JXB Advance Access originally published online on April 25, 2005
Journal of Experimental Botany 2005 56(416):1591-1604; doi:10.1093/jxb/eri154
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RESEARCH PAPER |
Comparison of changes in fruit gene expression in tomato introgression lines provides evidence of genome-wide transcriptional changes and reveals links to mapped QTLs and described traits
1Department of Plant Sciences, University of Oxford, South Parks Road, Oxford, OX1 3RB, UK
2Department of Animal and Plant Sciences, University of Sheffield, Sheffield, S10 2TN, UK
To whom correspondence should be addressed. Fax: +44 (0)1865 275074. E-mail: lee.sweetlove{at}plants.ox.ac.uk
Received 19 November 2004; Accepted 4 March 2005
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Abstract |
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Total soluble solids content is a key determinant of tomato fruit quality for processing. Several tomato lines carrying defined introgressions from S. pennellii in a S. lycopersicum background produce fruit with elevated Brix, a refractive index measure of soluble solids. The genetic basis for this trait can be determined by fine-mapping each QTL to a single gene, but this is time-consuming and technically demanding. As an alternative, high-throughput analytical technologies can be used to provide useful information that helps characterize molecular changes in the introgression lines. This paper presents a study of transcriptomic changes in six introgression lines with increased fruit Brix. Each line also showed altered patterns of fruit carbohydrate accumulation. Transcriptomic changes in fruit at 20 d after anthesis (DAA) were assessed using a 12 000-element EST microarray and significant changes analysed by SAM (significance analysis of microarrays). Each non-overlapping introgression resulted in a unique set of transcriptomic changes with 78% of significant changes being unique to a single line. Principal components analysis allowed a clear separation of the six lines, but also revealed evidence of common changes; lines with quantitatively similar increases in Brix clustered together. A detailed examination of genes encoding enzymes of primary carbon metabolism demonstrated that few of the known introgressed alleles were altered in expression at the 20 DAA time point. However, the expression of other metabolic genes did change. Particularly striking was the co-ordinated up-regulation of enzymes of sucrose mobilization and respiration that occurred only in the two lines with the highest Brix increase. These common downstream changes suggest a similar mechanism is responsible for large Brix increases.
Key words: Brix, carbohydrate metabolism, introgression, tomato microarray, yield
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Introduction |
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TopAbstractIntroductionMaterials and methodsResultsDiscussionSummaryReferences |
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Tomato fruit quality and yield are governed by a range of genetic and environmental factors that result in quantitative variation across varieties. The commercial value of processing tomato varieties is in part determined by a combination of total fruit yield and fruit soluble solids content (Brix). Ripe fruit with high soluble solids require the removal of less water to produce tomato-based food products of the appropriate consistency and taste. As such, the manipulation of fruit quality and yield are key targets of current tomato breeding programmes.
Different tomato varieties vary greatly in the form and abundance of the metabolites that determine Brix and the relationship between the concentrations of these metabolites and yield. Cultivated varieties such as Solanum lycopersicum are generally high yielding and develop ripe fruit with low soluble solids contents (Brix 3–4%) made up predominantly by the accumulation of glucose and fructose (Yelle et al., 1988
). By contrast, wild tomato progenitors, such as the green fruited Solanum pennellii, produce small fruit that store sucrose and have high (up to 15%) Brix contents (Stommel, 1992; Fridman et al., 2000). Although the biochemical pathways that are involved in the synthesis and storage of these metabolites are relatively well characterized in tomato fruit (Robinson et al., 1988; Yelle et al., 1991; N'tchobo et al., 1999) and differences between varieties have been described based on the measured activity of a small number of enzymes (Yelle et al., 1988; Miron and Schaffer, 1991; Sun et al., 1992; Stommel, 1992), a more complete analysis of regulatory and metabolic networks is required to understand the genetic basis of tomato fruit quality further.
In an attempt to gain an insight into the underlying genetic factors that govern differences between the cultivated and wild varieties, Zamir and colleagues generated a series of introgression lines in which defined genomic segments of the S. pennellii genome replaced homologous regions in a S. lycopersicum background (Eshed et al., 1992; Eshed and Zamir, 1995). A total of 76 lines were produced that collectively contained introgressions that covered the entire tomato genome. In a series of field studies, a number of phenotypic traits in these lines were quantified and QTLs identified (Eshed and Zamir, 1995; Gur et al., 2004). One such QTL for elevated Brix has been mapped to a single nucleotide substitution in a gene encoding an invertase enzyme (Fridman et al., 2000, 2004).
