Marion Prudent
email: marion.prudent@dijon.inra.fr

Nadia Bertin

Michel Genard

Stéphane Muños

Sophie Rolland

Virginie Garcia

Johann Petit

Pierre Baldet

Christophe Rothan

Mathilde Causse

Genotype-dependent response to carbon availability in growing tomato fruit

Keywords: Fruit growth, gene expression, genotype x environment interaction, hormone, metabolism, regulations, Solanum lycopersicum, stress response, sugar, transcriptome

Tomato fruit growth and composition depend on both genotype and environment. This paper aims (i) at studying how fruit phenotypic responses to changes in carbon availability can be influenced by genotype and (ii) at identifying genotype-dependent and -independent changes in gene expression underlying variations in fruit growth and composition. To this end, we grew a parental line (S. lycopersicum) and an introgression line from S. chmielewskii harboring QTL for fresh weight and sugar content under two fruit loads (FL). Lowering fruit load increased fruit cell number and reduced fruit developmental period in both genotypes. In contrast, fruit cell size was increased only in the parental line. Modifications in gene expression were monitored in expanding fruits using microarrays and RT-qPCR for a subset of genes. FL changes induced more deployments of regulation systems (transcriptional and post transcriptional) than massive adjustments of whole primary metabolism. Interactions between genotype and FL were especially noticeable for 99 genes mainly linked to hormonal and stress responses, and on gene expression kinetics during fruit development. Links between gene expression and fruit phenotype were found for aquaporin expression levels and fruit water content, and invertase expression levels and sugar content during fruit ripening phase. In summary, the present data emphasized age-and genotype-dependent responses of tomato fruit to carbon availability, at phenotypic as well as at gene expression level.

Introduction

Carbohydrate availability is a major factor limiting plant growth, in particular for sink organs such as fruits. In tomato, carbohydrates needed for fruit growth come from photosynthetic sources such as mature leaves, fruits having a low photosynthetic activity [START_REF] Farrar | Control of the rate of respiration in roots: compartmentation, demand and the supply of substrate[END_REF].

Increasing carbohydrate availability to the reproductive organs by reducing fruit load on the plant enhances cell division in the ovary and thereby the final fruit size [START_REF] Baldet | The expression of cell proliferation-related genes in early developing flowers is affected by a fruit load reduction in tomato plants[END_REF]. Conversely, a low carbohydrate supply during the phase of rapid cell expansion leads to a reduction of fruit size [START_REF] Bertin | Do genetic make-up and growth manipulation affect tomato fruit size by cell number, or cell size and DNA endoreduplication[END_REF][START_REF] Heuvelink | Effect of fruit load on dry matter partitioning in tomato[END_REF]) and even of dry matter [START_REF] Gautier | Modulation of competition between fruits and leaves by flower pruning and water fogging, and consequences on tomato leaf and fuit growth[END_REF]. In the case of severe carbon stress, sugar, protein and amino-acid contents can all be reduced [START_REF] Baldet | Contrasted responses to carbohydrate limitation in tomato fruit at two stages of development[END_REF][START_REF] Gary | Time-course of tomato whole-plant respiration and fruit and stem growth during prolonged darkness in relation to carbohydrate reserves[END_REF]. Fruit sugar content is the consequence of sucrose import, carbohydrate metabolism, and dilution by water [START_REF] Ho | The mechanism of assimilate partitioning and carbohydrate compartmentation in fruit in relation to the quality and yield of tomato[END_REF]. Sucrose enters the cells either via the apoplasm after conversion to hexose by a parietal invertase, or via the symplasm. In the cytoplasm, sucrose may be converted into fructose and glucose by invertase, or into fructose and UDP-glucose by sucrose synthase [START_REF] Frommer | Molecular analysis of carbon partitioning in Solanaceous species[END_REF][START_REF] Yelle | Sink metabolism in tomato fruit III. Analysis of carbohydrate assimilation in a wild species[END_REF]. Hexoses are then transformed into starch by the successive actions of fructokinase, hexokinase, phosphoglucoisomerase, phosphoglucomutase, ADP-glucose-pyrophosphorylase and starch synthase (Schaffer & Petreikov 1997a;Schaffer & Petreikov 1997b;[START_REF] Damari-Weissler | Evidence for intracellular spatial separation of hexokinases and fructokinases in tomato plants[END_REF].

Starch is transiently stored in the amyloplasts, and constitutes a carbon reservoir for hexose synthesis [START_REF] Dinar | The relationship between starch and accumulation of soluble solids content of tomato fruit[END_REF]. The accumulation of carbohydrates in the fruit leads to a gradient of osmotic pressure, causing first a massive entrance of water, notably via aquaporins (see [START_REF] Kaldenhoff | Aquaporins and plant water balance[END_REF] for review), and subsequently cell expansion [START_REF] Ho | An analysis of the accumulation of water and dry matter in tomato fruit[END_REF]. This expansion relies on cell wall plasticity, determined partly by the activity of enzymes related to the synthesis or degradation of cell wall components in the epidermis as well as in the pericarp [START_REF] Thompson | Regulation of tomato fruit growth by epidermal cell wall enzymes[END_REF]. Many of these processes are affected by hormones.

Cytokinins may affect fruit sugar content [START_REF] Martineau | Production of high solids tomatoes through molecular modification of levels of the plant growth regulator cytokinin[END_REF], while auxins and gibberellins can be involved in the regulation of cell enlargement, by controlling the expression of genes encoding cell wall modifying proteins like expansins [START_REF] Catala | Auxin-regulated genes encoding cell wallmodifying proteins are expressed during early tomato fruit growth[END_REF][START_REF] Bradford | Expression of an expansin is associated with endosperm weakening during tomato seed germination[END_REF][START_REF] Chen | A gibberellin-regulated xyloglucan endotransglycosylase gene is expressed in the endosperm cap during tomato seed germination[END_REF][START_REF] Guillon | Down-regulation of an auxin response factor in the tomato induces modification of fine pectin structure and tissue architecture[END_REF].

Large-scale quantification of gene expression in tomato has been shown to be a powerful tool for characterizing plant response to a variety of conditions, including salt stress [START_REF] Ouyang | Identification of early salt stress response genes in tomato root by suppression substractive hybridization and microarray analysis[END_REF], light [START_REF] Facella | Diurnal and circadian rhythms in the tomato transcriptome and their modulation by cryptochrome photoreceptors[END_REF], developmental changes [START_REF] Alba | Transcriptome and selected metabolite analyses reveal multiple points of ethylene control during tomato fruit development[END_REF][START_REF] Vriezen | Changes in tomato ovary transcriptome demonstrate complex hormonal regulation of fruit set[END_REF], differentiation of specialized tissues [START_REF] Lemaire-Chamley | Changes in transcriptional profiles are associated with early fruit tissue specialization in tomato[END_REF], mutants [START_REF] Kolotilin | Transcriptional profiling of high pigment-2(dg) tomato mutant links early fruit plastid biogenesis with its overproduction of phytonutrients[END_REF] or introgressions of genomic segments [START_REF] Baxter | Comparison of changes in fruit gene expression in tomato introgression lines provides evidence of genome-wide transcriptionnal changes and reveals links to mapped QTLs and described traits[END_REF]. To our knowledge, the effect of carbohydrate availability on tomato transcriptome has never been investigated, although many genes are known to be sugar sensitive [START_REF] Koch | Carbohydrate-modulated gene expression in plants[END_REF]. In fruit, the effect of carbohydrate availability was only investigated for target enzymes and genes at few developmental stages. For example, it was shown in peach fruit that high crop load leads to increased acid invertase activity at a final stage of fruit growth [START_REF] Morandi | Carbohydrate availability affects growth and metabolism in peach fruit[END_REF]. In tomato, an obscurity-induced carbohydrate limitation led to changes in the expression of some sugar transporters, and of enzymes involved in sugar-or amino-acid metabolism at two stages of fruit development (cell division and cell expansion) [START_REF] Baldet | Contrasted responses to carbohydrate limitation in tomato fruit at two stages of development[END_REF]. However, although recent studies emphasized that source-sink relationships between vegetative and reproductive organs are genetically controlled and are a central hub for controlling fruit metabolism in tomato [START_REF] Schauer | Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement[END_REF][START_REF] Lippman | An integrated view of quantitative trait variation using tomato interspecific introgression lines[END_REF], comparative analysis of fruit response to carbohydrate availability in different tomato genotypes has not been looked at in any depth.

This study aims at identifying genes and gene categories differentially regulated in growing fruit in response to changes in carbon availability induced by alteration of fruit load on the plant.

Transcriptome analysis using tomato microarrays was carried out on fruit pericarps harvested 21 days after anthesis, a stage of rapid fruit growth, cell expansion and storage of major carbohydrates such as starch. To get an insight into interactions between genotype and environment on gene expression, two closely-related tomato genotypes were grown at two contrasted fruit loads, one (high load, HL) inducing competition for carbon among fruits (trusses were not pruned), and the other (low load, LL) inducing low or no competition (trusses pruned to one fruit). The two accessions differed by an introgressed chromosome fragment on chromosome 9, carrying several quantitative trait loci whose expression is either independent of the fruit load (for fruit developmental duration, fresh weight, seed number, dry matter and sugar content) or specific to one fruit load (cell number, cell size or fruit cracking) [START_REF] Prudent | Genetic and physiological analysis of tomato fruit weight and composition: influence of carbon availability on QTL detection[END_REF]. In a last step, the expression of a subset of genes related to carbon metabolism, cell wall modification, and water fluxes were analyzed using RT-quantitative PCR along fruit expansion and ripening.

Materials and methods

Plant growth, fruit thinning and sampling

Two tomato genotypes, Solanum lycopersicum cv. Moneyberg (hereafter called M) and an introgression line (hereafter called C9d) carrying a fragment from the bottom of chromosome 9 of Solanum chmielewskii in the M genetic background, were grown under controlled greenhouse conditions at a day-night temperature set point of 25/15 °C during spring 2007 in Avignon, France.

