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High-throughput NMR based metabolic profiling of Braeburn apple in relation to internal browning
Postharvest Biology and Technology 80 (2013) 18–24
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Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio
High-throughput NMR based metabolic profiling of Braeburn apple in relation to internal browning Thomas Vandendriessche a , Hartmut Schäfer b , Bert E. Verlinden c , Eberhard Humpfer b , Maarten L.A.T.M. Hertog a,∗ , Bart M. Nicolaï a,c a
BIOSYST-MeBioS, University of Leuven, W. de Croylaan 42, 3001 Leuven, Belgium Application Method Development Group, Bruker BioSpin GmbH, Silberstreifen, 76287 Rheinstetten, Germany c Flanders Centre of Postharvest Technology, De Croylaan 42, 3001 Heverlee, Belgium b
a r t i c l e
i n f o
Article history: Received 26 November 2012 Accepted 20 January 2013 Keywords: Apple Braeburn Browning Metabolic profiling NMR
a b s t r a c t NMR is a valuable tool for metabolomics due to its short analysis time and reproducibility. However, this technique remains little used due to its high cost. Recently, cheaper NMR machines for high-throughput screening have been developed. In this study, NMR was used to study the effect of several pre- and postharvest factors on apple metabolite levels during air and controlled atmosphere storage, including metabolic changes related to the incidence of internal browning. The results show that the selected fertilizer treatments and fruit side (green versus red side) did not affect the metabolite levels. However, the different postharvest storage conditions (optimal CA and brown inducing CA) did result in significant changes in metabolite levels. In addition, differences (e.g., pyruvate, citrate, fumarate, alanine, chlorogenate, methanol, ethanol, acetaldehyde and acetoine) between brown and unaffected apples stored under the applied CA conditions could be demonstrated. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Metabolic analysis has been an integral part of plant and postharvest science and plays an essential role in the field of postharvest systems biology (Hertog et al., 2011). In practice, only a fraction of an estimated 150,000 of 1,000,000 metabolites is usually evaluated (Allwood et al., 2008; Nadella et al., 2012). Several extraction and analysis techniques are currently available to evaluate the complex plant matrices containing diverse groups of primary and secondary metabolites of considerable importance to plant function as well as horticultural and postharvest value. The analytical platforms differ in metabolite selectivity, resolution, analysis speed, and potential throughput (Fernie et al., 2004; Dunn et al., 2005; Allwood et al., 2008; Oms-Oliu et al., 2011; Lee et al., 2012b; Nadella et al., 2012). In addition, the extraction method of choice influences the point of focus of the analysis, biasing its outcome. Mass spectrometry and NMR are the current preferred analytical methods as they are based on the intrinsic physical properties of compounds which can be measured in a reproducible way (Dunn et al., 2005). Both NMR and mass spectrometry based techniques have their advantages and disadvantages. Mass spectrometry based
∗ Corresponding author. Tel.: +32 16322376; fax: +32 16322955. E-mail address: [email protected] (M.L.A.T.M. Hertog). 0925-5214/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.postharvbio.2013.01.008
techniques such as GC–MS and LC–MS, have a higher sensitivity than NMR and therefore may detect metabolites that are present in a concentration below the detection limit of NMR (Smolinska et al., 2012). Nevertheless, NMR has some advantages over these techniques. NMR-based metabolomics can be a valuable tool for studying biological systems as it provides a ‘holistic view’ of the metabolites (Kim et al., 2011). The main advantage of NMR is its analytical reproducibility in combination with a minimum of sample preparation (if any at all). As NMR does not rely on pre-separation of the analytes through techniques like chromatography or electrophoresis, the analysis time remains short providing potential for high-throughput analysis (Smolinska et al., 2012). The main downside of NMR, limiting its application in plant science laboratories, is its relatively high cost. With the recent development of dedicated NMR machines optimized for highthroughput screening (e.g., Spraul et al., 2009), the cost per sample is being reduced and specialized service laboratories can bring the technique within reach of smaller research laboratories and the industry for metabolite analyses and authenticity testing. To test the feasibility of using such high-throughput NMR devices for postharvest research, metabolite changes during storage and in relation to internal browning of ‘Braeburn’ apples were screened by NMR. The impact of different pre- and postharvest factors on the measured metabolite profiles were linked to the current understanding of internal browning.
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2. Materials and methods 2.1. Fruit materials Apples (Malus × domestica Borkh) cv. Braeburn were used for this study. Two cultivation treatments were applied: optimal fertilization (treated with 30 kg/ha calcium nitrate, 20 kg/ha phosphorus, no potassium) and suboptimal fertilization (treated with 30 kg/ha ammonium nitrate, 20 kg/ha phosphorus, 80 kg/ha potassium). The fertilization was applied on March 24, 2010. The apples were harvested on October 27, 2010, which was considered to be the optimal harvest date for long-term commercial storage for Belgium, as determined by the Flanders Centre of Postharvest Technology (VCBT, Belgium) (Peirs et al., 2001). Afterwards, apples from each fertilization treatment were stored under two types of controlled atmospheres (CA): browning inducing storage conditions (BCA; 1 kPa O2 , 5 kPa CO2 , 1 ◦ C), starting on October 29 shortly after harvest, and optimal storage conditions starting with a 3 weeks cooling period at 1 ◦ C under regular air and CA conditions being applied from November 16 onwards (OCA; 2.5 kPa O2 , 0.7 kPa CO2 , 1 ◦ C). NMR measurements were taken on samples collected at harvest (October 27), at the start of the delayed optimal CA application (November 16; regular air cold stored fruit), and four months after harvesting (March 4; BCA and OCA stored fruit). For the first two sampling dates a difference was made between samples coming from the green and red side of the fruit. Apples analyzed from the final sampling date showed internal browning. At this stage discrimination was made between tissue showing signs of internal browning versus unaffected tissue. 2.2. Determination of dry matter content and mineral composition Four replicate fruit samples of 20 apples each were washed, sliced (∼5 mm thick) and dried in an oven at 100 ◦ C for 48 h, after which the amount of dry matter was determined. The dried tissue was subsequently pooled and ground into fine powder (<1 mm particle size) for mineral analysis. Leaf and fruit powder was sent to a commercial laboratory for mineral composition analysis (Lancrop Laboratories, UK). The minerals analyzed were: Ca, Mg, Mn, B, Cu, Mo, Fe, N, P, K, S, Na, Cl and NO3 . The extraction of minerals was done by incubating the powder at 500 ◦ C, and digesting using concentrated hydrochloric acid. Nitrogen was extracted and quantified using the Dumas method. Chloride was extracted by wet digestion of the powder with concentrated nitric acid, potassium permanganate and silver nitrate. Chloride concentration was determined by titration with potassium thiocyanate. Nitrate was extracted with water and its concentration was determined using a nitrate selective electrode. The concentration of the other minerals was determined using an inductively coupled plasma analyzer (ICP). 2.3. NMR analysis Apple fruit samples were juiced and buffer was added (90% juice with 10% buffer). The buffer used contained 0.1% of TSP (sodium salt of 3-(trimethylsilyl)-propionate acid-d4 ) and 0.013% of sodium azide to suppress microorganism activity. A Bruker JuiceScreener NMR spectrometer was used for Spin Generated Fingerprint (SGF) Profiling (Rinke et al., 2007; Spraul et al., 2008, 2009). SGF Profiling is based on an Avance 400 NMR spectrometer with a 9.4-T UltrashieldTM Plus magnet and utilizes flow-injection NMR (BESTTM NMR) with a 4-mm flow-cell probe with Z-gradient and a Gilson liquids handler for sample storage, preparation and transfer. Samples were provided in bar-coded containers keeping them at low temperature (4 ◦ C) prior to injection.
