Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new ...

50 Pages·2009·0.84 MB·English

Plant Physiology Preview. Published on January 14, 2009, as DOI:10.1104/pp.108.133967
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Running head: Gene-metabolite regulations in tomato fruit

Corresponding Author:

Martine Lemaire-Chamley
INRA, UMR 619 Biologie du fruit, Centre INRA de Bordeaux, BP 81, 33883 Villenave d’Ornon Cedex,
France.
Tel: [phone]; Fax: [phone];
e-mail: [email protected]

Research Area:

System Biology, Molecular Biology, and Gene Regulation

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Gene and metabolite regulatory network analysis of early developing fruit tissues highlights
new candidate genes for the control of tomato fruit composition and development

Authors : Fabien Mounet1,2, Annick Moing1,2, Virginie Garcia1,2, Johann Petit1,2, Michael Maucourt1,2,3,
Catherine Deborde1,2,3, Stéphane Bernillon1,2,3, Gwénaëlle Le Gall4, Ian Colquhoun4, Marianne
Defernez4, Jean-Luc Giraudel5, Dominique Rolin1,2, Christophe Rothan1,2, Martine Lemaire-Chamley1,2

1 INRA - UMR 619 Biologie du Fruit, Centre de Bordeaux, F-33140 Villenave d'Ornon, France
2 Université de Bordeaux, UMR 619 Biologie du Fruit, F-33140 Villenave d'Ornon, France
3 Pôle Métabolome-Fluxome, IFR 103, INRA de Bordeaux, F-33140 Villenave d'Ornon, France
4 Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, UK
5 EPCA, ISM, UMR 5255, CNRS-Université Bordeaux 1, F-24000 Périgueux, France

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Footnotes:
This work was supported by Région Aquitaine (project n° 20051303006ABC and a Ph.D grant to
Fabien Mounet) and under the auspices of the EUSOL Integrated Project (grant no. FOOD–CT–2006–
016214), the EU STREP project META-PHOR (grant no. FOOD–CT–2006–036220), and the PAI
Alliance (grant no. 12155UC).

Corresponding Author:
Martine Lemaire-Chamley
e-mail: [email protected]

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Abstract

Variations in early fruit development and composition may have major impacts on the taste and the
overall quality of ripe tomato (Solanum lycopersicum L.) fruit. To get insights into the networks
involved in these coordinated processes and to identify key regulatory genes, we explored the
transcriptional and metabolic changes in expanding tomato fruit tissues using multivariate analysis and
gene-metabolite correlation networks. To this end, we demonstrated and took advantage of the
existence of clear structural and compositional differences between expanding mesocarp and locular
tissue during fruit development (12 to 35 days post anthesis – DPA). Transcriptome and metabolome
analyses were carried out respectively with tomato microarrays and analytical methods including
proton NMR and LC-MS. Pair-wise comparisons of metabolite contents and gene expression profiles
detected up to 37 direct gene-metabolite correlations involving regulatory genes, e.g. the correlations
between glutamine, bZIP and MYB transcription factors. Correlation network analyses revealed the
existence of major hub genes correlated with 10 or more regulatory transcripts and embedded in a
large regulatory network. This approach proved to be a valuable strategy for identifying specific
subsets of genes implicated in key processes of fruit development and metabolism, which are
therefore potential targets for genetic improvement of tomato fruit quality.

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INTRODUCTION

Fleshy fruit development leads to the formation of a juicy, expanded and generally sweet and colored
fruit (Coombe, 1976). Because they are rich in fibers, vitamins and minerals, fleshy fruits may have a
considerable effect on human health (Ness and Powles, 1997, 1999; WHO, 2003). As a consequence,
recommendations have recently reached five daily portions of fruit or vegetable. In addition to
nutritional quality, the sensorial quality of fruit (e.g. visual aspect, firmness and taste) is of utmost
importance for fruit consumption. Sensorial quality depends on numerous factors, including fruit
colour, texture, aroma, and composition in primary metabolites (sugars, organic acids and amino
acids). Both the nutritional and the sensorial attributes are built throughout the successive phases of
fruit development, namely cell division, cell expansion and ripening. Whilst the fruit-ripening process is
obviously important (Giovannoni, 2001), there is also growing body of evidence that supports the key
role of early fruit development for the acquisition of several fruit quality traits, including the
accumulation of sugars and organic acids (Guillet et al., 2002; Lemaire-Chamley et al., 2005; Carrari
et al., 2006; Petreikov et al., 2006), the determination of cell wall and texture characteristics (Chaib et
al., 2007) and the cuticle biosynthesis (Mintz-Oron et al., 2008). In the growing fruit, these processes
mainly take place during the cell expansion phase which sustains fruit growth by allowing a large
increase in fruit cell volume linked with membrane and cell wall/synthesis and the concomitant
accumulation of water, mineral ions and metabolites in the vacuoles, thereby conferring its fleshy
characteristics to the fruit. Increasing evidence links the enlargement of fruit cells with increase in
nuclear ploidy level (Gonzalez et al., 2007) but the way in which fruit growth and associated metabolic
changes are regulated and coordinated largely remains an open question.