The position of the introgression of genotype C9d as well as the quantitative trait loci (QTL) previously identified, are described in [START_REF] Prudent | Genetic and physiological analysis of tomato fruit weight and composition: influence of carbon availability on QTL detection[END_REF]. For both genotypes, all trusses were pruned to one fruit when flower 2 was at anthesis (low fruit load condition or LL) on 40 randomly selected plants, while trusses of 16 other plants were not pruned (high fruit load condition or HL).

Under HL condition, the average number of fruit sets per truss was similar in C9d and M (around seven fruits). Anthesis was recorded three times a week, allowing the determination of fruit age and fruit developmental duration. Fruits were harvested at five different developmental stages from the cell expansion period until maturity (21,28,35,42 days after anthesis (daa) and red ripe).

Fruits were weighed, and locular tissue and seeds were removed. The pericarp tissue was then weighed, frozen in liquid nitrogen and stored at -80°C until nucleic acid manipulations or stored at -20°C until phenotypic measurements.

Phenotypic measurements

At each developmental stage, six fruits per genotype and per fruit load were randomly harvested between the fourth and the ninth truss of the plants, at proximal positions: flowers 2, 3 or 4 under HL and only flower 2 under LL. Pericarp powders were lyophilized and weighed and pericarp water content was then deduced. Soluble sugars (glucose, fructose, sucrose) and starch were extracted, quantified by an enzymatic method [START_REF] Gomez | The microplate reader: an efficient tool for the separate enzymatic analysis of sugars in plant tissues -validation of a micromethod[END_REF][START_REF] Gomez | A new procedure for extraction and measurement of soluble sugars in ligneous plants[END_REF] and expressed in g per 100 g of pericarp dry matter. Additional phenotypic measurements were carried out on fruits harvested at the red ripe stage: seeds were counted, and the number and the mean size of pericarp cells were assessed according to the method described in [START_REF] Bertin | Number of cells in tomato fruit depending on fruit position and source-sink balance during plant development[END_REF]. An analysis of variance was performed on phenotypic measurements in order to study the effects of fruit age, fruit load, genotype and their two-or three-way interactions with R Software (http://www.r-project.org).

RNA extractions

All fruits used for RNA extraction were randomly harvested between the fourth and the ninth truss of the plants, at proximal positions, similarly to phenotypic measurements. Fruit samples used for microarray and quantitative real time PCR were different. Total RNA used for microarray experiment was isolated from two biological pools of 25 fruits at 21 daa, following the procedure described by Chang, [START_REF] Puryear | A simple and efficient method for isolating RNA from pine trees[END_REF]. Total RNA used for quantitative real time PCR was isolated from three biological pools of ten fruits (at 21, 28, 35, 42 daa and red ripe stage) with TRI Reagent® Solution (Ambion) following the procedure described by the manufacturer, with minor modifications.

Microarray experiments

TOM2 cDNA glass slides were fabricated by arraying Array-Ready Oligo Set™ for the Tomato Genome (Operon) onto Corning® UltraGAPS™ slides using a BioRobotics MicroGridII arrayer (Genomic Solutions) in Instituto de Biología Molecular y Celular de Plantas (Valencia, Spain). Slides contained 12,160 oligos corresponding to 11,862 unique and randomly selected transcripts of the tomato genome. Information about the oligo set was described on the Operon website (http://omad.operon.com/download/storage/lycopersicon_V1.0.2_datasheet. pdf). Fluorescent probes were prepared from 50 µg of total RNA, using the Amino Allyl MessageAmp TM II aRNA Amplification Kit (Ambion), and following manufacturer's specifications. Purified Cy3-or Cy5labelled AA-aRNAs were dried in a speed-vac, resuspended in 9 µL nuclease free water and fragmented using RNA Fragmentation Reagents (Ambion). Probes were mixed with 88 µL of the hybridization solution consisting of 0.5 µg.mL -1 denatured salmon sperm DNA (Stratagene), 5X SSC, 0.25% SDS and 5X Denhardt's solution and 50% formamide. The solution was denaturated for 1 min at 100°C and cooled down to 37°C. Labelled AA-aRNAs were hybridized with slides for 16h at 37°C under agitation. After hybridization, slides were washed at 30°C with 2X SSC, 0.2% SDS for 5 min, with 0.2X SSC, 0.1% SDS for 5 min, with 0.2X SSC for 3 min, and with 0.02X SSC for 30 s.

Finally, slides were dried with nitrogen gas for 3 min at 30°C before scanning.

Data analysis of microarray experiments

The microarray experimental design consisted of two biological replicates, and for each biological replicate the dyes were reversed (dye swap) for a total of four slides per comparison (Fig. 3a).

The raw data corresponding to the median spot intensities, with no background subtraction were analyzed using the Bioconductor (http://www.bioconductor.org) package R/Maanova v1.4.1 (Cui, Kerr & Churchill 2003) in R-2.6 (http://www.r-project.org). Data visualization, normalization, and statistical analysis, including multiple test adjustments (FDR) were performed as described by [START_REF] Mounet | Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development[END_REF]. F statistics computed on the James-Stein shrinkage estimates of the error variance [START_REF] Wu | MAANOVA: a software package for the analysis of spotted cDNA microarray experiments[END_REF] were calculated and genes with a P value < 5.10 -2 , FDR < 0.05, fold change ≥ 1.6 and average intensity > background mean + 2 background SD were selected.

In order to analyze specifically the interaction between the genotype and the fruit load, another statistical study was performed on normalized data using Bioconductor LIMMA package v2.13.8 (Smyth 2005a). The data were normalized using the printtiploess (within-array normalisation) and scale (between-arrays normalisation) functions (default parameters). Flagged spots were given a weight of 0.1 using the weight function. A factorial design analysis (Smyth 2005b) was performed and a linear model with a coefficient for each of the four factor combinations (C9dLL, C9dHL, MLL and MHL) was fit. The interaction term (C9dLL-C9dHL) -(MLL-MHL) was extracted. The P values resulting from moderated t test were corrected for multiple testing using the Benjamini-Hochberg FDR adjustment. As above, genes were considered to be significantly differentially expressed if adjusted P values were < 5.10 -2 , fold change was ≥ 1.6. For each of the four conditions, spots with an average intensity higher than background mean + 2 background SD were considered as detected.

Quantitative real-time PCR

The quantitative real-time PCR experiment followed the eleven golden rules proposed by [START_REF] Udvardi | Elevengolden rules of quantitative RT-PCR[END_REF]. Reverse transcription was performed with 2 µg of total RNA from each sample treated with DNAse in 50µL with oligo-dT (10 µM) and AMV Reverse Transcriptase (10 U/µL) (Promega), according to the manufacturer's instructions. The RT mix was diluted 5-fold in water and 2 µL aliquots were stored before use. Quantitative real-time PCR was carried out using a Stratagene Mx3005P ® thermocycler (Stratagene, Cedar Creek, TX) in a reaction volume of 20 µL, in 96-well plates. PCR were conducted using 7.5 µL SYBR Green mix (133 mM KCl, 27 mM Tris HCl pH 9, 0.2 % Triton® X-100, 8 mM MgCl 2 , 2 µL of 1500-fold dilution SYBR ® Green I Nucleic Acid Gel Strain 10000X (Lonza), 0.5 mM each dNTP, 2 µL of the 5-fold dilution of RT mix, 1 U Taq DNA polymerase, 9.8 µL H 2 O, and 0.12 µM of each primer. Primer sequences are detailed in Supporting Information Table S1. PCR conditions were: 2 minutes at 95°C, followed by 40 cycles at 95°C for 20 sec, 20 sec at the primer specific temperature (~55°C) and at 72°C for 35 sec. A thermal denaturation curve of the amplified DNA was carried out, in order to measure the melting temperature of the PCR product. For each reaction, three technical replicates were run. Relative gene expression was calculated by the [START_REF] Livak | Analysis of relative gene expression data using real-time quantitative PCR and the 2-[Delta][Delta]CT method[END_REF], with the eukaryotic translation initiation factor 4A-2 (eIF-4A-2) (U213502) as an internal control. The use of this unigene as a reference was validated under our experimental conditions, as advised by [START_REF] Gutierrez | The lack of a systematic validation of reference genes: a serious pitfall undervalued in reverse transcriptionpolymerase chain reaction (RT-PCR) analysis in plants[END_REF]. For each genotype, and at each developmental stage, fruit load effect on gene expressions was evaluated using a Student's t test with R software, and the significance of the interaction between genotype and fruit load was tested via a two-way analysis of variance in R. Normalized gene expression data were analyzed by principal component analysis (PCA) using the "princomp" function in R. The principal component scores were then plotted for individual observations.

T C   2 method

Functional categorization

Results

Fruit load effect on fruit phenotype

Tomato fruits of the two lines, Moneyberg (M) and the introgression line C9d, were harvested at five developmental stages from 21 daa to the red ripe stage, and their phenotype was described either throughout fruit development (Fig. 1), or only at maturity (Fig. 2). The analysis of variance (not shown) underlined significant interactions between fruit age and fruit load for all traits measured kinetically. At maturity, seed number was the only trait which was not affected by fruit load. For the other traits, the significant fruit load effect was either genotype-independent or associated to significant genotype x fruit load interactions. When fruit load was reduced to one fruit (LL), fruit development duration decreased and cell number increased, without any interaction with genotype (Fig. 2b). On the other hand, larger fruits (Fig. 1a), associated with higher cuticular macrocracks (Fig 2a), lower pericarp water and sugar contents (Fig. 1b and1d) and higher starch content (Fig. 1c) were consistently observed during fruit development in fruits grown under LL, when compared to HL fruits in both genotypes. However sugar contents were similar at maturity. For these traits, the effect of fruit load interacted significantly with genotype, for at least one fruit developmental stage. It was also the case of cell size, which was higher under LL conditions for M, but not for C9d (Fig. 2b).