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A heated sample transfer line from the Gilson unit to the probe and a heated capillary inside the probe pre-equilibrate the sample to the desired temperature (27 ◦ C) during the transfer. No additional temperature equilibration time was needed within the measurement cell. The overall experimental procedure was fully automated and controlled by Bruker’s SampleTrack software including temperature adjustment, tuning and matching, locking, shimming and the optimization of the pulses and presaturation power for each individual sample. The metabolite identification was based on an extensive spectroscopic database including over 12,000 NMR spectra from authentic juices and small molecule compounds for analysis of unknown ingredients. Metabolite quantification was based on integration of the 1 H NMR spectra of the identified compounds. Based on regression analysis of juice training data sets, additional parameters such as titratable acidity (Total Acid Juice) were evaluated. Since the pH of the samples was brought to pH 3, all organic acids were measured in the undissociated form (e.g. pyruvic acid). 2.4. Data analysis Principal component analysis (PCA) was applied to explore the multivariate dataset looking for correlations between metabolites. Partial Least Squares Discriminant Analysis (PLS-DA) was applied to investigate the effect of different pre- and postharvest factors on the measured metabolite profiles. PLS generates a multivariate regression model by projecting the independent variables and the dependent response variables to a new variable space as defined by the latent variables, maximizing the covariance between the dependent and independent variables. In PLS-DA the response variable is a binary variable. Based on the PLS-DA analysis, the VIP scores (Variable Importance in the Projection) were calculated (Wold et al., 2001). These scores are a measure of the importance of each explanatory variable in the model. Since the average of the squared VIP scores equals 1, ‘the greater than one rule’ was used as a criterion for variable selection (Chong and Jun, 2005). Multivariate statistics was applied using The Unscrambler® X.1 (CAMO Software, 2011) and the PLS Toolbox of MATLAB (MathWorks, Massachusetts, USA). To assess the effect of postharvest conditions on the NMR derived variables an analysis of variance (ANOVA) was carried out using SAS software, Version 9.2 (SAS Institute Inc., Cary, NC, USA). Duncan’s multiple range test was used to detect differences between different postharvest conditions on a 95% confidence level. Residuals were tested for heteroscedasticity in which case a log transform of the data was carried out to stabilize their variance. 3. Results and discussion The effects of pre- and postharvest factors on apple metabolomics were studied by NMR and chemometrics. Several preharvest factors affecting the development of browning disorders in apple fruit have been reported (Ferguson et al., 1999; Franck et al., 2007). Fertilization is known to potentially affect the incidence of browning (Rabus and Streif, 2000; Hunsche et al., 2003). The fertilization treatments applied in this study did not affect the metabolite level (data not shown). Of the minerals measured only potassium was affected. Apples grown under the optimal fertilization treatment had a 10% higher potassium content than the apples grown under suboptimal fertilization. As these fruit were the first year’s harvest after the fertilization was started, more pronounced effects are expected in the following years. Neither was a difference on metabolite level observed between samples taken from the green or red side of the fruits (data not shown). The postharvest conditions, however, resulted in clear
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Fig. 1. Principal component analysis of all data with the first three principal components describing respectively 28%, 21% and 10% of the observed variation.
Fig. 2. Values for the NMR derived variables under the different postharvest conditions. Bars represent the mean with the error bar indicating the standard error of the mean (at harvest and after 3 weeks storage n = 20; for the other treatments n = 10). The letters above each bar indicate the significance calculated as described in Section 2.
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Fig. 3. Partial Least Squares Regression analysis of the air-stored fruit data. The loadings are represented by blue spheres from which the size corresponds to the Variable Importance in the Projection value. The fruit samples are represented by their scores on the first two latent variables with different symbols discriminating between samples at harvest and after three weeks storage. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fig. 4. Partial Least Squares Regression analysis of the CA stored fruit data. The NMR derived variables are used as explaining variables to describe the difference in storage condition (OCA versus BCA) and tissue status (brown versus unaffected) after 4 months of storage. The loadings for the various NMR derived variables are represented by green dots. The fruit samples are represented by their scores on the first two latent variables with different symbols discriminating between samples from different storage conditions and tissue state. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
metabolic differences. Fig. 1 shows the PCA analysis of all data. The samples are colored according to storage conditions. A clear distinction between samples taken at harvest and samples stored for three weeks in air at 1 ◦ C can be observed in all score plots. Furthermore, both tissue of apples stored for 4 months under BCA as well as those stored under OCA conditions showing internal browning form two clearly separated groups. Finally, unaffected tissue of apples stored at these conditions tend to lie close to each other, but both form two distinct groups as well. These results indicate that under the different storage conditions used, clear shifts in metabolic activity occurred. A PLS-DA analysis was performed to study these changes in more detail (Figs. 3 and 4). Note that, as the metabolic profiling of brown tissue is a post hoc analysis because of its destructive nature, the corresponding results should be interpreted with caution as it is not always possible to distinguish between cause and results.
patterns (Fig. 2) and appear to be highly correlated as indicated by the position of their loadings (Fig. 3). Fructose was the dominating sugar, followed by sucrose and glucose (Figs. 2 and 3). The concentration of fructose and glucose changed significantly when the apples were stored for 3 weeks in air at 1 ◦ C. During storage, the fructose concentration tended to increase (Fig. 2). This is in agreement with Suni et al. (2000), but not with Roth et al. (2007) who showed constant fructose levels in time. The glucose concentration also increased during storage. This was probably the result of starch degradation which is highly related to climacteric and physiological changes in the apple (Veberic et al., 2010). Two other metabolites characteristic for storage under air at 1 ◦ C were methanol and d-galacturonate (Figs. 3 and 5) both increasing in concentration (Fig. 2). During postharvest storage, fruit firmness is lost as a result of pectin breakdown by, amongst others, pectin methyl esterase (PME) and endopolygalacturonases (PG) (Johnston et al., 2002). PME catalyses the demethylesterification of homogalacuronans, releasing acidic pectins and methanol (Micheli, 2001). This promotes the action of the pH-dependent PG cleaving the acidic pectins into d-galacturonic acid units (Micheli, 2001). This explains the observed changes in methanol and d-galacturonate during air storage. Although ethanol has a high VIP score (2.05, Fig. 5), the levels of ethanol at harvest and under air at 1 ◦ C remained small as compared to the other storage conditions (Fig. 2). Ethanol concentrations typically rise as a result of fermentation occurring in the case of hypoxia (see Section 3.2). The small, yet significant, rise in ethanol level observed when the apples were stored in air at 1 ◦ C most probably resulted from the sudden shift in metabolic balances induced by the reduced temperature, temporarily disturbing the dynamic steady state between ethanol production and ethanol consumption.
