phenylpropanoid in the C. E, cultivar may also play a role (Fig. 7). glutamine levels were the highest in the C. E. cultivar, whereas Recently, significantly increased caffeic acid content has been proline and GABA levels were the lowest. In contrast, the highest found in the grape cultivar Regent, compared with Trincadeira, levels of GABA and proline, along with the lowest levels of which are resistant and susceptible to fungi (Figueiredo et al., alanine and glutamine, were found in the C. S. cultivar. Furthermore, 2008). Caffeic acid, phenolic compound or phenylpropanoid, although glucose levels in C. E. were the lowest, alanine and produced from shikimic acid and a key intermediate in lignin glutamine levels were the highest. This could be due to the biosynthesis, has antimicrobial activity and inhibits the germination conversion of glutamate to glutamine and alanine rather than to of zoospores of the pathogenic fungi Phytophthora spp. (Dixon, proline and GABA, instead of increased alanine synthesis from 2001; Iwaki et al., 2006; Widmer and Laurent, 2006). Furthermore, glucose (Fig. 6). Figueiredo et al. (2005) suggested that constitutive the phenylpropanoid derivatives 5-O-feruloyl quinic acid and 3- accumulation of alanine and glutamine in the grape cultivar, O-caffeoyl quinic acid, as well as chlorogenic acid, reportedly Regent (Vitis vinifera L. resistant to fungi), is associated with improved host plant resistance to thrips in ornamentals and crops increased resistance to pathogens compared to Trincadeira (Leiss et al., 2009). (susceptible to fungi). Constitutively higher production of The activation of responses associated with the onset of glutamine has also been reported in wheat cultivar resistant to induced resistance, including the oxidation and polymerization of Fusarium head blight (FHB) than in a wheat cultivar susceptible preexisting phenols and the synthesis of new phenolic compounds to FHB (Hamzehzarghani et al., 2005). In addition, the chloroplast via activation of the phenylpropanoid pathway, has been reported glutamine synthetase-overexpressing transgenic rice clearly in endophytic bacterium-treated cucumbers (Benhamou et al., showed enhanced tolerance to salt stress compared to the control 2000), chickpeas (Singh et al., 2003), and grapevines (Barka et al., plant, leading to increased photorespiration capacity (Hoshida et 2000; 2002), resulting in stimulation of a defense reaction against al., 2000). The overexpression of glutamine synthetase was also the pathogen. Host defense response could be achieved through thought to contribute to cold-stress resistance in rice plants the formation of structural barriers, such as thickened cell wall (Hoshida et al., 2000), because cold stress causes photooxidative papillae due to the deposition of callose, or by the accumulation damage (Wise and Naylor, 1987). Numerous reports have shown of phenolic compounds or phenylpropanoids at the site of that alanine accumulation in plants and animals is accompanied pathogen attack (Benhamou et al., 2000; Compant et al., 2005). by exposure to a variety of stress conditions such as anoxia, Moreover, the improvement of cold tolerance in a plant growth- osmotic stress, extreme temperature, exposure to heavy-metal promoting rhizobacterium-treated Chardonnay cultivar has been ions, water shortage, and sinusoidally varying magnetic fields reported. The accumulations of starch, proline, and phenolic (Storey and Storey, 1986; Nissim et al., 1992; Andreev et al., compounds were correlated with the enhancement in cold 1996; Blasco and Puppo, 1999; Du et al., 1999; Monselise et al., tolerance (Barka et al., 2006). For these reasons, Barka et al. 2003). (2006) suggested that reaction to cold stress of plant could be In general, the grape cultivars used in