Although an extremely powerful and unbiased approach, delimiting a QTL to a single gene using genetic approaches is a time-consuming and technically demanding process (Fridman et al., 2000, 2004). Any additional information that could be linked to the observed traits in the introgression lines would therefore be beneficial in that it may provide clues as to the identity of the allele(s) responsible for a particular trait. Thus, metabolomic profiling of the introgression lines has been embarked upon to provide additional definition of the biochemical traits that are altered in each line (Overy et al., 2005). In addition, Causse and colleagues have taken a ‘candidate gene approach’ (Causse et al., 2004). They reasoned that many of the observed traits such as altered fruit soluble solids and yield are likely to be the result of alterations in fruit primary carbon metabolism. Therefore, they mapped a range of genes encoding enzymes of primary metabolism and were able to establish which genes lay in each introgression. In some cases, there were obvious links between the presence of S. pennelli alleles of these genes and the observed trait. Recent development of genomic resources for tomato has opened up an additional source of information: changes in the transcriptome of each introgression line. Given that one consequence of the presence of the introgression may be an altered expression pattern of the genes contained within it, a transcriptomic analysis could provide a route by which genetic changes can be linked to phenotype. This type of analysis will also reveal downstream changes as a result of the introgression that will provide important insight into the regulation of metabolic pathways that are related to the trait (Ruuska et al., 2002; Thimm et al., 2004; Sreenivasulu et al., 2004; Price et al., 2004).
There are currently available 180 000 tomato ESTs that have allowed the identification of approximately 30 000 unigenes across a range of tissues and developmental stages (Van der Hoeven et al., 2002; Fei et al., 2004). This has enabled the production of a tomato cDNA microarray containing 12 000 unique elements encoding 8500 genes covering a range of metabolic and developmental processes (http://bti.cornell.edu/CGEP/CGEP.html) (Alba et al., 2004). In this paper, this microarray resource is exploited in order to analyse transcriptomic changes in fruit of selected introgression lines. The focus is on a small group of lines that share a common phenotype of increased fruit Brix. These lines contain non-overlapping introgressions that would therefore be expected to lead to distinct primary transcriptomic changes. However, given the common phenotype, secondary transcriptomic changes (i.e. changes downstream of genes contained within the introgression) that share similar elements may also occur. The identification of such changes would reveal underlying regulatory mechanisms of fruit metabolism that lead to high Brix. To investigate the pattern of gene expression changes in the selected introgression lines, a replicated microarray analysis of fruit at 20 d after anthesis (DAA) from the S. lycopersicum parent and the introgression lines was performed. Multivariate statistics were used to analyse the pattern of changes between the lines as well as a directed analysis of statistically significant changes in expression of genes encoding enzymes of primary carbon metabolism. Wherever possible, these changes are related to the presence of known genes within each introgressed segment (Causse et al., 2004). The full dataset has been deposited in the public tomato expression database http://ted.bti.cornell.edu/cgi-bin/miame/home.cgi.
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Materials and methods |
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Plant growth conditions and fruit sampling
Solanum lycopersicum (cv. M82 accession LA3475) tomato plants and plants from introgression lines IL1-4, IL2-6, IL7-3, IL7-5, IL4-4, and IL12-4 were grown in a greenhouse with supplementary lighting providing an irradiance of 250–400 µmol m–2 s–1. Individual tomato plants were grown in pots (20 cm diameter) containing compost (Levingtons M3; 6 kg per pot) supplemented with Osmocote slow release fertilizer (30 g per pot). Plants were watered daily and prior to flowering given liquid fertilizer (Phostrogen plant food) on a weekly basis. Fruit were tagged at anthesis and harvested when they had reached the appropriate developmental stage. Individual fruit were removed from the plant, weighed, snap frozen in liquid nitrogen, and stored at –80 °C until required.
Measurement of fruit brix
Ripe fruit tissue was homogenized with a razor blade and the soluble solids (Brix) content of the resulting juice measured on a portable refractometer (Bellingham and Stanley Ltd, Kent, UK)
Carbohydrate assays
Frozen fruit powder was extracted with trichloroacetic acid (Sweetlove et al., 1996). Carbohydrates were assayed spectrophotometrically using the methods described in Baxter et al. (2003).
RNA isolation
Total RNA was isolated from homogenized, powdered tomato fruit tissue using a CTAB (hexadecyltrimethylammonium bromide) method (Chang et al., 1993).