3.1. Metabolic changes after 3 weeks storage in regular air at 1 ◦ C Storing apples for 3 weeks in regular air at 1 ◦ C prior to CA storage is an industry practice generally applied to reduce the incidence of Braeburn browning at later stages during storage. Fig. 3 displays the biplot corresponding to the PLS-DA used to study the effect of 3 weeks storage in air at 1 ◦ C on metabolic levels. The first two latent variables accounted for 53% of the total metabolic variance and 91% of the variance in storage time. Based on the PLS-DA, the VIP score of each metabolite was calculated. Those metabolites with a VIP score higher than 1 were considered as being responsible for the metabolic differences observed between at harvest and after 3 weeks of cold storage. Malate is the most abundant organic acid in apples and is responsible for the sour taste of the fruit. Malate is known to decrease during storage (Suni et al., 2000; Roth et al., 2007). The current data show a similar trend (Figs. 2 and 3). As malate is the major substrate for respiration, its content decreases during storage particularly when high levels of oxygen are present. Stored apples tend to taste less sour due to the decrease in malate. This is also shown by the decrease in Total Acid Juice (Fig. 2). As expected the different variables representing the acidity of the juice all show similar
3.2. Metabolic changes after 4 months optimal CA and brown inducing CA In order to study the metabolic changes underlying browning in apples, the fruit were stored under two different CA conditions: optimal (OCA) and browning inducing (BCA) conditions.
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Fig. 5. Variable Importance in the Projection values for each NMR derived variable, calculated from the Partial Least Squares Regression Analysis of the air (Fig. 3) and CA stored (Fig. 4) fruit data. The vertical line at VIP = 1, represents the threshold for being considered an important variable.
Fig. 4 displays the PLS-DA used to study on the metabolic level the difference between the two storage conditions and the difference between the two tissue states (unaffected versus brown). The first two PLS factors of the PLS-DA accounted for 36% of the total metabolic variance and 75% of the variance between the two storage conditions and 58% of the variance between the two tissue states (brown versus unaffected). Fig. 5 shows for each metabolite the VIP coefficient for the discrimination between the two storage conditions and between the two tissue states. The relevant metabolites with a VIP coefficient higher than 1 will be discussed in more detail. Most of the organic acids play an important role in discriminating between storage conditions and tissue states (Figs. 2, 4–5). A loss of malate was mainly observed in tissue showing internal browning of apples stored under BCA (Figs. 2 and 4). Although Lee et al. (2012a,b) state that the levels of organic acids, including the decrease in malate, are not associated or are not directly involved in the development of browning disorders, the data presented here show a clear correlation between the loss of malate and the development of browning disorder. In pears, malate was found to be a key metabolite to distinguish unaffected from brown pear tissue (Pedreschi et al., 2009). In brown pear tissue the expression of fumarase and malate dehydrogenase was found to be downregulated while the expression of malic enzyme was up-regulated (Pedreschi et al., 2007), causing a decrease in malate. As malic enzyme catalyses the decarboxylation of malate to pyruvate, CO2 and NADPH, this would lead to an increase in pyruvate. In our experiments, the concentration of pyruvate, however, was slightly smaller in apples stored under BCA conditions (Fig. 2). At the same time, the amino acid alanine increased. This might indicate a diversion of pyruvate toward alanine which is formed from pyruvate by reductive amination. On the other hand, in apples stored under elevated CO2 conditions showing browning symptoms, amino acid accumulation has been reported (Lee et al., 2012a). As such, it is possible that the accumulation of alanine results from enhanced proteolysis provoked by cell death in the brown tissue. Brown tissue from apples stored under BCA conditions have as well high levels of ethanol (Fig. 2). Therefore the decrease of pyruvate might as well be linked to the activation of the fermentation pathway (see further). The levels of fumarate are higher in both unaffected as well as brown tissue (Fig. 2). This is most likely a consequence of the downregulation of fumarase that catalyses the hydration of fumarate to malate. The decrease of malate is thus linked to the increase
of fumarate. In ‘Conference’ pears, fumarase has been shown to be down-regulated under oxidative stress (Pedreschi et al., 2007). In addition, it has been suggested that high CO2 partial pressure during hypoxia, may facilitate the conversion of oxaloacetate from phosphoenolpyruvate which via the reversal of the Krebs cycle might also lead to accumulation of fumarate (Pedreschi et al., 2009). As both in unaffected as well as brown tissue the fumarate concentration increased, this may have been a result of an impaired respiration metabolism due to adverse storage conditions (low O2 , high CO2 ) rather than an association with browning disorder. It has been reported that in high CO2 environments, without inducing CO2 injuries, succinate can accumulate in apple (Fernandez-Trujillo et al., 2001). Other studies report lower levels of succinate in brown as compared to healthy pear tissue (Pedreschi et al., 2007). In the study presented here, the succinate concentration was clearly elevated in apples stored under BCA (high CO2 ) and especially in brown tissue (Figs. 2 and 4). This indicates that indeed succinate accumulates under high CO2 conditions, most probably because of a CO2 -inhibition of succinate dehydrogenase (Fernandez-Trujillo et al., 2001; Franck et al., 2007). However, whether this causes the browning disorder is unclear. Interestingly, injured apples stored under OCA did not have elevated succinate levels (Fig. 2). Fernandez-Trujillo et al. (2001) showed that citramalate in the peel, a methyl-malic acid associated with the development of anthocyanins in apple skin, declined under CO2 treatment. They stated that this decrease is associated with a declined anthocyanin synthesis as reported in other CO2 -treated fruit, but that there is no evidence of a direct relation with CO2 injury. The data presented here show a clear increase of the citramalate concentration during storage both under OCA and BCA (Fig. 2). An additional strong increase was observed in brown tissue, with the highest levels reached in OCA were the CO2 level was the lowest (0.7% compared to 5%). Therefore, the current results suggest that the observed rise in citramalate was not dependent on the CO2 environment as such, but more tightly related to the browning disorder. Therefore, it would be interesting to study this particular metabolite in more detail in future experiments. Citrate increases significantly in brown tissue of apples stored under OCA (Fig. 2). The reason for this is unclear: one would rather expect citrate accumulation when the Krebs cycle is stalled under BCA conditions. Lactate is an indicator