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First published online August 7, 2003; 10.1104/pp.103.023572 Plant Physiology 133:84-99 (2003) © 2003 American Society of Plant Biologists Metabolic Profiling of Transgenic Tomato Plants Overexpressing Hexokinase Reveals That the Influence of Hexose Phosphorylation Diminishes during Fruit DevelopmentDepartment of Lothar Willmitzer, Max-Planck-Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1, 14476 Golm, Germany (U.R.-T., B.H., A.L., F.C., C.B., A.R.F.); and Institute for Field and Garden Crops, Agricultural Research Organization, Volcani Centre, Bet Dagan 50250, Israel (D.G.)
We have conducted a comprehensive metabolic profiling on tomato (Lycopersicon esculentum) leaf and developing fruit tissue using a recently established gas chromatography-mass spectrometry profiling protocol alongside conventional spectrophotometric and liquid chromatographic methodologies. Applying a combination of these techniques, we were able to identify in excess of 70 small-Mr metabolites and to catalogue the metabolite composition of developing tomato fruit. In addition to comparing differences in metabolite content between source and sink tissues of the tomato plant and after the change in metabolite pool sizes through fruit development, we have assessed the influence of hexose phosphorylation through fruit development by analyzing transgenic plants constitutively overexpressing Arabidopsis hexokinase AtHXK1. Analysis of the total hexokinase activity in developing fruits revealed that both wild-type and transgenic fruits exhibit decreasing hexokinase activity with development but that the relative activity of the transgenic lines with respect to wild type increases with development. Conversely, both point-by-point and principal component analyses suggest that the metabolic phenotype of these lines becomes less distinct from wild type during development. In summary, the data presented in this paper demonstrate that the influence of hexose phosphorylation diminishes during fruit development and highlights the importance of greater temporal resolution of metabolism.
Hexokinase (E.C. 2.7.1.1) catalyzes the phosphorylation of hexoses to form hexose monophosphates. This reaction is especially important in plants because the use of free phosphates is particularly complex in higher plants (Kruger, 1997  ). There have been many reports on the presence of glucokinase and hexokinase enzymes in a wide variety of plant species including tomato (Lycopersicon esculentum; Martinez-Barajaz and Randall, 1998), maize (Zea mays; Doehlert, 1989; Schnarrenberger, 1990; Galina et al., 1995), potato (Solanum tuberosum; Renz and Stitt, 1993; Veramendi et al., 1999), pea (Pisum sativum; Turner et al., 1977; Turner and Copeland, 1981), avocado (Persea americana; Copeland and Tanner, 1988), castor bean (Ricinus communis; Miernyk and Dennis, 1983), soybean (Glycine max; Copeland and Morrell, 1985), tobacco (Nicotiana tabacum; Sindelar et al., 1998), spinach (Spinacia oleracea; Wiese et al., 1999), and Arabidopsis (Jang et al., 1997).
Recently, transgenic manipulations of the activity of hexokinase have been carried out in tomato, potato, and Arabidopsis (Jang et al., 1997
When taken together, these results led us to postulate that although the basic mechanisms of sugar regulation are conserved in plants, but that the display of sugar-mediated phenotypes is dependent on the plant species, the physiological state of the plants, and other environmental factors (Veramendi et al., 2002
Evaluation of the Hexokinase Activity in Various Tissues of the Transgenic Lines
The aim of this work was to investigate the influence of the hexokinase reaction on metabolism in developing tomato plants. To this end, we decided to characterize previously generated plant material in which the Arabidopsis hexokinase AtHXK1 was expressed in tomato cv MP1 under the control of the cauliflower mosaic virus 35S promoter (Dai et al., 1999
Having confirmed the genetic identity of the lines and quantified the elevation in activity achieved in leaf tissues and at various developmental stages in the fruit, next wanted to biochemically phenotype material harvested in parallel to that used for the above determinations. To be able to analyze the levels of primary metabolites in various tissues of tomato, we adopted and optimized a GC-MS method established in our institute. For tomato leaves, the routine method of extraction, derivatization, measurement and chromatogram evaluation for Arabidopsis and potato leaves (Fiehn et al., 2000a
The analysis of tomato fruits represented a more difficult task because they contain high amounts of hexose sugars and citrate (Knee and Finger, 1992
To extend the characterization of tomato leaf and green and red fruit tissue, we quantified 22 amino acids, seven sugars, and six organic acids by establishment of calibration curves as described by Roessner et al. (2000