At 21 daa, which corresponds to the period of rapid fruit growth and to the developmental stage chosen for microarray transcriptome analysis, modification of fruit composition in response to changes in fruit load was significant in both genotypes for sugar content (Fig. 1d) and in M for water and starch content, indicating a strong significant interaction between genotype and fruit load (G x FL) for these two traits.

Fruit load effect on the transcriptome from 21 daa fruits and genotype-fruit load interactions

Microarray experimental design and data analysis

In the tomato genotypes studied, the 21 daa stage of fruit development corresponds to the cell expansion phase during which rapid fruit growth occurs and starch accumulation peaks [START_REF] Dinar | The relationship between starch and accumulation of soluble solids content of tomato fruit[END_REF]. At 21 daa, fruit load effect was obvious for starch and soluble sugar contents; no significant effect on fruit weight and water contents could be observed (Fig. 1a and1b). This stage of fruit development appears therefore critical for the control of fruit growth and of the concomitant changes in fruit composition.

In order to reduce the biological variability between samples, we used 40 randomly selected plants for the low load (LL) condition and 16 plants for the high load (HL) condition. For a given genotype and developmental stage, up to 50 fruits were randomly picked from the plants.

From these, large pools of 25 fruits each were constituted at random, as previously described [START_REF] Buret | Contribution to the study of sampling tomatoes for technological finishing[END_REF]. This sampling and pooling strategy effectively reduces the variability between biological replicates e.g. by excluding the environmental effects linked to plant location in the field, and allows thus to focus only on the effects of genotype and fruit load.

In addition, the microarray experiment was designed as a loop where C9dLL was compared to C9dHL itself compared to MHL itself compared to MLL itself compared to C9dLL (Fig. 3a).

Compared to single comparisons, this design decreases considerably the variance of estimated effects [START_REF] Churchill | Fundamentals of experimental design for cDNA microarrays[END_REF][START_REF] Yang | Design issues for cDNA microarray experiments[END_REF], allowing much more confidence in the estimation of the differentially expressed genes. Four technical replicates (including 2 dye swaps) were done per comparison i.e. 8 hybridizations per comparison (2 biological repeats X 4 technical repeats). To further increase the significance of the results from statistical analyses, the threshold for log 2 fold-change was set-up to 0.68 (1.6 fold change) i.e. a value higher than that commonly used in similar studies (e.g. [START_REF] Wang | Regulatory Features Underlying Pollination-Dependent and -Independent Tomato Fruit Set Revealed by Transcript and Primary Metabolite Profiling[END_REF]. As a result of this combination of pooling strategy, experimental design and statistical analyses, a smaller number of differentially expressed genes identified by transcriptome analysis can be anticipated but more robust results are expected. Indeed, in a previous study done in tomato using a similar design with fewer technical replicates, most of the results from microarray analysis were further validated by qRT-PCR [START_REF] Mounet | Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development[END_REF].

Impact of fruit load on fruit transcriptome

Because fruit load effect on fruit development and composition is the main focus of our study, we excluded from the statistical analyses the comparison of genotypes for the same load. The effect of fruit load on tomato fruit transcriptome in each genotype was analyzed by comparing gene expressions under HL and LL in M and in C9d (Fig. 3a). In total, 103 genes out of 11,862 (0.9 %) were differentially expressed in M, versus 147 in C9d (1.2 %) (Fig. 3b). Among them, 56 differentially expressed genes were common to C9d and M with similar responses whatever the genotype, except for one unigene showing an opposite response depending on the genotype.

To identify which biological processes are modified by a fruit load change, unigenes were classified into functional categories. Figure 4a displays the distribution of the differentially expressed unigenes into functional categories (Supporting Information Table S2 and Table S3). According to the Mapman classification, all biological processes were affected by fruit load but to a variable extent. As expected from the modification of the source/sink relationships under LL, the expression of genes implicated in primary and secondary metabolism was indeed affected.

Accordingly, LL fruits also displayed changes in cell wall related genes (mostly up-regulated) and in electron transport (mostly down-regulated). A large proportion of the differentially expressed genes, mostly down-regulated, have no function attributed. However, the most striking differences between HL and LL -in terms of expression level-, were observed for genes involved in signalling, in regulation of gene expression (hormonal responses, transcription) and in protein metabolism (protein synthesis, post-translational modifications and degradation). Details of several categories showing remarkable changes are displayed in Table 1. In primary metabolism, the enzyme exhibiting the highest changes under LL conditions in both M and C9d genotypes (log 2 fold-change = ~-1.6 in M, ~-1.5 in C9d) was the fructose-2,6-bisphosphatase (F26BPase). Several differentially expressed genes with putative function in signalling exhibited high fold-changes under low load conditions in both M and C9d (1.4 < log 2 fold-changes < 4.8). While functions of genes encoding proteins with calmodulin-binding motif and leucine rich repeats may remain elusive, the COP9 signalosome is a key player of the machinery controlling protein degradation that regulates a variety of processes in plants including light-regulated development and hormone signalling [START_REF] Chamovitz | Revisiting the COP9 signalosome as a transcriptional regulator[END_REF]). In addition, some ethylene, auxin and cytokinin-related genes with roles in hormone biosynthesis, degradation or responses were also up-or down-regulated under LL (Supporting Information Table S2 and Table S3). Among the 29 differentially expressed genes encoding transcription factors and other proteins involved in gene regulation, 9 displayed very consistent up-regulation (2 genes) or down-regulation (7 genes) in both M and C9d under LL.

Several transcription factor families were represented (HB, bHLH, AP2/EREBP, C2H2 zinc finger, bZIP …). Of these, the gene encoding a TAZ zinc finger protein exhibited very high and strikingly similar changes in M and C9d under low load. The fold change log 2 value was ~-5.3 for both genotypes, i.e. a ~40-fold reduction in transcript abundance in the 21 daa fruits from M and C9d cultivated under LL conditions. With the notable exception of the elongation factor 1-alpha implicated in the protein synthesis machinery and of protein phosphatase 2C, most of the genes classified into the protein metabolism category and differentially expressed under LL are involved in protein degradation.

Interactions between genotype and fruit load

The previous study showed independently the effect of fruit load on each genotype. Several genes displayed very similar fold changes in the two genotypes (e.g. the F26BPase and the TAZ transcription factor) while others were mostly affected in one or the other genotype (e.g. the AS2

or the CCAAT box transcriptional regulators preferentially up-regulated in C9d) (Table 1). In order to specifically identify the LL affected genes displaying a genotype-dependent change in gene expression, we analyzed the genotype x fruit load (G x FL) interaction using LIMMA. Ninety-nine genes showing significant G x FL interactions (Supporting Information Table S4) were identified and further classified into functional categories (Fig. 4b). Among them, a high proportion belonged to hormonal and stress response categories albeit all biological processes were targets of G x FL interactions. For all functional categories, most of the genes up-or down-regulated under low load conditions showed a greater variation of gene expression in one or the other genotype (Supporting Information Table S4). In this table, positive values indicates that the corresponding gene was more up-regulated or down-regulated under LL relative to HL conditions in C9d than in M while negative values indicate the opposite. Their absolute value gives an indication of the extent of the differences between the two genotypes. The largest category comprised genes related to various hormone biosynthetic pathways including ethylene (ACC synthase and ACC oxidase), auxin (IAA hydrolase), jasmonic acid (lipoxygenase, ent-kaurenoic acid hydroxylase) and phytosulfokines and salicylate (Table 2). All the hormone-related genes displaying G x FL interactions were more expressed in C9d while cell wall related genes were more or less expressed in one or the other genotype (Table 2).

Validation of microarray data

The abundance of transcripts from selected genes was monitored by qRT-PCR in order to validate the microarray data, on independent fruit samples. Quantitative PCR was carried out on eleven unigenes, and confirmed the changes in transcript abundance (Supporting Information Fig. S1).

Because of the difference in sensitivity of the two techniques (already observed in [START_REF] Mohammadi | Transcriptional profiling of hexaploid wheat (Triticum aestivum L.) roots identifies novel, dehydration-responsive genes[END_REF][START_REF] Mohammadi | Transcriptional profiling of hexaploid wheat (Triticum aestivum L.) roots identifies novel, dehydration-responsive genes[END_REF][START_REF] Fernandez | Transcriptomic identification of candidate genes involved in sunflower responses to chilling and salt stresses based on cDNA microarray analysis[END_REF], in a lot of cases the ratio values based on quantitative PCR were higher than those based on microarray but the Pearson's correlation coefficient between the two methods was high (r = 0.92, p = 7.10 -16 ).

Fruit load effect on expression of selected genes along fruit development

To gain further insight into how genes reacted to the fruit load change in the two genotypes, we performed qRT-PCR on five stages from the cell expansion phase to fruit maturity, in the two genotypes and under the two fruit loads. The functions of the 15 selected genes were related to processes potentially involved in fruit growth and carbohydrate accumulation. Some genes were chosen based on microarray results while others were selected because of their importance in metabolism (when absent from the DNA chip). The correspondence between gene codes, their annotations and their sequence references are detailed in Table 3. As shown on Fig. 5, for each of the 15 observed genes, we identified (i) a significant fruit load effect whatever the fruit developmental stage, for M, for C9d or for both genotypes and (ii) a significant G x FL interaction at least at two developmental stages.

Water flux-related genes

Two aquaporins: a delta-tonoplast integral protein (delta-TIP), and a plasma membrane intrinsic protein (PIP1), involved in water flux across biological membranes, were particularly affected by a change in fruit load. The down-regulation of delta-TIP under LL condition was confirmed for both genotypes (except at 42 daa) and PIP1 was similarly affected by fruit load. Moreover, a shift in expression with fruit load was observed for PIP1: its expression was down-regulated earlier under LL than under HL.

