Plant Physiology Preview. Published on October 27, 2006, as DOI:10.1104/pp.106.088534
* The metabolism of fruit development
Corresponding author:
Alisdair R. Fernie
MPI-MPP
Am Mühlenberg 1
14476 Golm
Germany
Email: [email protected]
Fax :+49-331-5678408
Journal area: Biochemical Processes and Macromolecular Structures
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Copyright 2006 by the American Society of Plant Biologists
Integrated analysis of metabolite and transcript levels reveals
the metabolic shifts that underlie tomato fruit development and
highlight regulatory aspects of metabolic network behavior
Fernando Carrari1,2, Charles Baxter3, Björn Usadel1, Ewa Urbanczyk-Wochniak1,
Maria-Ines Zanor1, Adriano Nunes-Nesi1, Victoria Nikiforova1, Danilo Centero,
Antje Ratzka1, Markus Pauly1, Lee Sweetlove3 and Alisdair R. Fernie1*
1
Max-Planck-Institut für Molekulare Pflanzenphysiologie, Am Mühlenberg 1,
14476 Golm-Postdam, Germany.
2
Instituto de Biotecnología, CICVyA, Instituto Nacional de Tecnología Agrícola
(IB-INTA) Argentina, partner group of the Max Planck Institut für Molekulare
Pflanzenphysiologie.
3
Department of Plant Sciences, University of Oxford, South Parks Rd, Oxford,
OX1 3RB, United Kingdom.
2
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*Corresponding author; email: [email protected]; fax: +49 331
5678408
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Abstract
Tomato is a well studied model of fleshy fruit development and ripening. Tomato
fruit development is well understood from a hormonal-regulatory perspective and
developmental changes in pigment and cell wall metabolism are also well
characterized. However, more general aspects of metabolic change during fruit
development have not been studied despite the importance of metabolism in the
context of final composition of the ripe fruit. In this study we quantified the
abundance of a broad range of metabolites by GC-MS, analyzed a number of
the principal metabolic fluxes and in parallel analyzed transcriptomic changes
during tomato fruit development. Metabolic profiling revealed pronounced shifts
in the abundance of metabolites of both primary and secondary metabolism
during development. The metabolite changes were reflected in the flux analysis
which revealed a general decrease in metabolic activity during ripening.
However, there were several distinct patterns of metabolite profile and statistical
analysis demonstrated that metabolites in the same (or closely related)
pathways changed in abundance in a coordinated manner indicating a tight
regulation of metabolic activity. The metabolite data alone allowed investigations
of likely routes through the metabolic network and as an example we analyze the
operational feasibility of different pathways of ascorbate synthesis. When
combined with the transcriptomic data, several aspects of the regulation of
metabolism during fruit ripening were revealed. First, it was apparent that
transcript abundance was less strictly coordinated by functional group than
metabolite abundance suggesting that post-translational mechanisms dominate
metabolic regulation. Nevertheless, there were some correlations between
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specific transcripts and metabolites and several novel associations were
identified that could provide potential targets for manipulation of fruit
compositional traits. Finally, there was a strong relationship between
ripening-associated transcripts and specific metabolite groups such as TCA
cycle organic acids and sugar phosphates underlining the importance of the
respective metabolic pathways during fruit development.
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Introduction
Fruits are not only colorful and flavorsome components of human and animal
diets, but they are also an important source of minerals, vitamins, fibers and
antioxidants in food and animal feed. For this reason a fuller comprehension of
the biosynthetic pathways for the production of these nutrients is of applied as
well as fundamental importance (Carrari and Fernie, 2006). Whilst plant model
systems such as Arabidopsis may be a suitable starting point in the search for
key regulatory mechanisms acting in fruit development and ripening (Liljegren et
al., 2004), it must be borne in mind that the term “fruit” encompasses an
enormous range of morphologies and anatomies. Thus, although fundamental
developmental processes may be shared among species this cannot be blithely
assumed since there are also dramatic developmental differences – even
between species of the same family (Fernie and Willmitzer, 2001). This is one
major reason why considerable effort is being put into genomic and
post-genomic study of species other than Arabidopsis (Goff et al., 2002; Carrari
et al., 2004; Desbrosses et al., 2005; Mueller et al., 2005). One example of this
is the use of tomato (Solanum lycopersicum), as a model system for plants
bearing fleshy fruits. Several features of the fruit make it a highly interesting
system to study, by and large all of them being linked to the dramatic metabolic
changes that occur during development. The most obvious of these changes is
the transition from partially photosynthetic to fully heterotrophic metabolism that
occurs coincidentally with the differentiation of chloroplasts into chromoplasts
(Bartley et al, 1994), marked shifts in cell wall composition (Rose et al., 2004a;
Scheible and Pauly, 2004), and the strict hormonal control of climacteric ripening
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(Barry et al., 2005; Alba et al., 2005). However, despite the clear importance of
metabolism in the developmental process a comprehensive analysis of
metabolic events along the developmental period has yet to be presented with
studies to date being limited either in the scope of metabolites measured or with
respect either to the number of different developmental stages analyzed, or both
(Boggio et al., 2000; Chen et al., 2001; Roessner-Tunali et al., 2003).
Given the recent development of an extensive array of tools to characterize the
various molecular entities of the cell (Alba et al., 2004; Fei et al., 2004; Fernie et
al., 2004; Rose et al., 2004b), it is now possible to access vast datasets at the
level of transcript abundance (Alba et al., 2004), protein abundance (Rose et al.,
2004), metabolite accumulation (Roesner-Tunali et al., 2003; Fernie et al.,2004)
and metabolic flux analysis (Ratcliffe and Shaker-Hill, 2006; Roessner-Tunali et
al., 2004). Integration of genomics datasets resulting from the application of such
diverse technology platforms is currently being attempted in plants (Rohde et al.,
2004; Tohge et al., 2005; Urbanczyk-Wochniak et al., 2003, Fernie and
Sweetlove, 2005). As a first step in this direction a recent study performed
transcript profiling across tomato fruit development was combined with selected
metabolite analysis (carotenoids, ethylene and ascorbate), in order to classify
points of regulatory control of development that were dependent on ethylene
(Alba et al., 2005).
In the current study we take a similar approach, but on a broader scale
measuring a total of 92 metabolites comprising sugars, sugar alcohols, organic
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acids, amino acids, vitamins and a select few secondary metabolites in addition
to pigments and the monosaccharide composition of the cell wall, in parallel to
transcript levels. We evaluated temporal changes in these parameters utilizing
the recently developed Solanaceous MapMan (Urbanczyk-Wochniak, 2006).
These data form a relatively comprehensive picture of changes in gene
expression and chemical composition of primary metabolism during fruit
development and furthermore give important insight into metabolic and
transcriptional programs underlying this process.
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Results
Experimental design
The two experiments described here were designed in order to cover the
developmental transition of fruit ripening processes in tomato. Under our
greenhouse conditions these processes occurred in a period of 70 days for fruits
set from flowers of the second and third floors. Fruits grew up until 35 days after
anthesis (DAA) with maximum fruit weight and size of 57 g and 5 cm diameter
per fruit, respectively (Figure 1). According to Gillaspy et al., (1993) the
developmental pattern of tomato fruits can be divided in four defined phases; cell
differentiation (P I), division (P II), expansion (P III) and ripening (P IV). In the
experiments described here and according to Seymour et al., (1993) these
phases correspond to small green fruits harvested from 10 to 21 DAA (P II), to
fruits of 28 DAA up to first visible carotenoind accumulation (56 DAA -breaker-, P
III) and to red fruits with a peak in respiration rate and ethylene biosynthesis (63
to 70 DAA -ripe-, P IV). Fruits harvested during the experiments were divided
longitudinally in two halves and pericarp samples were used for metabolite and
transcript determinations at the indicated time points (arrows above Figure 1).
Pigment content during fruit development
Changes in fruit colour are the most obvious visual character to define
processes occurring during tomato development (Pecker et al., 1992). Overall
colour change is the combined result of differential pigment accumulation. To
stage fruit development we therefore evaluated the pigment composition of the
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fruit samples analyzing chlorophylls a and b, ß-carotene, lutein, neoxanthin,
violaxanthin, and zeaxanthin, antheraxanthin and lycopene (Figure 1). About 85
% of the total pigment contents of fruits harvested from 10 to 49 DAA consisted
of chlorophyll, with chlorophyll a being the most highly represented (65 %). The
remaining 15 % was comprised of neoxanthin (2%), violaxanthin (2.5 %),
β-carotene (3.5 %), lutein (6.3%), antheraxanthin (0.4%) and zeaxanthin (0.8%),
respectively. No lycopene was detected in samples from these development
stages. However from 56 DAA onward here was a dramatic change in the
pigment composition of the fruits harvested; total chlorophyll levels decreased to
68 % whilst the relative proportion of violaxanthin, lutein, β -carotene, lycopene,
antheraxanthin and zeaxanthin, however reached levels in the ripe fruit that
were 2, 5, 10, 12, 9 and 9 times higher than those 56 DAA, respectively, .