Before utilizing the tomato method for the comparison of diverse genotypes, we carried out one final examination of its robustness. We performed recombination experiments, whereby extracts of potato and tomato were subjected to GC-MS investigations, both in isolation then as a stochiometric mixture, to validate that the peak identification for tomato samples was the same as that for potato tuber. This experiment is of particular importance given that different extract compositions can cause so-called "matrix effects" that can result in shifts in relative elution times (for example, see Wagner et al., 2003
Next, we evaluated the levels of starch, hexose phosphates, nucleotides, and the metabolites covered in the GC-MS protocol described above in ethanol, trichloroacetate, and methanol extracts of the homogenized pericarp tissue used for the enzyme determinations presented in Figure 1. The full data set comprising over 70 metabolites is presented in Table IV; the data set contains 73 metabolites of defined chemical structure, including starch, sugars, sugar alcohols, amino acids, organic acids, phosphorylated intermediates, and nucleotides. The majority of the compounds measured were found to alter both through development and across the genotypes. Furthermore, the relative changes observed between green and red fruit in the wild type were largely in accordance with the absolute data presented for the more limited data set of Table I. Although several of the changes in metabolite pools such as the large increases in hexose sugars and the transitory nature of starch accumulation have been documented in previous studies (Yelle et al., 1988
As would be expected, the starch content of orange fruit was markedly reduced from the level found in green fruits. Furthermore, the starch levels of lines HK4 and HK38 were significantly lower than that found in the wild-type control. At this time point, the levels of Fru and Glc were also reduced in these lines (in line HK38, the level of Suc was also reduced) and Glc-6-P increased in lines HK4 and HK38, whereas Fru-6-P decreased. As was observed in the green fruit, these changes corresponded to decreases in the level of ATP and, consequently, in the ATP to ADP ratio. Line HK4, furthermore, exhibits a decreased level of UDP-Glc. The levels of other sugars such as Man, maltose, and trehalose again were observed to be lower in the transgenic lines because they were at the earlier stage of development; however, at this time point, the differences between the pool sizes (with the exception of maltose) in the transformants and the wild type were not as great. Likewise, a similar pattern of change was observed in the amino acid pool sizes of orange fruit to that documented above for green fruits. As was noted for the sugar measurements, however (with the exceptions of Ala, Ile, and Val, which are even more strikingly increased at this developmental stage), the relative difference between the size of the amino acid pools in the transgenic and wild-type lines is smaller than that observed at the earlier stage of development. Another exception to the previous statement is the level of Gly, which was decreased in the transformants in early fruit development but increases dramatically in these lines by the orange stage. The relative pattern of change in other compounds between green and orange stages is fairly conserved, and despite minor increases in the differences in the relative levels of succinate and fumarate during this period, differences in the relative levels of other important metabolites such as shikimate and 6-phosphogluconate got much smaller in the same time period. Intriguingly there are also far fewer differences in the levels of the unknown metabolites in the transgenic lines, with respect to wild type, at this developmental stage (for details, see http://www.mpimp-golm.mpg.de). The metabolic profiling of the red fruit revealed even fewer differences in metabolite pool sizes between the transgenic and wild-type lines. Starch was undetectable at this stage, Glc and Fru were once again generally decreased (with the exception of the Glc level in line HK37), and Glc monophosphates generally increased, whereas Fru-6-P generally decreased in the transgenic lines. On the other hand ADP conversely increased in all lines, whereas the level of UDP was higher in line HK37 than it was in wild type at this developmental stage. With the exception of trehalose, which was depressed to lower levels in the transgenics, the levels of minor sugars and sugar alcohols were largely unchanged in the transformants. Far fewer changes in the amino acids were observed across the genotypes at this developmental stage, and those that did occur were generally not of the same order of magnitude as those seen in either of the earlier developmental stages. Similarly, the organic acid levels of the transformants were very similar to those of the wild type at this time point. The levels of 3PGA and 6-phosphogluconate, however, were increased in red fruits of the transformants, although as was the case for orange fruits, there were also far fewer differences in the levels of the unknown metabolites in the transgenic lines, with respect to wild type, at this developmental stage than there were in green fruit (for details, see http://www.mpimp-golm.mpg.de).