the present study, with similar to that of pathogenic attack, indicating linkage of the exception of C. E., Steuben, and Seibel cultivars, cannot be grown method of plant reaction to both biotic stress and abiotic stress. It in the wild vineyards of Korea, other than in greenhouses, mainly is therefore likely that phenylpropanoids are closely related to due to cold or freezing stress during the winter season and large phenolic compounds and also play roles in plant defense. The variations in seasonal temperatures. In Youngcheon City, the most identification and roles of phenylpropanoids in plants have been popular area for growing grapevine in Korea, the maximum daily extensively investigated (Liang et al., 2006; Leiss et al., 2009). In average was 32oC in July, whereas the minimum daily average the present study, the large accumulation of trans-feruloyl was −6.3oC in February 2008 (35.58 N and 128.57 E, Korea) derivative in C. E. could therefore be related to the constitutively (Source: Korea Meteorological Administration). Notably, the enhanced resistance to pathogenic fungi, as well as to environmental minimum daily average temperature of −6.3oC in Youngcheon stress such as cold stress. Moreover, the levels of trans-feruloyl City is very cold, compared to that of 2.7oC in January 2008 in derivative were highest in wines vinified with C. E., compared to Bordeaux (44.83 N and 0.70 W, France), and to that of 0.5oC in the wines vinified with C. S. and Shiraz cultivars in our previous January 2008 in Reims (49.18 N and 4.02 E, France) (Source: study (Son et al., 2008). Although the trans-feruloyl derivative Météo France), and may cause freezing stress in the grapevines in was not assigned in the previous study and thus remained as an Korea. C. S. and Chardonnay cultivars grew well in both unknown compound, a large amount of trans-feruloyl derivative Bordeaux and Reims. Therefore, the C. E. cultivar is likely to be in wines vinified with C. E. cultivar was present, which had been the most constitutively adapted or most strongly resistant to cold extracted from the grape berries during wine fermentation. or freezing stress among the grape cultivars used in this study, Recently, it was reported that the Chardonnay cultivar appeared resulting from large accumulation of alanine and glutamine. to be more resistant to both water deficit and salinity than the C. J Korean Soc Appl Biol Chem (2014) 57(4), 491−502 501

S. cultivar, as determined at the physiological level and by aspartate and alanine aminotransferase in Ruditapes philippinarum 122 proteome analysis (Vincent et al., 2007). Therefore, higher levels (Mollusca : Bivalvia). Comp Biochem Phys C , 253–63. p p Choi YH, Tapias EC, Kim HK, Lefeber AWM, Erkelens C, Verhoeven JTJ et of alanine ( <0.05), glutamine ( <0.05), and feruloyl quinic acid al. (2004) Metabolic discrimination of Catharanthus roseus leaves (p <0.01) in Chardonnay, detected in the present study, may infected by phytoplasma using 1H NMR spectroscopy and multivariate indicate a contribution to the stronger resistance of the Chardonnay data analysis. J Plant Physiol 135, 2398–410. cultivar to environmental stress, compared to the C. S. cultivar. Compant S, Reiter B, Sessitsch A, Nowak J, Clement C, and Barka EA (2005) Endophytic colonization of Vitis vinifera L. by plant growth Furthermore, comparing the white grape cultivars of Chardonnay promoting bacterium Burkholderia sp strain PsJN. Appl Environ Microb and Seibel, the higher levels of alanine, glutamine, and trans- 71, 1685–93. feruloyl derivative detected in the Seibel cultivar might provide Deluc LG, Grimplet J, Wheatley MD, Tillett RL, Quilici DR, Osborne C et al. evidence of the stronger resistance of Seibel cultivars to environ- (2007) Transcriptomic and metabolite analyses