Glass slide microarray
Glass slides containing arrayed tomato ESTs were obtained directly from The Centre for Gene Expression Profiling (CGEP) at the Boyce Thompson Institute (BTI), Cornell University. The tomato array contains approximately 12 000 unique elements randomly selected from cDNA libraries isolated from a range of tissues including leaf, root, fruit, and flowers and representing a broad range of metabolic and developmental processes. Technical details of the spotting and annotation of this file are provided on the BTI (http://bti.cornell.edu/CGEP/CGEP.html) website.
Fluorescent probe preparation and microarray hybridization
Microarray experiments were designed and conducted according to the MIAME guidelines (www.mged.org/miame) and all information relevant to this standard is presented in the Materials and methods and appropriate figure legends.
50 µg of total RNA was reverse transcribed to synthesize either Cy3 or Cy5 labelled cDNA probes. Total RNA was mixed with 1.25 µg Oligo d(T) primer (Invitrogen) and denatured at 65 °C for 5 min. A master mix containing 8 µl 5x first strand buffer, 4 µl low C dNTP mix (25 mmol each of dGTP, dATP, dTTP, and 10 mmol dCTP), 25 µmol Cy5 or Cy3 dCTP (Amersham), 4 µl 0.1 M DTT, and 40 U RNase out (Invitrogen) was added. Each sample was heated to 42 °C and then 2 µl (400 U) superscript II reverse transcriptase (Invitrogen, Karlsruhe) was added. The reaction was incubated for 2 h at 42 °C and stopped by the addition of 5 µl of 0.5 M EDTA. Template RNA was hydrolysed by the addition of 10 µl of 1 M NaOH and incubating at 65 °C for 30 min. The reaction was neutralized by the addition of 25 µl of 1 M TRIS (pH 8.0). Labelled cDNA was then precipitated by the addition of 8 µl of 3 M NaAc (pH 5.2) and 200 µl ethanol. Following incubation at –20 °C for 2 h, cDNA was pelleted by centrifugation at 12 000 g for 30 min at 4 °C. Pellets were allowed to dry, resuspended in 15 µl hybridization solution (0.1% SDS, 25% formamide, 5x SSC) and the Cy5 and Cy3 labelled probes were combined to a final volume of 30 µl. To account for dye bias, replicate experiments were done in which the Cy dyes used to label the samples to be compared were swapped.
Microarrays were prehybridized by immersion in prehybridization solution (0.1% (w/v) SDS, 25% (v/v) formamide, 5x SSC, 1% (w/v) BSA) for 90 min at 42 °C. Slides were rinsed in ddH2O and air-dried. For hybridization, 2 µl of liquid block solution (Amersham) was added to the purified combined Cy3 and Cy5 labelled probes and these were denatured at 95 °C for 5 min. Probe solution was added to the spotted surface of the slide and hybridization carried out under a hybri-slip (Sigma-Aldrich Chemie GmbH, Deisenhofen, Germany) in a humidified hybridization cassette (Telechem International, USA) for 16 h at 42 °C. Following hybridization, slides were washed for 5 min at 42 °C in 2x SSC, 0.1% (w/v) SDS, 10 min at room temperature in 0.1x SSC, 0.1% (w/v) SDS, and 4 min at room temperature in 0.1x SSC.
Microarray scanning and data analysis
Microarrays were scanned using an Affymetrix 428 Array scanner (Affymetrix, Inc, Santa Clara, Ca, USA) and acquisition software according to the manufacturer's instructions. After scanning images were analysed in Genepix Pro v. 4.1 software (Axon Instruments, Inc., USA) and the raw data collected and imported into Microsoft Excel for further analysis. To identify genes of interest whose expression levels change, the ratio of each feature (635 nm/532 nm) was calculated. Background fluorescence values were automatically calculated by the Genepix program and subtracted from all feature intensities prior to ratio calculation. It was determined that the array data should exclude all features that did not show a median pixel intensity of 2.5-fold greater than the overall mean slide background intensity. In order to account for differences in array-specific effects and to be able to average the results from replicate arrays, normalization between the Cy3 and Cy5 channels was further achieved by calculating the ratio for each spot based on the Cy3 and Cy5 fluorescence of the spot in relation to the total Cy3 and Cy5 fluorescence of the whole slide. Following data normalization and quality control all values were log transformed (log base 2) prior to further analysis.
Statistical analysis
Principal components analysis was performed on a complete microarray dataset consisting of log transformed expression values for each individual replicate. PCA was carried out using the R platform and relevant algorithms (http://www.r-project.org/). Briefly, the dimensionality of the dataset was reduced and described in a series of axes that defines the variance within the data. In this analysis 20.4% and 13.2% of the variance within the data was accounted for by the first two axes, respectively. These were used to plot the data and give an impression of the variation between the individual datasets; distances between points on the graph are indicative of the differences between replicates.