Cell wall-related genes

Five cell wall (CW) related genes were selected, two involved in synthesis, UDP-glucose-4epimerase (UDP-G-4-epi) and UDP-glucose-pryrophosphorylase (UDP-G-PPase), and three involved in CW degradation : polygalacturonase (PG), and two xyloglucan endotransglycosylases (XTH6 and BR1). The expression of UDP-G-4-epi was lower under LL than under HL during the fruit growth phase (the first three stages), while the opposite occurred during fruit ripening (the two last stages). The expression patterns of UDP-G-PPase and XTH6 were similar and slightly affected by fruit load, with a high G x FL interaction at 28 daa. BR1 and PG were highly affected by fruit load (figures are drawn with logarithmic scales), but for BR1 the variations depended on the fruit developmental stage. For M, a shift in expression was revealed as BR1 expression was upregulated earlier under LL than under HL. PG showed the highest fold-change expression values between HL and LL, during the whole of fruit development and its expression level was higher under LL than under HL during the fruit ripening for both genotypes.

Metabolism-related genes

Some genes involved in key processes of starch metabolism, sugar metabolism, organic-acid and amino-acid metabolism were analysed. Starch metabolism was represented by two genes involved in its synthesis, the ADP-glucose-pyrophosphorylase large sub-unit (ADP-G-PPase) and a starch branching enzyme (SBE), and one gene involved in its degradation: a beta-amylase (-AM). LL conditions mainly reduced the ADP-G-PPase expression during fruit ripening, whereas SBE expression was mainly reduced during the fruit growth phase. The expression of -AM was dependent on fruit load in both genotypes, but it was more affected during the fruit growth phase in C9d, and during fruit ripening in M.

Three genes involved in sugar metabolism were studied: two acid invertases (TIV1 and -FR) and a sucrose synthase (SUS2). Sucrose synthase is involved in phloem unloading and is mostly expressed at early stages of fruit development while vacuolar invertases cleave sucrose into glucose and fructose that are further accumulated into the vacuole of fruit cells. Sucrose synthase SUS2 exhibited a very strong interaction effect with fruit age since the highest expression level was observed at 28 daa in C9d, whatever the fruit load. In contrast, the transcript abundance of vacuolar invertase (TIV1 and -FR) was highly dependent on fruit load conditions and displayed a strong increase in both genotypes during fruit ripening.

A gene coding for a phosphoenolpyruvate carboxylase (PEPC) was studied because it is a multifaceted enzyme providing precursors for amino -acid and organic-acid synthesis [START_REF] Stitt | Nitrate regulation of metabolism and growth[END_REF].

Whatever the genotype, LL led to a slight decrease in PEPC expression, similarly to SUS2. A gene coding for glutamate decarboxylase (GAD1) was studied because this enzyme is involved in the synthesis of GABA, the major amino-acid stored in tomato fruit [START_REF] Akihiro | Biochemical mechanism on GABA accumulation during fruit development in tomato[END_REF]. In C9d, the level of GAD1 transcripts was lower under LL than under HL whatever the fruit developmental stage, while in M no clear tendency was deduced.

Principal component analysis (PCA)

Gene expression patterns throughout fruit development were studied via PCA (Fig. 6) in order to have a global view on the main changes that occurred at the two fruit loads. The first two principal components (PC) accounted for 56 % of the total gene expression variations. The first PC accounted for 33% of the variation and was mainly represented by expressions of PIP1, PEPC, SBE and UDP-G-PPase, ie water fluxes, organic and amino-acid metabolism, starch and cell wall synthesis while the second PC was mainly explained by the two studied invertases (-FR and TIV1), UDP-G-4-epi and PG ie soluble sugar metabolism and cell wall synthesis / degradation.

By plotting the PC scores during growth stages in relation to the first two PC axes, it appeared that the developmental courses of the fruits mostly differed between HL and LL rather than between the two genotypes. Under HL and for both genotypes, the beginning of the fruit growth phase was characterized by high expressions in BR1, followed by high expressions in GAD1 and ADP-G-PPase related to cell wall degradation, amino-acid metabolism, and starch synthesis, respectively, while the ripening phase was mainly characterized by reduced expressions of UDP-G-4-epi, and -AM, related to cell wall synthesis, and starch degradation. Under LL the same two phases could be distinguished: during the fruit growth phase, variations throughout fruit development were related to a down-regulation of UDP-G-4-epi while during fruit ripening, they were related to high expressions of the two invertases (TIV1, and -FR) and PG and low expressions of SUS2.

Discussion

In tomato, as in other plant species, the development and composition of strong sink organs such as seeds and fruits depend largely upon the size of the carbohydrate pool available and on the sink strength [START_REF] Baldet | The expression of cell proliferation-related genes in early developing flowers is affected by a fruit load reduction in tomato plants[END_REF][START_REF] Burstin | Developmental genes have pleiotropic effects on plant morphology and source capacity, eventually impacting on seed protein content and productivity in pea[END_REF]. Carbohydrate pool size and partitioning between the different plant tissues can be affected by a large variety of environmental conditions and by genetic factors. Environmental conditions, including cultural practises, are well-known determinants of fruit growth and quality [START_REF] Heuvelink | Effect of fruit load on dry matter partitioning in tomato[END_REF][START_REF] Gautier | Modulation of competition between fruits and leaves by flower pruning and water fogging, and consequences on tomato leaf and fuit growth[END_REF][START_REF] Bertin | Do genetic make-up and growth manipulation affect tomato fruit size by cell number, or cell size and DNA endoreduplication[END_REF]. Recent studies also highlighted the genetic control of fruit growth and composition at both plant [START_REF] Schauer | Comprehensive metabolic profiling and phenotyping of interspecific introgression lines for tomato improvement[END_REF]) and fruit [START_REF] Fridman | A recombination hotspot delimits a wild-species quantitative trait locus for tomato sugar content to 484 bp within an invertase gene[END_REF][START_REF] Fridman | Zooming in on a quantitative trait for tomato yield using interspecific introgressions[END_REF]) levels. In addition, detailed transcriptome analysis of tomato lines carrying introgressions from S. pennellii into S. lycopersicum genetic background indicated the coordinated up-regulation of enzymes of sucrose mobilization and respiration in early developing fruit in lines displaying high Brix values [START_REF] Baxter | Comparison of changes in fruit gene expression in tomato introgression lines provides evidence of genome-wide transcriptionnal changes and reveals links to mapped QTLs and described traits[END_REF]. However, the mechanisms by which environment and genotype interact and modulate fruit growth and quality remain poorly known.

In order to get some new insights upon these mechanisms, we have altered the size of the carbohydrate pool available to the fruit by reducing the fruit load (FL) of the plant and analyzed the transcriptome of the developing fruit under these conditions. This was done for two closely related genotypes differing for an introgression of 40 cM at the bottom of chromosome 9. The introgression carries quantitative trait loci whose expression is either independent of fruit load (fruit developmental duration, fresh weight, seed number, dry matter and sugar content) or specific to fruit load (cell number, cell size or fruit cracking), already indicating the occurrence of G

x FL interactions [START_REF] Prudent | Genetic and physiological analysis of tomato fruit weight and composition: influence of carbon availability on QTL detection[END_REF].

The C9d and the M tomato genotypes display both common and specific fruit phenotypic variations under low fruit load.

In both genotypes, the increased carbohydrate availability led to a spectacular increase in fruit size, with the appearance of large macro-cracks in the cuticle (Fig. 2a). Increase in fruit fresh weight can be related to an increase in cell number through the regulation of cell-proliferation genes at very early stages of flower and fruit development [START_REF] Baldet | The expression of cell proliferation-related genes in early developing flowers is affected by a fruit load reduction in tomato plants[END_REF], or to enhanced cell enlargement [START_REF] Bertin | Analysis of the tomato fruit growth response to temperature and plant fruit load in relation to cell division, cell expansion and DNA endoreduplication[END_REF]). In the present study, cell number was increased in both genotypes but cell expansion was affected only in the M genotype (Fig. 2b). In both genotypes, this resulted in the acceleration of fruit growth, more pronounced in the M genotype, and in the shortening of the duration of fruit development (Fig. 1a and 2b). As expected from previous studies [START_REF] Gautier | Modulation of competition between fruits and leaves by flower pruning and water fogging, and consequences on tomato leaf and fuit growth[END_REF], fruit composition was also affected since the decrease in fruit load led to a strong increase in starch content in M and C9d. Surprisingly, since starch degradation is the major source of soluble sugars that are further accumulated in the vacuole of ripening fruit cells [START_REF] Dinar | The relationship between starch and accumulation of soluble solids content of tomato fruit[END_REF], the soluble sugar content of the ripe fruit was not different under LL (Fig. 1d). This result, obtained in both genotypes, suggests that starch degradation products further enter fruit metabolism and/or are respired. Linked with the increased carbohydrate accumulation in the fruit, the dry matter content of the fruit was higher in the two genotypes, as indicated by the lower water concentration under LL (Fig. 1b). However, when considering both fruit weight (Fig. 1a) and water concentration in the fruit (Fig. 1b), the water flux towards the fruit was considerably enhanced under LL, in particular in the M genotype. In addition, the presence of cuticle macro-cracks under LL may have considerably increased the water flux in the fruit.

Fruit transcriptome analysis highlights regulatory processes as common targets of low fruit load conditions in both genotypes.