A similar analysis for leaves taken from 6-week-old plants was also performed (the full data set can be viewed at http://www.mpimp-golm.mpg.de). In contrast to what was observed in the fruits, the starch content in leaves was unaltered; however, the Glc and Fru content was significantly reduced in line HK4, whereas the hexose phosphate pools were increased up to 5-fold (Table V). The leaves of the transgenic lines were also characterized by dramatic decreases in the levels of all adenylates (ATP, ADP, and ADP-Glc), but this was most pronounced in ATP rendering the deduced ATP to ADP ratio much reduced. The UTP to UDP ratio of these lines was reduced in a similar manner, but given that the level of UDP-Glc increased in the transgenics, the total uridinylate pool size was unaltered. Analysis of the data from the GC-MS analysis of leaf extracts revealed large changes in the levels of the majority of metabolites in lines HK4 and HK38. The pattern of change in the amino acid pool sizes was similar to that observed in the green fruit, with a massive increase in Asn concomitant to a decrease in the level of Asp and also large increases in the level of Lys, Thr, Ile, homo-Ser, Tyr, Trp, Leu, Orn, Arg, and Val. The levels of organic acids tended to decrease in the transgenic lines with the exception of fumarate, which was up to 7-fold increased, and ascorbate, which was up to 15-fold increased. Interestingly, the vast majority of the unidentified peaks were dramatically decreased in leaves from the hexokinase overexpressors (see http://www.mpimp-golm.mpg.de).
Given that one of the primary objectives of this study was to evaluate the relative influence of hexose phosphorylation during fruit development, we next applied the statistical tool PCA to our combined data set. Several distinct clusters were clearly observable with the various developmental stages of fruit from both wild-type and transgenic lines largely separating along the first component axis, whereas the transgenic lines largely separated from the wild-type lines along the second component axis (Fig. 3). Intriguingly, in keeping with the findings for specific metabolites described above, the clusters of the transgenics were most distinct at the early stage of fruit development, with the orange fruit samples from the transgenics converging somewhat and with the wild-type samples of the red fruit even more. Furthermore, when a PCA of the leaf data set was performed, a similar pattern of separation to that of the green fruit was observed (data not shown).
This study illustrates the potential of comprehensive metabolic analysis coupled with statistical clustering methods for the analysis of the relative influence of an enzyme activity throughout development. We have used previously the techniques described in this paper to phenotype transgenic lines exhibiting enhanced Suc mobilization (Roessner et al., 2001a ) and restricted starch metabolism (Roessner et al., 2001b), demonstrating their power in phenotyping. In addition, we demonstrated that we were able to faithfully phenocopy genetic manipulations through alterations in environmental conditions (Roessner et al., 2001b). Here, we take a range of approaches to profile the primary metabolic complement of transgenic tomato lines overexpressing an Arabidopsis hexokinase AtHK1 (Dai et al., 1999), with particular focus on distinct phases of fruit development. Although many studies have followed developmental changes of a restricted number of parameters across diverse genotypes (for example, see Chen et al., 2002; Borisjuk et al., 2003; Hajirezaei et al., 2003; Kühn et al., 2003), and one unbiased analysis of metabolite change across strawberry (Fragaria uesca) fruit development has been carried out (Aharoni et al., 2002), to our knowledge this study represents the first comprehensive analysis of metabolic change during fruit development across diverse genotypes. Adaptation of the GC-MS method that we routinely use for analysis of metabolites from potato tissue allowed the detection of 92 metabolites in tomato leaf tissue and 62 metabolites in fruit tissue, of which 58 and 59 were of known chemical structure, respectively.