Microarray elements with significant changes in expression were identified using the significance analysis of microarrays protocol contained within the TIGR multiple experiment viewer (http://www.tigr.org/software/tm4/) (Tusher et al., 2001). For each introgression line (and wild-type S. lycopersicum) a dataset consisting of only those clones with expression values across each of the three replicated arrays was produced. SAM was performed on this dataset using the algorithms contained within the TIGR mev application. Lists of clones with significant changes in expression in comparison to wild-type S. lycopersicum were identified at delta values that gave a FDR of less than 10% (Tusher et al., 2001).
All other data generated in this study was analysed by t-test using the algorithm contained within Microsoft Excel software. Unless otherwise stated, all instances of the word ‘significant’ in the text denote a statistical significance of P <0.05 as determined by the t-test.
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Results |
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Selection of tomato introgression lines
Introgression lines with altered ripe fruit Brix were selected on the basis of previously published data from field-grown plants (Table 1) (Eshed and Zamir, 1995
). To minimize environmental variation that could complicate the subsequent interpretation of experimental results, plants were grown in controlled environment greenhouses and supplied with supplementary lighting. Under these growth conditions, changes in Brix were observed that were consistent with the traits recorded in field studies (Fig. 1; Table 1). On the basis of Brix and yield data the phenotypic variation within the selected lines could be grouped into three different categories; IL4-4 and IL7-3 were characterized by a large increase in Brix and a decrease in fruit weight and overall yield. IL2-6 and IL7-5 had a small increase in Brix, an increase in yield and a significant increase in Brixxyield (economic yield). Fruit from IL1-4 and IL12-4 displayed a small increase in Brix, with no significant effect on overall yield or Brixxyield (Fig. 1; Table 2).
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To define the biochemical phenotype imposed by each introgression further, fruit carbohydrate content was measured at three different stages of fruit development; 20 DAA, breaker (the stage of development when the fruit first begins to change from green to red) and ripe (Fig. 2). Generally, increased fruit Brix (Fig. 1) correlated with measured changes in the abundance of soluble carbohydrate (glucose, fructose, and sucrose) in ripe fruit (Fig. 2c). Specifically, in the high Brix, low yield lines IL4-4 and IL7-3, large increases (70–100%) in soluble carbohydrate were observed in the ripe fruit (Fig. 2c). This was due to increases in the abundance of glucose, fructose, and sucrose (Fig. 2c). In fruit from IL1-4 and IL12-4 (small Brix increase, no yield change), there were small changes in the abundance of soluble carbohydrate. Ripe fruit from IL2-6 and IL7-5, the lines with largest changes in Brixxyield, had similar soluble carbohydrate contents to the wild-type parent.
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Differences in the abundance of soluble carbohydrate between the introgression lines and the wild type were also evident at two earlier stages of fruit development (20 DAA and breaker stage) (Fig. 2a, b). With the exception of IL4-4, increases in the abundance of soluble carbohydrate at earlier stages of development were related to changes in the abundance of hexose sugars rather than sucrose.
There was no significant difference in the starch content of ripe fruit from any of the introgression lines (Fig. 2f); however, in a number of lines there were significant increases in fruit starch at earlier stages of development (Fig. 2d, e). Starch abundance was significantly higher (2–3-fold) than in the wild type in fruit at 20 DAA from IL4-4, IL7-3, IL7-5, and IL1-4 (Fig. 2d). By contrast, at the breaker stage of development, increases in starch content were most apparent in fruit from IL4-4 (30-fold increase), IL12-4 (2-fold) and IL2-6 (2-fold) (Fig. 2e).
Principle and design of experiments profiling tomato fruit transcript abundance
The choice was made to profile transcript abundance in fruit at 20 DAA as this represents the peak of starch accumulation (Robinson et al., 1988) and a developmental stage at which a number of the important enzymes of fruit carbohydrate metabolism are active (Yelle et al., 1988, 1991; Robinson et al., 1988). Measuring the variation in gene expression at this stage of fruit development will allow the differences between lines to be described and to focus on processes that play an important role in determining fruit Brix and yield.