Transcriptome analysis provides a systemslevel view of plant response to carbon availability, and allows a global approach of this response [START_REF] Smith | Coordination of carbon supply and plant growth[END_REF]. Transcript profiles under two conditions of fruit load in two genotypes were analysed during cell expansion phase (21 daa) because (i) fruit load affected cell size in M but not in C9d (Fig. 2b), (ii) fruit load affected starch storage (Fig. 1c) which is maximum during this period [START_REF] Dinar | The relationship between starch and accumulation of soluble solids content of tomato fruit[END_REF]. Despite the large changes in starch accumulation in the fruit, indicating a change of its carbohydrate status (Fig. 1c), main gene categories affected by fruit load and common to both genotypes were mostly related to transcriptional and post-transcriptional regulation processes, to stress and to unknown functions, but not to primary and secondary metabolism (Fig. 4a). Several hypotheses can be considered to explain this result. First, microarray transcriptome analyses were done at only one stage, 21 daa, which may not be representative for the metabolic processes occurring during the whole fruit development. However, the transcriptome analysis of introgression lines by [START_REF] Baxter | Comparison of changes in fruit gene expression in tomato introgression lines provides evidence of genome-wide transcriptionnal changes and reveals links to mapped QTLs and described traits[END_REF], done at the same stage of development, effectively highlighted sugar-metabolism related genes in lines differing in fruit composition. Second, analyses of transcriptome data could have been too stringent, excluding a large group of differentially expressed genes. This hypothesis is sustained by the comparatively low number of differential genes identified. At last, fruit primary metabolism can be mainly controlled at other levels than transcript abundance via for instance protein abundance, which can be uncoupled from transcript level [START_REF] Piques | Ribosome and transcript copy numbers, polysome occupancy and enzyme dynamics in Arabidopsis[END_REF], and enzymatic regulation, as proposed by [START_REF] Gibon | Adjustment of growth, starch turnover, protein content and central metabolism to a decrease of the carbon supply when Arabidopsis is grown in very short photoperiods[END_REF].

The most striking and unexpected result was the strong up-or down-regulation in response to changes in carbon availability of genes involved in signaling, in regulation of gene expression (hormonal responses, transcription) and in protein metabolism (protein synthesis, posttranslational modifications and degradation). Carbohydrate status of the plant is known to affect a wide range of processes in the fruit, such as fruit cell division [START_REF] Bertin | Analysis of the tomato fruit growth response to temperature and plant fruit load in relation to cell division, cell expansion and DNA endoreduplication[END_REF], whole-plant respiration rate [START_REF] Gary | Time-course of tomato whole-plant respiration and fruit and stem growth during prolonged darkness in relation to carbohydrate reserves[END_REF], fruit cuticle elasticity [START_REF] Gilbert | Cuticular cracking on nectarine fruit surface: Spatial distribution and development in relation to irrigation and thinning[END_REF]), fruit transpiration [START_REF] Guichard | Analysis of growth and water relations of tomato fruits in relation to air vapor pressure deficit and plant fruit load[END_REF], ethylene emission [START_REF] Génard | ETHY. A theory of fruit climacteric ethylene emission[END_REF], and carbon-and nitrogenmetabolism [START_REF] Baldet | Contrasted responses to carbohydrate limitation in tomato fruit at two stages of development[END_REF]. Accordingly, in our experiment, fruit growth and composition were notably affected in both genotypes. According to the results presented here, the fruit may cope with the new nutritional status of the plant under LL by setting up new regulations in order to adjust its growth and its metabolism, whatever the genotype. It is noteworthy that several of the most highly induced or repressed genes, which displayed high and almost identical variations in expression under LL in both genotypes, may potentially regulate whole pathways or processes, like the fructose-2,6-bisphosphatase (F26BPase) and the TAZ transcription factor. In plants, the F26BPase enzyme is involved in both the synthesis and the degradation of the Fru-2,6-P2, which is believed to be involved in the regulation of sink metabolism [START_REF] Nielsen | Fructose-2,6-bisphosphate: a traffic signal in plant metabolism[END_REF]. The Arabidopsis BT2 gene homologous to the tomato TAZ transcription factor described here encodes a protein with BTB and TAZ domains which has recently been shown to be a central component of an interconnected signalling network that detects and responds to multiple inputs, including sugars and hormones [START_REF] Mandadi | BT2, a BTB protein, mediates multiple responses to nutrients, stresses and hormones in Arabidopsis[END_REF]). In addition, several other transcription factors such as the b-ZIP, AP2 and C2H2 zinc finger protein, known to be involved in the mediation of diverse hormone, light, circadian rhythm, stress and metabolic effects [START_REF] Liu | ELF3 encodes a circadian clock-regulated nuclear protein that functions in an Arabidopsis PHYB signal transduction pathway[END_REF][START_REF] Mcgrath | Repressor-and activator-type ethylene response factors functioning in jasmonate signaling and disease resistance identified via a genome-wide screen of Arabidopsis transcription factor gene expression[END_REF][START_REF] Nakano | Identification of genes of the plant-specific transcription-factor families cooperatively regulated by ethylene and jasmonate in Arabidopsis thaliana[END_REF][START_REF] Rushton | Tobacco transcription factors: novel insights into transcriptional regulation in the Solanaceae[END_REF][START_REF] Robert | BTB and TAZ domain scaffold proteins perform a crucial function in Arabidopsis development[END_REF][START_REF] Yáñez | An abiotic stress-responsive bZIP transcription factor from wild and cultivated tomatoes regulates stress-related genes[END_REF], were also regulated by fruit load. Among the possible functions of the other large "protein modification" category, which includes proteins with diverse roles in protein synthesis and fate, there is the post-transcriptional control of protein abundance and activity e.g. for enzymes from primary metabolism.

Genotype x fruit load interactions are unravelled by fruit transcriptome analysis

and by qRT-PCR analysis of specific target genes along fruit development. All functional categories analyzed showed significant G x FL interactions (Supporting Information Table S4). Genes showing G x FL interactions were (i) differentially expressed in LL conditions relative to HL conditions in at least one genotype but (ii) more or less expressed in one or the other genotype, if both genotypes were affected. As expected from the fruit growth phenotype (Fig. 1a), the presence of cuticle macro-cracks which may be perceived as wounds (Fig. 2a) and the large difference in seed numbers between the two lines (Fig. 2b and/or in stress response such as ethylene, jasmonic acid and abscisic acid (Table 2). Accordingly, several transcription factors showing differential expression in the two genotypes under LL such as AP2/EREBP and WRKY are known as regulators of hormone mediated stress response [START_REF] Birnbaum | A gene expression map of the arabidopsis root[END_REF][START_REF] Ülker | WRKY transcription factors: from DNA binding towards biological function[END_REF]. Hormonal control may also play a role in the differential expression in both genotypes of genes involved in cell wall modifications (Table 2) [START_REF] Catala | Auxin-regulated genes encoding cell wallmodifying proteins are expressed during early tomato fruit growth[END_REF][START_REF] Bradford | Expression of an expansin is associated with endosperm weakening during tomato seed germination[END_REF][START_REF] Chen | A gibberellin-regulated xyloglucan endotransglycosylase gene is expressed in the endosperm cap during tomato seed germination[END_REF][START_REF] Guillon | Down-regulation of an auxin response factor in the tomato induces modification of fine pectin structure and tissue architecture[END_REF]) and in the possibly linked fruit cell size (Fig. 2b). Interestingly, several metabolism genes (isocitrate lyase, GABA transaminase, tryptophan synthase) displayed significant G x FL interactions (Supporting Information Table S4), confirming the need for large scale metabolomic investigation of the LL effect on fruit composition in both genotypes. Thus, the function of the genes showing significant G x FL interactions could be to modulate the fruit response to the LL condition in accordance with the specific plant and fruit characteristics of a given genotype.

As discussed above, the transcriptome study was done at only one stage of fruit development. It may therefore have overlooked some fruit responses to LL conditions and G x FL interactions. For that reason, we analyzed the expression patterns along fruit development of selected genes implicated in various aspects of plant metabolism and growth (Fig. 5 and Fig. 6).

The strongest effect was observed for the vacuolar invertases (TIV1 and -fructosidase), which catalyze the cleavage of sucrose to glucose and fructose further stored in the vacuole (for a review, see [START_REF] Sturm | Invertases. Primary structures, functions, and roles in plant development and sucrose partitioning[END_REF]) and for the polygalacturonase gene involved in cell wall degradation [START_REF] Hadfield | Polygalacturonases: many genes in search of a function[END_REF]. Both showed consistent changes of expression in both genotypes during fruit ripening, suggesting a strong regulation by carbohydrate availability or by induced changes in the fruit. In addition, increased invertase expression could be linked with the increased starch degradation (Fig. 1c) and soluble sugar accumulation (Fig. 1d) under LL in both genotypes.

In the case of the aquaporins, which allow fluxes of water across biological membranes [START_REF] Baiges | Plant aquaporins[END_REF][START_REF] Tyerman | Plant aquaporins: multifunctional water and solute channels with expanding roles[END_REF][START_REF] Maurel | Plant aquaporins: membrane channels with multiple integrated functions[END_REF], decreased aquaporin expression could be linked with decreased fruit water content (Fig. 1b) under LL in both genotypes. However, though the expression patterns of many genes during fruit development were in agreement with previous studies dealing with carbon-metabolism related genes [START_REF] Alba | Transcriptome and selected metabolite analyses reveal multiple points of ethylene control during tomato fruit development[END_REF][START_REF] Kortstee | Differences in regulation of carbohydrate metabolism during early fruit development between domesticated tomato and two wild relatives[END_REF]), a consistency between gene expressions on the one hand and possible phenotypic changes under LL on the other hand was not found for all the analyzed genes. It was for example the case for starch synthesis and degradation related genes. Possible reasons are the limited number of genes studied by qRT-PCR or the existence of other genes with similar functions in the fruit. For example, -amylase is not the only enzyme responsible for starch degradation as -amylase and starch phosphorylase both fulfil similar roles (Robinson, Hewitt & Bennett 1988).