for apples suffering from browning disorders while stored under OCA (Fig. 4). Fig. 2 shows that the concentrations of lactate in these apples were, however, the same as in apples analyzed at harvest. For all other apples, lactate tended to decline especially in unaffected tissue of apples stored under BCA. The reason for this decline remains to be elucidated. The phenolic compound chlorogenate was an indicator for brown tissue of apples stored under BCA conditions (Figs. 2 and 4). The occurrence of browning is due to the enzymatic oxidation of phenolic compounds by polyphenoloxidase (PPO) to o-quinones, which are very reactive and form brown colored polymers (Franck et al., 2007). This browning reaction is triggered by decompartmentalization due to membrane damage bringing PPO, originally present in the plastids, into contact with chlorogenate from the vacuoles (Amaki et al., 2011). The decline of the chlorogenate concentration in brown tissue of apples stored under BCA as compared to that in unaffected tissue of apples stored under the same condition, supports the view that chlorogenate serves as a substrate for PPO during browning (Fig. 2). Interestingly, brown tissue of apples stored under OCA had the same level of chlorogenate as those that were not affected (Fig. 2). Accumulation of acetaldehyde and ethanol in apples treated with high CO2 is a well-studied phenomenon (Fernandez-Trujillo et al., 2001). The current results show indeed such an accumulation
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in brown tissue of apples stored under BCA (Fig. 2). This accumulation has been suggested to be the result rather than the cause of tissue disorganization (Fernandez-Trujillo et al., 2001). However, Larrigaudiere et al. (2004) showed that during core breakdown in pear, the accumulation of ethanol induces the characteristic cell collapse whereas during brown heart development, oxidative processes were considered as the most important causes (Larrigaudiere et al., 2004). While the initial events in these two browning disorders are different, limited gas diffusion is believed to be at the base of both cases (Lammertyn et al., 2003a,b; Ho et al., 2006a,b, 2009; Franck et al., 2007). Ho et al. (2010, 2011) used a detailed gas transport model to show that during CA storage the oxygen concentration in the center of the apple may become low enough to cause the metabolism to switch to fermentation. The low energy yield of the latter is insufficient for repairing membrane damage, and cell death in the inner parts of the bulky fruit may result. This would explain the higher levels of ethanol and acetaldehyde in brown apples stored under BCA conditions. The reason that unaffected tissue of apples stored under BCA-conditions did not have increased ethanol and acetaldehyde levels is related to spatial differences within a single fruit. Methanol tends to be an indicator for brown tissue of apples stored under BCA conditions (Fig. 4). Interestingly, in these apples the methanol concentration is as high as in apples stored for three weeks in air (Fig. 2). In the latter, this rise of methanol could be linked to the rise in d-galacturonate (see above). However, dgalacturonate did not increase in brown apples stored under BCA (Fig. 2). Of course, cell wall degradation during CA is considerably suppressed as compared to air storage (Johnston et al., 2002). Depending on a possibly differential inhibition of the PME and PG action the parallel rise in methanol and d-galacturonate is disturbed. In addition, it is possible that the produced d-galacturonate is immediately used for the biosynthesis of l-ascorbic acid since l-ascorbic acid has been proven to be important for the protection against browning (Valpuesta and Botella, 2004; Franck et al., 2007). Finally, an increase in acetoine could be noticed in brown tissue of apples stored under BCA (Fig. 2). The reason for this increase is, however, unclear.
4. Conclusions In this study, we used NMR for a high-throughput metabolic profiling of Braeburn apples treated with different preharvest and postharvest factors. Furthermore, the technique was used to analyze the metabolites characteristic for apples affected by browning disorders. The short analysis time and good reproducibility make NMR-based metabolomics a valuable tool for studying postharvest disorders as demonstrated in the presented experiment. Even with the default Spin Generated Fingerprint technique optimized for fruit juices in general (Spraul et al., 2009) interesting results could be obtained based on 24 variables. The use of the preharvest factors fertilization and green/red side of the fruit did not show clear differences at the metabolite level. However, the storage conditions applied resulted in large effects at the metabolite level. In addition, the differences in metabolites between brown and unaffected apples, confirmed previous reports but also raised some additional questions requiring further indepth studies. Especially interesting discrepancies between brown apples stored under optimal CA conditions and brown inducing CA conditions were found (e.g., pyruvate, citrate, fumarate, alanine, chlorogenate, methanol, ethanol, acetaldehyde and acetoine). However, due to limited information some of these changes could not be clarified and need additional experiments such as in depth metabolic profiling in combination with a metabolic flux analysis.
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Acknowledgements The authors thank the Research Council of the K.U.Leuven (OT 12/055), the Flanders Fund for Scientific Research (project G.0603.08) and the EU (project InsideFood FP7-226783) for financial support. The opinions expressed in this document do by no means reflect their official opinion or that of its representatives. Thomas Vandendriessche is a post-doctoral fellow of the Research Council of the K.U.Leuven.
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Pedreschi, R., Vanstreels, E., Carpentier, S., Hertog, M., Lammertyn, J., Robben, J., Noben, J.P., Swennen, R., Vanderleyden, J., Nicolai, B.M., 2007. Proteomic analysis of core breakdown disorder in Conference pears (Pyrus communis L.). Proteomics 7, 2083–2099. Peirs, A., Lammertyn, J., Ooms, K., Nicolaï, B.M., 2001. Prediction of the optimal picking date of different apple cultivars by means of VIS/NIR-spectroscopy. Postharvest Biol. Technol. 21, 189–199. Rabus, C., Streif, J., 2000. Effect of various preharvest treatments on the development of internal browning disorders in ‘Braeburn’ apples. In: Proceedings of the XXV International Horticultural Congress, Pt 8, pp. 151–215. Rinke, P., Moitrier, S., Humpfer, E., Keller, S., Moertter, M., Godejohann, M., Hofmann, G., Schaeffer, H., Spraul, M., 2007. An 1 H NMR-technique for high throughput screening in quality and authenticity control of fruit juice and fruit raw materials. Fruit Process 17, 10–18. Roth, E., Berna, A., Beullens, K., Yarramraju, S., Lammertyn, J., Schenk, A., Nicolai, B., 2007. Postharvest quality of integrated and organically produced apple fruit. Postharvest Biol. Technol. 45, 11–19. Smolinska, A., Blanchet, L., Buydens, L.M.C., Wijmenga, S.S., 2012. NMR and pattern recognition methods in metabolomics: from data acquisition to biomarker discovery: a review. Anal. Chim. Acta 750, 82–97.
