- Open Access
Integration of metabolite with transcript and enzyme activity profiling during diurnal cycles in Arabidopsisrosettes
© Gibon et al.; licensee BioMed Central Ltd. 2006
- Received: 11 May 2006
- Accepted: 17 August 2006
- Published: 17 August 2006
Genome-wide transcript profiling and analyses of enzyme activities from central carbon and nitrogen metabolism show that transcript levels undergo marked and rapid changes during diurnal cycles and after transfer to darkness, whereas changes in activities are smaller and delayed. In the starchless pgm mutant, where sugars are depleted every night, there are accentuated diurnal changes in transcript levels. Enzyme activities in this mutant do not show larger diurnal changes; instead, they shift towards the levels found in the wild type after several days of darkness. This indicates that enzyme activities change slowly, integrating the changes in transcript levels over several diurnal cycles.
To generalize this conclusion, 137 metabolites were profiled using gas and liquid chromatography coupled to mass spectroscopy. The amplitudes of the diurnal changes in metabolite levels in pgm were (with the exception of sugars) similar or smaller than in the wild type. The average levels shifted towards those found after several days of darkness in the wild type. Examples include increased levels of amino acids due to protein degradation, decreased levels of fatty acids, increased tocopherol and decreased myo-inositol. Many metabolite-transcript correlations were found and the proportion of transcripts correlated with sugars increased dramatically in the starchless mutant.
Rapid diurnal changes in transcript levels are integrated over time to generate quasi-stable changes across large sectors of metabolism. This implies that correlations between metabolites and transcripts are due to regulation of gene expression by metabolites, rather than metabolites being changed as a consequence of a change in gene expression.
- Transcript Level
- Additional Data File
- Diurnal Cycle
- Metabolite Level
- Diurnal Change
A full understanding of metabolic networks requires quantitative data about transcript levels, protein levels or enzyme activities, and metabolite levels. Interactions between these three functional levels will depend on the structure of the metabolic and signaling network, and on the dynamics of transcript, protein and metabolite turnover. Many inputs, including changes in metabolite levels, contribute to the regulation of gene expression. Changes in the levels of transcripts modify the levels of the encoded enzymes and the levels of metabolites or, more broadly, the metabolic phenotype. The impact of changes in transcript levels on metabolism will depend on the rates of turnover of the encoded proteins, their contribution to the control of the metabolic pathways that they are involved in, and the rates of turnover of the metabolites that are in, or are produced by, these pathways. There have been many focused studies on the impact of altered expression of single genes on protein and metabolite levels [1, 2], and broader genomics studies that link changes at the levels of transcripts and proteins or enzymes [3, 4], or transcripts and metabolites [5, 6], but relatively few global studies of responses at all three levels . Most studies have also concentrated on comparing individual conditions, rather than analyzing the temporal dynamics during a time series.
The paucity of multilevel studies is partly because of technical reasons. While global changes in expression can be routinely analyzed using custom-made or commercial arrays [8–10], it is more difficult to obtain quantitative information about the accompanying changes in protein levels and metabolites. Quantitative proteomics is still in its infancy [3, 11]. The importance of analyzing changes in protein levels is underlined by the growing evidence that, at least in eukaryotes, protein levels can change independently of the levels of the transcripts that encode them [3, 12]. We recently developed a robotized system to measure the activities of >20 enzymes involved in central carbon and nitrogen metabolism using optimized assays, in which the measured activity reflects changes in protein levels . This platform was used to analyze changes in enzyme activities during diurnal light/dark cycles and during several days of darkness in Arabidopsis leaves. Most enzyme activities changed less and much more slowly than transcripts, and the attenuation and delay varied from enzyme to enzyme. Routine analysis of large numbers of metabolites is complicated by the vast number and chemical diversity of the metabolites in a given organism [13–16]. Methods have been developed for the profiling of metabolites using gas chromatography-mass spectroscopy (GC-MS) [17, 18] and liquid chromatography-mass spectroscopy (LC-MS)  or nuclear magnetic resonance (NMR) [20, 21], but to date relatively few studies have applied these technologies in combination with global analysis of levels of transcripts [5, 6, 22, 23] or proteins [24, 25].
Normalization, analysis and display of multilayered data sets also pose challenges. While considerable progress has been achieved for transcript arrays [26–28], there is no consensus on normalization strategies for metabolites and/or proteins. Typically, log fold-change normalization is used when metabolites are involved. Combined network analysis with implemented causality has been used to generate putative gene-metabolite communication networks  and protein-metabolite networks . Deeper insights are provided when the experimental data are integrated with information about the structure of metabolic or signaling pathways, as illustrated in a recent study of glucosinolates and primary metabolism [5, 6]. Although general metabolic pathway databases such as KEGG exist to support the integration of previous knowledge, it is often necessary to edit or extend them for use with a specific organism or set of organisms. Some specific plant metabolome/transcriptome pathway databases have been developed recently [16, 22, 31]. Software tools are also emerging that allow multiple facets of data to be displayed on a common interface . However, such approaches quickly run into the limitation that only small sectors of metabolism can be usefully visualized when items are being viewed at different levels.
Plants typically grow in a diurnal light/dark cycle, providing an amenable system to analyze the temporal dynamics of changes in gene expression and metabolism. In the light, photosynthetic CO2 fixation drives the synthesis of sucrose in leaves and its export to the remainder of the plant to support growth and storage, whereas at night the plant becomes a net consumer of carbon [33–36]. The following experiments analyze changes in transcripts, enzyme activities and metabolites during a diurnal cycle and under two further conditions that accentuate changes in sugars; a prolonged dark treatment and the starchless pgm mutant. Prolongation of the night leads within a few hours to total exhaustion of starch and a collapse of sugars and related metabolites, even in wild-type (WT) plants . This provides a system to investigate the responses of transcript levels, enzyme activities and metabolite levels over a longer time frame than is available in the 24 h light/dark cycle. Starch normally accumulates in leaves in the light and is remobilized and converted to sucrose at night [4, 37]. The pgm mutant lacks plastid phosphoglucomutase activity, which is an essential enzyme for photosynthetic starch synthesis . It accumulates very high levels of sugars in the day, but has very low levels of sugars in the second part of the night [36–38]. This provides a system to investigate how recurring accentuated changes in the levels of sugars impact on the diurnal responses of transcript levels, enzyme activities and other metabolites.
The responses of transcript levels and 23 enzyme activities during the diurnal cycle and an extended dark treatment in WT Arabidopsis, and during the diurnal cycle in starchless pgm mutants, were presented in [4, 37]. In WT, over 30% of the genes expressed in rosettes exhibit significant diurnal changes in their transcript levels, mainly driven by changes of sugars and by the circadian clock . Prolongation of the night leads to marked changes of hundreds of transcripts within 4 to 6 h , and thousands of transcripts after 1 to 2 days (O Blaesing, unpublished data). The accentuated diurnal changes in sugar levels in the starchless pgm mutant lead to exaggerated diurnal changes in the levels of >4,000 transcripts . These are mainly due to the low levels of sugars at night; in the light period the global transcript levels in pgm resemble those in WT, whereas in the dark the global transcript profile in pgm resembles WT after a 4 to 8 h extension of the night [4, 37]. The responses of enzyme activities were smaller and much slower than those of transcripts , both during diurnal cycles and the extended dark treatment in WT, and when WT is compared with pgm. In particular, whereas transcript levels in pgm resembled WT after a 6 hour extension of the night (see above), enzyme activities in pgm resembled WT after several days of darkness [4, 22, 37].