Additional explanations are the existence of strong G x FL interactions. Indeed, significant genotype effects and G x FL interactions were detected in this study for most genes studied, as well as interactions between responses to carbon availability and fruit developmental stage. The significant difference in fruit development between the two genotypes and the shift between the two fruit loads (fruit developmental duration is shorter under LL than under HL), could possibly explain the genotype effect and the G x FL interactions for some genes. At early stages of fruit development, a clear effect of the M genotype is observed for the sucrose cleaving enzyme sucrose synthase SUS2 which has a prominent role in sink tissues [START_REF] Claussen | Influence of sucrose and hormones in the activity of sucrose synthase and invertase in detached leaves and leaf sections of eggplants (Solanum melongena)[END_REF][START_REF] Sung | Indentification of actively filling sucrose sinks[END_REF][START_REF] Amor | A membraneassociated form of sucrose synthase and its potential role in synthesis of cellulose and callose in plants[END_REF][START_REF] Kleczkowski | Glucose activation and metabolism through UDP-glucose pyrophosphorylase in plants[END_REF], possibly reflecting the differences in competition for assimilates between the two genotypes. Shift in fruit development between HL and LL may also account for e.g. the differences in ADP-G-PPase expression patterns in M compared to C9d. However, several other genes, including the cell wall degradation gene BR1, the starch degradation gene -amylase and the GABA biosynthesis gene GAD1, displayed clear G x FL interactions independently from the fruit development shift under LL. Considering the phenotypic data available, no link could be established between the G x FL interactions at gene expression level and the G x FL interactions at fruit phenotypic level, again highlighting the need for more global and comprehensive analysis of the mechanisms involved in fruit response to increased carbon availability.

Conclusion

The present paper aimed at identifying processes that were influenced by a change in fruit carbon availability in two tomato genotypes. Our results suggested that a change in carbon availability affected very few genes but all biological processes and that primary metabolism was globally less modified at the transcriptional level than regulation systems. To get a better insight on these regulations, a translatome study could be envisaged similarly to the work of [START_REF] Mustroph | Profiling translatomes of discrete cell populations resolves altered cellular priorities during hypoxia in Arabidopsis[END_REF], as well as a metabolome study in order to identify the regulation networks between metabolites and genes, similarly to the work of [START_REF] Mounet | Gene and metabolite regulatory network analysis of early developing fruit tissues highlights new candidate genes for the control of tomato fruit composition and development[END_REF]. This study also emphasized the recurrent interactions between genotype and carbon availability, at the phenotypic level as well as at the gene expression level throughout fruit development. All these interactions, arising at different levels, thus raise the difficulty of a consistent characterization of responses to various environments, if conducted on a single genotype at a single developmental stage. Therefore, once enough data will be available on tomato, meta-analyses will offer the possibility to decipher those interactions.

Tables Table 1: or low load (LL) conditions. For each trait, the fruit load effect and the genotype effect were evaluated by t-tests and interactions between genotype and fruit load were evaluated via a two-way analysis of variance. S3: List of transcripts up-regulated under LL conditions. Genes were classified according to their belonging to a biological process. Expression fold-change values (expressed in log 2 -fold change) between the two fruit load conditions were indicated, as well as the sense of differential expression when significant at the 0.05 probability level, for Moneyberg (M) and C9d.

Supporting information

  [START_REF] Prudent | Genetic and physiological analysis of tomato fruit weight and composition: influence of carbon availability on QTL detection[END_REF], the 21 daa fruits submitted to LL conditions displayed genotype-dependent variations of expression for genes involved in hormone synthesis, hormone response, stress response, transcription and cell wall modifications. Most of the hormones concerned are either involved in fruit growth control, such as auxins, gibberellins, cytokinins, and brassinosteroid (Gillapsy,[START_REF] Gillaspy | Fruits : a developmental perspective[END_REF]

  Transcripts affected by fruit load and belonging to (A) primary metabolism, (B) signaling, (C) transcription and (D) protein metabolism. Expression fold-change values (expressed in log 2 -fold change) between the two fruit load conditions were indicated, as well as the sense of differential expression when significant at the 0.05 probability level,

  probability level and belonging to (a) hormonal responses and (b) cell wall modification. Differences in fruit load fold change expression between Moneyberg (M) and C9d are indicated and expressed in log 2 fold-change.

Figure legends Figure 1 :

 1 Figure legends

Figure 2 :

 2 Figure 2: Phenotypic characteristics of fruits at the red ripe stage.

Figure 3 :

 3 Figure 3: Microarray analysis of gene expression under HL and LL conditions, in M and C9d.

Figure 4 :Figure 5 :

 45 Figure 4: Distribution of differentially expressed genes into biological classes, according to

Figure 6 :

 6 Figure 6: Principal component analysis (PCA) of gene expression during fruit development

  Figure 2.

  photosynthesis primary metabolism

Table S4 :Figure S1 :

 S4S1 Photosynthesis sgn-U212734 0.70

Table 2 : Transcripts showing significant genotype x fruit load interactions at

 2 the 0.05

	Primary
	metabolism
	sgn-U214573 delta-1-pyrroline-5-carboxylate dehydrogenase	0.72	+LL	0.38	ns
	sgn-U223594 formamidase	0.75	+LL	0.44	ns
	sgn-U214006 aldehyde dehydrogenase	0.98	+LL	0.78	+LL
	sgn-U213084 S-adenosylmethionine decarboxylase	1.07	+LL	0.5	ns
	sgn-U225810 adenine phosphoribosyltransferase	1.21	+LL	0.45	ns
	sgn-U233626 phospholipid hydroperoxide glutathione peroxidase	3.49	+LL	0.49	ns
	sgn-U219737 galactinol synthase	0.54	ns	0.76	+LL
	sgn-U212595 histidine decarboxylase	-0.12	ns	1.20	+LL
	sgn-U225897 aldo/keto reductase	0.37	ns	2.54	+LL
	sgn-U214272 succinate dehydrogenase	0.22	ns	2.90	+LL
	sgn-U221770 dihydrolipoamide S-acetyltransferase	0.14	ns	3.73	+LL
	sgn-U234104 Fruc2,6BisPase	-1.83	-LL	-1.56	-LL
	sgn-U230970 glycosyltransferase	-1.64	-LL	-1.52	-LL
	sgn-U212765 alcohol dehydrogenases	-0.78	-LL	-0.85	-LL
	sgn-U217846 bis(5'-adenosyl)-triphosphatase	-0.12	ns	-0.99	-LL
	B) Signalling
	sgn-U222606 transducin	0.73	+LL	0.44	ns
	sgn-U220461 calreticulin 3	0.75	+LL	0.63	ns
	sgn-U238875 receptor kinase DUF 26	1.38	+LL	2.56	+LL
	sgn-U223661 COP9 signalosome	2.18	+LL	4.07	+LL
	sgn-U222229 calmodulin-binding motif	2.27	+LL	3.53	+LL
	sgn-U231007 leucine rich repeat VIII-2	3.56	+LL	4.78	+LL
	sgn-U213199 GCN5-related N-acetyltransferase	0.51	ns	0.76	+LL
	sgn-U219767 calmodulin-like MSS3	0.62	ns	0.78	+LL
	sgn-U218626 calmodulin-related protein	0.1	ns	0.87	+LL
	sgn-U218749 G-proteins	0.23	ns	2.29	+LL
	sgn-U219841 transducin	-0.05	ns	2.96	+LL
	sgn-U222057 ubiquitin E3 SCF FBOX	0.61	ns	3.24	+LL
	sgn-U231736 COP9	-0.09	ns	-2.31	-LL
	gi-AF130423 cryptochrome 1	-0.07	ns	-2.27	-LL
	sgn-U218306 phospholipase C	-0.47	ns	-0.78	-LL

Table 3 : List of the 18 genes used for qRT-PCR analysis.

 3 For each gene studied in qRT-PCR, the code used in the text, annotation and reference of the sequence are added. Asterisk means that the gene was chosen according to microarray results.

	Code	Annotation	Accession number	References
	* PIP1	plasma membrane intrinsec protein 1c (PIP1c)	U212567	(Johanson et al., 2001)
	* delta-TIP	delta tonoplast integral protein 1 (delta-TIP)	U214295	(Bayer et al., 2006)
	* UDP-G-4-epi UDP-glucose-4-epimerase	U214352	(Oomen et al., 2004)
	* UDP-G-PPase UDP-glucose-4-pyrophosphorylase	U213088	(Borovkov et al., 1997)
	* XTH6	xyloglucan-endotransglycosylase (XTH6)	U215860	(Saladie et al., 2006)
	BR1	xyloglucan-endotransglycosylase (BR1)	TC 94351	(Koka et al., 2000)
	* PG	polygalacturonase 2A	U213213	(Grierson et al., 1986)
	ADP-G-PPase ADP-glucose-pyrophosphorylase large sub-unit (AGP-S1) U81033	(Chen et al., 1998)
	* SBE	starch branching enzyme II (SBE II)	U221404	(Larsson et al., 1998)
	* -AM	beta-amylase 1	U220865	(Ren et al., 2007)
	TIV1	acid-invertase TIV1	M81081	(Klann et al., 1992)
	* -FR	beta-fructosidase	U212903	(Ohyama et al., 1992)
	SUS2	sucrose synthase 2 (sus2)	AJ011535	(Fu and Park, 1995)
	PEPC	phosphoenolpyruvate carboxylase	U219207	(Koizumi et al., 1991)
	GAD1	glutamate decarboxylase 1 (GAD1)	U314961	(Yun and Oh, 1998)
	* ACC-ox	1-aminocyclopropane-1 carboxylate oxidase	U212787	(Kock et al., 1991)
	* E8	2-oxoglutarate-dependent dioxygenase	U213299	(Trentmann and Kende, 1995)
	* eIF-4A-2	eukaryotic initiation factor 4A-2	U213502	(Owttrim et al., 1991)

Table S1 : List of the 18 genes and primers used for qRT-PCR analysis.