In most cases, fruit development and metabolism are clearly interconnected (Carrari et al., 2004) and
undergo major shifts that coincide with transitions between successive phases of fruit development
(Lemaire-Chamley et al., 2005; Carrari and Fernie, 2006; Carrari et al., 2007). Reverse genetic
approaches have demonstrated that alterations in fruit metabolism/composition may impact fruit
development and vice-versa. As an example, increasing fruit sucrose content by down-regulating
invertase or vacuolar ATPase genes affects fruit growth and fruit size (Klann et al. 1996; Amemiya et
al. 2006). Conversely, altering the ethylene-dependent signal transduction networks has a
considerable effect on fruit ripening and associated changes, including fruit colour, aroma, sugar and
organic acids (Wilkinson et al., 1995; Barry and Giovannoni, 2006; Carrari and Fernie, 2006;
Giovannoni, 2007). For early stages of fruit development, auxin and brassinosteroids are believed to
act as major regulatory signals (Balbi and Lomax, 2003; Montoya et al. 2005). The auxin-dependent
signal transduction network has been the most studied because of its role in fruit set and cell
expansion phase (Gillapsy, 1993; Catala et al., 2000; Balbi and Lomax, 2003; Lemaire-Chamley et al.,
2005). In addition, cross-talks between auxin and ethylene have been suggested for regulating early
fruit development (Balbi and Lomax, 2003) and onset of ripening (Jones et al., 2002).

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Besides signalling pathways linked to plant hormones, elements of regulatory networks controlling fruit
development and composition have been discovered in recent years by map-based cloning strategies,
using well-characterized pleiotropic mutants with altered fruit ripening or colour (rin, Cnr, hp1, hp2
mutants; Giovannoni, 2007; Seymour et al., 2008; Manning et al., 2006). Until now, with the exception
of the Nr mutant encoding an ethylene-receptor (Wilkinson et al., 1995) and the hp3 mutation affecting
an ABA biosynthetic gene (Galpaz et al., 2008), the ripening mutations with known biochemical
functions that have been identified affect signal transduction proteins, being either transcription factors
(the MADS-box and SBP-box transcription factors) or proteins with a possible role in light signalling
(DET1 and DDB1) (Liu et al., 2004; Giovannoni; Wang et al., 2008). These studies further
demonstrated that the alteration of a single regulatory gene may affect processes ranging from
independent biosynthetic pathways controlling several phytonutrients (Davuluri et al., 2005; Wang et
al., 2008) to whole developmental processes such as ripening. So far, we have very little insight into
the molecular networks regulating the complex and concerted modifications occurring in the early
developing fruit, before the onset of fruit ripening. In addition, little is also known about the regulation
of biochemical pathways controlling the accumulation of metabolites at these stages and their
connection with fruit development. Though several mutants affected in the regulation of early fruit
development and composition have been identified (own unpublished results), the isolation of the
corresponding alleles through forward genetics approaches, as it has been done for the tomato
ripening mutants, remains a long term objective. The acquisition and integration of omic data
(transcriptome, proteome, metabolome), coupled with functional analysis of the candidate regulatory
genes identified by such approaches, may constitute an alternative and complementary strategy to
identify target genes regulating either single biochemical pathways or more complex developmental
programs in the growing fruit.