Small molecule metabolite content during fruit development
Having determined the pigment profile and thus developmental stage of the fruit
we next turned our attention to evaluating shifts in the contents of soluble
carbohydrates using an established GC-MS method (Fernie et al., 2004). Whilst
the levels of a limited number of metabolites across fruit development has been
reported previously (see for example Boggio et al., 2000; Chen et al., 2001), and
a broad range analysis was carried out at three defined developmental time
points in our previous assessment of the temporal influence of hexokinase on
tomato fruit metabolism (Roessner-Tunali et al., 2003), a comprehensive survey
of shifts in steady state metabolite levels has not yet been performed. For this
reason we took the exact same samples as were used for pigment analysis and
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performed methanol extraction and metabolite profiling as described in the
materials and methods. Evaluation of the GC-MS chromatograms revealed large
changes in metabolite levels through development (Figure 2). Sucrose
decreased more than two-fold between 7 and 10 DAA and continued to decline
(albeit at a slower rate) until approximately 28 DAA, whereas glucose and
fructose accumulated in an essentially linear manner. The changes in major
sugars during ripening largely mirror those reported previously for these
carbohydrates and for the enzymes involved in their interconversion (see for
example (Baxter et al., 2005; Robinson et al., 1988; Schaffer and Petreikov
1997; Lambeth et al., 1964). An obvious exception to this statement is the
sucrose accumulating wild species Solanum habrochaites which is not
characterised by depletion in the levels of sucrose (Miron and Schaffer, 1991),
however, nearly all cultivars of S lycopersicum are characterised, to a greater or
lesser extent, by such changes. In contrast to Suc, Glc and Fru, Mannose and
maltose were characterized by less simple kinetics – gradually accumulating
during fruit development and then peaking dramatically in ripe fruit. Trehalose, in
contrast, was high during very early stages of fruit development but depleted 15
DAA before gradually recovering to levels similar to those found in early fruit
development (Figure 2A). The levels of the minor sugar pools also displayed
major shifts. Interestingly, in nearly all instances there is a clear switch in
metabolite contents between 20 and 28 DAA and between 49 and 56 DAA.
Rhamnose and fucose rapidly accumulate to high levels at the first of these time
points and are equally rapidly depleted at the second, whilst galactose, xylose
and arabinose largely display the converse behavior (although in the case of
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xylose and arabinose the change in metabolite poolsizes are far more gradual).
The ribose content behaves atypically of the minor sugars being largely stable
until 63 DAA when it is present at increased levels (Figure 2A).
Sugar alcohol levels tend to decline during the developmental course, although
the levels of mannitol recover somewhat at late stages of ripening and those of
glycerol and the peak corresponding to sorbitol and galactitol displayed
considerable variation throughout development (Figure 2B). In contrast, the
levels of the phosphorylated intermediates and fatty acids that were reliably
detectable displayed essentially hyperbolic decreases with respect to
developmental time (Figures 2C and 2D).
The levels of organic acids of the TCA cycle showed similar trends to the
phosphorylated intermediates with the exception that there was a second peak
in their levels at around 56 DAA. In most cases this was a relatively minor
increase but for citrate the increase was substantial (Figure 2E). These changes
largely mirror changes in activities of TCA cycle enzymes decline during the
chloroplast-chromoplast transition in tomato fruit (Jeffery et al. 1986) and with
previous reports reports that document changes in the major acids – citrate and
malate (Stevens, 1972; Davies, 1965; 1966). However, it is worth noting that the
magnitudes of these changes in organic acid content are somewhat variable
between American and European cultivars. Irrespective of the cultivar though,
the levels of citrate and isocitrate keep high until later stages and together with
the fact that NADP-ICDH activity peak in ripe pericarp Gallardo et al (1995) most
probably in order to suplí the 2-oxoglutarate for amino-acid biosynthesis and
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ammonia assimilation (Chen and Gadal 1990; Galvez et al, 1999). Levels of
organics acids that are not associated with the TCA cycle generally displayed
different behavior with respect to developmental stage. Whilst the levels of
saccharate, phosphorate, gluconate, threonate, benzoate, nicotinate, c-caffeate,
shikimate and quinate also revealed hyperbolic decay over time, in contrast to
the TCA cycle intermediates there was generally no clear second peak of these
metabolites correlating with the onset of ripening (this was only apparent in the
case of glycerate). In contrast t-caffeate, ascorbate, dehydroxyascorbate,
galacturonate and galactonate 1,4-lactone increased either gradually or rapidly
during later stages of fruit development and maleate, gulonate 1,4-lactone
displayed variable behavior during the course of the experiment (Figure 2F).
The levels of amino acids were also highly variable during the time-course of
development (Figure 2G). Gradual declines in metabolite levels were observed
for GABA, -alanine, Arg, Asn, Gln, pyroglutamate, Orn, Leu and Val, whilst the
levels of Ser, Ala and Pro decreased sharply. In contrast, Trp, Cys, Glu, Asp, Lys,
Met and putrescine increase to peak at fruit ripening. One of the most prominent
changes associated with these processes in ripening tomatoes is the increase in
Glu content (Grierson et al. 1985), which has been reported to increase by up to
20-fold in tomato pericarp during ripening (Gallardo et al. 1993). The change
observed here was far less dramatic only 2-fold but this may be due to use of
different cultivars in the two studies – notably an increase of approximately 8-fold
was documented during ripening of the processing cultivar M82 (Baxter et al.,
2005). Given that Glu is a direct precursor for chlorophyll biosynthesis its
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accumulation, during a time period when the majority of compounds associated
with nitrogen assimilation decrease, it is tempting to speculate that this increase
is, at least in part, due to the downregulation of chlorophyll biosynthesis at this
time point. The remaining amino acid and high N compounds; Tyr, Phe, Thr and
tyramine increase transiently peak at between 15 and 35 DAA whilst Iso is
invariant throughout development. Interesting, those amino acids which increase
during development can act as alternative respiratory substrates at times when
sugar supply is low (Ishizaki et al., 2005). Their decline could therefore imply a
partial reliance of the mitochondrial electron transport chain on these pools
during later stages of development in which carbon demand is met entirely by
source leaves of the plants.
Cell wall matrix monosaccharide composition during fruit development
Given that it is well documented that cell wall metabolism is developmentally regulated
in ripening fruit (Hadfield and Bennett, 1998; Rose et al., 2004), we evaluated the
monosaccharide composition of the cell wall in the exact same extracts. As would be
expected from previous studies these data suggested a dramatic shift in the
relative proportion of monosaccharide composition. The most prominently
observable change was a decrease in the amount of glucose which was
diminished to 20% of that recorded during early fruit development. Most of this
glucose can probably be attributed to starch - since no starch removing
procedure was employed. Since most of the starch diminished the remaining
material had a higher proportion of the wall sugars Man, Xyl and Uronic acids.
However, Fuc was only abundant at very low levels, Ara and Rha were invariant
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throughout development while a decrease in the levels of Gal were observed
(Figure 2H). This trend in this data is consistent with earlier work that indicated
particularly a degradation of pectin derived arabinan and galactans during fruit
development (Sakurai and Nevins, 1993).