Based on these results, we propose that: changes in enzyme activities are strongly delayed compared to changes in transcript levels; and a series of transient but recurring changes in transcript levels are integrated over time as changes in enzyme activities. This conclusion is based on an analysis of 23 enzymes involved in central carbon and nitrogen metabolism. The following paper generalizes this conclusion by analyzing the responses of 137 metabolites, measured using GC-MS and LC-MS. The underlying hypothesis is that changes in the metabolite profile will integrate the responses of hundreds of enzymes across several sectors of metabolism.
Changes in transcript levels and enzyme activities
Many of the 82 genes show diurnal changes in transcript levels in WT (the second column). The amplitude and timing varies from gene to gene (Figure 1). Most show an accentuated diurnal change in pgm (the fourth column), including some that do not show marked diurnal changes in WT. Almost all of the genes show marked changes in their transcript levels after a prolonged night (the third column labeled XN). In most cases, the response after the prolonged night treatment represents an extension of the changes towards the end of the night in WT or pgm. A few genes show a change after the prolonged night that is opposite to that during the later part of the diurnal cycle in WT; for example, two genes (NIA1, NIA2) encoding nitrate reductase and one of the two genes encoding ferredoxin-glutamate synthase rose at the end of the night in WT but fell during a prolonged night. For most of these, the diurnal response in pgm also differs from that in WT, and the response during a prolonged night resembles that in the last part of the night in the pgm mutant.
The same normalization was used to depict changes in enzyme activities (Figure 1). As discussed in , the amplitudes of the diurnal changes of enzyme activities are unrelated to the changes of the encoding transcript levels, and the daily peak of enzyme activity is delayed compared to the peak of transcript level by an interval that varies from enzyme to enzyme. Two further aspects of the data highlight that transcript levels and enzyme activities respond with very different dynamics. First, when plants are subjected to prolonged darkness there are widespread and coordinated changes in the transcript levels for many genes within 6 h, whereas the changes in enzyme activity require several days (compare transcript levels and activities). Second, instead of showing larger diurnal changes, enzyme activities in pgm are typically shifted to a new value that qualitatively resembles the WT after a prolonged dark treatment. For example, transcripts for glutamate dehydrogenase and invertase show a rapid overshoot and a lower but sustained increase in WT in an extended night, and increase transiently at the end of the night in pgm (Figure 1). The activities rise gradually over several days in an extended night, and show a marked increase in pgm that is maintained across the entire diurnal cycle. An analogous response is found for many enzymes involved in respiratory metabolism, nitrogen assimilation and amino acid synthesis, including fructokinase, NAD-glyceraldehyde-3P dehydrogenase, PPi-phosphofructokinase, phosphoenolpyruvate carboxylase, NADP-isocitrate dehydrogenase, ferredoxin-glutamate synthase, alanine and aspartate aminotransferases, fumarase, shikimate dehydrogenase, and transketolase. In this case, the transcript levels fall rapidly in a prolonged night, but the activities do not decrease until several days later. Their activities during the diurnal cycle are lower in pgm than WT.
Our approach requires that these measurements of enzyme activity can be used as a surrogate for measurements of protein levels. In these assays, the reaction product is determined via highly sensitive enzymatic cycling systems , which allow the use of highly diluted extracts. All optimized assays were shown to be linear with time and independent of the extract concentration, indicating that they are not compromised by inhibitory compounds in the extracts. Substrate levels and other assay conditions were optimized to allow measurement of Vmax activity . In selected cases, immunoassays were used to confirm that the changes in activity match the changes in protein level, measured by  (and unpublished data).
Diurnal changes in metabolite levels in WT Arabidopsis
Metabolites with smooth diurnal oscillations in Arabidopsis Col0 WT plants growing in 12 h day and 12 h night cycles.
Ubiquinone-50 (Coenzyme Q10)
Linoleate (C18:cis[9, 12]2)
Glycerol-3-P (polar fraction)
Particularly large diurnal changes were found for sugars (sucrose, glucose, fructose), photorespiratory intermediates (glycine, serine and glycerate) and, to a lesser extent, other amino acids (Figure 2). Hexoses peaked relatively early in the photoperiod (2 to 4 h), as has also been seen in other species [33, 34]. UDP-glucose peaked at 6 h and sucrose at the end of the day. Malate and fumarate rose until the end of the light period, while succinate decreased during the day and rose during the first hours of darkness (Figure 2). Accumulation of malate during the light period has been previously reported in other species, and may be related to the accumulation of malate as a counter-anion of nitrate, which decreases during the light period due to rapid assimilation of nitrate .
Among the fatty acids, palmitolenate (C16:2), stearate (C18:0), linolenate (C18:cis[9,12]2) and palmitate (C16:0) had a clear diurnal rhythm (Figure 2), with maxima at the end of the day and minima at the end of the night. The chloroplast contains up to 85% of the total lipids in Arabidopsis rosettes, mainly in the thylakoids , making it likely that large diurnal changes must reflect changes in this compartment. Palmitolenate (C16:2), which exhibits the strongest oscillations, is exclusively located within the chloroplast. This fatty acid is mainly present as a constituent of 1-18:2-2-16:2-monogalactosyldiacylglycerol, and is synthesized via the glycosylglyceride desaturation pathway, which takes place in the chloroplast .
Changes in metabolites in a prolonged night and during diurnal changes in the starchless pgmmutant
During a prolonged night, many metabolites showed gradual but marked changes. This included a large decrease in the levels of organic acids and shikimate (an intermediate in the aromatic amino acid biosynthesis pathway), a marked decrease in C16:2 and smaller decreases in other fatty acids, including C18:0. C18:2, C18:3, and C20:1, a decrease in inositol, ribonate, gluconate and isopentenyl pyrophosphate, and a marked increase in many amino acids due to release during catabolism of proteins .
The pgm mutant is characterized by a daily alternation between elevated levels of sugars in the light, and low levels of sugars in the dark. It has already been shown that most of the genes that undergo larger diurnal changes in pgm are responding to the low levels of sugars in the night, rather than the higher levels of sugars in the day . The finding that the metabolite profile of pgm leaves (with the exception of sugars and a few other metabolites) resembles that of WT plants after a prolonged dark treatment reveals that carbon starvation acts via long term mechanisms to regulate the levels of many metabolites, and generate a low-carbon metabolic phenotype. This phenotype will reflect the response of large numbers of enzymes across several sectors of central metabolism. It provides general support for the conclusions drawn from a subset of 23 enzymes in .