Systems biology approaches have recently emerged as highly powerful tools for discovering links
between co-regulated genes and pathways and, ultimately, for predicting gene function and for
identifying regulatory genes in plants (Saito et al., 2007). Co-expression analyses allowed the
identification of genes regulating tissue and cell development (Persson et al., 2005; Gifford et al.,
2008), environmental responses (Wang et al., 2006) and plant metabolism (Hirai et al., 2007;
Gutiérrez et al., 2008) and were further validated by reverse genetics strategies. With the current
availability of large scale gene expression and metabolite profiling techniques in many plants including
tomato (Alba et al., 2004, 2005; Lemaire-Chamley et al., 2005; Moco et al., 2006; Mounet et al., 2007;
Saito et al., 2007; Mintz-Oron et al., 2008; Schauer et al. 2008), large gene expression and metabolite
data sets can be combined through correlation and clustering analyses (Urbanczyk-Wochniak et al.,
2003) and further represented as a network of relationships between genes and metabolites (Aoki et
al., 2007; Saito et al., 2007; Hoefgen and Nikiforova, 2008). These approaches have been
successfully applied to the discovery of regulatory and biosynthetic genes involved in the control of
metabolite production - e.g. the glucosinolate metabolism (Hirai et al., 2004, 2007). In tomato, the
combination of metabolome and transcriptome data obtained during fruit development recently led to
the identification of novel associations (Carrari et al., 2006), opening the way for manipulation of fruit
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compositional traits by using transgenic approaches or natural and artificially-induced genetic
variability (Rothan and Causse, 2007).

In the present study, we explored the cell expansion phase of tomato fruit development at the level of
transcriptome and metabolome to identify regulatory genes involved in the control of developmental
and metabolic processes that may impact fruit quality. Attention was focussed on the two major
expanding tissues in tomato fruit, mesocarp and locular tissue. We took advantage of both the
similarity and variability existing at the morphological, cellular, transcript and composition levels
between these two tissues during early fruit development to undertake a combined analysis of
transcript and metabolite data under a range of tissue conditions. Data integration achieved through
correlation analyses revealed numerous correlations, common to both tissues, between metabolites
and transcripts involved in regulatory processes, embedded in a large network. Many correlations
were novel and unpredictable and highlighted candidate genes that should be of great potential for
modifying tomato fruit composition by genetic means.

RESULTS

Cell expansion during fruit development proceeds differently in mesocarp and locular tissues

Mesocarp and locular tissue (Fig. 1) both undergo cell expansion in developing tomato fruit.
Mesocarp (Fig. 1B) develops from the carpel wall after fertilization and is the most abundant tissue in
tomato fruit, representing ~ 50% (w/w) of the fruit fresh weight (data not shown). Its quantitative
importance remains stable along fruit development. The differentiation of the locular tissue during fruit
development is particularly impressive: in the cultivar studied here it starts to develop from columella
at around 4 DPA (Lemaire et al, 2005 and data not shown) and fill the locular cavity as early as 20
DPA (Fig. 1C). At 35 DPA, locular tissue is the second most abundant tissue in tomato fruit,
representing 23% (w/w) of the fruit fresh weight. The proportions of exocarp, columella and seed in the
fruit slowly decrease during early development and remain between 10 and 17% (w/w) of the fruit
fresh weight.
In order to characterize precisely mesocarp and locular tissue during fruit development,
detailed cytological analyses were performed from anthesis to the ripe fruit stage (45 DPA for the Ailsa
Craig cultivar under our culture conditions). As described for other tomato cultivars (Cheniclet et al.,
2005), cell division occurred very transiently after fruit set and resulted in an increase of pericarp cell
layers from 11.3 +/- 0.8 to 20.6 +/- 0.5 between 2 and 6 DPA. After 6 DPA, the number of cell layers
remained stable in the pericarp, but its thickness increased dramatically due to cell expansion. Indeed,
the mean mesocarp cell size doubled between 12 and 20 DPA (early expansion) and tripled between
20 and 35 DPA (late expansion, Fig. 1B). In locular tissue, the mean cell size doubled during early
expansion but was multiplied only by 1.5 during late expansion (Fig. 1C). Locular tissue cells were
smaller than mesocarp cells during the entire cell expansion period, and their size were more
homogeneous when compared to mesocarp cells which contained both small and large cells at 35
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DPA (Fig. 1B). In addition, the cytological analysis suggested a different cell wall structure between
the two tissues, locular tissue cell walls appearing undulated in comparison to mesocarp cell walls
(Fig. 1B and Fig. 1C) in our conditions of analysis.