Correlative behaviour in metabolite levels suggests concerted regulation
of distinct metabolic pathways
Results described above allow a quantitative interpretation of the patterns of
metabolite levels during tomato fruit development and ripening. However, the
data also allows a more sophisticated assessment of the behaviour of the
metabolic network. As a second objective we were interested in the combinatorial
analysis of metabolites by running all data points through pairwise correlation
analysis. Of the 4,140 possible pairs analysed, 2,527 resulted in significant
correlations (p<0.05). Out of this number, 763 correlations showed high
correlation coefficients, 614 positive (r2 > 0.65) and 149 negative (r2 < -0.65). The
full data set of correlation coefficients is presented in the heat map of Figure 3. To
simplify the interpretation metabolites are grouped by compound class and we
will restrict discussion here to those correlations which showed coefficients
above (and below) 0.65 (-0.65). When these correlations are scrutinized several
trends become apparent. Phosphorylated intermediates display by far the
greatest number of significant correlations to other metabolites most probably
indicating their centrality in primary metabolism. The phosphorylated
intermediates are followed by fatty acids, organic and amino acids, sugar
alcohols, cell wall components and soluble sugars. The pigments displayed the
lowest number of significant correlations. Within each group of metabolites,
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sugars and cell wall components showed more negative than positive
correlations, whilst the opposite occurred with sugar phosphates, fatty acids,
sugar alcohols, amino acids, organic acids and pigments. All the metabolites
measured presented significant correlations to compounds outside of their
compound class (with the exception of amino acids and the pigments). The
individual metabolites that showed the highest number of correlations were
linoleic acid, glycerol 1-P, α-ketoglutarate, myoinositol-phosphate, GABA,
shikimate, glucose-6-P, quinate and fructose-6-P with 27, 26, 25, 25, 24, 23, 23,
20 and 20 associations, respectively. Whilst fucose, glycerol, cis-caffeate,
gulonate-1, 4-lactone, aconitate, maleate, nicotinate, Tyr, Orn and the cell wall
components Gal and Fuc showed no significant correlation. Moreover there are
several clusters of highly correlated metabolites that are conserved between
metabolites of similar chemical composition. For example, organic acids
correlated positively with sugar phosphates, sugar alcohols, cell wall sugars and
with other organic acids. Furthermore, they tended to correlate negatively with
free sugars (especially mannose). Similarly, myo-inositol and myo-inositol
phosphate negatively correlated with all monosaccharides (with the exception of
arabinose) but positively correlate with the disaccharide sucrose. That said
almost all metabolite classes showed negative correlation with free sugars, with
the exception of the minor organic acids.
Changes in the major carbon fluxes during fruit development
In order to relate the information obtained from the analysis of steady state levels
of metabolites to actual metabolic change we next assessed the major fluxes of
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carbon metabolism at three time points of development 21, 35 and 49 DAA. For
this purpose pericarp discs were cut from the fruit and incubated in 10 mM
MES-KOH (pH 6.5) supplemented with 20 mM cold glucose and 2 µCi (3
mCi/mmol) of [U-14C]Glc. The fruits were metabolically active with the dominant
flux by far being that of sucrose synthesis (Table I). This finding is in keeping with
previous reports that suggest a considerable cycle of sucrose synthesis and
degradation occurs in tomato fruits or cells derived from them (Nguyen-Quoc and
Foyer, 2001; Rontein et al., 2002). In contrast the glycolytic flux rate is relatively
minor, as are those of starch, cell wall and protein synthesis. Interesting trends
were observed in label incorporation with that recovered in organic acids
decreasing at 35 DAA before peaking at 49 DAA, a similar trend was also
observed in the amino acid levels. In contrast starch, cell wall and carbon dioxide
evolution all declined through development and the recovery of radiolabel in
protein increased during this period. Given the relative stability of the hexoseP
poolsize its specific activity was largely invariant at all three time points. On
estimating fluxes (using the assumptions described in Geigenberger et al., 2000),
a similar trend in sucrose, starch and cell wall biosynthesis was apparent, whilst
the rate of protein synthesis appeared to drop 35 DAA before peaking at 49 DAA.
Glycolytic flux (which was estimated using the summed label accumulation in
carbon dioxide, protein, organic and amino acids) declined sharply between 21
and 35 DAA but fell no further thereafter. In addition in order to assess whether
the disc feeding system yielded results that were representative of the in vivo
situation radiolabel was injected into the collumella of fruit at 21 DAA. Estimated
fluxes were highly similar between these experiments (with in vivo fluxes
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estimated at 2002 ± 504, 59 ± 28, 305 ± 106, 163 ± 48 and 726 ± 108 nmol
hexose equivalents gFW-1. 2h-1 for Suc, starch, cell wall, protein and glycolysis,
respectively), validating the experimental set-up chosen.
Transcript levels during fruit development
We chose to profile transcript abundance in fruit at 10, 15, 20, 21, 35, 49 DAA,
breaker (56 DAA) and ripe (70 DAA) stages, as these represent well defined
phases during the developmental process (Seymour et al. 1993). To obtain a
well represented variation inherent to fruits at each stage and in order to facilitate
comparison to the metabolome data sets presented above, we pooled RNA
extracted from exactly the same samples used above. We used pools of RNAs
coming from 6 different fruits and performed 2 to 6 array hybridizations for each
stage. Using 2.5 fold background as a threshold we flagged those spots showing
medians above the local background in all replicates (Alba et al, 2006; Baxter et
al, 2005a; and Carbone et al, 2005). Out of the 12,900 ETS clones arrayed on
TOM1 (van der Hoeven et al., 2002; Alba et al., 2004) 5,184; 4,230; 2784; 3,383;
4,941; 2,798; 6,486 and 4,308 spots displayed signals above the threshold at 10,
15, 20, 21, 35, 49, 56 DAA and ripe (70 DAA) stages, respectively. A Venn
diagram representation of the number of spots detected at each stage grouped
by developmental phases can be viewed online (Supplementary Figure 1).
During this period of analysis 1,420 spots showed signals above the threshold
representing 810 different genes expressed at all stages during fruit
development. By applying hierarchical cluster analysis (HCA) we ordered these
genes by their expression patterns across development (Supplementary Figure
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2A). Nine major clusters containing 784 genes were differentiated by this
analysis (Supplementary Figure 2B). Approximately 50 % of these genes could
not be assigned to any of the previously defined MapMan functional ontologies
(Thimm et al, 2004; Usadel et al, 2005). This group includes those genes
annotated by similarity with Arabidopsis predicted proteins and also those
showing no similarities with any known protein (unknowns). Despite this fact,
each cluster showed a specific category composition; the 245 genes showing a
relatively high and constant expression levels (cluster 1) were mainly
represented by genes (46%) that fell into BIN29 (protein), 8% fell into BIN34
(transport), 6% into BIN27 (RNA) and 5% into BIN26 (miscellaneous enzyme
families). Clusters 2 and 13 grouped 64 and 44 genes respectively with similar
expression patterns; a high relative expression level at the beginning of the
analyzed period and declining after on. Sixteen and 21% of the genes clustered
here fell into BIN1 (photosynthesis) and 14% and 11% fell into BIN13 (amino
acids metabolism). Cluster 4 and 12 grouped those genes (164) with depressed
levels between 20 and 56 DAA which mainly fell into RNA, DNA, transport, cell,
protein and stress BINs. Stress related genes (BIN20) were highly represented
in clusters 7 (26%), 9 (9%) and 11 (8%). Cluster 14, grouped those genes
expressed at constant low levels containing genes into BINs 29 (protein: 29%),
27 (RNA: 19%), 17 (hormones: 11%) and 1 (photosynthesis: 7%). To facilitate
the identification of patterns of transcriptional change in pathways associated
with the metabolites which we determined above we visualized this data in the
recently released Solanaceous MapMan (Urbanczyk-Wochniak et al., 2006).
The entire Maps can be viewed at www.mpimp-golm.mpg.de/fernie here we
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highlight changes in the transcript levels of transcripts associated with energy,
starch and amino acid metabolism (Figure 4). As would be expected
photosynthetic gene expression shuts down during fruit development (illustrated
here with the light reactions), however, it appears to exhibit a bi-phasic response
with a massive decrease occurring relatively early on followed by a secondary
decline. This pattern of change is largely unique to the photosynthetic genes with
expression of other genes associated with metabolism generally exhibiting
restrictions in expression only at later stages. However, during fruit development
there is a clear tendency of transcript levels of metabolically associated genes to
be reduced. For example, this is also the case for starch synthesis (and
degradation for that matter) – as could be expected in an organ displaying
transient starch synthesis (Beckles et al., 2001). However, perhaps surprisingly
this is also true for genes associated with amino acid synthesis, glycolysis and
the TCA cycle despite the dependence of at least the latter pathways for energy
metabolism under conditions where these pathways are not augmented by fruit
photosynthesis.
Correlative behaviour in transcript levels suggests fewer functionally
concerted changes throughout development than observed in the
metabolite levels
As would be anticipated from the fact that the levels of so many transcripts
displayed similar patterns of change across fruit development the overall level of
correlation in transcript levels is much greater than that of the metabolite data set.
This is illustrated in Figure 5 by utilizing a subset of the transcript data set,
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selected on the basis of involvement of genes in processes previously described
to be important in fruit development (see Carrari and Fernie, 2006; Giovannoni,
2004). However, a similar pattern emerges when the entire data set is queried
indicating that the conclusion made above is not overly influenced by the
process of transcript selection (data not shown). This finding aside, when the
transcripts are analysed from the perspective of functionally groupings they
display a far less concerted pattern of change than that displayed by the
metabolites. Despite the relative paucity of correlations between functionally
similar transcripts close inspection of the correlations presented in Figure 5
revealed some interesting features especially with respect to ethylene pathway
and cell wall associated genes. The ethylene pathway associated genes clearly
displayed a large degree of both positive and negative correlations with the other
ripening associated genes. Intriguingly, the correlations observed for the
transcript corresponding to the ethylene receptor 1 showed opposing behaviour
to the rest of the ethylene receptor transcripts. Whilst this observation is
currently difficult to interpret, since both ethylene receptor 1 and 2 exhibit
constitutive and stable expression patterns, it may not be greatly significant from
a functional viewpoint since at later stages of development receptors 3 and 4 are
expressed at much higher levels (see Alba et al., 2005 and the current study).
