Isolation of Fruit Quality QTL

Fine mapping experiments that allow the precise mapping of QTL within a chromosomal region is the first step towards positional cloning (Paterson et al. 1990; Fraryet al. 2003b; Lecomte et al. 2004b). Fine mapping may reveal the existence of several linked QTLs. For example, Lecomte et al. (2004b) identified two linked QTLs with moderate effects for fruit weight within a 20 cM region that were confounded as one major effect QTL.

Correspondence of map location of QTLs and genes related to carbon metabolism allowed the identification of several putative candidate genes (Causse et al. 2004), but the validation of their role in trait variation required fine-mapping and the identification of a causal polymorphism that was not obvious. Mutations of enzymes involved in carbon metabolism that alter sugar composition in fruit have been found in S. chmielewskii and in S. habrochaites. The sucr mutation in an invertase gene in S. chmielewskii provides fruit with sucrose instead of glucose and fructose (Chetelat et al. 1995). In S. habrochaites, an allele of the ADP glucose pyrophosphorylase enzyme was identified as being much more efficient than the allele of cultivated tomato, leading to an increase in the final sugar content of fruit (Schaffer et al. 2000).

Positional Cloning The first QTLs cloned in tomato influenced fruit quality traits (fruit weight, fruit shape, soluble solids) and were isolated through positional cloning. A fruit weight QTL (fw2.2) responsible for about 30% of the variation of this character was isolated using the classical strategy of high-resolution mapping by screening 3,472 F2 plants, identifying 53 recombinants (between two markers 4.2 cM apart) and screening a YAC library. From a YAC likely to contain the responsible gene, a cosmid library was screened and three clones used to transform a tomato variety. The cosmid leading to differences in fruit size after transformation was sequenced and the two sequences corresponding to ORFs were used in a second round of transformation. This allowed the definitive identification of the clone corresponding to the QTL (Frary et al. 2000) (See Sect. 1.8.3). Nesbitt and Tanksley (2002) sequenced the fw2.2 gene in a set of wild and cultivated accessions. They showed that the small-fruited accessions did not carry the same allele. No significant association could be detected between fruit size and fw2.2 genotypes, highlighting the role of other QTLs. Their results suggested that the polymorphism responsible for the QTL variation was not in the coding region but probably in the promoter region. Recently, Cong and Tanksley (2006) showed that fw2.2 regulates cell division by interacting with a kinase protein involved in the cell cycle.

The ovate gene, responsible for pear shape and elongated fruit shape was cloned by Liu et al. (2002). It corresponded to a nucleus-localized putative regulatory protein. A stop codon was responsible for the change in shape from round to elongated.

A major QTL for soluble solids content was mapped on chromosome 9 in the population of ILs derived from S. pennellii (Eshed and Zamir 1996). The line IL9-2-5 that contains a 9 cM genomic region of S. pennellii showed an increase in Brix┬░ and thus restricted the QTL confidence interval to this region (See Sect. 1.8.3). The QTL was then cloned by a map-based cloning strategy (Fridman et al. 2000). The gene responsible for the QTL is an apoplastic invertase encoded by the lin5 gene (Godt and Roitsch 1997). The mutation responsible for the trait variation was first delimited to a 484 bp region of the gene and then restricted to a single nucleotide mutation that leads to an amino acid change in the sequence of the invertase protein, positioned near the catalytic site of the enzyme (Fridman et al. 2004). A complex trait such as sugar content variation has thus been simplified into a SNP, introducing the concept of quantitative trait nucleotide (QTN, Fridman et al. 2004).

Transcriptomics for QTL Characterization Comparison of the transcriptomes of two samples leads to a list of differentially expressed genes. Several methods are available to compare transcriptomes including serial analysis of gene expression (SAGE), cDNA-AFLP, and differential display. Microarrays and DNA chips are currently the most accessible tools for tran-scriptomics.