Mesocarp and locular tissue differ in their metabolic composition

Since cytological analyses indicated obvious differences at cellular level between the expanding
mesocarp and locular tissue, we investigated whether metabolic composition also differed between
these two tissues. For this, we characterized the metabolite composition of tomato mesocarp and
locular tissue from the same fruits during cell expansion as follows: i) primary metabolites were
measured by 1H-NMR (quantification of 20 identified and 5 unknown metabolites) and enzymatic
reactions (starch); ii) secondary metabolites were determined by HPLC-DAD (quantification of 8
metabolites) and by LC-MS (relative quantification of 8 metabolites). The data were analyzed using
two unsupervised statistical methods (Principal Component Analysis (PCA) on all 42 metabolites and
Kohonen’s self–organizing maps (SOM)) on the 34 metabolites with absolute quantification in order to
compare both tissues and the global modification of their metabolic composition throughout fruit
development. The PCA scores revealed that the metabolic composition of the mesocarp and the
locular tissue are different at each stage of development (Fig. 2A). Indeed, the first principal
component (PC1), explaining 41% of the total variability, clearly separated the locular tissue samples
on the positive side from the mesocarp samples on the negative side. The examination of PC1
loadings (Fig. 2B) allowed the identification of metabolites involved in these differences and revealed
discriminatory metabolites, as detailed below. The SOM analysis allowed us to map the 18 tissue
samples (2 tissues x 3 development stages x 3 replicates) according to their metabolic composition
(Fig. 3). The construction of different maps with increasing unit numbers (from 4 to 15 units) allowed
us to increase the map resolution and test its robustness. As observed for the PCA analysis, a clear
separation between mesocarp and locular tissue samples was visible in the four maps (Figs. 3A to D),
pointing out the differences in metabolic composition of both tissues. This result was particularly
obvious in the 4-unit map (Fig. 3A) since: i) for each tissue, the three developmental stages (12, 20
and 35 DPA) were grouped together and ii) the two tissues were mapped to different units. The 6-unit
map (Fig. 3B) revealed a closer proximity of metabolic composition in both tissues at the earlier
stages, since 12 and 20 DPA samples remained in the same unit whereas 35 DPA samples were
grouped in a neighbouring unit. The 9-unit and 16-unit maps allowed a full discrimination of all sample
types with a perfect clustering of the biological replicates (Fig. 3C and D). The 9-unit map, which
showed a clear separation of both tissues and developmental stages, was used for the component
plane representation of each of the 29 identified and quantified metabolites (Fig. 3E) in order to
highlight discriminant metabolites as detailed in the next paragraph.
For the PCA analysis, examination of PC1 loadings (Fig. 2B) suggested that the differences
between the locular tissue and mesocarp samples involved sucrose, citrate, malate, amino acids
(aspartate, GABA, glutamate), caffeoylquinates (chlorogenate and another caffeoyl-quinate isomer
(CQ1)), choline and unknown compounds (unkD6.2, unkS8.5) on the positive side, and glucose,
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fructose, starch, trigonelline, amino acids (glutamine, isoleucine, leucine, threonine, valine) and two
unknown compounds (unkSD5.1 and unkS5.4) on the negative side. The SOM component plane
representation of each of the 29 identified and quantified metabolites (Fig. 3E) and the individual
metabolite changes in both tissues during fruit development (Fig. 4) confirmed these tendencies. In
agreement with the PCA results, major differences between mesocarp and locular tissues were
observed for sugar and organic acid composition. Indeed, fructose, glucose, mannose, starch and
unknown sugar D5.1 were more abundant in mesocarp than locular tissue, whereas sucrose and
UDP-glucose were more abundant in locular tissue. The organic acids (citrate, malate and fumarate)
were more abundant in locular tissue than in mesocarp. Some differences in amino acids and
secondary metabolites were also visible. In particular, aspartate, GABA, and glutamate were more
abundant in locular tissue whereas glutamine, isoleucine, leucine, threonine, and valine were more
abundant in mesocarp. Some secondary metabolites were more abundant in locular tissue:
chlorogenate, and caffeoyl-quinate CQ1, choline and tomatidenol/tomatidine.

Mesocarp and locular tissue follow parallel metabolite trajectories during fruit development