Comparison of the amplitudes of the changes in transcript levels, enzyme activities and metabolites in diurnal cycles
Figure 7 shows the frequency distribution of the amplitudes of the diurnal changes of transcript levels for all 2,433 genes assigned to metabolism by the MapMan ontology [22, 32] (Figure 7a; [Additional data file 2]), the 82 genes that encode the enzymes treated in this paper (Figure 7b), 23 enzyme activities (Figure 7c), and 137 metabolites (Figure 7d). The data for WT and pgm diurnal cycles are shown separately. The x axis shows the amplitude of the diurnal change (expressed as (max-min)/max), and the y-axis shows the proportion of genes that show an amplitude in that magnitude. Although current data processing of Affymetrix arrays may underestimate the extent of changes in transcript levels by a factor of two to three [41, 42], this should not lead to serious error when the amplitudes are compared because this involves comparison of relative changes.
In WT, the peak values were approximately 0.15 for transcripts, and approximately 0.2 for metabolites and enzymes. The amplitudes of the changes of transcript levels were slightly larger for the 82 transcripts that encode the enzymes measured in  than for all 2,433 genes assigned to metabolism. The spread of amplitudes is larger for transcripts than enzymes. While most metabolites show smaller amplitudes, some show comparable diurnal changes to the most strongly responding transcripts. The pgm mutant has larger diurnal changes in transcript levels (approximately 0.28) but similar diurnal changes in enzyme activities and metabolites (approximately 0.2) to those in WT. There was a shift to a bi-modal distribution curve in pgm, with substantial numbers of transcripts, some enzyme activities and a few metabolites (mainly sugars) undergoing a diurnal change with larger amplitude. This analysis illustrates in a condensed form that large diurnal changes in transcript levels do not lead to a systematic increase of the amplitudes of the diurnal changes in enzyme activities or metabolites.
Comparison of the temporal dynamics of the changes in transcript levels, enzyme activities and metabolites in a prolonged night
An analogous approach was taken to compare the speed and extent of the changes in metabolites, transcript levels and enzyme activities in WT during a prolonged night (Figure 8). All values were normalized on a reference value at the end of the normal night. The normalized values are shown as a series of frequency plots, which compare the amplitudes of the changes of transcript levels (Figure 8a), enzyme activities (Figure 8b) and metabolites (Figure 8c) after different times in an extended dark treatment. Figure 8a shows the changes for all 2,433 genes assigned to metabolism by the MapMan ontology. A similar result was obtained with the genes encoding the set of enzymes (not shown). After a 2 h extension of the night, a small subset of metabolites, including glucose, fructose, and glycerate, showed a marked change in their level. By 4 h, changes in transcript levels were becoming marked and by 8 h these were more widespread than the changes in metabolites. At this time, there were only minimal changes in enzyme activities. After 24 and 48 h, the changes in transcript levels became even larger and changes in enzyme activities became apparent, while the changes in metabolites became only slightly larger. Only data for enzyme activities and metabolites are available for 72 and 144 h. At these times, there were large changes in sets of enzyme activities and metabolites.
Comparison of changes in specific transcripts, enzyme activities and metabolites
The data set was next inspected to identify examples where changes of metabolites in pgm or prolonged darkness can be associated with the induction or repression of specific pathway genes and/or variations in enzyme activities.
In central carbon metabolism, the accumulation of sugars in pgm in the light includes an increase of sucrose and a particularly large increase of glucose and fructose. The pgm mutant has increased levels of transcripts for most of the gene family for sucrose-P synthase, a small increase in sucrose-P synthase activity, a large increase in the levels of transcripts for two and a small increase in the levels of transcripts for another three genes encoding acid invertase, and a large increase in acid invertase activity (Figures 1 and 4). The lower levels of transcripts and activities for enzymes involved in glycolysis and organic acid synthesis in WT in prolonged darkness and in pgm is accompanied by lower average levels of pyruvate and, to a lesser extent, malate and fumarate. It should be noted that there are also changes in these metabolites within the diurnal cycles, and that these are not related to momentary changes in the enzyme activity (as measured in optimized conditions in vitro). Thus, changes in enzyme levels contribute to the mid-term shifts of metabolite levels, but are not responsible for the shorter-term changes within an individual diurnal cycle. The same holds for many of the other metabolites discussed in this section.
Qualitative agreement was also found between changes in transcript levels, enzyme activities and mid-term changes in metabolites in nitrogen metabolism. The levels of glutamine and glutamate were always lower in pgm than in WT Arabidopsis (Figure 4), as were the activities of nitrate reductase, glutamine synthetase and ferredoxin-glutamate synthase and transcript levels for the corresponding genes (Figure 1). It is known that nitrate reductase expression is regulated by sugars, acting at the level of transcription, translation and protein stability . The levels of most minor amino acids, including the aromatic and branched chain amino acids, increased in a prolonged night and in pgm. This was associated with increased levels of transcripts for genes assigned to amino acid degradation, including GDH and several genes annotated as branched chain amino acid dehydrogenases [22, 37], and increased glutamate dehydrogenase activity (Figure 4).
In some cases, there is agreement between the changes in the levels of transcripts and metabolites, but enzyme activities are not available to establish a clear correlation between all three levels. For example, the lower levels of inositol found in pgm and WT plants exposed to several days of darkness were associated with the strong induction of MIOX2 and MIOX4, which encode related inositol oxidases  (Figure 9b; [Additional data file 3]). The decreased levels of isopentenyl pyrophosphate observed after several days of prolonged darkness and in the pgm mutant were related to coherent changes in the levels of transcripts of a large proportion of genes encoding enzymes from the non-mevalonate pathway. Typically, these transcripts dropped strongly at night in pgm, or in WT plants transferred to a prolonged night ([Additional data file 3]). In contrast, no consistent changes were found within the mevalonate pathway. This suggests that at low carbon levels, the decrease in isopentenyl pyrophosphate synthesis mainly occurs within the chloroplast, as the non-mevalonate pathway is located in the plastids and the mevalonate pathway is in the cytosol .
In other cases, there are discrepancies between the functional levels. Cytosolic fructose-1,6-bisphosphatase and ADP-glucose pyrophosphorylase activity change independently of the levels of the corresponding transcripts. This discrepancy indicates that translation or degradation of these enzymes is regulated. These two enzymes and NADP-glyceraldehyde-3P dehydrogenase activity also respond differently in pgm and in a prolonged night, with activity being lower during the diurnal cycle in pgm, especially in the light, but unchanged or even increased in a prolonged night (Figure 1). One possibility is that the high sugar levels during the light period in pgm inhibits translation and/or promotes degradation of these proteins. Another example relates to shikimate dehydrogenase: lower levels of shikimate in pgm and in a prolonged dark treatment correlate with decreased activity of shikimate dehydrogenase, but there are no marked changes in SDH transcript levels. This indicates that post-transcriptional mechanisms contribute to the regulation of this enzyme.
There are also cases where discrepancies are already apparent, even though only two of the three functional levels have been analyzed. The increase in tocopherols in extended darkness and in pgm could not be related to any clear change at the level of transcripts for genes involved in tocopherol synthesis (data not shown). A similar picture emerged for ferulate, which decreased in a prolonged night and was lower in the pgm mutant. In these examples, measurements of enzyme activity or protein will be needed to define whether the changes in metabolites are due to translational or post-translational regulation.
Comparison of the global relationship between metabolite levels and transcript levels
The data set was also analyzed to detect correlations between metabolite and transcript levels. The relatively slow response of enzyme activities and most metabolites to changes in transcript levels indicates that most correlations during short term responses will be due to regulation of gene expression by metabolites, rather than vice versa.