of Cabernet Sauvignon grape berry development. BMC Genomics 8, 429. mental stress, such as cold stress (Figs. 9 and 10), because the Diaz P, Borsani O, Marquez A, and Monza J (2005) Osmotically induced Seibel cultivar is able to survive the minimum daily average proline accumulation in Lotus corniculatus leaves is affected by light and temperature of −6.3oC in a wild Korean vineyard, as described nitrogen source. Plant Growth Regul 46, 223–32. above, whereas the Chardonnay cultivar cannot. Of the grape Dickinson JR, Eshantha L, Salgado J, and Hewlins MJE (2003) The catabolism of amino acids to long chain and complex alcohols in cultivars used in this study, C. E., Steuben and Seibel cultivars are Saccharomyces cerevisiae. J Biol Chem 278, 8028–34. crosses of two or more grape species, whereas C. S., Merlot and Dickinson JR, Harrison SJ, and Hewlins MJE (1998) An investigation of the Chardonnay cultivars are single species (see Materials and Methods). metabolism of valine to isobutyl alcohol in Saccharomyces cerevisiae. J 273 trans Biol Chem , 25751–6. The accumulations of alanine, glutamine, and -feruloyl Dickinson JR, Harrison SJ, Dickinson JA, and Hewlins MJE (2000) An derivative in the crossbred grape cultivars would therefore indicate investigation of the metabolism of isoleucine to active amyl alcohol in the resistance traits of the grapes in the present study against cold Saccharomyces cerevisiae. J Biol Chem 275, 10937–42. or freezing stress, consistent with results of a report of multiple Dickinson JR, Lanterman MM, Danner DJ, Pearson BM, San P, Harrison SJ Vitis et al. (1997) A C-13 nuclear magnetic resonance investigation of the crosses involving wild species (Eibach and Toepfer, 2003). metabolism of leucine to isoamyl alcohol in Saccharomyces cerevisiae. The present study revealed clear differences in the metabolome The J Biol Chem 272, 26871–8. of grape berries according to the grape cultivar, and metabolic Dieterle F, Ross A, Schlotterbec G, and Senn H (2006) Probabilistic quotient association with adaptation or resistance against environmental normalization as robust method to account for dilution of complex biological mixtures. Application in 1H NMR metabonomics. Anal Chem stress, through a metabolomics approach with global profiled 78, 4281–90. metabolites. In particular, metabolic phenotyping of grape berries Dixon RA (2001) Natural products and plant disease resistance. Nature 411, could provide a better understanding of the potential effects of the 843–7. grape metabolome on grape wine. Du Toit PG, Dry PR, and Loveys BR (2003) A preliminary investigation on partial rootzone drying (PRD) effects on grapevine performance, nitrogen assimilation and berry composition. S Afr J Enol Vitic 24, 43– 54. References Du YC, Nose A, and Wasano K (1999) Effects of chilling temperature on photosynthetic rates, photosynthetic enzyme activities and metabolite Ali K, Maltese F, Zyprian E, Rex M, Choi YH, and Verpoorte R (2009) NMR levels in leaves of three sugarcane species. Plant Cell Environ 22, 31–24. metabolic fingerprinting based identification of grapevine metabolites Eibach R and Toepfer R (2003) Success in resistance breeding ‘Regent’ and associated with downy mildew resistance. J Agr Food Chem 57, 9599– its steps into the market. Acta Hortic 603, 68–91. 