To obtain consistent and statistically valid comparisons of gene expression in each of the lines, triplicated samples from individual plants were hybridized against a pooled RNA sample obtained from wild-type S. lycopersicum (Fig. 3). The variation inherent in this pooled wild-type sample was itself accounted for by triplicated arrays comparing the pooled sample to individual wild-type plants (Fig. 3). This allowed the ratio of expression generated for each line to be reliably compared across the population of plants and significant changes in gene expression to be identified using the significance analysis of microarrays (SAM) method described by Tusher et al. (2001). SAM has a lower false discovery rate than conventional tests and can be used to identify statistically significant changes in gene expression by assimilating a set of gene-specific t-tests (Tusher et al., 2001). SAM was performed on a set of data for each line, containing expression values with an average signal intensity of 2.5-times background in all three replicated arrays (Table 2). Data identified from triplicated arrays represent reliable and accurate measurements of gene expression: individual clones were represented in all replicates with a hybridization signal of 2.5-fold background. Clones identified in this way accounted for between 25% and 65% of the targets present on the array for individual lines (Table 2).
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Comparative analysis of fruit gene expression across introgression lines
To demonstrate the degree of diversity between selected introgression lines at the level of the transcriptome, the array dataset was analysed using principal components analysis (PCA) (Fig. 4). PCA revealed that each line clustered independently and there was a large degree of separation between lines. The overall clustering pattern formed from PCA additionally discriminated between lines with different fruit phenotypes (Figs 1, 4); the high Brix, low yield IL4-4 and IL7-3 clustered at the upper end of both components, whilst the moderate Brix, high yield IL7-5 and IL2-6 clustered at the lower end (Fig. 4).
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In a complementary approach to PCA, the number of lines in which individual transcripts change significantly in expression in comparison to the S. lycopersicum parent were identified and analysed (Fig. 5a). Of the genes displaying significant changes in expression the majority (78%) are unique to a single introgression line (Fig. 5a). A smaller proportion of significantly altered transcripts (12.6%) changed in expression in two of the six lines and only 1.5% were common to three or more lines. Transcripts common to more than one introgression line were evenly distributed across functional classes (Fig. 5a). A number of genes (8.1%) whose expression changed in more than one line appeared to be oppositely regulated in different lines (present as both black and grey bars in Fig. 5a).
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The classification of significantly up-regulated or down-regulated genes from individual introgression lines spanned a range of functional categories (Fig. 5b); however, the number of genes in each functional category differed between lines. Although the unidentified group was the largest category in all lines, the metabolism subset of genes formed a proportionally greater part of the classification in IL2-6 (increased Brixxyield) and IL4-4 (large increase in Brix, decreased yield) than other lines (Fig. 5b). By contrast, a greater percentage of the transcripts changing in expression in IL12-4 (altered Brix, no change in yield) were in the electron transport (i.e. energy production), transcription, and DNA synthesis categories, whilst a larger number of the genes identified in IL1-4 (altered Brix, no change in yield) were classified as signal transduction or photosynthetic genes (Fig. 5).
Changes in the expression of genes encoding enzymes of fruit primary metabolism
Common changes in fruit carbohydrate accumulation in the six introgression lines (Fig. 2) suggests that similar metabolic pathways are affected (albeit to different degrees) by the different introgressions. Although the primary transcriptomic changes in each introgression line must clearly be different (as each introgression is in a different, non-overlapping part of the genome) there may be common downstream transcriptomic changes that reflect common regulatory mechanisms of carbohydrate metabolism. Therefore the abundance of transcripts encoding enzymes of known metabolic function across the selected introgression lines was compared, in order to focus on the changing expression of genes related directly to carbohydrate metabolism (Table 3). Analysis was focused on the pathways that are known to play a direct role in the uptake, storage, and metabolism of carbohydrate transported to the fruit (Fig. 6) and was broken down into six processes (Table 3). Changes in transcripts relating to proteins involved in amino acid biosynthesis have also been included.
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In general, what emerges from this analysis is a clear dichotomy in the transcriptomic response amongst the introgression lines (Table 3). In the high Brix, low yield lines, IL4-4 and IL7-3, there are striking co-ordinated increases in expression of genes encoding enzymes of sucrose metabolism, glycolysis, and the TCA cycle. By contrast, in the other four lines with small increases in Brix, relatively few significant changes in expression of metabolic genes were observed. Most changes in these lines were decreases in expression and there was little evidence of co-ordinated changes within metabolic pathways.