Spraul, M., Humpfer, E., Schäfer, H., Schütz, B., Mörtter, M., Rinke, P., 2008. NMR-based mixture analysis on the example of fruit juice quality control using statistics and quantification. In: Holzgrabe, U., Wawer, I., Diehl, B. (Eds.), NMR Spectroscopy in Pharmaceutical Analysis. Elsevier, Amsterdam, pp. 319–339. Spraul, M., Schutz, B., Rinke, P., Koswig, S., Humpfer, E., Schafer, H., Mortter, M., Fang, F., Marx, U.C., Minoja, A., 2009. NMR-based multi parametric quality control of fruit juices: SGF profiling. Nutrients 1, 148–155. Suni, M., Nyman, M., Eriksson, N.A., Bjork, L., Bjorck, I., 2000. Carbohydrate composition and content of organic acids in fresh and stored apples. J. Sci. Food Agric. 80, 1538–1544. Valpuesta, V., Botella, M.A., 2004. Biosynthesis of l-ascorbic acid in plants: new pathways for an old antioxidant. Trends Plant Sci. 9, 573–577. Veberic, R., Schmitzer, V., Mikulic-Petkovsek, M., Stampar, F., 2010. Impact of shelf life on content of primary and secondary metabolites in apple (Malus domestica Borkh.). J. Food Sci. 75, S461–S468. Wold, S., Sjöström, M., Eriksson, L., 2001. PLS-regression: a basic tool of chemometrics. Chemometr. Intell. Lab. 58, 109–130.
Contents lists available at SciVerse ScienceDirect
Postharvest Biology and Technology journal homepage: www.elsevier.com/locate/postharvbio
High-throughput NMR based metabolic profiling of Braeburn apple in relation to internal browning Thomas Vandendriessche a , Hartmut Schäfer b , Bert E. Verlinden c , Eberhard Humpfer b , Maarten L.A.T.M. Hertog a,∗ , Bart M. Nicolaï a,c a
BIOSYST-MeBioS, University of Leuven, W. de Croylaan 42, 3001 Leuven, Belgium Application Method Development Group, Bruker BioSpin GmbH, Silberstreifen, 76287 Rheinstetten, Germany c Flanders Centre of Postharvest Technology, De Croylaan 42, 3001 Heverlee, Belgium b
a r t i c l e
i n f o
Article history: Received 26 November 2012 Accepted 20 January 2013 Keywords: Apple Braeburn Browning Metabolic profiling NMR
a b s t r a c t NMR is a valuable tool for metabolomics due to its short analysis time and reproducibility. However, this technique remains little used due to its high cost. Recently, cheaper NMR machines for high-throughput screening have been developed. In this study, NMR was used to study the effect of several pre- and postharvest factors on apple metabolite levels during air and controlled atmosphere storage, including metabolic changes related to the incidence of internal browning. The results show that the selected fertilizer treatments and fruit side (green versus red side) did not affect the metabolite levels. However, the different postharvest storage conditions (optimal CA and brown inducing CA) did result in significant changes in metabolite levels. In addition, differences (e.g., pyruvate, citrate, fumarate, alanine, chlorogenate, methanol, ethanol, acetaldehyde and acetoine) between brown and unaffected apples stored under the applied CA conditions could be demonstrated. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Metabolic analysis has been an integral part of plant and postharvest science and plays an essential role in the field of postharvest systems biology (Hertog et al., 2011). In practice, only a fraction of an estimated 150,000 of 1,000,000 metabolites is usually evaluated (Allwood et al., 2008; Nadella et al., 2012). Several extraction and analysis techniques are currently available to evaluate the complex plant matrices containing diverse groups of primary and secondary metabolites of considerable importance to plant function as well as horticultural and postharvest value. The analytical platforms differ in metabolite selectivity, resolution, analysis speed, and potential throughput (Fernie et al., 2004; Dunn et al., 2005; Allwood et al., 2008; Oms-Oliu et al., 2011; Lee et al., 2012b; Nadella et al., 2012). In addition, the extraction method of choice influences the point of focus of the analysis, biasing its outcome. Mass spectrometry and NMR are the current preferred analytical methods as they are based on the intrinsic physical properties of compounds which can be measured in a reproducible way (Dunn et al., 2005). Both NMR and mass spectrometry based techniques have their advantages and disadvantages. Mass spectrometry based
∗ Corresponding author. Tel.: +32 16322376; fax: +32 16322955. E-mail address: [email protected] (M.L.A.T.M. Hertog). 0925-5214/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.postharvbio.2013.01.008
techniques such as GC–MS and LC–MS, have a higher sensitivity than NMR and therefore may detect metabolites that are present in a concentration below the detection limit of NMR (Smolinska et al., 2012). Nevertheless, NMR has some advantages over these techniques. NMR-based metabolomics can be a valuable tool for studying biological systems as it provides a ‘holistic view’ of the metabolites (Kim et al., 2011). The main advantage of NMR is its analytical reproducibility in combination with a minimum of sample preparation (if any at all). As NMR does not rely on pre-separation of the analytes through techniques like chromatography or electrophoresis, the analysis time remains short providing potential for high-throughput analysis (Smolinska et al., 2012). The main downside of NMR, limiting its application in plant science laboratories, is its relatively high cost. With the recent development of dedicated NMR machines optimized for highthroughput screening (e.g., Spraul et al., 2009), the cost per sample is being reduced and specialized service laboratories can bring the technique within reach of smaller research laboratories and the industry for metabolite analyses and authenticity testing. To test the feasibility of using such high-throughput NMR devices for postharvest research, metabolite changes during storage and in relation to internal browning of ‘Braeburn’ apples were screened by NMR. The impact of different pre- and postharvest factors on the measured metabolite profiles were linked to the current understanding of internal browning.