606. Figueiredo A, Fortes AM, Ferreira S, Sebastiana M, Choi YH, Sousa L et al. Andreev VY, Generozova IP, Polyakova LI, and Vartapetian BB (1996) (2008) Transcriptional and metabolic profiling of grape (Vitis vinifera L.) Glycolytic activity and resistance to anoxia in excised roots of Pisum leaves unravel possible innate resistance against pathogenic fungi. J Exp sativum L. Russ J Plant Physl+ 43, 236–41. Bot 59, 3371–81. Barka EA, Belarbi A, Hachet C, Nowak J, and Audran JC (2000) Forde BG and Lea PJ (2007) Glutamate in plants: metabolism, regulation, and Enhancement of in vitro growth and resistance to gray mould of Vitis signaling. J Exp Bot 58, 2339–58. vinifera co-cultured with plant growth-promoting rhizobacteria. FEMS Hamzehzarghani H, Kushalappa AC, Dion Y, Rioux S, Comeau A, Yaylayan Microbiol Lett 186, 91–5. V et al. (2005) Metabolic profiling and factor analysis to discriminate Barka EA, Gognies S, Nowak J, Audran JC, and Belarbi A (2002) Inhibitory quantitative resistance in wheat cultivars against fusarium head blight. effect of endophyte bacteria on Botrytis cinerea and its influence to Physiol Mol Plant P 66, 119–33. promote the grapevine growth. Biol Control 24, 135–42. Holmes E, Loo RL, Stamler J, Bictash M, Yap IKS, Chan Q et al. (2008) Barka EA, Nowak J, and Clement C (2006) Enhancement of chilling Human metabolic phenotype diversity and its association with diet and resistance of inoculated grapevine plantlets with a plant growth- blood pressure. Nature 453, 396–400. promoting rhizobacterium, Burkholderia phytofirmans strain PsJN. Appl Hoshida H, Tanaka Y, Hibino T, Hayashi Y, Tanaka A, and Takabe T (2000) Environ Microb 72, 7246–52. Enhanced tolerance to salt stress in transgenic rice that overexpresses Bell SJ and Henschke PA (2005) Implications of nitrogen nutrition for grapes, chloroplast glutamine synthetase. Plant Mol Biol 43, 103–11. fermentation and wine. Aust J Grape Wine R 11, 242–95. Hotelling H (1931) The generalization of Student's ratio. Ann Math Statist 2, Benhamou N, Gagne S, Le Quere D, and Dehbi L (2000) Bacterial-mediated 360–78. induced resistance in cucumber: Beneficial effect of the endophytic Huang X, Xiao X, Zhang S, Korpelainen H, and Li C (2009) Leaf bacterium Serratia plymuthica on the protection against infection by morphological and physiological responses to drought and shade in two Pythium ultimum. Phytopathology 90, 45–6. Populus cathayana populations. Biol Plantarum 53, 588–92. Blasco J and Puppo J (1999) Effect of heavy metals (Cu, Cd and Pb) on Iwaki K, Koya-Miyata S, Kohno K, Ushio S, and Fukuda S (2006) 502 J Korean Soc Appl Biol Chem (2014) 57(4), 491−502

Antimicrobial activity of Polygonum tinctorium Lour: extract against 131–40. oral pathogenic bacteria. J Nat Med 60, 121–5. Skogerson K, Runnebaum R, Wohlgemuth G, de Ropp J, Heymann H, and Lee JE, Hwang GS, Lee CH, and Hong YS (2009a) Metabolomics reveals Fiehn O (2009) Comparison of gas chromatography-coupled time-of- alterations in both primary and secondary metabolites by wine bacteria. flight mass spectrometry and 1H nuclear magnetic resonance Agr Food Chem 57, 10772–83. spectroscopy metabolite identification in white wines from a sensory Lee JE, Hwang GS, Van Den Berg F, Lee CH, and Hong YS (2009b) study investigating wine body. J Agr Food Chem 57, 6899–907. Evidence of vintage effects on grape wines using 1H NMR-based Son HS, Hwang GS, Ahn HJ, Park WM, Lee CH, and Hong YS (2009a) metabolomic study. Anal Chim Acta 648, 71–6. Characterization of wines from grape varieties through multivariate Leiss KA, Maltese F, Choi YH, Verpoorte R, and Klinkhamer PGL (2009) statistical analysis of 1H NMR spectroscopic data. Food Res Int 42, Identification of chlorogenic acid as a resistance factor for thrips in 1483–91. chrysanthemum. J Plant Physiol 150, 1567–75. Son HS, Hwang GS, Kim KM, Ahn HJ, Park WM, van den Berg F et al. Liang YS, Kim HK, Lefeber AWM, Erkelens C, Choi YH, and Verpoorte R (2009b) Metabolomic studies on geographical grapes and their wines (2006) Identification of phenylpropanoids in methyl jasmonate treated using 1H NMR analysis coupled with multivariate statistics. J Agr Food Brassica rapa leaves using two-dimensional nuclear magnetic resonance Chem 57, 1481–90. spectroscopy. J Chromatogr A 1112, 