In fruit from IL4-4 there was evidence for synchronized changes in gene expression in a number of pathways. These changes included the significant down-regulation of an apoplastic invertase and up-regulation of a sucrose transporter in the pathway of phloem unloading, the up-regulation of sucrose synthase and fructokinase in cellular sucrose breakdown and an ADPGpyrophosphorylase involved in starch synthesis (Table 3; Fig. 6). In addition, there was significant up-regulation of most of the genes encoding enzymes involved in glycolysis (significant up-regulation of all genes detected with the exception of aldolase) and the TCA cycle (pyruvate dehydrogenase, citrate synthase, aconitase, succinate dehydrogenase, malate dehydrogenase, and malic enzyme) (Table 3).
Patterns of metabolic gene expression in IL7-3 were similar to IL4-4, particularly in terms of co-ordinated pathway-wide up-regulation of transcripts involved in glycolysis, and the TCA cycle (Table 3). However, in this line, notable differences were observed in phloem unloading (up-regulation of apoplastic invertase), cellular sucrose breakdown (down-regulation of acid invertase), starch metabolism (down-regulation of starch synthase, starch branching enzyme, and ß-amylase) and in the decrease in the abundance of transcripts encoding cytosolic malate dehydrogenase (Table 3).
In IL2-6 significant down-regulation of expression was observed for genes encoding enzymes of glycolysis (aldolase, GapDH, enolase) and starch metabolism (ADPGpyrophosphorylase), whilst transcripts encoding pyruvate decarboxylase and glucose-6-phosphate dehydrogenase were up-regulated. A number of non-significant changes in gene expression in fruit from IL7-5 were similar to the significant changes measured in IL2-6, particularly for transcripts encoding glycolytic enzymes (Table 3). In addition, significant up-regulation of phosphoglucomutase and citrate synthase and down-regulation of aldolase were detected in IL7-5.
In IL12-4 and IL1-4 there were very few significant changes in gene expression in fruit at 20 DAA. In these lines measured changes in gene expression were confined to the down-regulation of transcripts encoding vacuolar acid invertase (IL12-4), PEPcarboxylase (IL1-4), pyruvate dehydrogenase (IL1-4), and up-regulation of a hexose transporter (IL1-4) (Table 3).
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Discussion |
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Fruit from introgression lines with increased Brix have altered patterns of carbohydrate accumulation
The introgression line population of tomato plants produced from a cross between S. pennellii and S. lycopersicum (cv. M82) (Eshed et al., 1992
) provides a powerful source of phenotypic variation in a defined genetic background and has facilitated the identification of a number of QTLs related to plant yield and fruit quality (Eshed and Zamir, 1995; Eshed et al., 1996; Liu et al., 2003; Causse et al., 2004). In this study, six introgression lines were selected to form the basis of an investigation into the genome-wide changes in the transcriptome of tomato fruit that underpin altered fruit Brix.
The growth of selected lines in the greenhouse resulted in a similar growth habit to that described previously (Eshed and Zamir, 1995; Causse et al., 2004) suggesting that the selected ripe fruit characteristics were robust in differing growth environments (Fig. 1; Table 2). Previously, QTL effects in tomato have been shown to be consistent across different environments (Monforte et al., 2001). In all of the lines a significant increase in ripe fruit Brix was measured in comparison to the wild-type parent S. lycopersicum (Fig. 1). Although significant increases in Brix were recorded for all lines, they were largest in IL4-4 and IL7-3.
In tomato fruit, sugars and organic acids are the major metabolites contributing towards Brix (Grierson and Kader, 1986; Roessner-Tunali et al., 2003) and in many varieties soluble carbohydrate forms the most significant component of this value (Yelle et al., 1988; Stommel, 1992). In this study, increases in fruit Brix in three of the lines correlated with significant increases in the abundance of soluble carbohydrate in the ripe fruit (Fig. 2c).
In all of the selected introgression lines increases in the abundance of soluble carbohydrate and starch earlier in fruit development (Fig. 2) appear to underpin the significant increases in Brix observed in ripe fruit (Fig. 1) and suggest that all lines show an altered pattern of carbohydrate metabolism in comparison with the S. lycopersicum parent.
Overall pattern of transcriptomic changes in the selected introgression lines
Each line used in this study contains a unique introgressed segment of the S. pennellii genome that results in a number of significant changes in fruit gene expression for each line. As one might predict, given that each of the introgressions are non-overlapping, the majority of changes in transcript abundance are unique to each line (Fig. 5a). Indeed, of those genes exhibiting significant changes in expression compared with the S. lycopersicum parent, 78% were unique to a single introgression line. This analysis reveals that the lines selected in this study each have a transcriptome that is largely specific for a particular introgression.