To provide independent evidence that expression of these genes may be regulated by sugars or closely related metabolites, several transcript profiling data sets from published experiments in which sugar levels were changed by several different methods were inspected to complete a list of 1,312 'sugar responsive' genes. The criteria were that the genes show: a >2-fold change after addition of 15 mM glucose or 15 mM sucrose to Arabidopsis seedlings that had been carbon-starved for two days; and a >2-fold change between Arabidopsis rosettes that had been illuminated for 4 h in the presence of ambient [CO2] or low [CO2] to prevent photosynthesis . The procedure is described in , where it is additionally shown that about 70% of these sugar-regulated genes show diurnal changes in WT, and even more in pgm. A list of these genes is provided in [Additional data file 4]. Figure 11a shows, for the genes whose transcript levels correlate positively or negatively with glucose, sucrose or glucose-6-P in the combined data set, what proportion is found in this list of 1,312 'sugar-responsive' genes. There was an increasingly large overlap as the p value was increased (Figure 11b). At p values <0.001, the highest overlap was found for the genes that correlated with glucose-6-P and sucrose (847 genes, that is to say >60% of sugar responsive genes).
In a reverse comparison, we asked what proportion of the 1,312 sugar-responsive genes shows highly significant p values (<10-3) with sucrose, glucose-6-phoshopate or glucose in the combined data set. To do this, the genes showing a correlation with glucose, sucrose or glucose-6-P with a p value <10-3 (Figure 11a) were filtered, by requiring that they should be in the list of 1,312 'sugar-responsive' genes. This operation can be performed in a spreadsheet provided in [Additional data file 4]. Of the 1,312 genes in the 'sugar-responsive' set, 426 showed positive and 125 negative correlations with glucose, 626 positive and 408 negative correlations with sucrose, and 579 positive and 371 negative correlations with glucose-6-phosphate in the combined data set. More genes correlate with changes of sucrose or glucose-6-P than with glucose. Further, sucrose and glucose-6-P induce and repress similar proportions of genes during diurnal cycles, whereas glucose acts mainly to induce gene expression (Figure 11a). In total, this analysis identified a robust core of 1,141 genes whose transcripts correlate with endogenous changes in sugars, and also changes in response to addition of sugars and to a treatment that alters the endogenous sugar level.
Hexoses are sensed via at least three pathways, including one that does not require phosphorylation, one that requires hexokinase as a sensor, and one that requires phosphorylation of glucose , whereas there may be separate sucrose-specific sensing and signaling pathways . The sensing mechanisms for sucrose are largely unknown. There is considerable overlap between genes that correlate with sucrose or glucose-6-phosphate, but little overlap between these and the genes that correlate with glucose (Figure 11b). It is tempting to speculate that glucose and sucrose act via different pathways. To further test this hypothesis, we inspected the responses of a set of 363 genes that respond >2-fold 30 minutes after adding sucrose to carbon-starved seedlings (Osuna D, Usadel B, Morcuende R, Gibon Y, Bläsing OE, Höhne M, Günter M, Kamlage B, Trethewey R, Scheible WR, and Stitt M, unpublished). Of these 363 genes, 194 and 188 were correlated at a p value of <0.001 to sucrose or glucose-6-P, respectively. Only 68 genes were found to be correlated to glucose at this p value.
Blaesing et al.  used a set of filters to identify genes whose expression might be subject to sugar-regulation during the diurnal cycle. The correlative approach in the present paper provides a refined list of candidate genes that may be regulated by a particular sugar or their derivatives ([Additional data file 4]). To assign genes to a given sugar, we performed a ranking based on the p values ([Additional file 4]). With stringent p values below 10-6, 8 genes were assigned to glucose, 493 to sucrose, and 211 to glucose-6-P. Strikingly, all six sugar-regulated genes encoding trehalose phosphate synthases or phosphatases were highly correlated to sucrose and/or to glucose-6P, namely At4g17770 (TPS5), At1g70290 (TPS8), At1g23870 (TPS9), At1g60140 (TPS10) and At2g18700 (TPS11) and At2g22190 (TPP H). An over-representation analysis using Fisher exact tests as in  was performed using MapMan [22, 32] BINs as categories to identify functional groups of genes that may respond to a given sugar. This approach was carried out with genes that were positively correlated to the various sugars at different p value thresholds. Genes involved in protein biosynthesis were over-represented among genes for which transcript levels are positively correlated to sugars (see [Additional data file 4] for accession codes) and genes involved in protein degradation were over-represented among the negatively correlated genes (Fisher exact test; see [Additional data file 4] for accession codes). This suggests the occurrence of tight links between sugar sensing and protein turnover. Previous studies have shown that a number of genes involved in protein synthesis are repressed and genes involved in protein degradation are induced in carbon starved Arabidopsis [22, 50].
About 5,500 genes correlate (p <10-3) with sucrose, glucose or glucose-6-P in diurnal changes and extended night treatments (Figure 10a,b), but are absent from the list of 1,312 'sugar-responsive' genes. These correlations may be due to secondary effects, or the genes might have been excluded from the list of 'sugar responsive' genes because they do not respond in one of the treatments used as filters in compiling this list. Similarly, genes that are regulated by a sugar-related input will not be necessarily correlated to endogenous sugar levels in a given set of treatments. For some genes, sugar repression only takes places below a given threshold, which is not passed in a diurnal cycle in WT plants. For example, the transcripts encoding inositol oxidases, MIOX2 and MIOX4, show little or no diurnal variations in WT (Figure 8b), but respond strongly to carbon starvation in both WT in a prolonged night and pgm at night.
Further experiments are needed to validate these correlations, and establish whether they reflect a causal relationship in which metabolites directly or indirectly regulate gene expression. Use of a wider range of conditions might exclude some false positives. However, stringent validation will require additional strategies, for example, the use of reverse genetics to generate small changes in the levels of specific metabolites. A two- to three-fold decrease in protein level and enzyme activity typically has little or no impact on the pathway flux, but often leads to small shifts in the levels of the substrates, products and other ligands of the enzyme, and other closely linked metabolites [51, 52]. A partial inhibition of gene expression can be obtained using techniques like antisense RNA or interference RNA. The availability of large collections of knock-out mutants may allow a general strategy to be used, in which heterozygotes are used to partially inhibit enzyme activity. For many enzymes, activity is halved in a heterozygote between the WT and a null mutant . A further possibility is the use of inducible gene expression to generate small and reversible changes in the levels of specific metabolites.