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2. Materials and methods 2.1. Fruit materials Apples (Malus × domestica Borkh) cv. Braeburn were used for this study. Two cultivation treatments were applied: optimal fertilization (treated with 30 kg/ha calcium nitrate, 20 kg/ha phosphorus, no potassium) and suboptimal fertilization (treated with 30 kg/ha ammonium nitrate, 20 kg/ha phosphorus, 80 kg/ha potassium). The fertilization was applied on March 24, 2010. The apples were harvested on October 27, 2010, which was considered to be the optimal harvest date for long-term commercial storage for Belgium, as determined by the Flanders Centre of Postharvest Technology (VCBT, Belgium) (Peirs et al., 2001). Afterwards, apples from each fertilization treatment were stored under two types of controlled atmospheres (CA): browning inducing storage conditions (BCA; 1 kPa O2 , 5 kPa CO2 , 1 ◦ C), starting on October 29 shortly after harvest, and optimal storage conditions starting with a 3 weeks cooling period at 1 ◦ C under regular air and CA conditions being applied from November 16 onwards (OCA; 2.5 kPa O2 , 0.7 kPa CO2 , 1 ◦ C). NMR measurements were taken on samples collected at harvest (October 27), at the start of the delayed optimal CA application (November 16; regular air cold stored fruit), and four months after harvesting (March 4; BCA and OCA stored fruit). For the first two sampling dates a difference was made between samples coming from the green and red side of the fruit. Apples analyzed from the final sampling date showed internal browning. At this stage discrimination was made between tissue showing signs of internal browning versus unaffected tissue. 2.2. Determination of dry matter content and mineral composition Four replicate fruit samples of 20 apples each were washed, sliced (∼5 mm thick) and dried in an oven at 100 ◦ C for 48 h, after which the amount of dry matter was determined. The dried tissue was subsequently pooled and ground into fine powder (<1 mm particle size) for mineral analysis. Leaf and fruit powder was sent to a commercial laboratory for mineral composition analysis (Lancrop Laboratories, UK). The minerals analyzed were: Ca, Mg, Mn, B, Cu, Mo, Fe, N, P, K, S, Na, Cl and NO3 . The extraction of minerals was done by incubating the powder at 500 ◦ C, and digesting using concentrated hydrochloric acid. Nitrogen was extracted and quantified using the Dumas method. Chloride was extracted by wet digestion of the powder with concentrated nitric acid, potassium permanganate and silver nitrate. Chloride concentration was determined by titration with potassium thiocyanate. Nitrate was extracted with water and its concentration was determined using a nitrate selective electrode. The concentration of the other minerals was determined using an inductively coupled plasma analyzer (ICP). 2.3. NMR analysis Apple fruit samples were juiced and buffer was added (90% juice with 10% buffer). The buffer used contained 0.1% of TSP (sodium salt of 3-(trimethylsilyl)-propionate acid-d4 ) and 0.013% of sodium azide to suppress microorganism activity. A Bruker JuiceScreener NMR spectrometer was used for Spin Generated Fingerprint (SGF) Profiling (Rinke et al., 2007; Spraul et al., 2008, 2009). SGF Profiling is based on an Avance 400 NMR spectrometer with a 9.4-T UltrashieldTM Plus magnet and utilizes flow-injection NMR (BESTTM NMR) with a 4-mm flow-cell probe with Z-gradient and a Gilson liquids handler for sample storage, preparation and transfer. Samples were provided in bar-coded containers keeping them at low temperature (4 ◦ C) prior to injection.
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A heated sample transfer line from the Gilson unit to the probe and a heated capillary inside the probe pre-equilibrate the sample to the desired temperature (27 ◦ C) during the transfer. No additional temperature equilibration time was needed within the measurement cell. The overall experimental procedure was fully automated and controlled by Bruker’s SampleTrack software including temperature adjustment, tuning and matching, locking, shimming and the optimization of the pulses and presaturation power for each individual sample. The metabolite identification was based on an extensive spectroscopic database including over 12,000 NMR spectra from authentic juices and small molecule compounds for analysis of unknown ingredients. Metabolite quantification was based on integration of the 1 H NMR spectra of the identified compounds. Based on regression analysis of juice training data sets, additional parameters such as titratable acidity (Total Acid Juice) were evaluated. Since the pH of the samples was brought to pH 3, all organic acids were measured in the undissociated form (e.g. pyruvic acid). 2.4. Data analysis Principal component analysis (PCA) was applied to explore the multivariate dataset looking for correlations between metabolites. Partial Least Squares Discriminant Analysis (PLS-DA) was applied to investigate the effect of different pre- and postharvest factors on the measured metabolite profiles. PLS generates a multivariate regression model by projecting the independent variables and the dependent response variables to a new variable space as defined by the latent variables, maximizing the covariance between the dependent and independent variables. In PLS-DA the response variable is a binary variable. Based on the PLS-DA analysis, the VIP scores (Variable Importance in the Projection) were calculated (Wold et al., 2001). These scores are a measure of the importance of each explanatory variable in the model. Since the average of the squared VIP scores equals 1, ‘the greater than one rule’ was used as a criterion for variable selection (Chong and Jun, 2005). Multivariate statistics was applied using The Unscrambler® X.1 (CAMO Software, 2011) and the PLS Toolbox of MATLAB (MathWorks, Massachusetts, USA). To assess the effect of postharvest conditions on the NMR derived variables an analysis of variance (ANOVA) was carried out using SAS software, Version 9.2 (SAS Institute Inc., Cary, NC, USA). Duncan’s multiple range test was used to detect differences between different postharvest conditions on a 95% confidence level. Residuals were tested for heteroscedasticity in which case a log transform of the data was carried out to stabilize their variance. 3. Results and discussion The effects of pre- and postharvest factors on apple metabolomics were studied by NMR and chemometrics. Several preharvest factors affecting the development of browning disorders in apple fruit have been reported (Ferguson et al., 1999; Franck et al., 2007). Fertilization is known to potentially affect the incidence of browning (Rabus and Streif, 2000; Hunsche et al., 2003). The fertilization treatments applied in this study did not affect the metabolite level (data not shown). Of the minerals measured only potassium was affected. Apples grown under the optimal fertilization treatment had a 10% higher potassium content than the apples grown under suboptimal fertilization. As these fruit were the first year’s harvest after the fertilization was started, more pronounced effects are expected in the following years. Neither was a difference on metabolite level observed between samples taken from the green or red side of the fruits (data not shown). The postharvest conditions, however, resulted in clear
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Fig. 1. Principal component analysis of all data with the first three principal components describing respectively 28%, 21% and 10% of the observed variation.
Fig. 2. Values for the NMR derived variables under the different postharvest conditions. Bars represent the mean with the error bar indicating the standard error of the mean (at harvest and after 3 weeks storage n = 20; for the other treatments n = 10). The letters above each bar indicate the significance calculated as described in Section 2.