For this study, we decided to concentrate on these metabolites alone because when they are augmented by the parallel analysis of nucleotides, starch, hexose phosphates, and sugars, a fairly complete picture of primary metabolism is achieved. To check the applicability of our potato method for tissues of tomato, we determined the absolute concentrations of a range of compounds including sugars, organic acids, and amino acids in wild-type tomato leaf, green fruit, and red fruit extracts (Table II). The absolute values we obtained for these metabolites were very similar to those previously reported for these tissues (for example, see Boggio et al., 2000
Next, we turned our attention to confirming the genetic identity of the transgenic lines used in this study. Perhaps surprising is that the increase in activity observed in fruit was greatest in later stages of development. However, this is most probably explained by the fact that hexokinase activity in the wild type decreases to vanishingly low levels, nearing the point of detection (for example, see Schaffer and Petreikov, 1997
Although the major purpose of this study was to combine multiparallel metabolite analysis with bioinformatic tools for data analysis in the evaluation of the influence of hexokinase on fruit metabolism, the comprehensive analysis afforded by metabolic profiling alone allowed some important conclusions to be made. In addition to analysis of fruit metabolism, we performed a parallel analysis of leaf samples because it is highly possible that changes in the metabolite pools of the leaves could influence those of the fruit. Despite this cautionary note, several of the changes observed in the leaves are probably the result of different factors in the leaf and the fruit. One such example is the changes in the adenylate pool sizes: Although both leaf and fruit tissue are characterized by a reduction in the adenylate pools, the reasons behind this are likely to be, at least partially, different. The initial characterization of the hexokinase transformants revealed an inhibition of photosynthesis in these lines, which could explain why the drop in adenylates and in the ATP to ADP ratio was more severe in the leaf tissue. However, it should be noted that although reduced photosynthesis can correlate with reduction in the adenylate pool size (for example, see Lytovchenko et al., 2002b
Although the changes in the transgenics largely correlate to the level of expression of the transgene (being far more predominant in the cases of lines HK4 and HK38), this is not exclusively the case, implying that some of the metabolic changes are secondary consequences of the genetic manipulation. Although the exact reasons underlying this remain unclear, the previous study of these plants also documented that they were characterized by a reduction in fruit growth (Dai et al., 1999
As mentioned above, the hexokinase overexpressors were also characterized by a dramatic increase in the levels of several key amino acids both in leaf and fruit tissue. Because the level of protein is unaltered in either tissue (data not shown), it seems likely that in both instances these changes are the result of an elevated carbon partitioning through glycolysis toward the amino acid pool. However, we cannot directly rule out that changes in the amino acid composition of the fruit are due to increased import of amino acids from the leaves. Nevertheless, this possibility seems unlikely because the pattern of change in amino acids in the fruits is distinct from that observed in leaves. Furthermore, characterization of key activities of glycolysis revealed an induction of this pathway occurs in green fruit of the transgenics similar to that observed on induction of glycolysis in potato tubers exhibiting enhanced Suc mobilization (Trethewey et al., 1998
Interestingly, as was also observed in these potato transgenics (Roessner et al., 2001), fruits (but not leaves) of the hexokinase plants displayed large increases in the levels of Ala and succinate. When considered alongside the changes in the energy levels seen in these fruits, this suggests that the fruit may tend toward hypoxia (Geigenberger, 2003
When the changes in the metabolite pools of the transgenics at the independent time points are assessed in the context of fruit development, with a few notable exceptions, the pattern of change in largely reflects that observed for the wild type, although the magnitude of the changes between the different stages is on occasion much larger. Although many of the metabolic changes during development have been reported previously (for example, see Schaffer and Petreikov, 1997 In conclusion, the influence of hexokinase on primary metabolism diminishes markedly over developmental time. Thus, the results presented in this study support our earlier postulate that the influence of hexokinase in metabolism is highly dependent on the developmental and/or environmental situation. Furthermore, although these results do not preclude a role for hexokinase-mediated sugar sensing, they can all be rationalized purely on the basis of the enzymatic activity of the protein.
Plant Material
Three independent transgenic tomato (Lycopersicon esculentum) lines overexpressing the Arabidopsis hexokinase AtHXK1 (for description, see Dai et al., 1999
All chemicals were purchased from Sigma-Aldrich Chemie GmbH (Deisenhofen, Germany), with the exception of the starch determination kit and biochemical enzymes (Boehringer Mannheim, Mannheim, Germany), N-methyl-N-[trimethylsilyl]trifluoroacetamide (Macherey-Nagel GmbH & Co. KG, Düren, Germany), and radiolabel (Amersham International, Braun-schweig, Germany).