A particular problem in multicellular eukaryotes is that cellular or subcellular compartmentation can mask correlations between a specific pool of a metabolite and the transcript level. For example, whereas sucrose is distributed between the cytoplasm and vacuole in leaves, the vast majority of the glucose is located in the vacuole . The poor correlation between transcript levels and glucose noted above shows that vacuolar glucose is not a major signal, but it remains possible that other smaller pools of glucose in other compartments, or fluxes of glucose between compartments, act as signals. In principle, techniques are available to allow comprehensive measurements of subcellular metabolite levels . However, such measurements would be very time consuming, and would not provide reliable information about minor pools due to errors in correcting for cross-contamination. Powerful technologies are emerging that use imaging techniques to measure the local concentrations of specific metabolites [54, 55]. A complementary strategy would be to use reverse genetics to generate targeted changes in metabolites in specific compartments. For example, overexpression of invertase in the vacuole, the cytosol and the cell wall space can be used as a strategy to alter the sucrose/reducing sugar ratio in these different metabolic compartments . Notwithstanding current limitations, the occurrence of highly significant correlations in light/dark cycles and their independent validation in independent experiments in which sugars are added or endogenous pools are manipulated by changing [CO2] provides an initial step in dissecting these interactions.
It is not yet possible to systematically establish a comprehensive gene-protein-metabolite network in plants, due to theoretical limitations in current gene annotations and technical limitations that prevent the measurement of all enzymes and metabolites (see Background). However, analysis of the dynamics of enzymes and metabolites that are technically accessible does allow a general comparison of responses and dynamics at these different levels of metabolic function, provided enough parameters are analyzed to obtain a representative picture of the response at each functional level. In the experimental systems studied in this article, levels of transcripts and some metabolic intermediates in central metabolism show rapid changes, but the majority of the 137 metabolites investigated show slow changes, which reflect the dynamics with which changes in transcript levels lead to changes in 23 enzyme activities. These results have two important implications. First, the enzyme activity profile and the metabolite profile represent an integration, over time, of faster but more transient changes in transcript levels. This may reflect the fact that plants are subject to recurrent diurnal changes and many other irregular fluctuations with a time frame of hours, days or weeks. The temporal dynamics of enzyme turnover may be hardwired to allow the metabolic phenotype to adjust to changes when they are maintained for several days, while being largely independent of short term fluctuations or recurring diurnal changes. Second, the slow response of enzyme activities, and the resulting time delay between changes in transcript levels and changes in metabolite levels, implies that correlations between transcripts and metabolites are likely to reflect a regulatory impact of metabolites on gene expression, rather than the impact of changes of gene expression on metabolism.
Even though a comprehensive analysis is not yet possible, analysis of the responses of individual parameters at all three levels already provides some functional information. Analysis of the responses of transcript and metabolite levels identified over 1,141 transcripts, whose levels are strongly correlated with endogenous changes of sugars, and half of which could be cross-validated by analyzing how they respond to changes in levels of sugars in other experimental systems. Further experiments will be required, for example using genotypes with altered levels of specific enzymes or transporters, to generate more specific changes in the levels of individual metabolites and their subcellular pools and identify the underlying metabolic signals more precisely. Analyses of the combined changes in transcript levels, enzyme activities and metabolites also identified cases where changes in expression could contribute to the changes in metabolism, and other cases where additional regulation at the level of protein synthesis or degradation is probably required.
More generally, our results show that comprehensive information about protein levels is required to dissect the causal relationship between transcription and cellular responses. The present analysis used robotized enzyme assays as a proxy for protein levels. Enzyme activity measurements are precise, have a relatively high throughput, do not require expensive or complex infrastructure and can be used in species where comprehensive genome sequence data are not available. However, it is sometimes not possible to unambiguously link the measured enzyme activity to a single gene, and this approach is only applicable to a small sector of the genome, that is, genes that encode enzymes whose activities can be easily measured. Advances in hardware and data evaluation software are currently allowing advances in quantitative proteomics [3, 12]. This will ultimately allow comprehensive analysis of the dynamics of all types of proteins, including those involved in signaling and cellular structure.
Arabidopsis thaliana var Col0, WT, and a plastidic pgm  were grown in an 8 h day growth chamber. At least 3 weeks before their use, the plants were transferred into a small growth cabinet with a 12 h day of 160 μE and 20°C throughout the day/night cycle. Harvests of 15 plant rosettes at a time point were carried out sequentially every 2 or 4 h within a day/night cycle, or after 0, 2, 4, 8, 24, 48, 72 and 144 h in total darkness. Each sample typically contained 3 rosettes, equivalent to approximately 500 mg fresh weight. The entire sample was powdered under liquid nitrogen and stored at -80°C until its use. Each experiment was repeated two times.
RNA isolation and expression analysis with 22K Affymetrix arrays
Isolation of total RNA, cDNA synthesis, cRNA labeling and the hybridization on the GeneChip Arabidopsis ATH1 genome array was done as described in  and recommended by the manufacturer (part no. 900385, Affymetrix UK Ltd., High Wycombe, UK). The microarray suite software package (MAS 5.0, Affymetrix) was used to evaluate probe set signals of the array. The generated data files (.cel) were the input for the software package RMAExpress, which was used to normalize and estimate raw signal intensities . Normalization was performed on the entire set of transcript profiles used in the present study, that is, WT and pgm diurnal cycles , and WT transferred to 0, 2, 4, 8, 24 and 48 h of extension of the night ([Additional data file 2]).
Extraction and assay of metabolites
Batches of 15 samples, and 5 control samples obtained from 200 Col0 rosettes grown in the greenhouse, pooled and homogenized, were used. The analysis of metabolites by GC-MS was performed as described in . The LC-MS , analyses were performed using an Agilent 1100 capillary LC system (Agilent, Technologies, Waldbronn, Germany) coupled with an Applied Biosystems/MDS SCIEX API 4000 triple quadrupole mass spectrometer (Applied Biosystems, Darmstadt, Germany). After reversed phase HPLC separation, detection and quantification was performed in the multiple reaction monitoring (MRM) mode . Results are expressed as ratios between samples and the median calculated for control samples. Sucrose, glucose, fructose and glucose-6P were extracted and measured as in .
Extraction and assay of glycerol-3P dehydrogenase
Aliquots of 20 mg fresh weight were extracted by vigorous mixing with 1 ml extraction buffer. The composition of the extraction buffer was 20% (v/v) glycerol, 0.25% (w/v) bovine serum albumin, 1% (v/v) Triton-X100, 50 mM Hepes/KOH pH 7.5, 10 mM MgCl2, 1 mM EDTA, 1 mM EGTA, 1 mM benzamidine, 1 mM aminocapronic acid, 10 μM leupeptin and 1 mM DTT.
Glycerol-3P dehydrogenase activity was determined in microplates using a stopped assay, in which 2 μl extract as well as glycerol-3P standards prepared in the extraction buffer and ranging from 0 to 100 μM were incubated for 30 minutes in 20 μl medium containing 50 mM Hepes/KOH pH 7.5, 10 mM MgCl2, 0.5 mM NADH, and 0 or 1 mM dihydroxyacetone-P. The reaction was stopped with 20 μl of 0.5 M NaOH. After heating for 10 minutes at 95°C to destroy dihydroxyacetone-P , the wells were neutralized with 20 μl of 0.5 M HCl. Glycerol-3P was then determined as in .
Standard procedures were carried out using functions of the Microsoft Excel program. Heat maps were generated in Excel using a macro. Smoothness was calculated as described in . Density plots were calculated in R  using the 'density plot' function. Smoothing was achieved by calculating means for two consecutive points.