148–55. Son HS, Hwang GS, Kim KM, Kim EY, van den Berg F, Park WM et al. Monselise EB, Parola AH, and Kost D (2003) Low-frequency electromagnetic (2009c) 1H NMR-Based metabolomic approach for understanding the fields induce a stress effect upon higher plants, as evident by the fermentation behaviors of wine yeast strains. Anal Chem 81, 1137–45. universal stress signal, alanine. Biochem Bioph Res Co 302, 427–34. Son HS, Kim KM, van den Berg F, Hwang GS, Park WM, Lee CH et al. Nicholson JK, Lindon JC, and Holmes E (1999) ‘Metabonomics’: (2008) 1H Nuclear magnetic resonance-based metabolomic characterization Understanding the metabolic response of living systems to of wines by grape varieties and production areas. J Agr Food Chem 56, pathophysiological stimuli via multivariatae statistical analysis of 8007–16. biological NMR spectroscopic data. Xenobiotica 29, 1181–9. Storey KB and Storey JM (1986) Freeze tolerant frogs-cryoprotectants and Nissim I, Hardy M, Pleasure J, and States B (1992) A mechanism of glycine tissue metabolism during freeze thaw cycles. Can J Zoolog 64, 49–56. and alanine cytoprotective action-stimulation of stress-induced HSP70 Trygg J and Wold S (2002) Orthogonal projections to latent structures (O- messenger-RNA. Kidney Int 42, 775–82. PLS). J Chemometr 16, 119–28. Pereira GE, Gaudillere JP, Van Leeuwen C, Hilbert G, Lavialle O, Maucourt Veselkov KA, Lindon JC, Ebbels TMD, Crockford D, Volynkin VV, Holmes M et al. (2005) H-1 NMR and chemometrics to characterize mature E et al. (2009) Recursive segment-wise peak alignment of biological 1H grape berries in four wine-growing areas in Bordeaux, France. J Agr NMR spectra for improved metabolic biomarker recovery. Anal Chem Food Chem 53, 6382–9. 81, 56–66. Pereira GE, Gaudillere JP, Van Leeuwen C, Hilbert G, Maucourt M, Deborde Vincent D, Ergul A, Bohlman MC, Tattersall EAR, Tillett RL, Wheatley MD C et al. (2006) H-1 NMR metabolite fingerprints of grape berry: et al. (2007) Proteomic analysis reveals differences between Vitis vinifera Comparison of vintage and soil effects in Bordeaux grapevine growing L. cv. Chardonnay and cv. Cabernet Sauvignon and their responses to areas. Anal Chim Acta 563, 346–52. water deficit and salinity. J Exp Bot 58, 1873–92. Pereira GE, Gaudillere JP, Van Leeuwen C, Hilbert G, Maucourt M, Deborde Widmer TL and Laurent N (2006) Plant extracts containing caffeic acid and C et al. (2007) 1H NMR metabolic profiling of wines from three rosmarinic acid inhibit zoospore germination of Phytophthora spp. cultivars, three soil types and two contrasting vintages. J Int Sci Vigne pathogenic to Theobroma cacao. Eur J Plant Pathol 115, 377–88. Vin 41, 103–9. Widodo, Patterson JH, Newbigin E, Tester M, Bacic A, and Roessner U Perez-Tornero O, Tallon CI, Porras I, and Navarro JM (2009) Physiological (2009) Metabolic responses to salt stress of barley (Hordeum vulgare L.) and growth changes in micropropagated Citrus macrophylla explants due cultivars, Sahara and Clipper, which differ in salinity tolerance. J Exp to salinity. J Plant Physiol 166, 1923–33. Bot 60, 4089–103. Simoh S, Quintana N, Kim HK, Choi YH, and Verpoorte R (2009) Metabolic Wise RR and Naylor AW (1987) Chilling-enhanced photooxidation-evidence changes in Agrobacterium tumefaciens-infected Brassica rapa. J Plant for the role of singlet oxygen and superoxide in the breakdown of Physiol 166, 1005–14. pigments and endogenous antioxidants. Plant Physiol 83, 278–82. Singh UP, Sarma BK, and Singh DP (2003) Effect of plant growth-promoting Xin ZG and Browse J (1998) eskimo1 mutants of Arabidopsis are rhizobacteria and culture filtrate of Sclerotium rolfsii on phenolic and constitutively freezing tolerant. Proc Natl Acad Sci USA 95, 7799–804. salicylic acid contents in chickpea (Cicer arietinum). Curr Microbiol 46,