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Fig. 3. Partial Least Squares Regression analysis of the air-stored fruit data. The loadings are represented by blue spheres from which the size corresponds to the Variable Importance in the Projection value. The fruit samples are represented by their scores on the first two latent variables with different symbols discriminating between samples at harvest and after three weeks storage. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
Fig. 4. Partial Least Squares Regression analysis of the CA stored fruit data. The NMR derived variables are used as explaining variables to describe the difference in storage condition (OCA versus BCA) and tissue status (brown versus unaffected) after 4 months of storage. The loadings for the various NMR derived variables are represented by green dots. The fruit samples are represented by their scores on the first two latent variables with different symbols discriminating between samples from different storage conditions and tissue state. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of the article.)
metabolic differences. Fig. 1 shows the PCA analysis of all data. The samples are colored according to storage conditions. A clear distinction between samples taken at harvest and samples stored for three weeks in air at 1 ◦ C can be observed in all score plots. Furthermore, both tissue of apples stored for 4 months under BCA as well as those stored under OCA conditions showing internal browning form two clearly separated groups. Finally, unaffected tissue of apples stored at these conditions tend to lie close to each other, but both form two distinct groups as well. These results indicate that under the different storage conditions used, clear shifts in metabolic activity occurred. A PLS-DA analysis was performed to study these changes in more detail (Figs. 3 and 4). Note that, as the metabolic profiling of brown tissue is a post hoc analysis because of its destructive nature, the corresponding results should be interpreted with caution as it is not always possible to distinguish between cause and results.
patterns (Fig. 2) and appear to be highly correlated as indicated by the position of their loadings (Fig. 3). Fructose was the dominating sugar, followed by sucrose and glucose (Figs. 2 and 3). The concentration of fructose and glucose changed significantly when the apples were stored for 3 weeks in air at 1 ◦ C. During storage, the fructose concentration tended to increase (Fig. 2). This is in agreement with Suni et al. (2000), but not with Roth et al. (2007) who showed constant fructose levels in time. The glucose concentration also increased during storage. This was probably the result of starch degradation which is highly related to climacteric and physiological changes in the apple (Veberic et al., 2010). Two other metabolites characteristic for storage under air at 1 ◦ C were methanol and d-galacturonate (Figs. 3 and 5) both increasing in concentration (Fig. 2). During postharvest storage, fruit firmness is lost as a result of pectin breakdown by, amongst others, pectin methyl esterase (PME) and endopolygalacturonases (PG) (Johnston et al., 2002). PME catalyses the demethylesterification of homogalacuronans, releasing acidic pectins and methanol (Micheli, 2001). This promotes the action of the pH-dependent PG cleaving the acidic pectins into d-galacturonic acid units (Micheli, 2001). This explains the observed changes in methanol and d-galacturonate during air storage. Although ethanol has a high VIP score (2.05, Fig. 5), the levels of ethanol at harvest and under air at 1 ◦ C remained small as compared to the other storage conditions (Fig. 2). Ethanol concentrations typically rise as a result of fermentation occurring in the case of hypoxia (see Section 3.2). The small, yet significant, rise in ethanol level observed when the apples were stored in air at 1 ◦ C most probably resulted from the sudden shift in metabolic balances induced by the reduced temperature, temporarily disturbing the dynamic steady state between ethanol production and ethanol consumption.
3.1. Metabolic changes after 3 weeks storage in regular air at 1 ◦ C Storing apples for 3 weeks in regular air at 1 ◦ C prior to CA storage is an industry practice generally applied to reduce the incidence of Braeburn browning at later stages during storage. Fig. 3 displays the biplot corresponding to the PLS-DA used to study the effect of 3 weeks storage in air at 1 ◦ C on metabolic levels. The first two latent variables accounted for 53% of the total metabolic variance and 91% of the variance in storage time. Based on the PLS-DA, the VIP score of each metabolite was calculated. Those metabolites with a VIP score higher than 1 were considered as being responsible for the metabolic differences observed between at harvest and after 3 weeks of cold storage. Malate is the most abundant organic acid in apples and is responsible for the sour taste of the fruit. Malate is known to decrease during storage (Suni et al., 2000; Roth et al., 2007). The current data show a similar trend (Figs. 2 and 3). As malate is the major substrate for respiration, its content decreases during storage particularly when high levels of oxygen are present. Stored apples tend to taste less sour due to the decrease in malate. This is also shown by the decrease in Total Acid Juice (Fig. 2). As expected the different variables representing the acidity of the juice all show similar
3.2. Metabolic changes after 4 months optimal CA and brown inducing CA In order to study the metabolic changes underlying browning in apples, the fruit were stored under two different CA conditions: optimal (OCA) and browning inducing (BCA) conditions.
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Fig. 5. Variable Importance in the Projection values for each NMR derived variable, calculated from the Partial Least Squares Regression Analysis of the air (Fig. 3) and CA stored (Fig. 4) fruit data. The vertical line at VIP = 1, represents the threshold for being considered an important variable.
Fig. 4 displays the PLS-DA used to study on the metabolic level the difference between the two storage conditions and the difference between the two tissue states (unaffected versus brown). The first two PLS factors of the PLS-DA accounted for 36% of the total metabolic variance and 75% of the variance between the two storage conditions and 58% of the variance between the two tissue states (brown versus unaffected). Fig. 5 shows for each metabolite the VIP coefficient for the discrimination between the two storage conditions and between the two tissue states. The relevant metabolites with a VIP coefficient higher than 1 will be discussed in more detail. Most of the organic acids play an important role in discriminating between storage conditions and tissue states (Figs. 2, 4–5). A loss of malate was mainly observed in tissue showing internal browning of apples stored under BCA (Figs. 2 and 4). Although Lee et al. (2012a,b) state that the levels of organic acids, including the decrease in malate, are not associated or are not directly involved in the development of browning disorders, the data presented here show a clear correlation between the loss of malate and the development of browning disorder. In pears, malate was found to be a key metabolite to distinguish unaffected from brown pear tissue (Pedreschi et al., 2009). In brown pear tissue the expression of fumarase and malate dehydrogenase was found to be downregulated while the expression of malic enzyme was up-regulated (Pedreschi et al., 2007), causing a decrease in malate. As malic enzyme catalyses the decarboxylation of malate to pyruvate, CO2 and NADPH, this would lead to an increase in pyruvate. In our experiments, the concentration of pyruvate, however, was slightly smaller in apples stored under BCA conditions (Fig. 2). At the same time, the amino acid alanine increased. This might indicate a diversion of pyruvate toward alanine which is formed from pyruvate by reductive amination. On the other hand, in apples stored under elevated CO2 conditions showing browning symptoms, amino acid accumulation has been reported (Lee et al., 2012a). As such, it is possible that the accumulation of alanine results from enhanced proteolysis provoked by cell death in the brown tissue. Brown tissue from apples stored under BCA conditions have as well high levels of ethanol (Fig. 2). Therefore the decrease of pyruvate might as well be linked to the activation of the fermentation pathway (see further). The levels of fumarate are higher in both unaffected as well as brown tissue (Fig. 2). This is most likely a consequence of the downregulation of fumarase that catalyses the hydration of fumarate to malate. The decrease of malate is thus linked to the increase