Some dramatic changes in the expression level of cell wall metabolism related
genes were observed during fruit development including those encoding
structural glycoproteins, cellulose synthases and nucleotide sugar conversion
enzymes, xyloglucan endotransglycosylases and other glycosylhydrolases.
More than 50% of the genes were related to pectin degradation, in particular
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endopolygalacturonases and pectinmethylesterases, whose activities are known
to become dominant during fruit ripening (Cheng and Huber, 1997). Interestingly,
even when analysed at a higher level of resolution there was nearly no
correlation
within
gene
families
of
wall
metabolic
enzymes
e.g.
polygalacturonases (Figure 5), instead single members of those gene families
correlated with other single members of other gene families postulated to be of
importance in ripening. This finding therefore implies a low level of functional
redundancy within these gene families during tomato fruit metabolism and
development. That said almost all of the wall-related transcripts correlated
positively with at least one ADP-glucose pyrophosphorylase isoform, whilst
ascorbate reductases and peroxidases also showed positive correlations with
the transcript levels of this enzyme. In contrast, ADP-glucose pyrophosphorylase
correlated negatively with ACC oxidase and ethylene receptor and responsive
genes. Surprisingly, the transcript levels of MAP kinases appear to exhibit very
low correlations with genes associated to ripening. However, several unigenes
representing the ripening inducible transcription factor TDR4 and other MADS
box genes, displayed dramatic negative correlation with a high number of
previously defined ripening related genes represented in Figure 5. These results
thus corroborate previous genetic evidence for the importance of this class of
genes in the ripening process (Vrebalov et al., 2000).
Transcript to metabolite correlations reveals certain types of metabolite
have very strong correlations to a large number of transcripts
We next turned our attention to analysing the correlation between metabolites
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
and transcripts. For this purpose we evaluated the correlations between the
levels of all measured metabolites and the subset of transcripts defined above
(Figure 6). The general level of correlation was relatively low, however, there
were a number of clusters that indicate highly positive or negative correlations
amongst functionally similar molecular entities. When assessed from the
metabolite viewpoint these clusters were most prominent in the case of
sugar-phosphates, organic acids of the TCA cycle and pigments. Of particular
note from a functional perspective is that fact that the acids galacturonate,
L-ascorbate and dehydroascorbate positively correlated with ACC-oxidase,
ethylene receptor and with the ripening inducible transcription factor TDR4
expression. Whilst shikimate showed high positive correlation coefficients with
ethylene response genes, ADP-glucose pyrophosphorylase, dehydroascorbate
reductase and several wall related gene but negative correlation with
ACC-oxidase and with an ethylene receptor gene. From a more general
perspective, organic acids of the TCA cycle also exhibited significant correlation
with many ripening associated transcripts. Analysis of the pigments revealed that
the levels of neoxanthin, violaxanthin, lutein and the chlorophylls are very highly
correlated with transcript levels. Intriguingly, these pigments all display similar
correlations with the exact same genes.
Analysis of this data matrix from the transcript point of view identified that genes
associated with the ethylene pathway, carbohydrate metabolism and cell wall
displaying a high number of associations (for example cellulose synthases
correlate negatively with free hexose and sugar constituents of the cell wall but
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
positively correlate with myo-inositol levels). However, surprisingly there was
very little correlation between cell wall polysaccharide content and genes
associated with cell wall metabolism althoughsome of the pectin degrading
enzymes exhibited strong correlations with sugar phosphates suggesting that
the degraded wall components are taken back up into the cell to feed the carbon
metabolic network of the cell.
Thus far we have only considered correlations between known metabolites and/
or transcripts in an attempt to discern regulatory motifs during ripening. We next
analysed of the behaviour of transcript levels of unknown genes, throughout the
developmental series, relative to those of metabolites (Supplementary Figure 3)
and of known genes (Supplementary Figure 4). The purpose of this was two-fold
(i) such approaches have been reported to by highly useful in the prediction or
even the proof of gene function (Tohge et al., 2005; Morikawa et al., 2005;
Broekling et al., 2005), (ii) this approach can providing an alternative to the QTL
approach in the identification of candidate genes for biotechnological
improvement (Urbanczyk-Wochniak et al., 2003). Here we were able to identify
genes which correlated with specific metabolites for example three genes
(SGN-U147356,
-U144736 and
-U155430)
homologous to Arabidopsis
membrane proteins (At1g72480 and At1g64720) and to a putative glutamate
permease displayed high negative correlation coefficients with sucrose.
Interestingly, the Arabidopsis homologs of these genes were also recently
identified as sucrose responsive (Bläsing et al., 2005). However, two of these
genes (SGN-U147356 and -U147061) correlate negatively with several soluble
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sugars, all sugar-phosphate measured and several organic acids. The lack of
specificity in correlation was a common feature in the data with many genes
showing strong correlation with a wide range of metabolites (Supplementary
Figure 3), for example a gene similar to a zinc ion binding protein
(SGN-U155837), a gene similar to an Arabidopsis protein phosphatase
(At2g27210) (SGN-U154577) and a gene similar to a Fagus glutamate
permease (SGN-U146076) correlated positively with chlorophylls a and b, lutein,
neoxanthin, valine, ß-alanine, glutamine and myo-inositol levels. The vast
majority of genes, however, exhibited associations with many different
metabolites for example gene SGN-U143517 (which a blast search revealed as
similar to the vtc2 gene of ascorbate biosynthesis; Mueller-Moule et al, 2003)
correlated with zeaxanthin, citrate, arabinose, oxoproline, mannitol and fru-6-P
and with an unknown metabolite that co-elutes with isoascorbate. To further aid
in categorisation of gene function, we additionally looked through the expression
data to see which of these genes were co-expressed with genes that had been
assigned a MapMan category, throughout ripening (Supplementary Figure 4).
This analysis revealed that the unknown genes analyzed also displayed a large
proportion of connections with categorized genes (ranging from 22 to 190
significant correlations). Transcripts associated with RNA and protein are highly
represented here, however metabolism associated transcripts also show highly
correlative behaviour. SGN-U155430 (a putative glutamate permease) showed a
high number of connections with photosynthesis-related genes (with 12 % of
all correlating transcripts falling into this functional category) and SGN-U143484
(a putative nucleoside-diphosphate-sugar epimerase) showed a high number of
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correlations with major carbohydrates-related genes (7 % of all correlating
transcripts), amino acids metabolism (6 % of all correlating transcripts) and also
with photosynthesis-related genes (14 % of all correlating transcripts).
Interestingly, SGN-U167243 showed a relative high number of connections with
amino acids-related genes (31 %). This unknown gene also correlates
negatively with the levels of the amino acids proline, glutamine and ß-alanine
and also with a high number of organic acids (gluconate, quinate, shikimate,
fumarate, phosphorate, α-keto-glutarate, malate and succinate). Such analyses
should aid in assigning putative functions for the many non-annotated ESTs
available for tomato.
In addition to looking at the data point-by-point on the basis of a priori knowledge
we also attempted to analyse network connections that persist during fruit
development. Networks, similar to those obtained from combined metabolite and
transcript profiling of sulphur starvation in Arabidopsis (Nikiforova et al., 2005),
can be constructed from the dataset presented here (Supplementary Figures 5A
and 5B). However, when elements that have been suggested to be important in
the developmental process are demarcated few clear trends emerge. One
reason for this could be the sheer size of dataset analysed. To circumvent this
problem analysis of correlation sub-clusters was additionally carried out
(Supplementary Figure 5B). These analyses revealed close associations
indicative of specialist functions i.e. between the sugar Xyl in its free form and as
a cell wall constituent, between pectin degrading enzymes and cell wall Fuc or
merely between pectin methylesterases themselves and between inositol
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phosphate and other sugar phosphates as well as between ethylene, ascorbate
and constituents of the cell wall machinery. Whilst these data are currently
difficult to interpret it is likely that this network will provide a useful foundation for
future research looking into fruit developmental processes as well as a useful
reference tool that could aid in future gene functional annotation studies.