This is confirmed by PCA in which replicates from individual lines clearly clustered together (Fig. 4). At first glance, this observation would imply that despite the common biochemical characteristics of ripe fruit from the selected lines, there are no shared transcriptional changes that explain the increased fruit Brix phenotype. However, on closer examination, it is clear that PCA reveals apparent similarities in the overall transcriptome of fruit with similar phenotypes. Specifically, the high Brix, low yield lines (IL4-4 and IL7-3) cluster more closely to each other and at the opposite end of each component to the increased Brix and yield lines (IL2-6 and IL7-5).
Common changes in gene expression as a downstream consequence of the different introgressed genes
The effect of the introgression on fruit carbohydrate metabolism could either be a result of genes present on the introgressed section having altered expression patterns compared with the parent S. lycopersicum alleles or encoding proteins with altered properties. Indeed, recent characterization of the S. pennellii allele of an apoplastic invertase gene responsible for high fruit Brix identified an invertase protein with altered kinetic properties (Fridman et al., 2004). Thus, transcriptomic changes in the introgression lines could either be the result of altered expression of the introgressed alleles or downstream gene expression changes resulting from the presence of proteins with altered properties (leading to, for example, altered metabolite signalling of gene expression). In an attempt to discriminate between these two possibilities, a detailed look was taken at the expression of genes of carbon metabolism that have been previously mapped (Table 4).
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For each introgression line, the expression of a range of genes encoding enzymes of carbohydrate metabolism, including those that have been mapped to locations within the introgressions (Table 4), was investigated (Causse et al., 2004
). In general, genes known to occur within the introgression did not show significantly altered expression levels. Of the mapped genes described in Table 4, only fructokinase (IL4-4) and malate dehydrogenase (IL7-3) changed significantly in expression (Table 3). This suggests that, in general, the introgressed alleles conferred changes in fruit metabolism by encoding proteins with altered properties, rather than being expressed at different levels. However, some caution must be exercized in making this statement since this transcriptomic study was conducted at a single time point in fruit development (20 DAA). Allele-specific changes in gene expression at other stages of fruit development, or in tissues other than fruit cannot be ruled out. Furthermore, it is possible that the kinetics of carbohydrate accumulation and fruit development may be different between the lines (although fruit growth analysis suggests this is not the case; data not shown).
Although few changes in the expression of introgressed alleles encoding enzymes of carbon metabolism were observed, there were significant changes in many other metabolic genes that are not contained within the introgressed regions (Tanksley et al., 1992; Fuglevand et al., 1998; Schaffer et al., 2000; Causse et al., 2004; Table 3). Such changes are presumably the result of perturbations in signalling and regulatory networks as a result of the presence of the introgressed alleles.
Common changes in the metabolic transcriptome in the two lines with the highest Brix increase
The observation that there are widespread downstream transcriptomic changes as a result of the introgressions raises the question as to whether the nature of these changes in the different introgression lines reveals common regulatory mechanisms that relate to fruit carbohydrate metabolism. The fact that PCA clustered lines with similar fruit Brix changes more closely together suggests that there are, in fact, some common elements of transcriptomic change (Fig. 4). Indeed, when the expression of genes encoding enzymes of carbohydrate metabolism were analysed, a clear distinction could be made between the two lines with the largest increases in fruit Brix (IL4-4 and IL7-3) and the other lines with smaller increases in Brix. In the former, there is a pronounced and co-ordinated up-regulation of large sections of metabolism that does not occur in the latter. This suggests that a common mechanism for high Brix exists within these lines.