Correlation networks were generated by calculating all pair-wise correlations in R and considering all pair-wise correlations with a significant p value (0.05) calculated as described earlier  as connected. The resulting network was then displayed using Pajek  using the Kamada layout process option. When displaying correlations of transcripts and metabolites, the number of transcripts correlated with a metabolite was indicated by the size of the metabolite token.
Genes showing a significant and strong correlation to glucose, fructose and sucrose were extracted from the list of sugar regulated genes generated by  using an R script. These were then tested for a deviation of biological processes defined by MapMan BINS from all approximately 1,312 sugar regulated genes using Fisher's exact test.
All scripts and macros are available upon request.
The following additional data are available with the online version of this paper. Additional data file 1 lists metabolite levels in WT and pgm throughout a 12 h day/12 h night cycle, and in WT plants transferred to an extended night for 2, 4, 8, 24, 48, 72 and 148 h. Data are expressed as means and standard deviation (n = 5) of ratios to the median calculated for a set of 5 control samples. Additional data file 2 lists Affymetrix array data for the conditions described. Data are given as means of RMA-normalized data, and numbers of replicates are indicated. Additional data file 3 lists levels of transcripts encoding enzymes involved in the glycerolipid and inositol metabolism, and the mevalonate and non-mevalonate pathways. Transcript levels were determined using the Affymetrix ATH1 array.Additional data file 4 lists correlation coefficients (Pearson) calculated between sugar responsive genes and sucrose, glucose or fructose. Sugar responsive genes were retrieved from . Significant correlation coefficients are indicated in bold.
This research was supported by the Max Planck Society and by the German Ministry for Research and technology, in the framework of the German plant genomics program GABI (0312277A, 0313110). The support of Ralf Looser and the Bioanalytics Technical Center at metanomics in performing the metabolic analyses is gratefully acknowledged. Thanks are due to Linda Bartezko and Manuela Guenther for technical assistance.
- Willmitzer L: The use of transgenic plants to study plant gene-expression. Trends Genet. 1988, 4: 13-18. 10.1016/0168-9525(88)90122-9.PubMedView ArticleGoogle Scholar
- Stitt M, Sonnewald U: Regulation of metabolism in transgenic plants. Annu Rev Plant Biol. 1995, 46: 341-361. 10.1146/annurev.pp.46.060195.002013.View ArticleGoogle Scholar
- Greenbaum D, Colangelo C, Williams K, Gerstein M: Comparing protein abundance and mRNA expression levels on a genomic scale. Genome Biol. 2003, 4: 117-10.1186/gb-2003-4-9-117.PubMedPubMed CentralView ArticleGoogle Scholar
- Gibon Y, Blaesing OE, Hannemann J, Carillo P, Hoehne M, Hendriks JHM, Palacios N, Cross J, Selbig J, Stitt M: A Robot-based platform to measure multiple enzyme activities in Arabidopsis using a set of cycling assays: comparison of changes of enzyme activities and transcript levels during diurnal cycles and in prolonged darkness. Plant Cell. 2004, 16: 3304-3325. 10.1105/tpc.104.025973.PubMedPubMed CentralView ArticleGoogle Scholar
- Hirai MY, Yano M, Goodenowe DB, Kanaya S, Kimura T, Awazuhara M, Arita M, Fujiwara T, Saito K: Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci USA. 2004, 101: 10205-10210. 10.1073/pnas.0403218101.PubMedPubMed CentralView ArticleGoogle Scholar
- Hirai MY, Klein M, Fujikawa Y, Yano M, Goodenowe DB, Yamazaki Y, Kanaya S, Nakamura Y, Kitayama M, Suzuki H, et al: Elucidation of gene-to-gene and metabolite-to-gene networks in arabidopsis by integration of metabolomics and transcriptomics. J Biol Chem. 2005, 280: 25590-25595. 10.1074/jbc.M502332200.PubMedView ArticleGoogle Scholar
- Oresic M, Clish CB, Davidov EJ, Verheij E, Vogels J, Havekes LM, Neumann E, Adourian A, Naylor S, van der Greef J, Plasterer T: Phenotype characterisation using integrated gene transcript, protein and metabolite profiling. Appl Bioinformatics. 2004, 3: 205-217. 10.2165/00822942-200403040-00002.PubMedView ArticleGoogle Scholar
- DeRisi JL, Iyer VR, Brown PO: Exploring the metabolic and genetic control of gene expression on a genomic scale. Science. 1997, 278: 680-686. 10.1126/science.278.5338.680.PubMedView ArticleGoogle Scholar
- Redman JC, Haas BJ, Tanimoto G, Town CD: Development and evaluation of an Arabidopsis whole genome Affymetrix probe array. Plant J. 2004, 38: 545-561. 10.1111/j.1365-313X.2004.02061.x.PubMedView ArticleGoogle Scholar
- The Arabidopsis Functional Genomics Network. [http://web.uni-frankfurt.de/fb15/botanik/mcb/AFGN/atgenex.htm]
- Peck SC: Update on proteomics in Arabidopsis. Where do we go from here?. Plant Physiol. 2005, 138: 591-599. 10.1104/pp.105.060285.PubMedPubMed CentralView ArticleGoogle Scholar
- Horak CE, Snyder M: Global analysis of gene expression in yeast. Funct Integr Genomics. 2002, 2: 171-180. 10.1007/s10142-002-0065-3.PubMedView ArticleGoogle Scholar
- Fiehn O, Kopka J, Dormann P, Altmann T, Trethewey RN, Willmitzer L: Metabolite profiling for plant functional genomics. Nat Biotechnol. 2000, 18: 1157-1161. 10.1038/81137.PubMedView ArticleGoogle Scholar
- Sumner LW, Mendes P, Dixon RA: Plant metabolomics: large-scale phytochemistry in the functional genomics era. Phytochemistry. 2003, 62: 817-836. 10.1016/S0031-9422(02)00708-2.PubMedView ArticleGoogle Scholar
- Stitt M, Fernie AR: From measurements of metabolites to metabolomics: an 'on the fly' perspective illustrated by recent studies of carbon-nitrogen interactions. Curr Opin Biotechnol. 2003, 14: 136-144. 10.1016/S0958-1669(03)00023-5.PubMedView ArticleGoogle Scholar
- Kopka J, Fernie A, Weckwerth W, Gibon Y, Stitt M: Metabolite profiling in plant biology: platforms and destinations. Genome Biol. 2004, 5: 109-10.1186/gb-2004-5-6-109.PubMedPubMed CentralView ArticleGoogle Scholar
- Roessner U, Wagner C, Kopka J, Trethewey RN, Willmitzer L: Technical advance: simultaneous analysis of metabolites in potato tuber by gas chromatography-mass spectrometry. Plant J. 2000, 23: 131-142. 10.1046/j.1365-313x.2000.00774.x.PubMedView ArticleGoogle Scholar