Discussion
Fruit development is a highly complex process that has received much research
attention in recent years with the vast majority of these being focussed towards
hormonal regulation (Lanahan et al., 1994; Adams-Phillips et al., 2004; Barry
and Giovannoni, 2006), or aspects of pigmentation (Fraser et
al., 1994;
Giuliano et al., 1993; Ronen et al., 2000) or sugar and cell wall metabolism (Yelle
et al., 1991; Rose et al., 2004; Fridman et al., 2004) with only a handful of
studies looking at more general aspects of metabolism. In this paper we report a
comprehensive analysis of changes in metabolism occurring during tomato fruit
development using a combination of transcriptomic and GC-MS based
metabolite profiling approaches. This revealed that metabolism is very tightly
regulated during the transition of the fruit from a partially photosynthetic to a fully
heterotrophic organ. Moreover the pattern of metabolite accumulation provides
information that is important for understanding what defines the metabolite
composition of the ripe fruit but also reveals the underlying developmental shifts
in metabolism that lead to this composition.
The kinetic nature of the metabolite data presented here facilitates the
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evaluation of the potential routes through the metabolic network. As an example
of this approach, we investigated the possible routes of ascorbate biosynthesis
within the fruit. To date four putative pathways of ascorbate biosynthesis have
been postulated in plants (Ischikawa et al., 2006). The best characterised of
these the Smirnoff-Wheeler pathway has been resolved to a fairly high degree
with genes now identified that encode almost every reaction step postulated
(Conklin et al., 2006). Such strong evidence does not exist for the alternative
pathway which was proposed merely on the observation of GDP-L-gulose as an
alternative product of the GDP mannose epimease reaction (Wolucka and Van
Montagu, 2003), however, the conversion of L-gulose to ascorbate in whole
tissue has been demonstrated (Jain and Nessler, 2000). Recently the
overexpression
of
a
strawberry
D-galacturonic
acid
reductase
in
Arabidopsis thaliana led to two- to three-fold increase in the ascorbate content of
foliar tissue (Agius et al., 2003) leading the authors to suggest that the
degradation of pectins facilitated ascorbate accumulation. Finally biochemical
evidence has been presented to suggest that myo-inositol oxygenase could
potential be a further entry point into plant ascorbate biosynthesis (Lorence et al.,
2004). Evidence presented in the current study suggest that the D-galacturonic
acid and myo-inositol routes are unlikely to be major precursors for ascorbate
biosynthesis in the tomato given their kinetic profiles relative to that of ascorbate,
dehydroascorbate and threonate. In contrast, the poolsizes of galactonolactone
and gulonolactone are considerable prior to the large increase in ascorbate
content. The presence of homologs of all enzymes of the Smirnoff-Wheeler
pathway in tomato strengthens the suggestion that the GDP-Mannose pathways
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are the predominant route of ascorbic acid biosynthesis in the tomato. Whilst
D-galacturonic
acid reductase has purportedly been mapped in the tomato
genome (Zuo et al., 2006), this claim was not supported by functional evidence.
Despite our belief that it is unlikely to be of high importance in ascorbate
metabolism the fact that both myo-inositol and myo-inositol phosphate
negatively correlates with all monosaccharides (with the exception of arabinose)
but positively correlate with the disaccharide sucrose suggest it is potentially
interesting. Correlation of myo-inositol phosphate with sucrose has previously
been observed across an introgression population of tomato (Schauer et al.,
2006), and during a diurnal period in Arabidposis (Morenthal et al., 2005). Given
the involvement of myo-inositol phosphates in diverse processes spanning,
amongst others, signal transduction, osmoprotection and auxin metabolism
(Gomez-Merino et al., 2005), it is important that its levels are highly regulated
and would appear to follow very closely the momentary level of sucrose. The fact
that its levels appear to be highly responsive to those of sucrose therefore
implicates inositol and its derivatives as potentially important molecules in the
regulation of fruit development. In keeping with this suggestion myo-inositol
phosphate levels were one of only a handful of metabolites that were strongly
linked to yield associated traits in the Zamir introgression line population with
other metabolites of such high importance being sucrose, sugar phosphates and
GABA (Schauer et al., 2006). Interestingly all of these molecules have been
postulated to be signal metabolites in plants and with the exception of GABA all
were shown in the current study to display high correlations with transcript levels
of genes thought to be important in fruit development. That similar results
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emerged from radically different way of approaching phenotype association goes
a long way to validating them and also suggests that the “guilt by association”
approach represents a viable alternative approach for identifying candidate
genes for trait improvement.
In addition to providing potential targets for the engineering of metabolism this
data set also allows a general assessment of metabolic regulation during tomato
fruit development. The levels of metabolites of the same compound class display
closely coordinated changes throughout development (Figure 3) despite the fact
that, generally speaking, structurally similar metabolites do not display the same
correlations with transcript levels (Figure 6). This fact suggests that a large
proportion of the regulation of metabolism occurs at the post-translational level.
This is not a highly surprising finding since similar conclusions have been
reached in several recent studies in plant and non-plant systems (Gibon et al.,
2004; Urbanczyk-Wochniak et al., 2005; Kümmel et al., 2006). Previously
experiments looking at the diurnal regulation of primary metabolism have
documented that there is generally very little correlation between changes in
transcript and changes in enzyme levels (Gibon et al., 2004) and even less
between transcript and metabolite levels (Urbanczyk-Wochniak et al., 2005).
Similarly, tandem profiling of these molecular entities in microbial systems (Pir et
al., 2006) is in support of theoretical assessments (TerKuile and Westerhof,
2001) that in these systems too metabolic regulation occurs pre-dominantly at
the post translational level. Moreover, a recent study using a combination of
metabolome data and computation of reaction thermodynamics indicated that
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the majority of metabolic regulation is likely to be related to allostery (Kümmel et
al., 2006).
Despite the apparent dominance of post-translational regulation of
metabolism, we were able to identify several correlation hotspots between
transcripts and metabolites. For example, sugar phosphates, organic acids and
pigments are all highly correlated to selected ripening-associated transcripts.
This observation has important biotechnological implications given that the
manipulation of cellular levels of transcripts is now relatively facile. It must be
borne in mind that the correlation of metabolite and transcript levels by no means
proves that the metabolite level is under transcriptional control (it could equally
imply that gene transcription or transcript stability is under metabolite control).
However, the fact that such chemically diverse compounds correlate to the same
ripening associate transcripts leads us to contend that the levels of these
metabolites are developmentally regulated at the level of gene transcript.
Irrespective, of whether this is indeed the case, the strong correlative behaviour
between organic acids, sugar phosphates and pigments with genes associated
to the ethylene and cell wall pathways underscore the importance of these
metabolic intermediates in the process of ripening.
Understanding of pigment composition of fruits is of high commercial importance
and considerable advances have been made in defining and understanding the
metabolic pathways underlying their regulation (Lewinsohn et al., 2005; Fernie et
al., 2006).That said as highlighted by a recent QTL and positional mapping study
not all the genes that are responsible for the accumulation of pigments in tomato
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have yet been identified (Liu et al., 2003). Moreover, there have been few
studies to date that attempt to integrate changes in primary metabolism to those
in pigment composition. Whilst this study reveals that the levels of few of the
primary metabolites correlate strongly with pigment contents it does show that
several organic acids display considerable correlation with many of the same
transcripts. This result is particularly interesting given the fact that several
tomato genotypes deficient in TCA cycle function exhibit elevated pigment
content (Carrari et al., 2003; Studart-Guimeraes et al., submitted), providing
support that this approach can be readily utilized as a means of identifying
candidate genes for biotechnology. That the organic acids and sugar
phosphates also show such considerable correlative behaviour with similar or
even the same transcripts is intriguing. Ethylene regulated respiratory changes
are a dominant feature of climacteric fruit ripening (Giovannoni, 2004; Herner
and Sink, 1973), as a rule the TCA cycle intermediates decline gradually,
however, a second peak in their levels is clearly observable 56 DAA. Whilst we
favour the hypothesis that the TCA cycle intermediates are regulated at the
transcriptional level we cannot currently exclude the possibility that in plants, as
in animals (He et al., 2004), these intermediates could play a key role in
mediating retrograde regulated gene expression (Doicinovic et al., 2004;
Zarkovic et al., 2005) .
Thus far we have only considered correlative behaviour between single entities
or at most between groups of similar entities. This is largely due to inherent
difficulties in the complexity of interpretation of larger networks (Sweetlove and
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Fernie, 2005). The application of network analysis to plant metabolism and
development is in its infancy. That said the first transcript profiles of fruit
developmental mutants of tomato have been published (Alba et al., 2005) and
the tomato genome sequencing project is well underway (Mueller et al., 2005).