 S1 For each gene studied in qRT-PCR, the code used in the text and annotation of the sequence are added.

	Code	Annotation	Accession number	Forward primer 5'--> 3'	Reverse primer 5'--> 3'
	PIP1	plasma membrane intrinsec protein 1c (PIP1c)	U212567	GTCTTGGTGCTATCTGTGG	GTCCATCACCTTTGGTGTAAC
	delta-TIP	delta tonoplast integral protein 1 (delta-TIP)	U214295	GTGGGTTAGCTGGTCTTATC	CTGCAAACCAAAAACTTAAC
	UDP-G-4-epi UDP-glucose-4-epimerase	U214352	GTATATGGCAACGACTACCC	GAAGTCTCTGAAGTGCAAC
	UDP-G-PPase UDP-glucose-4-pyrophosphorylase	U213088	CTGAAAAGCTCAACAACCTC	CTTAGATAGCGACCGACAAG
	XTH6	xyloglucan-endotransglycosylase (XTH6)	U215860	GAGGGACCATACTCACACAC	CTCCCATGAAACTTAAATC
	BR1	xyloglucan-endotransglycosylase (BR1)	TC 94351	CTCGTTGCTGGAAACTCTG	GTAGGGTCAAACCAAAGG
	PG	polygalacturonase 2A	U213213	GAGAATATCAAGGGCACAAG	TCTAGTGAAGTGCAGTGTGG
	ADP-G-PPase ADP-glucose-pyrophosphorylase large sub-unit (AGP-S1) U81033	CATATTAGGAGGAGGCGAAG	GAGCAATGTGACGATTCAG
	SBE	starch branching enzyme II (SBE II)	U221404	GAGCTTCTTGACGTTATCTG	CACTTCATCTCTGCGTATG
	b-AM	beta-amylase 1	U220865	CCTGACTTATTCCATCCTG	GCTTCCCTCTCTACCTGTTC
	TIV1	acid-invertase TIV1	M81081	GACAGTGAATCTGCTGACC	GGCCACTGAAGTAGATGTG
	b-FR	beta-fructosidase	U212903	CATGTAGGTTTCAGTTGCTC	GTTAGCTCAGATAGCGTTTG
	SUS2	sucrose synthase 2 (sus2)	AJ011535	TATTGATCCATATCACGGTGAGCAAG ATAAACAGCAGCCAGCGTCAGTAGTC
	PEPC	phosphoenolpyruvate carboxylase	U219207	TGGCCATTCTTTAGGGTAAC	CTCTTCGTACTTGGACCTC
	GAD1	glutamate decarboxylase 1 (GAD1)	U314961	GCAAGGGTACTAAGAGAAGG	GAGCATCTGGTGGCATAGTG
	ACC-ox	1-aminocyclopropane-1 carboxylate oxidase	U212787	GGCAGAGGAAAGTACACAAG	TTGAATTGGGATCTAAGCAC
	E8	2-oxoglutarate-dependent dioxygenase	U213299	GTCGATTCAGGGATAGTTC	CTACGTTGAAAATCGTCTCC
	eIF-4A-2	eukaryotic initiation factor 4A-2	U213502	GATCAGCGTATCCTTCAGAG	GGCATTGTAGCAGAGAAAAC

Table S2 : List of transcripts down-regulated under LL conditions.

 S2 Genes were classified according to their belonging to a biological process. Expression fold-change values (expressed in log 2 -fold change) between the two fruit load conditions were indicated, as well as the sense of differential expression when significant at the 0.05 probability level, for

	Transport	sgn-U230991	-4.01	-LL	0.08	ns	metabolite transporters at the mitochondrial membrane
		sgn-U214295	-1.23	-LL	-1.01	-LL	major intrinsic protein TIP
		sgn-U218010	-0.09	ns	-3.89	-LL	mannitol transporter
		sgn-U216406	-0.46	ns	-0.73	-LL	amino acids transport
	Unknown	sgn-U222947	-5.52	-LL	-0.08	ns	unknown
		sgn-U239621	-4.88	-LL	-1.56	-LL	unknown
		sgn-U240531	-2.49	-LL	-0.45	ns	unknown
		sgn-U220450	-2.24	-LL	0.05	ns	unknown
		sgn-U242297	-1.97	-LL	-1.75	-LL	unknown
		sgn-U225758	-1.95	-LL	0.09	ns	unknown
		sgn-U224962	-1.77	-LL	-2.88	-LL	unknown
	sgn-U228268 Moneyberg (M) and C9d. -1.75 sgn-U239771 -1.67	-LL -LL	-1.66 -0.58	-LL ns	unknown unknown
		sgn-U220856	-1.41	-LL	-0.86	-LL	unknown
	Functional category	Gene ID sgn-U225490 sgn-U222595 sgn-U229617	Log 2 -fold change M (LL/HL) -1.38 -1.37 -1.25	Sense in M -LL -LL -LL	-0.17 Log 2 -fold change -1.01 C9d (LL/HL) -0.79	ns Sense in C9d -LL -LL	unknown Annotation unknown unknown
	Cell wall modification	sgn-U215187 sgn-U216459 sgn-U215382 sgn-U231794 sgn-U229103 sgn-U239488 sgn-U217425	-1.70 -1.07 -0.88 -0.07 -0.01 0.03 -0.45	-LL -LL -LL ns ns ns ns	-1.18 -0.62 -1.47 -3.76 -1.91 -1.50 -1.33	-LL ns -LL -LL -LL -LL -LL	keratin elastin unknown unknown unknown xyloglucan endotransglycosylase unknown
	Electron	sgn-U225019 sgn-U218174	-5.30 -0.39	-LL ns	-5.13 -0.72	-LL -LL	4Fe-4S ferredoxin iron-sulfur binding unknown
	transport	sgn-U228865	-1.76	-LL	-1.52	-LL	cytochrome P450
		sgn-U230985	-1.54	-LL	-0.08	ns	glutaredoxins
		sgn-U220890	-1.23	-LL	-0.65	ns	glutaredoxins
	sgn-U221959 sgn-U232104 ns: non-significant fruit load effect -0.83 -LL 0.07 ns	-0.37 -4.00	ns -LL	glutaredoxins cytochrome c reductase
		sgn-U215744	0.51	ns	-0.83	-LL	F1-ATPase
	Hormonal	sgn-U212706	-1.76	-LL	-0.86	-LL	cytokinin repressed protein
	responses	sgn-U214919	-2.54	-LL	-0.77	-LL	1-aminocyclopropane-1-carboxylate oxidase
		sgn-U212787	-1.93	-LL	-0.64	ns	1-aminocyclopropane-1-carboxylate oxidase
		sgn-U217786	-0.95	-LL	-0.84	-LL	auxin induced protein
	Photosynthesis sgn-U230677	-0.12	ns	-4.03	-LL	PSII polypeptide subunits
		sgn-U234083	-0.48	ns	-0.71	-LL	PSII LHC-II
	Primary	sgn-U234104	-1.83	-LL	-1.56	-LL	Fruc2,6BisPase
	metabolism	sgn-U230970	-1.64	-LL	-1.52	-LL	glycosyltransferase
		sgn-U212765	-0.78	-LL	-0.85	-LL	alcohol dehydrogenases
		sgn-U217846	-0.12	ns	-0.99	-LL	bis(5'-adenosyl)-triphosphatase
	Protein	sgn-U230025	-3.70	-LL	-3.94	-LL	peptidase
	metabolism	sgn-U236595	-2.35	-LL	0.01	ns	receptor like cytoplasmatic kinase VIII
		sgn-U223430	-1.66	-LL	-1.71	-LL	degradation
		sgn-U229834	-1.64	-LL	-0.56	ns	receptor like cytoplasmatic kinase VII
		sgn-U221944	-1.62	-LL	-1.43	-LL	protein phosphatase 2C
		sgn-U220298	-1.07	-LL	-0.90	-LL	peptidylprolyl isomerase
	Signalling	sgn-U231736	-0.09	ns	-2.31	-LL	COP9
		gi-AF130423	-0.07	ns	-2.27	-LL	cryptochrome 1
		sgn-U218306	-0.47	ns	-0.78	-LL	phospholipase C
	Stress	sgn-U221778	-2.62	-LL	-2.97	-LL	disease resistance protein
	responses	sgn-U216469	-1.61	-LL	-0.29	ns	heat shock protein
		gi-U72396	-1.50	-LL	-0.43	ns	heat shock protein
		sgn-U216965	-1.32	-LL	0	ns	stress induces protein
		gi-AJ225049	-1.07	-LL	-0.78	-LL	heat shock protein
		sgn-U216888	-1.07	-LL	-0.68	-LL	DNAJ
		sgn-U212696	-0.99	-LL	-0.87	-LL	heat shock protein
		sgn-U215426	-0.98	-LL	-0.53	ns	SENU1
		sgn-U217418	-0.44	ns	-0.96	-LL	heat shock protein HSP81-1
	Transcription	sgn-U230225	-5.36	-LL	-5.28	-LL	TAZ zinc finger family protein / BTB/POZ domain-containing protein
		sgn-U217339	-3.49	-LL	-0.68	-LL	putative transcription regulator
		sgn-U218544	-2.30	-LL	-2.24	-LL	Ring3-related bromodomain protein
		sgn-U236234	-2.12	-LL	-1.75	-LL	ELF3 /
		sgn-U222954	-1.74	-LL	-1.78	-LL	C2H2 zinc finger family
		sgn-U228377	-1.74	-LL	-0.1	ns	chromatin remodeling factors
		sgn-U240661	-1.62	-LL	-1.44	-LL	transcriptional regulator
		sgn-U216671	-1.45	-LL	-1.17	-LL	bZIP transcription factor family
		sgn-U225321	0.03	ns	-2.95	-LL	DHHC-type zinc finger