Total RNA was isolated from leaves and developing tomato fruits as described by Hughes and Galau (1988
Leaf or fruit pericarp samples were rapidly frozen in liquid N2, and enzymes were extracted and desalted according to Geigenberger and Stitt (1993
Leaf or fruit pericarp samples were rapidly frozen in liquid N2 and extracted in either ethanol (for starch and sugar measurements) or in trichloroacetic acid (for hexosephopshates, nucleotide, and nucleotide sugar measurements). Starch and sugars were determined spectrophotometrically as described by Fernie et al. (2001a
Metabolite analysis by GC-MS was carried out by a method modified from that described by Roessner et al. (2000 Residues after reduction were redissolved in and derivatized for 90 min at 37°C (in 40 µL of 20 mg mL-1 methoxyamine hydrochloride in pyridine) followed by a 30-min treatment with 60 µL of N-methyl-N-[trimethylsilyl]trifluoroacetamide at 37°C. Eight microliters of a retention time standard mixture (0.029% [v/v] n-dodecane, n-pentadecane, n-nonadecane, n-docosane, n-octacosane, n-dotracontane, and n-hexatriacontane dissolved in pyridin) was added before trimethylsilylation. Sample volumes of 1 µL were then injected onto the GC column using a hot needle technique.
The GC-MS system used comprised an AS 2000 autosampler, a GC 8000 gas chromatograph, and a Voyager quadrupole mass spectrometer (ThermoFinnigan, Manchester, UK). The mass spectrometer was tuned according to the manufacturer's recommendations using Tris-(perfluorobutyl)-amine (CF43). GC was performed on a 30-m Rtx_5Sil MS column with 0.25-µm film thickness with a 10-m Integra precolumn (Restek, Bad Homburg, Germany). The injection temperature was set at 230°C, the interface at 250°C, and the ion source adjusted to 200°C. Helium was used as the carrier gas at a flow rate of 1 mL min-1. The analysis was performed under the following temperature program; 5 min of isothermal heating at 70°C, followed by a 5°C min-1 oven temperature ramp to 350°C, and a final 5-min heating at 330°C. The system was then temperature equilibrated for 1min at 70°C before injection of the next sample. Mass spectra were recorded at 2 scan s-1 with a mass-to-charge ratio of 50 to 600 scanning range. Both chromatograms and mass spectra were evaluated using the MASSLAB program (ThermoQuest, Manchester, UK), and the resulting data were prepared and presented as described by Roessner et al. (2001a
PCA was carried out exactly as detailed by Roessner et al. (2001a
We are especially grateful to Lothar Willmitzer for supporting our work. We also thank Dr. Oliver Kreft for the determination of Glu in tomato fruit extracts using HPLC. We thank Dr. Joachim Kopka for helpful discussion concerning data mining and Dr. Markus Pauly for discussions concerning cell walls. Received March 14, 2003; returned for revision May 9, 2003; accepted May 21, 2003. * Corresponding author; e-mail fernie{at}mpimp-golm.mpg.de; fax 49-0-331-5678408.
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ABSTRACT
RESULTS
for PGM in leaves and TPI in fruit activities are presented in µmol min-1 g fresh wt-1.
-aminobutyrate (GABA) TMS; 8, threonate TMS; 9, Glu TMS; 10, ribitol TMS (quantification standard); 11, citrate TMS; 12, quinate TMS; 13, Fru MEOX1 TMS; 14, Fru MEOX2 TMS; 15, Glc MEOX1 TMS; 16, Glc MEOX2 TMS co-eluting with nonadecane (time reference); 17, gluconate TMS; 18, inositol TMS; 19, docosane (time reference); 20, Suc TMS; 21, octacosane (time reference); 22, chlorogenate TMS; 23, dotriacontane; 24, phosphate TMS; 25, Thr TMS; 26, 
-Ala TMS; 27, Asn TMS; 28, unknown substance; 29, Gln TMS; and 30, unknown substance. Derivatives are TMS unless otherwise indicated. Major peaks in the expanded region are identified directly on the chromatogram. An asterisk indicates novel metabolites detected within tomato fruit extracts compared with tomato leaf extracts.
-Ketoglutarate also increased 2-fold in lines HK37 and HK38 but was conversely reduced to approximately 40% of the wild-type level in the strongest line. In addition to these changes, we also observed decreased levels of ascorbate and increased levels of both shikimate and 6-phosphogluconate in the transformants with further changes in the levels of some 18 metabolites of unknown chemical structure (for details, see 