Given the advances that can be anticipated in gene annotation it is likely that the
data presented here will also prove a useful reference data set for future data
interpretation and allow us to proceed further in functional annotation than we
were able to in the present study. Nevertheless, from this study we were able to
draw several important conclusions concerning metabolic regulation during
tomato fruit development. Firstly, our data demonstrate that primary metabolism
is highly co-ordinately regulated throughout this period with up and
downregulation of the accumulation of compounds of the same chemical class
prevalent. Secondly, metabolite abundance appears to be strictly controlled
whereas transcript abundance is more variable. This suggests that during
tomato fruit development, as was observed in the response of Arabidopsis to
extended darkness (Gibon et al., 2004), the transcriptional response does not
always equate to a proportional functional response. Detailed point by point
analysis was however able to identify the likely pathway of ascorbate
metabolism in the fruit as well as to identify areas of metabolism that seem to be
of high importance to the ripening process. Future studies in our laboratories will
focus both on analysing the consequences on fruit development of reverse
genetic manipulation of organic acid and sugar phosphate metabolism and on
determining the broad metabolic consequences of perturbing fruit ripening and
development.
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
Materials and Methods
Plant and chemical materials
Except where otherwise stated all enzymes and materials were purchased from
Roche (Mannheim, Germany). Tomato (Solanum lycopersicum cv. Moneymaker)
plants were obtained from Meyer Beck (Berlin, Germany), and were handled as
described in the literature (Carrari et al., 2003).
Sampling of Fruits
Individual flowers were tagged at anthesis to accurately follow fruit ages through
development. Fruits were harvested in the middle of the light period in two
separate experiments; firstly at 10, 15, 20 days after anthesis and at braker (56
DAA) and ripe (70 DAA) stages, and secondly at 7 days intervals from 21 DAA to
70 DAA (ripe). These periods fully covered the transition from green to fully ripe
red fruit. The fruits were weighed and measured immediately upon harvesting.
Harvested fruits were cut in two with a scalpel blade and the pericarp was
separated from the placental tissue. The pericarp was immediately frozen in
liquid nitrogen before being kept at -80°C until use.
Pigment determination
The determination of the levels of chlorophylls A and B, lutein, neoxanthine,
violxanthin, anteraxanthin and zeaxanthin were performed in acetone extracts,
essentially as described in Thayer and Björkman (1990). The pigments were
separated in extracts (100µl injection volume) by high performance liquid
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
chromatography on a 5µm non-endcapped 25 by 4,5 mm Zorbax-ODS
reverse-phase column. Pigments were detected by their absorbance at 450nm
and identified by co-chromatography with authentic standards. The quantity of
each pigment was detemined by comparison of the sample peak areas to a
standard curve. Mobile phases were [A] 88% (v/v) acetonitrile, 10% (v/v)
methanol, 2% (v/v) 100mM Tris-HCl (pH 8.0) and [B] 67% (v/v) methanol, 33%
(v/v) acetic acid ethylester. The gradient employed was 21 minutes at 100% [A]
followed by a linear gradient to100% [B] over 6 minutes at a flow rate of 0.8 ml
min-1. At 29 minutes the flow rate was increased to 1.0 ml min-1, on 37 minutes a
linear gradient over 14 minutes to 100% [A] was applied and these conditions
were maintained for 10 minutes
Metabolite analysis
The relative levels of metabolites were determined from frozen pericarp samples
as described in Roessner et al. (2001a), with the modifications for tomato tissue
documented in Roessner-Tunali et al. (2003). Data are presented normalized to
the control (7 days after anthesis), as described by Roessner et al. (2001b).
Cell wall analysis
Cell wall analysis was carried out essentially as described in Baxter et al.
(2005b). In brief, the insoluble residue after metabolite extraction was washed
with ice-cold 70% (v/v) ethanol followed by a washing step with a 1: 1 (v/v)
mixture of methanol/chloroform. The dried residue was then subjected to 2 M
trifluoroacetic acid hydrolysis for 1 h at 121°C to release the monosaccharides of
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
the wall matrix polysaccharides. The released monosaccharides were quantified
as their alditol acetate derivatives by GC–MS (Albersheim et al., 1967), with the
exception that data is presented normalized to the control (7 days after anthesis),
as per the soluble metabolite analysis
Incubation of plant material with [U-14C]-glucose
Developing fruits were removed at 21, 35 and 49 days post anthesis and a 10
mm diameter latitudinal core was taken. Both cuticular and locular tissues were
removed and the residual pericarp material was sliced into 2mm slices and
washed three times in fresh incubation medium (10 mM MES-KOH: pH 6.5) and
then incubated (8 discs in 5 ml incubation medium containing [U-14C]-glucose,
(1.4 MBq mmol-1) to a final concentration of 10 mM. Samples were then
incubated for 2h before washing again three times in unlabelled incubation
medium, and freezing in liquid N2, until further analysis. All incubations were
performed in a sealed 100 ml flask, at 25°C and shaken at 150 rpm. The evolved
14
CO2 was collected in 0.5 ml of 10% (w/v) KOH.
In vivo labelling of tomato fruit
Labelling experiments were carried out following modification of the conditions
for intact potato tubers described by Bologa et al. (2003). A fine channel (1-2 mm
in diameter) was bored into the columella tissue directly to the abscission zone
of the pedicel
and 7.4 MBq/ml of [U-14C]-glucose (specific activity 11.5
GBq/mmol), equivalent to approximately 37MBq per fruit, was injected into the
borehole. After 2h the fruit was removed from the plant dissected and frozen in
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
liquid N2, until further analysis
Fractionation of 14C labelled material
Tissue was fractionated exactly as described by Fernie et al. (2001), with the
exception that hexoses were fractionated enzymatically rather than utilizing thin
layer chromatography. Labelled sucrose levels were determined after 4 hours
incubation of 200 µl of total neutral fraction with 4Units/ml of hexokinase in
50mM Tris-HCl, pH 8.0, containing 13.3 mM MgCl2 and 3.0 mM ATP at 25°C. For
labelled glucose and fructose levels 200 µl of neutral fraction were incubated
with 1Unit/ml of glucose oxidase and 32 Units/ml of peroxidase in 0.1M
potassium phosphate buffer, pH 6, for a period of 6 hours at 25°C. After the
incubation time, all reactions were stopped by heating at 95°C for 5 minutes. The
label was separated by ion-exchange chromatography as described by Fernie et
al. (2001). The reliability of these fractionation techniques have been thoroughly
documented (Fernie et al., 2001; Runquist and Kruger, 1999) previously with the
exception of the hexose fractionation. Recovery experiments performed in the
current study determined that the quantitative recovery of radiolabel following
this novel method of hexose fractionation was acceptable (90-105%).
RNA isolation
Total RNA from tomato pericarp was isolated as described in Obiadalla-Ali et al.,
2004. RNA was hybridized against glass slide microarrays as defined below.
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
Glass slide microarray
Glass slides containing arrayed tomato ESTs were obtained directly from The
Center for Gene Expression Profiling (CGEP) at the Boyce Thompson Institute
(BTI), Cornell University, The Geneva Agricultural Experiment Station, and The
USDA Federal Plant and Nutrition Laboratory. The tomato array (TOM1)
contains approximately 12,000 unique elements randomly selected from cDNA
libraries isolated from a range of tissues including leaf, root, fruit, and flowers
and representing a broad range of metabolic and developmental processes.
Technical
details
of
the
spotting
are
provided
as
MIAME
http://www.mpimp-golm.mpg.de/fernie . Further annotation of this file was
carried out to provide gene identities and putative functions for the ESTs
described on the Solanaceae Genomics Network (http://soldb.cit.cornell.edu/)
website. Fluorescent probe preparation and microarray hybridization was exactly
as described previously (Baxter et al., 2005a; Urbanczyk-Wochniak et al., 2005).
In brief microarrays were scanned using an Affymetrix 428 Array scanner
(Affymetrix, Inc. Santa Clara, Ca. USA) and acquisition software according to the
manufactures instructions. After scanning images were analysed in Genepix Pro
vs 4.1 software (Axon Instruments Inc., Ca. USA) and raw data collected and
incorporated into Microsoft Excel for further analysis. Data was normalised and
quality controlled exactly as described previously (Baxter et al., 2005), values
were then transformed (log base 2) prior to comparison between other sampling
points.
Detailed
information
is
included
into
http://www.mpimp-golm.mpg.de/fernie
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
MIAME
MAPMAN analyses
The 34 MapMan BINs currently used for the Arabidopsis MapMan classification
(Usadel et al., 2005, Thimm et al., 2004), have been adopted for tomato as
defined in Urbanczyk-Wochniak et al. (2006). For visualization the data was
loaded into MapMan, which displays indivual genes mapped on their pathways
as false color coded rectangles. The software can be downloaded, as well as
help obtained, from http://gabi.rzpd.de/projects/MapMan. Moreover its use is
documented in the aforementioned publications.