of fumarate. In ‘Conference’ pears, fumarase has been shown to be down-regulated under oxidative stress (Pedreschi et al., 2007). In addition, it has been suggested that high CO2 partial pressure during hypoxia, may facilitate the conversion of oxaloacetate from phosphoenolpyruvate which via the reversal of the Krebs cycle might also lead to accumulation of fumarate (Pedreschi et al., 2009). As both in unaffected as well as brown tissue the fumarate concentration increased, this may have been a result of an impaired respiration metabolism due to adverse storage conditions (low O2 , high CO2 ) rather than an association with browning disorder. It has been reported that in high CO2 environments, without inducing CO2 injuries, succinate can accumulate in apple (Fernandez-Trujillo et al., 2001). Other studies report lower levels of succinate in brown as compared to healthy pear tissue (Pedreschi et al., 2007). In the study presented here, the succinate concentration was clearly elevated in apples stored under BCA (high CO2 ) and especially in brown tissue (Figs. 2 and 4). This indicates that indeed succinate accumulates under high CO2 conditions, most probably because of a CO2 -inhibition of succinate dehydrogenase (Fernandez-Trujillo et al., 2001; Franck et al., 2007). However, whether this causes the browning disorder is unclear. Interestingly, injured apples stored under OCA did not have elevated succinate levels (Fig. 2). Fernandez-Trujillo et al. (2001) showed that citramalate in the peel, a methyl-malic acid associated with the development of anthocyanins in apple skin, declined under CO2 treatment. They stated that this decrease is associated with a declined anthocyanin synthesis as reported in other CO2 -treated fruit, but that there is no evidence of a direct relation with CO2 injury. The data presented here show a clear increase of the citramalate concentration during storage both under OCA and BCA (Fig. 2). An additional strong increase was observed in brown tissue, with the highest levels reached in OCA were the CO2 level was the lowest (0.7% compared to 5%). Therefore, the current results suggest that the observed rise in citramalate was not dependent on the CO2 environment as such, but more tightly related to the browning disorder. Therefore, it would be interesting to study this particular metabolite in more detail in future experiments. Citrate increases significantly in brown tissue of apples stored under OCA (Fig. 2). The reason for this is unclear: one would rather expect citrate accumulation when the Krebs cycle is stalled under BCA conditions. Lactate is an indicator for apples suffering from browning disorders while stored under OCA (Fig. 4). Fig. 2 shows that the concentrations of lactate in these apples were, however, the same as in apples analyzed at harvest. For all other apples, lactate tended to decline especially in unaffected tissue of apples stored under BCA. The reason for this decline remains to be elucidated. The phenolic compound chlorogenate was an indicator for brown tissue of apples stored under BCA conditions (Figs. 2 and 4). The occurrence of browning is due to the enzymatic oxidation of phenolic compounds by polyphenoloxidase (PPO) to o-quinones, which are very reactive and form brown colored polymers (Franck et al., 2007). This browning reaction is triggered by decompartmentalization due to membrane damage bringing PPO, originally present in the plastids, into contact with chlorogenate from the vacuoles (Amaki et al., 2011). The decline of the chlorogenate concentration in brown tissue of apples stored under BCA as compared to that in unaffected tissue of apples stored under the same condition, supports the view that chlorogenate serves as a substrate for PPO during browning (Fig. 2). Interestingly, brown tissue of apples stored under OCA had the same level of chlorogenate as those that were not affected (Fig. 2). Accumulation of acetaldehyde and ethanol in apples treated with high CO2 is a well-studied phenomenon (Fernandez-Trujillo et al., 2001). The current results show indeed such an accumulation
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in brown tissue of apples stored under BCA (Fig. 2). This accumulation has been suggested to be the result rather than the cause of tissue disorganization (Fernandez-Trujillo et al., 2001). However, Larrigaudiere et al. (2004) showed that during core breakdown in pear, the accumulation of ethanol induces the characteristic cell collapse whereas during brown heart development, oxidative processes were considered as the most important causes (Larrigaudiere et al., 2004). While the initial events in these two browning disorders are different, limited gas diffusion is believed to be at the base of both cases (Lammertyn et al., 2003a,b; Ho et al., 2006a,b, 2009; Franck et al., 2007). Ho et al. (2010, 2011) used a detailed gas transport model to show that during CA storage the oxygen concentration in the center of the apple may become low enough to cause the metabolism to switch to fermentation. The low energy yield of the latter is insufficient for repairing membrane damage, and cell death in the inner parts of the bulky fruit may result. This would explain the higher levels of ethanol and acetaldehyde in brown apples stored under BCA conditions. The reason that unaffected tissue of apples stored under BCA-conditions did not have increased ethanol and acetaldehyde levels is related to spatial differences within a single fruit. Methanol tends to be an indicator for brown tissue of apples stored under BCA conditions (Fig. 4). Interestingly, in these apples the methanol concentration is as high as in apples stored for three weeks in air (Fig. 2). In the latter, this rise of methanol could be linked to the rise in d-galacturonate (see above). However, dgalacturonate did not increase in brown apples stored under BCA (Fig. 2). Of course, cell wall degradation during CA is considerably suppressed as compared to air storage (Johnston et al., 2002). Depending on a possibly differential inhibition of the PME and PG action the parallel rise in methanol and d-galacturonate is disturbed. In addition, it is possible that the produced d-galacturonate is immediately used for the biosynthesis of l-ascorbic acid since l-ascorbic acid has been proven to be important for the protection against browning (Valpuesta and Botella, 2004; Franck et al., 2007). Finally, an increase in acetoine could be noticed in brown tissue of apples stored under BCA (Fig. 2). The reason for this increase is, however, unclear.
4. Conclusions In this study, we used NMR for a high-throughput metabolic profiling of Braeburn apples treated with different preharvest and postharvest factors. Furthermore, the technique was used to analyze the metabolites characteristic for apples affected by browning disorders. The short analysis time and good reproducibility make NMR-based metabolomics a valuable tool for studying postharvest disorders as demonstrated in the presented experiment. Even with the default Spin Generated Fingerprint technique optimized for fruit juices in general (Spraul et al., 2009) interesting results could be obtained based on 24 variables. The use of the preharvest factors fertilization and green/red side of the fruit did not show clear differences at the metabolite level. However, the storage conditions applied resulted in large effects at the metabolite level. In addition, the differences in metabolites between brown and unaffected apples, confirmed previous reports but also raised some additional questions requiring further indepth studies. Especially interesting discrepancies between brown apples stored under optimal CA conditions and brown inducing CA conditions were found (e.g., pyruvate, citrate, fumarate, alanine, chlorogenate, methanol, ethanol, acetaldehyde and acetoine). However, due to limited information some of these changes could not be clarified and need additional experiments such as in depth metabolic profiling in combination with a metabolic flux analysis.
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Acknowledgements The authors thank the Research Council of the K.U.Leuven (OT 12/055), the Flanders Fund for Scientific Research (project G.0603.08) and the EU (project InsideFood FP7-226783) for financial support. The opinions expressed in this document do by no means reflect their official opinion or that of its representatives. Thomas Vandendriessche is a post-doctoral fellow of the Research Council of the K.U.Leuven.
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