- Roessner U, Luedemann A, Brust D, Fiehn O, Linke T, Willmitzer L, Fernie A: Metabolic profiling allows comprehensive phenotyping of genetically or environmentally modified plant systems. Plant Cell. 2001, 13: 11-29. 10.1105/tpc.13.1.11.PubMedPubMed CentralView ArticleGoogle Scholar
- von Roepenack-Lahaye E, Degenkolb T, Zerjeski M, Franz M, Roth U, Wessjohann L, Schmidt J, Scheel D, Clemens S: Profiling of Arabidopsis secondary metabolites by capillary liquid chromatography coupled to electrospray ionization quadrupole time-of-flight mass spectrometry. Plant Physiol. 2004, 134: 548-559. 10.1104/pp.103.032714.PubMedPubMed CentralView ArticleGoogle Scholar
- Ratcliffe RG, Shachar-Hill Y: Probing plant metabolism with NMR. Annu Rev Plant Physiol Plant Mol Biol. 2001, 52: 499-526. 10.1146/annurev.arplant.52.1.499.PubMedView ArticleGoogle Scholar
- Krishnan P, Kruger NJ, Ratcliffe RG: Metabolite fingerprinting and profiling in plants using NMR. J Exp Bot. 2005, 56: 255-265. 10.1093/jxb/eri010.PubMedView ArticleGoogle Scholar
- Thimm O, Blaesing O, Gibon Y, Nagel A, Meyer S, Kruger P, Selbig J, Muller LA, Rhee SY, Stitt M: MAPMAN: a user-driven tool to display genomics data sets onto diagrams of metabolic pathways and other biological processes. Plant J. 2004, 37: 914-939. 10.1111/j.1365-313X.2004.02016.x.PubMedView ArticleGoogle Scholar
- Urbanczyk-Wochniak E, Baxter C, Kolbe A, Kopka J, Sweetlove LJ, Fernie AR: Profiling of diurnal patterns of metabolite and transcript abundance in potato (Solanum tuberosum) leaves. Planta. 2005, 221: 891-903. 10.1007/s00425-005-1483-y.PubMedView ArticleGoogle Scholar
- Lafaye A, Junot C, Pereira Y, Lagniel G, Tabet JC, Ezan E, Labarre J: Combined proteome and metabolite-profiling analyses reveal surprising insights into yeast sulfur metabolism. J Biol Chem. 2005, 280: 24723-24730. 10.1074/jbc.M502285200.PubMedView ArticleGoogle Scholar
- Mijalski T, Harder A, Halder T, Kersten M, Horsch M, Strom TM, Liebscher HV, Lottspeich F, de Angelis MH, Beckers J: Identification of coexpressed gene clusters in a comparative analysis of transcriptome and proteome in mouse tissues. Proc Natl Acad Sci USA. 2005, 102: 8621-8626. 10.1073/pnas.0407672102.PubMedPubMed CentralView ArticleGoogle Scholar
- Irizarry RA, Bolstad BM, Collin F, Cope LM, Hobbs B, Speed TP: Summaries of Affymetrix GeneChip probe level data. Nucleic Acids Res. 2003, 31: e15-10.1093/nar/gng015.PubMedPubMed CentralView ArticleGoogle Scholar
- Li C, Hung Wong W: Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application. Genome Biol. 2001, 2: research0032.1-0032.11. 10.1186/gb-2001-2-8-research0032.Google Scholar
- Thimm O, Blaesing O, Usadel B, Gibon Y: Evaluation of the transcriptome and genome to inform the study of metabolic control. Control of Primary Metabolism in Plants. Edited by: Plaxton B, McManus M. 2006, Oxford (UK): Blackwell Publishing, 22: 1-23. Robert J (series editor): Annual Plant Reviews.View ArticleGoogle Scholar
- Nikiforova VJ, Daub CO, Hesse H, Willmitzer L, Hoefgen R: Integrative gene-metabolite network with implemented causality deciphers informational fluxes of sulphur stress response. J Exp Bot. 2005, 56: 1887-1896. 10.1093/jxb/eri179.PubMedView ArticleGoogle Scholar
- Morgenthal K, Wienkoop S, Scholz N, Weckwerth W: Correlative GC-TOF-MS-based metabolite profiling and LC-MS-based protein profiling reveal time-related systemic regulation of metabolite-protein networks and improve pattern recognition for multiple biomarker selection. Metabolomics. 2005, 1: 109-121. 10.1007/s11306-005-4430-9.View ArticleGoogle Scholar
- Mueller LA, Zhang P, Rhee SY: AraCyc: a biochemical pathway database for Arabidopsis. Plant Physiol. 2003, 132: 453-460. 10.1104/pp.102.017236.PubMedPubMed CentralView ArticleGoogle Scholar
- Usadel B, Nagel A, Thimm O, Redestig H, Blaesing OE, Palacios-Rojas N, Selbig J, Hannemann J, Piques MC, Steinhauser D, et al: Extension of the visualization tool MapMan to allow statistical analysis of arrays, display of corresponding genes, and comparison with known responses. Plant Physiol. 2005, 138: 1195-1204. 10.1104/pp.105.060459.PubMedPubMed CentralView ArticleGoogle Scholar
- Matt P, Geiger M, Walch-Liu P, Engels C, Krapp A, Stitt M: The immediate cause of the diurnal changes of nitrogen metabolism in leaves of nitrate-replete tobacco: a major imbalance between the rate of nitrate reduction and the rates of nitrate uptake and ammonium metabolism during the first part of the light period. Plant Cell Environ. 2001, 24: 177-190. 10.1111/j.1365-3040.2001.00676.x.View ArticleGoogle Scholar
- Matt P, Geiger M, Walch-Liu P, Engels C, Krapp A, Stitt M: Elevated carbon dioxide increases nitrate uptake and nitrate reductase activity when tobacco is growing on nitrate, but increases ammonium uptake and inhibits nitrate reductase activity when tobacco is growing on ammonium nitrate. Plant Cell Environ. 2001, 24: 1119-1137. 10.1046/j.1365-3040.2001.00771.x.View ArticleGoogle Scholar
- Smith SM, Fulton DC, Chia T, Thorneycroft D, Chapple A, Dunstan H, Hylton C, Zeeman SC, Smith AM: Diurnal changes in the transcriptome encoding enzymes of starch metabolism provide evidence for both transcriptional and posttranscriptional regulation of starch metabolism in Arabidopsis leaves. Plant Physiol. 2004, 136: 2687-2699. 10.1104/pp.104.044347.PubMedPubMed CentralView ArticleGoogle Scholar
- Gibon Y, Blaesing OE, Palacios N, Pankovic D, Hendriks JHM, Fisahn J, Hoehne M, Günter M, Stitt M: Adjustment of diurnal starch turnover to short days: Depletion of sugar during the night leads to a temporary inhibition of carbohydrate utilisation, accumulation of sugars and post-translational activation of ADPglucose pyrophosphorylase in the following light period. Plant J. 2004, 39: 847-862. 10.1111/j.1365-313X.2004.02173.x.PubMedView ArticleGoogle Scholar
- Blaesing OE, Gibon Y, Guenther M, Hoehne M, Morcuende R, Osuna D, Thimm O, Usadel B, Scheible WR, Stitt M: Sugars and circadian regulation make major contributions to the global regulation of diurnal gene expression in Arabidopsis. Plant Cell. 2005, 17: 3257-3281. 10.1105/tpc.105.035261.View ArticleGoogle Scholar