To facilitate comparison of the different colors a legend explaining the changes is
included by MapMan, which associates the color representation with the log fold
changes in expression. Since MapMan uses an ontology to display data, it sorts
data by biological processes and displays them in a group wise format. For the
time course analysis presented here we selected some major processes and
collated them in Figure 4. For our analysis the Mapping file SGN-UnigeneR2_
commodity was used, which is freely available from within MapMan or from
http://gabi.rzpd.de/database/java-bin/Mapping Downloader or converted to excel
format on request.
Heat maps
Heat maps were created using the ‘heatmap’ module of the statistical software
Python IDLE (http://www.python.org/IDLE) version 1.1.1. False colour imaging
was performed to visualize correlations between metabolites, transcripts and
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
between metabolites and transcripts by applying Spearman algorithm by using
SSPS software. Expression profile data clustering was done on the log2-based
relative expression values of the genes using EPCLUST
(http://ep.ebi.ac.uk/EP/EPCLUST/) with the correlation-based distance measure
and the average linkage clustering method. These are also available as
interactive Figures at http://mapman.mpimp-golm.mpg.de/pageman/outerspace/
- these are zoomable if the Adobe SVG viewer is installed. Moreover moving the
mouse over a given square reveals the parameters under consideration.
Statistical analysis
Microarray experiment slides were normalized with print tip loess and moving
minimum background subtraction using the Bioconductor limma package
framework (Gentleman et al., 2004). Microarray slides were subsequently scale
normalized, adjusting the log-ratios to have the same median absolute deviation
across arrays (Yang et al., 2002), (Smyth and Speed, 2003). Moderated
t-statistics were used to detect any genes likely to be differentially expressing
(Smyth, 2004). MAPMAN files were constructed from resulting analysis log2 fold
change values, where any poor quality spots created during the experimental
process were down-weighted essentially as described in Urbanczyk-Wochniak
et al. (2006). Gene-metabolite network analysis was performed as described by
Nikiforova et al. (2005).
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
Acknowledgements
We are indebted to Christian Kristukat for help with the Python IDLE software.
We are grateful for financial support from the Max Planck Socitey (in the form of
a Max-Planck partner laboratory grant to FC and ARF) and for two independent
grants in the BMBF GABI Program (to BU and to ARF and MIZ) as well as from
the BBSRC (to CB and LJS), CONICET, INTA and EMBO (to FC).
41
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Table I. Redistribution of radiolabel and absolute fluxes in pericarp discs isolated
from developing fruits at varying DAA.
Days after anthesis
21
1
1
Total Uptake (Bq gFW- )
Metabolized (Bq gFW- )
35
49
565 ± 137
362 ± 18
1372 ± 200
160 ± 35
197 ± 10
352 ± 50
1
Label incorporation (Bq gFW- )
Sucrose
111 ± 28
183 ± 9
Organic acids
8.4 ± 0.4
5.5 ± 0.5
17.20 ± 1.88
Amino acids
6.5 ± 0.1
4.3 ± 0.5
9.96 ± 2.39
Starch
12.6 ± 1.2
6.3 ± 0.4
6.91 ± 0.66
Protein
1.5 ± 0.4
3.2 ± 0.3
4.49 ± 0.62
Cell Wall
7.9 ± 0.8
3.9 ± 0.8
4.26 ± 0.33
Carbon dioxide
2.2 ± 0.1
0.5 ± 0.1
0.31 ± 0.03
17.0 ± 3.7
14.1 ± 4.2
16.1 ± 2.7
0.06 ± 0.01
0.05 ± 0.01
Hexoses-P
184 ± 27
Specific activity hexose phosphates
1
(Bq nmol- )
0.08 ± 0.01
-1
Metabolic flux (nmol hexose equivalents gFW )
Sucrose synthesis
1809 ± 294
3540 ± 966
1875 ± 316
Starch synthesis
168 ± 32
121 ± 41
95 ± 17
Cell wall synthesis
157 ± 52
67 ± 22
62 ± 16
Protein synthesis
41 ± 11
33 ± 3
61 ± 11
510 ± 143
315 ± 96
385 ± 52
Glycolytic
Discs were cut washed three times in buffer and then incubated for 2 h in 10 mM
MES-KOH (pH 6.5) supplemented with 10 mM [U-14C]-glucose, (specific activity
1.4 MBq mmol-1). At the end of the incubation discs were again washed three
times extracted and anaylsed for radiolabel in organic and amino acids, starch,
protein, cell wall, phosphoesters and sucrose. In addition 14CO2 evolved during
the experiment was trapped in KOH and the level of radiolabel determined by
liquid scintillation counting. Absolute rates of flux were calculated from the label
incorporation data using the specific activity of the hexose phosphate pool in
order to account for isotopic dilution factors. Data presented are the mean ± SE,
n = 4. Values that were determined to be significantly different from fruit
harvested at 21 DAA (P < 0.05)
61
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Figure legends
Figure 1. Experimental design. Tomato flowers, from plants growing under
greenhouse conditions, were labeled daily and the fruits harvested at the
indicated time-point post anthesis. Six fruits from the second or third floors were
collected per time-point. Fresh weight and fruit diameter were measured in these
fruits and values presented correspond to means ± SE from 6 determinations.
Levels of chlorophylls a and b, β-carotene, lutein, neoxanthine, antheraxanthine,
violaxanthine, lycopene and zeaxanthine were measured in acetone extracts
from 100-150 mg of frozen pericarp tissue as described in Thayer and Björkman
(1990). Pigments contents are presented as percentages of the total measured.
White triangles and black square denote two different harvests. Arrows above
the plot indicate fruit stages used for metabolic profile (gray) and transcript
profile (black) analysis.
Figure 2. Metabolic profiles of tomato fruit along development. Relative
metabolite contents of fruits harvested from 7 days after anthesis until
post-ripening (70 days after anthesis). Metabolite contents were identified and
quantified by gas chromatography-mass spectrometry (GC-MS) and their
relative amounts were calculated as described in Roessner-Tunalli et al (2003)
relative to 7 days after anthesis. Histograms show the relative amounts of
soluble sugars (A), sugar-alcohols (B), sugar-phosphates (C), fatty acids (D),
organic acids (E), TCA cycle intermediates (F) amino acids (G) and cell wall
components (H).
Figure 3. Visualization of metabolite-metabolite correlations. Heatmap of
metabolite-metabolite correlations along developmental period of tomato fruits
(from 7 to 70 DAA). Metabolites were grouped by compound class and each
square represents the correlation between the metabolite heading the column
with the metabolite heading the row. Correlation coefficients and significances
(2-tailed) were calculated by applying Spearman algorithm by using SSPS
software. Out of 4232 pairs analysed, 2430 resulted in significant correlations
(p<0.05). Each dot indicates a given r value resulting from a Spearman
correlation analysis in a false colour scale. The web-version of this Figure allows
mouse-over annotation that facilitates point-by-point evaluation of the data to
facilitate its detailed interrogation.
62
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Figure 4. Differences in transcript levels during tomato fruit development for
genes associated with the photosynthetic light reactions, the TCA cycle,
glycolysis, amino acid synthesis and degradation and starch synthesis and
degradation. All material was harvested in the middle of the day. Red and blue
represent a decrease and an increase of expression with respect to the average
of all time points. Here each unigene which has been assigned to a process is
represented by a single colored box. The color scale which was used is
reproduced in the figure. This data is best viewed, and all data point annotations
provided at http://gabi.rzpd.de/projects/MapMan (see Materials and Methods).
This website also gives simple instructions to facilitate its ease of use.
Figure 5.. Heatmap of correlations between selected transcripts on the basis of
involvement of processes previously described to be important in fruit
development. Transcripts were grouped by functionality on the bases of
MapMan gene ontology. In analogy to Figure 3 each square represents the
correlation between the transcript heading the column with the transcript heading
the row. Correlation coefficients and significances (2-tailed) were calculated by
applying Spearman algorithm by using SSPS software. Each dot indicates a
given r value resulting from a Spearman correlation analysis in a false colour
scale. RI TFs TDR: ripening related transcription factors (TDR family).
CHO-AGPses: carbohydrate metabolism-ADP-glucose pyrophosphorylases.
The web-version of this Figure allows mouse-over annotation that facilitates
point-by-point evaluation of the data that facilitates point-by-point evaluation of
the data to facilitate its detailed interrogation.
Figure 6. Selected transcript-metabolite correlations visualization. Heatmap
surface of selected transcript-metabolite correlations was drawn and correlation
coefficients were calculated as described for Figures 3 and 7.