The significant changes in gene expression in IL4-4 and IL7-3 mainly related to pathways involved in sucrose mobilization and respiration (Table 3). The question then is what is the mechanism that links these changes in gene expression to increased fruit Brix? The identification of an apoplastic invertase as a QTL for increased Brix (Fridman et al., 2004) highlights one possibility, an increased supply of sucrose to the fruit tissue. The introgressed apoplastic invertase allele encodes a kinetically more efficient enzyme that is proposed to lead to an enhanced capacity for the movement of sucrose unloaded from phloem into the fruit tissues (Fridman et al., 2004; Baxter et al., 2005). There are, of course, many other possible routes to increasing the supply of sugar to the fruit (increased photosynthesis, increased partitioning of leaf photoassimilate to sucrose export, increased efficiency of phloem loading and unloading, as well as increased capacity for the uptake of sucrose into the fruit) and it is possible that this mechanism could be common amongst the high Brix introgression lines studied here. Thus, one explanation for the co-ordinated increase in expression of genes encoding enzymes involved in sucrose mobilization and respiration in IL4-4 and IL7-3 could be that these changes reflect an increased availability of sucrose in the fruit. Thus, the response is to increase the expression of enzymes of sugar mobilization and increase respiratory capacity. It is interesting that, in the opposite situation (sugar starvation), co-ordinated decreases in the same pathways occur (Thimm et al., 2004), suggesting that the transcriptional regulation of these pathways is tightly governed by sugar supply. In addition, an analysis of gene expression during Arabidopsis seed filling also highlighted synchronized induction of sucrose breakdown and glycolytic gene expression, although, in this study co-ordinated changes appeared to be specific to cytosolic or plastidial isoforms (Ruuska et al., 2002). Such specificity is not observed in the significant gene expression changes that were measured here, perhaps highlighting differences in sink metabolism between tomato and Arabidopsis. However, given that the array used in this study used EST as probes rather than gene-specific oligonucleotides, it is possible that cross-hybridization has limited the ability to distinguish closely related members of gene families. Apart from IL4-4 and IL7-3, there was no evidence for a co-ordinated up-regulation of genes encoding enzymes of sucrose mobilization and respiration in the other introgression lines in this study. Given that the changes in carbohydrate metabolism were much less pronounced in these lines compared with IL4-4 and IL7-3, it may be that there is a threshold of sugar-related changes required before re-programming of the metabolic transcriptome is observed.
The transcriptomic changes in fruit from IL4-4 and IL7-3 also provide insight into the regulation of starch metabolism in developing tomato fruit. Fruit from IL4-4 and IL7-3 at 20 DAA contained elevated levels of starch compared with fruit from the S. lycopersicum parent at the same stage (Fig. 2). In general, there didn't appear to be a co-ordinated change in the expression of genes encoding enzymes of starch metabolism that correlated with this change. In both lines, an increase in expression of the large subunit of ADPglucose pyrophosphorylase was observed. However, the increase was much smaller in IL4-4 despite the fact that fruit from both lines contained similar increases in starch (Fig. 2; Table 3). In IL4-4 no other significant changes in the expression of starch metabolism genes were observed. However, many of the genes were not detected on the arrays. In IL7-3, there were actually significant down-regulations of a starch synthase and a branching enzyme gene. These observations support previous work suggesting that the rate of starch synthesis in sink tissues is dependent on substrate supply rather than the amount of enzymes in the starch synthetic pathway (Schaffer and Petreikov, 1997; N'tchobo et al., 1999; Baxter et al., 2005). By contrast, in Arabidopsis, altered carbohydrate supply led to co-ordinated changes in the expression of a number of genes encoding enzymes of starch metabolism (Price et al., 2004). This highlights the different ways in which metabolism is regulated in storage organs such as fruit and in developing seedlings.
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Summary |
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TopAbstractIntroductionMaterials and methodsResultsDiscussionSummaryReferences |
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Transcriptomic changes have been described in a series of tomato introgression lines that share the common trait of increased ripe fruit soluble solids and increased accumulation of fruit carbohydrate. Each non-overlapping introgression leads to a distinct set of transcriptomic changes that are sufficient to be able to distinguish clearly between the lines using multivariate statistics. However, there are a small number of common changes that reveal mechanistic relationships between the lines. In particular, the two lines with the largest increases in fruit soluble solids both show a co-ordinated up-regulation of genes encoding enzymes of sucrose mobilization and respiration. By contrast, lines with smaller increases in Brix show relatively few changes in the expression of genes encoding enzymes of primary carbon metabolism. At the developmental stage analysed in this study, few genes that have been mapped within the introgressions changed in expression, raising the possibility that the majority of transcriptomic changes are a downstream consequence of the expression of the introgressed genes. This transcriptomic approach provides additional information that characterizes the Zamir introgression lines and adds to the growing collection of data that includes metabolome profiles (Overy et al., 2005
) and map positions of candidate genes (Causse et al., 2004). The complete transcriptome dataset from this study has been deposited in the tomato expression database http://ted.bti.cornell.edu/cgi-bin/miame/home.cgi. |
Acknowledgements |
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Financial support to LJS and WPQ by the BBSRC is acknowledged.
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Footnotes |
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* These authors contributed equally to this work.
Present address: Département de Biochimie, Faculté de Médecine, Université de Montréal, CP 6128, Succ. Centre-ville, Montréal, Qc H3C 3J7, Canada.
Abbreviations: DAA, days after anthesis; IL, introgression line; PCA, principal components analysis; SAM, significance analysis of microarrays; QTL, quantitative trait loci; EST, expressed sequence tag.
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