Table

delta-1-pyrroline-5-carboxylate dehydrogenase sgn-U223594

		sgn-U213010	1.05	+LL	0.38	ns	phenazine biosynthesis PhzC/PhzF family protein
		sgn-U216112	1.58	+LL	1.81	+LL	ascorbate oxidase SRG1
		sgn-U218068	0.19	ns	0.68	+LL	cinnamoyl-CoA reductase
		sgn-U217320	0.35	ns	0.79	+LL	redox.glutaredoxins
		sgn-U221863	-0.15	ns	2.29	+LL	secondary metabolism.flavonoids.flavonols.oxidoreductase
	Signalling	sgn-U222606	0.73	+LL	0.44	ns	transducin
		sgn-U220461	0.75	+LL	0.63	ns	calreticulin 3
		sgn-U238875	1.38	+LL	2.56	+LL	receptor kinase DUF 26
		sgn-U223661	2.18	+LL	4.07	+LL	COP9 signalosome
		sgn-U222229	2.27	+LL	3.53	+LL	calmodulin-binding motif
		sgn-U231007	3.56	+LL	4.78	+LL	leucine rich repeat VIII-2
		sgn-U213199	0.51	ns	0.76	+LL	GCN5-related N-acetyltransferase
		sgn-U219767	0.62	ns	0.78	+LL	calmodulin-like MSS3
		sgn-U218626	0.1	ns	0.87	+LL	calmodulin-related protein
		sgn-U218749	0.23	ns	2.29	+LL	G-proteins
	Functional category	Gene ID sgn-U219841 sgn-U222057	Log 2 -fold change M -0.05 0.61	Sense in M ns ns	Log 2 -fold change C9d 2.96 3.24	Sense in C9d +LL +LL	transducin Annotation ubiquitin E3 SCF FBOX
	Cell wall modification	sgn-U217045 sgn-U215860 sgn-U213213 sgn-U215393 gi-Y12640 sgn-U218488 sgn-U220299 sgn-U213790 sgn-U222710 sgn-U212922 sgn-U215711 sgn-U213021 sgn-U213668 sgn-U225818 sgn-U218566	(LL/HL) 0.69 1.38 1.79 0.72 0.76 0.97 -0.16 1.99 0.31 2.58 -0.08 -0.58 0 0.48 0.14	+LL +LL +LL +LL +LL +LL ns +LL ns +LL ns ns ns ns ns	(LL/HL) 0.49 0.83 1.90 0.69 0.48 0.6 0.76 0.99 0.85 1.23 2.92 0.70 3.58 0.71 0.71	ns +LL +LL +LL ns ns +LL +LL +LL +LL +LL +LL +LL +LL +LL	pectate lyases and polygalacturonases xyloglucan endotransglycosylase peroxidases Cf-4A gene disease resistance protein-related polygalacturonase glycosyl hydrolase elastin PR protein beta-1,4-xylosidase protease inhibitor expansin Avr 09 pectate lyases and polygalacturonases heat shock protein
	Electron transport	sgn-U217593 sgn-U214691 sgn-U225537 sgn-U214985 gi-M13938 sgn-U215231 sgn-U220136 sgn-U213023 sgn-U213338	0.72 1.13 0.15 0.55 -0.08 0.5 0.35 -0.3 -0.31	+LL +LL ns ns ns ns ns ns ns	0.42 0.86 1.73 0.76 0.77 0.80 2.20 0.97 1.12	ns +LL +LL +LL +LL +LL +LL +LL +LL	thioredoxin alternative oxidase hevein-related protein precursor (PR-4) proteinase inhibitor I gene peroxidases 5'-adenylylsulfate reductase protease inhibitor mitochondrial carrier protein family trypsin inhibitor
	Hormonal responses	sgn-U213299 sgn-U216133 sgn-U212578 sgn-U227798	1.01 1.35 -0.27 0.43	+LL +LL ns ns	0.59 1.26 1.42 3.02	ns +LL +LL +LL	2-oxoglutarate-dependent dioxygenase cytokinin oxidase transferase peroxidases
	sgn-U227363 sgn-U218918 Transcription sgn-U222912 sgn-U212798 sgn-U213736 sgn-U241804 gi-BT012912 sgn-U215161 sgn-U222648 sgn-U212800 sgn-U223859 sgn-U212799 sgn-U241461 sgn-U236633 sgn-U242104 sgn-U217991	0.04 0.47 0.68 -0.05 0.77 -0.07 0.93 0.12 1.36 0.34 2.66 0.47 2.76 -0.06 3.19 -0.1	ns ns +LL ns +LL ns +LL ns +LL ns +LL ns +LL ns +LL ns	0.70 0.72 -0.21 0.73 0.23 0.79 0.54 0.83 0.63 0.97 4.52 1.10 0.6 1.67 3.41 0.80	+LL +LL ns +LL ns +LL ns +LL ns +LL +LL +LL ns +LL +LL +LL	auxin induced protein unspecified 2-oxoglutarate-dependent dioxygenase RNA helicase 2-oxoglutarate-dependent dioxygenase MYB-related transcription factor family auxin induced protein GCN5-related N-acetyltransferase auxin induced protein HB,Homeobox transcription factor family 2-oxoglutarate-dependent dioxygenase bHLH 2-oxoglutarate-dependent dioxygenase AP2/EREBP, APETALA2/Ethylene-responsive element binding protein family auxin induced protein AP2/EREBP, APETALA2/Ethylene-responsive element binding protein family
	Photosynthesis sgn-U213011 sgn-U225111 sgn-U212665 sgn-U216769 sgn-U223602 sgn-U216297 sgn-U218605	0.86 0.15 0.92 0.19 -0.07 0.39 0.35	+LL ns +LL ns ns ns ns	0.53 0.85 0.83 0.85 2.77 0.87 0.90	ns +LL +LL +LL +LL +LL +LL	WRKY domain transcription factor family rubisco subunit2A zinc finger protein (PMZ) -related PSII polypeptide subunits AP2/EREBP, APETALA2/Ethylene-responsive element binding protein family chlorophyll synthetase WRKY domain transcription factor family
	Primary metabolism	sgn-U214573 sgn-U214006 sgn-U213139 sgn-U214213 sgn-U217394 sgn-U213084 sgn-U218476 sgn-U225810 sgn-U224219 sgn-U233626 sgn-U239113	0.72 0.75 0.98 0.25 0.22 0.65 1.07 0.02 1.21 0.21 3.49 -0.02	+LL +LL +LL ns ns ns +LL ns +LL ns +LL ns	0.38 0.44 0.78 0.92 0.96 0.97 0.5 1.31 0.45 2.43 0.49 2.84	ns ns +LL +LL +LL +LL ns +LL ns +LL ns +LL	formamidase C2H2 zinc finger family PHOR1 C2H2 zinc finger family aldehyde dehydrogenase bHLH S-adenosylmethionine decarboxylase histone deacetylase adenine phosphoribosyltransferase phospholipid hydroperoxide glutathione peroxidase
		sgn-U219737	0.54	ns	0.76	+LL	galactinol synthase
		sgn-U212595	-0.12	ns	1.20	+LL	histidine decarboxylase
		sgn-U225897	0.37	ns	2.54	+LL	aldo/keto reductase
		sgn-U214272	0.22	ns	2.90	+LL	succinate dehydrogenase
		sgn-U221770	0.14	ns	3.73	+LL	dihydrolipoamide S-acetyltransferase
	Protein	sgn-U222514	0.84	+LL	0.26	ns	AAA type protein
	metabolism	sgn-U214901	1.03	+LL	0.62	ns	26S proteasome regulatory subunit
		sgn-U220965	1.08	+LL	2.16	+LL	serine protease
		sgn-U213363	1.23	+LL	1.59	+LL	proteinase inhibitor
		sgn-U225461	1.39	+LL	1.84	+LL	ubiquitin E1
		sgn-U215055	1.78	+LL	0.57	ns	Rer1A
		sgn-U212847	2.09	+LL	0.93	+LL	elongation factor 1-alpha
		sgn-U221937	0.18	ns	0.71	+LL	phosphatase 2C
		sgn-U213770	0.11	ns	0.72	+LL	cysteine protease XBCP3
		sgn-U215101	0.34	ns	0.81	+LL	postranslational modification
		sgn-U213693	0.38	ns	0.90	+LL	ubiquitin E3 RING
		sgn-U215602	0.38	ns	0.92	+LL	AAA-type ATPase family
		sgn-U218603	0.18	ns	0.96	+LL	30S ribosomal protein
		sgn-U214535	0.5	ns	1.12	+LL	ubiquitin E3 RING
		sgn-U222008	0.08	ns	1.49	+LL	ubiquitin E3 RING
		sgn-U234442	0.03	ns	1.60	+LL	metalloprotease
		sgn-U217270	-0.08	ns	2.78	+LL	receptor like cytoplasmatic kinase VIII
		sgn-U215048	0.14	ns	4.39	+LL	protein kinase family

AS2,Lateral Organ Boundaries Gene Family sgn-U233205

		0.23	ns	3.78	+LL	ribonucleases
	sgn-U224917	0.03	ns	4.02	+LL

CCAAT box binding factor family, HAP2

Acknowledgements

We are grateful to the greenhouse experimental team and to Yolande Carretero for taking care of the plants. We thank Jean-Claude L'Hotel and Michel Pradier for their technical support during harvests, Beatrice Brunel and Esther Pelpoir for managing cell and seed counting, Emilie Rubio and Doriane Bancel for sugar analyses, Cécile Garchery and Caroline Callot for their help in RNA extractions. Many thanks to Rebecca Stevens for English revising. Keygene, The Netherlands is acknowledged for providing seeds of the tomato population. This work was funded by the European EU-SOL Project PL016214-2 and Marion Prudent was supported by a grant from INRA and Région Provence Alpes Côte d'Azur (France).