Although mesocarp and locular tissues were different with respect to their global metabolite
composition, they followed parallel trajectories during development as highlighted by the PCA analysis
where the second principal component (PC2), explaining 29% of the total variability, separated late
(on the positive side) from early (on the negative side) stages of mesocarp and locular tissue
development. Examination of PC2 loadings (Fig. 2B) suggested that these parallel changes involved
fructose, mannose, tyrosine and phenylalanine on the positive side, and UDP-glucose, GABA,
fumarate, isoprenoids (chlorophyll a and b, lutein, carotene), three caffeoyl-quinates (chlorogenate
and two other caffeoyl-quinate isomers), rutin and three alkaloids (tomatidine, one tomatidine
glycoside (TG2) and tomadidenol) on the negative side. Indeed, examination of individual metabolite
concentrations of virtual tissue samples in a SOM component plane representation (Fig. 3E) and of
the samples (Fig. 4) shows that some metabolites increased in both tissues during fruit development
(fructose, mannose, tyrosine, aspartate and phenylalanine), whereas others decreased (UDP-glucose,
GABA, fumarate, isoprenoids (chlorophyll a and b, lutein, carotene), three caffeoyl-quinates
(chlorogenate and two other caffeoyl-quinate isomers), rutin and three alkaloids (tomatidine, a
tomatidine glycoside (TG2) and tomatidenol).
Common behavior in metabolic networks in both tissues was revealed by the calculation of
pairwise correlation coefficients between metabolites. Of the 861 possible metabolite pairs, 400 pairs
resulted in significant correlations (P < 0.05, Fig. 5) and amongst these, 276 correlations were highly
significant (P < 0.01). All known metabolites showed highly significant correlations to compounds
outside of their compound class except asparagine and astragalin. The individual metabolites that
gave a number of correlations superior or equal to 20 were unknown sugarD5.1, isoleucine, aspartate
and unknownS5.4. Starch was correlated to 14 other metabolites. Sugars exhibited positive and
negative correlations with a given class of compound except for pigments where only positive
correlations were observed. Soluble sugars gave eight negative and two positive correlations with
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organic acids, 11 negative and 14 positive correlations with amino acids, six negative and five positive
correlations with phenolics, and two negative and five positive correlations with alkaloids. Pigments
(except xanthophylls) were correlated positively with glucose. Phenylalanine and tyrosine were
negatively correlated with several pigments, phenolics and alkaloids but they showed no positive
correlations.

Few genes are differentially expressed during early cell expansion (12-20 DPA) in mesocarp
and locular tissue

To investigate gene expression changes during fruit development in mesocarp and locular
tissue, we monitored the transcriptome of these tissues during the expansion phase in tomato (Fig.
6A) using TOM1 cDNA microarray slides (Van der Hoeven et al., 2002; Alba et al., 2004). MA-ANOVA
analysis followed by elimination of low quality data on the basis of background threshold and flags
(see “Material and Methods”) allowed the selection of 1139 spots with a significant variation of
expression (P value < 0.01 and FDR < 0.05). Amongst them, 322 corresponded to genes of unknown
function (Supplemental Table S1). Our experimental design (Fig. 6A) allowed to compare gene
expression between 12 and 35 DPA in mesocarp (expression ratios M12/M20, M20/M35), in locular
tissue (expression ratios L12/L20, L20/L35) and to compare gene expression in the two tissues at 12,
20 and 35 DPA (expression ratios L12/M12, L20/M20, L35/M35). Among the 1139 transcripts, 582
showed at least a 2-fold variation during either mesocarp or locular tissue development (ratio 12/20
and/or 20/35 >2 or <0.5) or in the comparison of both tissues (ratio L12/M12, L20/M20 and/or L35/M35
>2 or <0.5). More transcripts showed variation in locular tissue than in mesocarp tissue during early
(ratio 12/20) and late (ratio 20/35) expansion (Fig. 6B). This suggests that the locular tissue
transcriptome changed more than that of the mesocarp. Alternatively, this difference may result from
the higher homogeneity of locular tissue cells compared to mesocarp cells. Indeed, the mesocarp
samples were composed of heterogeneous-sized cells (Fig. 1). The gene expression changes
detected by the microarray experiment in this tissue reflect the mean of the expression changes in
different-sized cells and therefore may be lower than would be measured for the most expanding cells.
In contrast, locular tissue was composed of more homogeneous cells, with a possible better
synchronisation of gene expression changes.
A low number of genes were differentially detected in both tissues between 12 and 20 DPA (Fig.
6B, Supplemental Table S1). Indeed only 4.7% (53 / 1139) and 9.4% (107 / 1139) of the transcripts
varied in mesocarp and locular tissues between 12 and 20 DPA (ratio 12/20 >2 or <0.5). Several, not
necessarily mutually exclusive, hypotheses can explain this result: i) the genes involved in early
expansion are absent from the microarray slide used in this study, ii) the variation of gene expression
during the early cell expansion phase is too low to be detected due to microarray sensitivity, iii) early
cell expansion involves post-transcriptional modifications of enzyme activities rather than
transcriptional regulations. Most of the differences in gene expression were observed between 20 and
35 DPA (ratio 20/35 >2 or <0.5) in mesocarp and locular tissue (Fig. 6B, Supplemental Table S1), as
described by other authors for pericarp (Alba et al., 2005; Carrari et al., 2006). Indeed 15.3% (174 /
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