Each dot indicates a given r value resulted from a Spearman correlation analysis
in a false colour scale. RI TFs TDR: ripening related transcription factors (TDR
family).
CHO-AGPses:
carbohydrate
metabolism-ADP-glucose
pyrophosphorylases. The web-version of this Figure allows mouse-over
annotation that facilitates point-by-point evaluation of the data to facilitate its
63
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detailed interrogation.
64
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Supplementary figure legends
Supplementary Figure 1. Venn diagrams grouping flagged spots (if median
signal > 2.5 fold local background) detected within the microarray experiment at
different points of fruit development (P – II: cell division phase, P – III: cell
expansion phase and P – IV using the classification of Gillaspy et al., 1993)
Supplementary Figure 2. Hierarchical Cluster Analysis (HCA) of ubiquitous
transcripts. Genes whose expression was detected at all analyzed stages in at
least two replicates were clustered using the EPCLUST software
(http://ep.ebi.ac.uk/EP/EPCLUST/) following parameters described in the
Materials and Methods (A). Histograms of nine of the major clusters revealing
the pattern of change in steady-state transcript levels (B).
Supplementary Figure 3. Heatmap surface of unknown transcripts-metabolite
correlations. Correlations were ordered by resulted r values for better
visualization. Correlations were calculated as described for Figures 3, 7 and 8.
Only correlations with r values above (or below) 0.65 (or –0.65) are shown in a
false color scale.
Supplementary Figure 4. Heatmap surface of correlations between unknown
and categorized transcripts. Correlations were calculated as described for
Figures 3, 7 and 8. Only correlations with r values above (or below) 0.65 (or
–0.65) are shown in a false color scale.
Supplementary Figure 5. Causally directed gene-metabolite correlation
network based of fruit development. Correlation distributions for paired entities
have been determined to be significantly correlated by comparing the Pearson
correlation coefficients of the experimental and shuffled (randomized) data sets.
The threshold level for a “significance call” was set as the minimum Pearson
correlation coefficient that returns less than 10% of the correlations in the
shuffled dataset than in the original experimental dataset (in this case R >
65
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0.923). All associations whose level of correlation was lower than the threshold
were filtered out from the network analysis. Given that ripening is a complex
process and is not directed by a single metabolic or genetic cue the choice of
apex for the projection of the data is difficult. Here we chose the pigment
neoxanthin since it display a linear pattern of change with developmental time
(A). However, irrespective of which is the most appropriate apex the graph
indicates the vast complexity of the gene-metabolite network of tomato fruit
development. To highlight connectivities of interest several of these have been
expanded (B). eth pathway, ethylene pathway; TFs ripening, ripening related
transcription factors (TDR family); CHO, carbohydrates.
66
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Figure 1
80
80
Fruit fresh weight (g) (
60
60
40
40
20
20
0
Pigment content (% of total)
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
)
100
0
7 10 15 2021
28
35
42
49
Days after anthesis
Brk
63
ripe
Neoxanthin
Violaxanthin
Antheraxanthin
Lutein
Zeaxanthin
Chlorophyll b
Chlorophyll a
ß-carotene
Lycopene
A
1.4
sucrose
trehalose
1.0
0.6
0.2
0.2
glucose
1.0
myo-inositol
1.0
3
0.2
ononitol
1.0
0.5
fructose
ribose
1
7
0.5
25
1
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Copyright © 2006 American Society of Plant Biologists. All rights reserved.
mannose
xylose
15
0.6
0.6
0.2
1.4
6
0.2
2
1.5
0.6
0.5
mannitol
1.0
0.6
rhamnose
fucose
4
7 15 28 42 56 70
10 20 35 49 63
7 15 28 42 56 70
10 20 35 49 63
0.2
2.0
1.6
1.2
0.8
0.4
0.0
D
glycerol-1-P
1.0
0.6
1
tryptophan
0.2
5
glycerol
3
1
0
7 15 28 42 56 70
10 20 35 49 63
1.0
0.2
1.4
1.0
ß-alanine
phenylalanine
0.6
0.2
30
tyrosine
cysteine
10
1.4
glutamate
1.0
0.6
0.2
4
methionine
0.2
quinate
10
stearate 18:0
oleic acid
0.8
1.0
0.6
0.6
1.0
0.2
0.6
0.6
1.2
0.2
1.2
0.2
fructose-6-P
0.8
0.4
0.4
0.0
7 15 28 42 56 70
10 20 35 49 63
gluconate
0.0
0.2
9.12(Z.Z)-18:2
linoleic acid
1.0
palmitate 16:0
1.2
threonate galacturonate
galactonate 1,
4-lactone
0.8
7 15 28 42 56 70
10 20 35 49 63
aconitate 1.4
fumarate
1.0
1.0
0.6
0.6
0.2
0.2
citrate
succinate
1.0
0.6
10
2
8000
0.2
α-ketoglutarate
1.2
0
5
1.4
1.0 3
0.6
0.2 1
3.5
14 2.5
10
1.5
6
2 0.5
1.4
30 1.0
lysine
aspartate
2
0
7 15 28 42 56 70 7 15 28 42 56 70
1020 35 49 63
1020 35 49 63
2.5 putrescine
leucine 1.4
1.0
1.5
0.6
0.5
threonine 1.4 glutamine
valine
1.0
arginine
0.6
0.2
1.4
0.2
1.4
isocitrate 1.2
0.8
0.8
0.4
0.4
0.0
7 15 28 42 56 70
10 20 35 49 63
7 15 28 42 56 70 0.0
10 20 35 49 63
serine
0.6
2.5 manose
0.6
0.6
0.2
2.5
1.4
0.2
3.5
0.2
1.4
tyramine
isoleucine
glycine
1.0
1.5
0.6
0.5
0.2
2.0
1.2
proline
ornithine
0.8
7 15 28 42 56 70
1020 35 49 63
0.4
0.0
fucose
1.0
7 15 28 42 56 70 7 15 28 42 56 70
1020 35 49 63
1020 35 49 63
0.0
galacturonate
0.2 0
7 101520 2835424956 6370
1.5
1.5
0.5
0.5
2.5
3
0.6 1
0.2
alanine 1.0
1.4
1.0
0.6
1.0
0.6
1.51.0
0.6
0.50.2
10
4
6
2
2
0 0
arabinose
0.2
1.0
asparagine
galactose
1.0
2.5 xylose
rhamnose
2.0
1.5
1.0
0.5
7 15 28 42 56 70
10 20 35 49 63
1.0
0.6
0.2
1.6
4000
0.0 7 15 28 42 56 70
10 20 35 49 63
0.2
7 15 28 42 56 70 0.0
10 20 35 49 63
0.4
7 15 28 42 56 70
1020 35 49 63
0.6
1.4
6
benzoate
malate
1.0
0.4
1
gulonate-1,4-lactone 1.8
1.4
1.0
0.6
0.2
0.2
0.2
1.4
0.8
2
5
1.2
3
1.0
10
pyroglutamic
0.6
0.2
14
0.6
0.6
glucose-6-P
phosphorate dehydroascorbate
1.0
F
1.4
H
2
1.0 GABA
glycerate
1.0
maleate L-ascorbate
G
6
c-caffeate
t-caffeate
0.6
2
10
0.6
sorbitol/galactitol
1.0
arabinose
5
0
2.5
1.0
0.2
1.4
6
1.4
0.2
maltose
3
8
1.0
1.0
6
0.2
3
shikimate
1.4
0.2
5
1.5
5
7
0.6
nicotinate
0.6
myo-ino-1-P
1.0
saccharate
0.6
C
0.6
galactose
1.5
2.5
1.4
1.0
0.6
2.5
E
B
1.4
7 15 28 42 56 70
10 20 35 49 63
0.0
pigments
f.a.
amino
acids
organic
acids
CWCHO
sugars-P
sugar-ols
amino
acids
r
organic
acids
CWCHO
sugars-P
sugar-ols
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sugars
Figure 3
pigments
f.a.
sugars
Figure 4
Light Reactions
Calvin Cycle
AA Synthesis
Starch: Synthesis
and Degradation
Glycolysis
TCA & electron
transport
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Development and Ripening
Cell wall
MAP kinases
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CHO -AGPsesRI TFs -TDR-
Ethylene
pathway
r
Ascorbate
Ethylene
pathway
MAP kinases
RI TFs -TDRCHO -AGPses-
Ascorbate
Cell wall
Figure 5
Metabolites
pigments
f.a.
amino
acids
organic
acids
CWCHO
sugars-P
sugars-ol
r
CHO metADPGases
TFripening
MAP kinasesfruit
Ethylene
pathway
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sugars
ASC met
Transcripts
Figure 6
Cell wall