- Caspar T, Huber SC, Somerville C: Alterations in growth, photosynthesis, and respiration in a starchless mutant of Arabidopsis-thaliana (L) deficient in chloroplast phosphoglucomutase activity. Plant Physiol. 1985, 79: 11-17.PubMedPubMed CentralView ArticleGoogle Scholar
- Stitt M, Mueller C, Matt P, Gibon Y, Carillo P, Morcuende R, Scheible WR, Krapp A: Steps towards an integrated view of nitrogen metabolism. J Exp Bot. 2002, 53: 959-970. 10.1093/jexbot/53.370.959.PubMedView ArticleGoogle Scholar
- Somerville C, Browse J: Dissecting desaturation: plants prove advantageous. Trends Cell Biol. 1996, 6: 148-153. 10.1016/0962-8924(96)10002-7.PubMedView ArticleGoogle Scholar
- Czechowski T, Bari RP, Stitt M, Scheible WR, Udvardi MK: Real-time RT-PCR profiling of over 1400 Arabidopsis transcription factors: unprecedented sensitivity reveals novel root- and shoot-specific genes. Plant J. 2004, 38: 366-379. 10.1111/j.1365-313X.2004.02051.x.PubMedView ArticleGoogle Scholar
- Scheible WR, Morcuende R, Czechowski T, Fritz C, Osuna D, Palacios-Rojas N, Schindelasch D, Thimm O, Udvardi MK, Stitt M: Genome-wide reprogramming of primary and secondary metabolism, protein synthesis, cellular growth processes, and the regulatory infrastructure of Arabidopsis in response to nitrogen. Plant Physiol. 2004, 136: 2483-2499. 10.1104/pp.104.047019.PubMedPubMed CentralView ArticleGoogle Scholar
- Meyer C, Stitt M: Nitrate reduction and signalling. Plant Nitrogen. Edited by: Morot-Gaudry JF, Lea PJ. 2001, INRA (France): Springer-Verlag, 37-59.View ArticleGoogle Scholar
- The Arabidopsis Lipid Gene Database. [http://www.plantbiology.msu.edu/lipids/genesurvey/Presentation_database.htm]
- Kanter U, Usadel B, Guerineau F, Li Y, Pauly M, Tenhaken R: The inositol oxygenase gene family of Arabidopsis is involved in the biosynthesis of nucleotide sugar precursors for cell-wall matrix polysaccharides. Planta. 2005, 221: 243-254. 10.1007/s00425-004-1441-0.PubMedView ArticleGoogle Scholar
- Hsieh M-H, Goodman HM: The Arabidopsis IspH homolog is involved in the plastid nonmevalonate pathway of isoprenoid biosynthesis. Plant Physiol. 2005, 138: 641-653. 10.1104/pp.104.058735.PubMedPubMed CentralView ArticleGoogle Scholar
- Moore B, Zhou L, Rolland F, Hall Q, Cheng WH, Liu YX, Hwang I, Jones T, Sheen J: Role of the Arabidopsis glucose sensor HXK1 in nutrient, light, and hormonal signalling. Science. 2003, 300: 332-336. 10.1126/science.1080585.PubMedView ArticleGoogle Scholar
- Smeekens S: Sugar-induced signal transduction in plants. Annu Rev Plant Physiol Plant Mol Biol. 2000, 51: 49-81. 10.1146/annurev.arplant.51.1.49.PubMedView ArticleGoogle Scholar
- Hannah MA, Heyer AG, Hincha DK: A global survey of gene regulation during cold acclimation in Arabidopsis thaliana. PLoS Genetics. 2005, 1: e26-10.1371/journal.pgen.0010026.PubMedPubMed CentralView ArticleGoogle Scholar
- Contento AL, Kim SJ, Bassham DC: Transcriptome profiling of the response of Arabidopsis suspension culture cells to Suc starvation. Plant Physiol. 2004, 135: 2330-2347. 10.1104/pp.104.044362.PubMedPubMed CentralView ArticleGoogle Scholar
- Sonnewald U, Brauer M, von Schaewen A, Stitt M, Willmitzer L: Transgenic tobacco plants expressing yeast-derived invertase in either the cytosol, the vacuole or the apoplast; a powerful tool to study sucrose metabolism and sink-source interactions. Plant J. 1991, 1: 95-106. 10.1111/j.1365-313X.1991.00095.x.PubMedView ArticleGoogle Scholar
- Neuhaus HE, Kruckeberg AL, Feil R, Gottlieb L, Stitt M: Dosage mutants of phosphoglucose isomerase in the cytosol and chloroplasts of Clarkia xantiana. II. Study of the mechanisms which regulate photosynthate partitioning. Planta. 1989, 178: 110-122. 10.1007/BF00392534.PubMedView ArticleGoogle Scholar
- Stitt M, Lilley RMC, Gerhardt R, Heldt HW: Determination of metabolite levels in specific cells and subcellular compartments of plant leaves. Methods Enzymol. 1989, 174: 518-552.View ArticleGoogle Scholar
- Fehr M, Frommer WB, Lalonde S: Visualization of maltose uptake in living yeast cells by fluorescent nanosensors. Proc Natl Acad Sci USA. 2002, 99: 9846-9851. 10.1073/pnas.142089199.PubMedPubMed CentralView ArticleGoogle Scholar
- Frehr M, Okumoto S, Deuschle K, Lager I, Looger LL, Person J, Kozhukh L, Lalonde S, Frommer WB: Development and use of fluorescent nanosensors for metabolite imaging in living cells. Biochem Soc Trans. 2005, 33: 287-290. 10.1042/BST0330287.View ArticleGoogle Scholar
- Bolstad BM, Irizarry RA, Astrand M, Speed TP: A comparison of normalization methods for high density oligonucleotide array data based on bias and variance. Bioinformatics. 2003, 19: 185-193. 10.1093/bioinformatics/19.2.185.PubMedView ArticleGoogle Scholar
- Niessen WM: Progress in liquid chromatography-mass spectrometry instrumentation and its impact on high-throughput screening. J Chromatogr A. 2003, 1000: 413-436. 10.1016/S0021-9673(03)00506-5.PubMedView ArticleGoogle Scholar
- Gergov M, Ojanperä I, Vuori E: Simultaneous screening for 238 drugs in blood by liquid chromatography-ionspray tandem mass spectrometry with multiple-reaction monitoring. J Chromatogr B. 2003, 795: 41-53. 10.1016/S1570-0232(03)00498-7.View ArticleGoogle Scholar
- Gibon Y, Vigeolas H, Tiessen A, Geigenberger P, Stitt M: Sensitive and high throughput metabolite assays for inorganic pyrophosphate, ADPGlc, nucleotide phosphates, and glycolytic intermediates based on a novel enzymic cycling system. Plant J. 2002, 30: 221-235. 10.1046/j.1365-313X.2001.01278.x.PubMedView ArticleGoogle Scholar
- The R Project for Statistical Computing. [http://www.R-project.org]
- Steinhauser D, Usadel B, Luedemann A, Thimm O, Kopka J: CSB.DB: a comprehensive systems-biology database. Bioinformatics. 2004, 20: 3647-3651. 10.1093/bioinformatics/bth398.PubMedView ArticleGoogle Scholar
- Batagelj V, Mrvar A: Pajek - Program for Large Network Analysis. Connections. 1998, 2: 47-57.Google Scholar
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