SummaryFruit ripening is characterized by dramatic changes in gene expression, enzymatic activities and metabolism. Although the process of ripening has been studied extensively, we still lack valuable information on how the numerous metabolic pathways are regulated and co-ordinated. In this paper we describe the characterization of FaMYB1, a ripening regulated strawberry gene member of the MYB family of transcription factors. Flowers of transgenic tobacco lines overexpressing FaMYB1 showed a severe reduction in pigmentation. A reduction in the level of cyanidin 3-rutinoside (an anthocyanin) and of quercetin-glycosides (¯avonols) was observed. Expression of late¯avonoid biosynthesis genes and their enzyme activities were aversely affected by FaMYB1 overexpression. Two-hybrid assays in yeast showed that FaMYB1 could interact with other known anthocyanin regulators, but it does not act as a transcriptional activator. Interestingly, the C-terminus of FaMYB1 contains the motif pdLNL D / E Lxi G / S . This motif is contained in a region recently proposed to be involved in the repression of transcription by AtMYB4, an Arabidopsis MYB protein. Our results suggest that FaMYB1 may play a key role in regulating the biosynthesis of anthocyanins and¯avonols in strawberry. It may act to repress transcription in order to balance the levels of anthocyanin pigments produced at the latter stages of strawberry fruit maturation, and/or to regulate metabolite levels in various branches of the¯avonoid biosynthetic pathway.

Variation for metabolite composition and content is often observed in plants. However, it is poorly understood to what extent this variation has a genetic basis. Here, we describe the genetic analysis of natural variation in the metabolite composition in Arabidopsis thaliana. Instead of focusing on specific metabolites, we have applied empirical untargeted metabolomics using liquid chromatography-time of flight mass spectrometry (LC-QTOF MS). This uncovered many qualitative and quantitative differences in metabolite accumulation between A. thaliana accessions. Only 13.4% of the mass peaks were detected in all 14 accessions analyzed. Quantitative trait locus (QTL) analysis of more than 2,000 mass peaks, detected in a recombinant inbred line (RIL) population derived from the two most divergent accessions, enabled the identification of QTLs for about 75% of the mass signals. More than one-third of the signals were not detected in either parent, indicating the large potential for modification of metabolic composition through classical breeding.Metabolites are critical in biology, and plants are especially rich in diverse biochemical compounds. It has been estimated that over 100,000 metabolites can be found in plants, and each species may contain its own chemotypic expression pattern 1 . Moreover, substantial quantitative and qualitative variation in metabolite composition is often observed within plant species 2 .Although knowledge on the regulation of metabolite formation is increasing, for thousands of metabolites, their function in the plant, their biosynthetic pathway and the regulation thereof is still unknown. QTL analysis of natural variation, which can affect metabolites 3 , in segregating populations can identify loci explaining the observed variation 4 . In recent years, a few studies have focused on identifying QTLs regulating a specific group of known metabolites using detection methods directed toward specific metabolite groups 5-9 . However, recent advances in mass spectrometry-based metabolomics and data processing techniques should now allow large-scale QTL analyses of untargeted metabolic profiles, which may uncover previously unknown regulatory functions of loci in metabolic pathways. Using dedicated alignment software, it is now possible to perform an unbiased comparison of large numbers of metabolite-derived masses detectable in large numbers of samples arising from inherently large sets of genotypes (which are required for accurate mapping of QTLs) in an RIL population 10,11 . QTL mapping will result in the localization of loci, and ultimately genes, causal for the observed variation and will allow the discovery of coregulated compounds. In this way, genomewide genetic correlative metabolic analysis now becomes feasible, as we demonstrate here. RESULTS Metabolite variation is abundant and genetically controlledTo assess the natural variation in metabolite content present in A. thaliana, we performed HPLC-QTOF MS-based untargeted metabolic fingerprinting of acidified aqueous methanol extracts fr...

To take full advantage of the power of functional genomics technologies and in particular those for metabolomics, both the analytical approach and the strategy chosen for data analysis need to be as unbiased and comprehensive as possible. Existing approaches to analyze metabolomic data still do not allow a fast and unbiased comparative analysis of the metabolic composition of the hundreds of genotypes that are often the target of modern investigations. We have now developed a novel strategy to analyze such metabolomic data. This approach consists of (1) full mass spectral alignment of gas chromatography (GC)-mass spectrometry (MS) metabolic profiles using the MetAlign software package, (2) followed by multivariate comparative analysis of metabolic phenotypes at the level of individual molecular fragments, and (3) multivariate mass spectral reconstruction, a method allowing metabolite discrimination, recognition, and identification. This approach has allowed a fast and unbiased comparative multivariate analysis of the volatile metabolite composition of ripe fruits of 94 tomato (Lycopersicon esculentum Mill.) genotypes, based on intensity patterns of .20,000 individual molecular fragments throughout 198 GC-MS datasets. Variation in metabolite composition, both between-and within-fruit types, was found and the discriminative metabolites were revealed. In the entire genotype set, a total of 322 different compounds could be distinguished using multivariate mass spectral reconstruction. A hierarchical cluster analysis of these metabolites resulted in clustering of structurally related metabolites derived from the same biochemical precursors. The approach chosen will further enhance the comprehensiveness of GC-MS-based metabolomics approaches and will therefore prove a useful addition to nontargeted functional genomics research.

For the description of the metabolome of an organism, the development of common metabolite databases is of utmost importance. Here we present the Metabolome Tomato Database (MoTo DB), a metabolite database dedicated to liquid chromatography-mass spectrometry (LC-MS)-based metabolomics of tomato fruit (Solanum lycopersicum). A reproducible analytical approach consisting of reversed-phase LC coupled to quadrupole time-of-flight MS and photodiode array detection (PDA) was developed for largescale detection and identification of mainly semipolar metabolites in plants and for the incorporation of the tomato fruit metabolite data into the MoTo DB. Chromatograms were processed using software tools for mass signal extraction and alignment, and intensity-dependent accurate mass calculation. The detected masses were assigned by matching their accurate mass signals with tomato compounds reported in literature and complemented, as much as possible, by PDA and MS/MS information, as well as by using reference compounds. Several novel compounds not previously reported for tomato fruit were identified in this manner and added to the database. The MoTo DB is available at http://appliedbioinformatics.wur.nl and contains all information so far assembled using this LC-PDA-quadrupole time-of-flight MS platform, including retention times, calculated accurate masses, PDA spectra, MS/MS fragments, and literature references. Unbiased metabolic profiling and comparison of peel and flesh tissues from tomato fruits validated the applicability of the MoTo DB, revealing that all flavonoids and a-tomatine were specifically present in the peel, while several other alkaloids and some particular phenylpropanoids were mainly present in the flesh tissue.