Comparison oftranscriptomes can be used to elucidate the modulation network of genes during a specific process or as a consequence of a specific mutation. For example, a transcriptomic analysis was performed by Alba et al. (2005) to identify genes implicated in fruit maturation. The transcript level of approximately 8,500 genes was followed during tomato fruit development in the pericarp. This study identified 869 genes differentially expressed during fruit development. Gene expression levels were strongly dependent on ethylene, which plays a central role in fruit ripening. In combination with the analysis of fruit development, transcript profiling of the Nr mutant (defective for an ethylene receptor, see above) was performed. From the 869 genes that were differentially expressed during fruit development, 37% were also affected by the Nr mutation. Together these data identified 72 candidate genes which could be responsible, in part, for the regulation of fruit development. It would be of great interest to map these genes to discover whether or not they co-localize with firmness QTL. This study further elucidated regulatory networks responsible for fruit ripening.

The comparison of transcriptome profiles of NILs can also identify differentially expressed genes, among which some may be located in genomic regions for which the lines differ. These genes then become candidates for QTLs.

Baxter et al. (2005) compared transcriptome profiles of six ILs derived from S. pennellii in the genetic background of the processing cultivar M82. All six ILs had a higher soluble solids content compared to M82. Each IL was characterized by a large set of genes differentially expressed (at 20 days post-anthesis) and 78% of significant changes were unique to a single line. Very few carbon related genes were altered in expression and very few genes differentially expressed were located in introgressed regions. This experiment gave clues as to which metabolic pathways were most perturbed by each introgression.

Proteomics for QTL Characterization Proteomic analysis reveals differentially expressed genes at the protein level (Rose et al. 2004). This approach is less frequently used than DNA chips but it reveals another level of genome expression, closer to the phenotype. Faurobert et al. (2007) have described proteome modifications during fruit development and ripening. The intensity of 1,791 spots which approximately corresponded to the same number of proteins was monitored. From these 1,791 spots, 148 were significantly differentially expressed. The corresponding proteins were identified for 90 of the 148 spots. Protein levels of genes involved in amino acid metabolism and protein synthesis were more abundant at early developmental stages than during ripening. Conversely, genes related to carbon metabolism were highly expressed in mature fruit.

Proteome analysis may also be used to analyze genetic variation (Pawlowskii et al. 2005; Rocco et al. 2006). Both quantitative (spot amount) and qualitative variation (protein shift due to allelic variation or spot absence due to a null allele) can be detected. The analysis of the two lines (Cervil and Levovil) used for mapping QTL for sensory quality described above (See Sect. 1.15.1) showed that more than 90% of the spot positions overlapped and that about 10% of the spots showed quantitative variation (Mihr et al. 2005). The comparison of proteome profiles ofNILs derived from this cross allowed the identification of candidate proteins for fruit quality QTLs (Faurobert et al. 2006).

Metabolomics for QTL characterization New tools in biochemistry allow the simultaneous quantifica tion of multiple metabolites present within a sample. Because organoleptic quality of a fruit is correlated to the amount of sugars, acids, and volatile compounds, these tools can be useful to characterize tomato quality (see Sect. 1.16). The evolution of metabolome profiling during fruit development has been described (Carrari et al. 2006), showing that the abundance of metabolites, from either primary or secondary metabolism, is very dynamic during fruit development. The variation of metabolite abundance is coordinated and strongly partitioned by metabolic pathways, illustrating the tight link between metabolites. Carrari et al. (2006) compared metabolite profiles to transcriptome profiles during tomato fruit development. Although some links were clearly evident between variation in metabolites and transcripts, gene transcript levels were less coordinated than metabolite levels. Metabolomic profiling combined with genetic studies may provide insights into the physiological bases of quantitative traits and yield clues as to which candidate genes to screen (Overy et al. 2005). Schauer et al. (2006) characterized metabolic profiles of fruit of the ILs derived from S. pennellii. They identified 889 QMLs (quantitative metabolic loci) among which half were associated with QTLs for yield-associated traits. This analysis highlights the value of combined genetic, physiological and biochemical profiling to identify the major components of fruit quality.

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