In plants, genomic DNA methylation which contributes to development and stress responses can be actively removed by DEMETER-like DNA demethylases (DMLs). Indeed, in Arabidopsis DMLs are important for maternal imprinting and endosperm demethylation, but only a few studies demonstrate the developmental roles of active DNA demethylation conclusively in this plant. Here, we show a direct cause and effect relationship between active DNA demethylation mainly mediated by the tomato DML, SlDML2, and fruit ripeningan important developmental process unique to plants. RNAi SlDML2 knockdown results in ripening inhibition via hypermethylation and repression of the expression of genes encoding ripening transcription factors and rate-limiting enzymes of key biochemical processes such as carotenoid synthesis. Our data demonstrate that active DNA demethylation is central to the control of ripening in tomato.active DNA demethylation | DNA glycosylase lyase | epigenetic | tomato | fruit ripening G enomic DNA methylation is a major epigenetic mark that is instrumental to many aspects of chromatin function, including gene expression, transposon silencing, or DNA recombination (1-4). In plants, DNA methylation can occur at cytosine both in symmetrical (CG or CHG) and nonsymmetrical (CHH) contexts and is controlled by three classes of DNA methyltransferases, namely, the DNA Methyltransferase 1, Chromomethylases, and the Domain Rearranged Methyltransferases (5-7). Indeed, in all organisms, cytosine methylation can be passively lost after DNA replication in the absence of methyltransferase activity (1). However, plants can also actively demethylate DNA via the action of DNA GlycosylaseLyases, the so-called DEMETER-Like DNA demethylases (DMLs), that remove methylated cytosine, which is then replaced by a nonmethylated cytosine (8

The grape berry microclimate is known to influence berry quality. The effects of the light exposure of grape berry clusters on the composition of berry tissues were studied on the "Merlot" variety grown in a vineyard in Bordeaux, France. The light exposure of the fruiting zone was modified using different intensities of leaf removal, cluster position relative to azimuth, and berry position in the cluster. Light exposures were identified and classified by in situ measurements of berry temperatures. Berries were sampled at maturity (>19 Brix) for determination of skin and/or pulp chemical and metabolic profiles based on (1) chemical and physicochemical measurement of minerals (N, P, K, Ca, Mg), (2) untargeted 1H NMR metabolic fingerprints, and HPLC targeted analyses of (3) amino acids and (4) phenolics. Each profile defined by partial least-square discriminant analysis allowed us to discriminate berries from different light exposure. Discriminant compounds between shaded and light-exposed berries were quercetin-3-glucoside, kaempferol-3-glucoside, myricetin-3-glucoside, and isorhamnetin-3-glucoside for the phenolics, histidine, valine, GABA, alanine, and arginine for the amino acids, and malate for the organic acids. Capacities of the different profiling techniques to discriminate berries were compared. Although the proportion of explained variance from the 1H NMR fingerprint was lower compared to that of chemical measurements, NMR spectroscopy allowed us to identify lit and shaded berries. Light exposure of berries increased the skin and pulp flavonols, histidine and valine contents, and reduced the organic acids, GABA, and alanine contents. All the targeted and nontargeted analytical data sets used made it possible to discriminate sun-exposed and shaded berries. The skin phenolics pattern was the most discriminating and allowed us to sort sun from shade berries. These metabolite classes can be used to qualify berries collected in an undetermined environment. The physiological significance of light and temperature effects on berry composition is discussed.

Non-structural carbohydrates (NSC) in plant tissue are frequently quantified to make inferences about plant responses to environmental conditions. Laboratories publishing estimates of NSC of woody plants use many different methods to evaluate NSC. We asked whether NSC estimates in the recent literature could be quantitatively compared among studies. We also asked whether any differences among laboratories were related to the extraction and quantification methods used to determine starch and sugar concentrations. These questions were addressed by sending sub-samples collected from five woody plant tissues, which varied in NSC content and chemical composition, to 29 laboratories. Each laboratory analyzed the samples with their laboratory-specific protocols, based on recent publications, to determine concentrations of soluble sugars, starch and their sum, total NSC. Laboratory estimates differed substantially for all samples. For example, estimates for Eucalyptus globulus leaves (EGL) varied from 23 to 116 (mean = 56) mg g(-1) for soluble sugars, 6-533 (mean = 94) mg g(-1) for starch and 53-649 (mean = 153) mg g(-1) for total NSC. Mixed model analysis of variance showed that much of the variability among laboratories was unrelated to the categories we used for extraction and quantification methods (method category R(2) = 0.05-0.12 for soluble sugars, 0.10-0.33 for starch and 0.01-0.09 for total NSC). For EGL, the difference between the highest and lowest least squares means for categories in the mixed model analysis was 33 mg g(-1) for total NSC, compared with the range of laboratory estimates of 596 mg g(-1). Laboratories were reasonably consistent in their ranks of estimates among tissues for starch (r = 0.41-0.91), but less so for total NSC (r = 0.45-0.84) and soluble sugars (r = 0.11-0.83). Our results show that NSC estimates for woody plant tissues cannot be compared among laboratories. The relative changes in NSC between treatments measured within a laboratory may be comparable within and between laboratories, especially for starch. To obtain comparable NSC estimates, we suggest that users can either adopt the reference method given in this publication, or report estimates for a portion of samples using the reference method, and report estimates for a standard reference material. Researchers interested in NSC estimates should work to identify and adopt standard methods.

Metabolic profiling by 1-dimensional (1-D) 1 H-nuclear magnetic resonance (NMR) was tested for absolute quantification of soluble sugars, organic acids, amino acids and some secondary metabolites in fruit, roots and leaves. The metabolite responsible for each peak of the 1 H-NMR spectra was identified from spectra of pure compounds. Peak identity was confirmed by the addition of a small amount of commercially-available pure substance. 1 H-NMR spectra acquisition was automated. 1 H-NMR absolute quantification was performed with a synthesised electronic reference signal and validated by comparison with enzymatic or HPLC analyses; the correlation coefficients between 1 H-NMR data and enzymatic or HPLC data were highly significant. Depending on the species and tissues, 14-17 metabolites could be quantified with 15-25 min acquisition time. The detection limit was approximately 1-9 µg in the NMR tube, depending on the compound. Quantitative data were used for (1) a genetic study of strawberry fruit quality, (2) a functional study of tomato transformants overexpressing hexokinase and (3) a study of Arabidopsis phosphoenolpyruvate carboxylase transformants with several lines showing decreased activity of the enzyme. Biochemical phenotyping of the fruits of a strawberry offspring allowed the detection of quantitative trait loci (QTL) controlling fruit quality. Comparison of the roots of wild types and hexokinase tomato transformants using principal component analysis of metabolic profiles revealed that environmental factors, i.e. culture conditions, can significantly modify the metabolic status of plants and thus hide or emphasise the expression of a given genetic background. The decrease in phosphoenolpyruvate carboxylase activity (up to 75%) in Arabidopsis transformants impacted on the metabolic profiles without compromising plant growth, thus supporting the idea that the enzyme has a low influence on the carbon flux through the anaplerotic pathway.

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A metabolomics approach combining (1)H NMR and gas chromatography-electrospray ionization time-of-flight mass spectrometry (GC-EI-TOFMS) profiling was employed to characterize melon (Cucumis melo L.) fruit. In a first step, quantitative (1)H NMR of polar extracts and principal component analyses (PCA) of the corresponding data highlighted the major metabolites in fruit flesh, including sugars, organic acids, and amino acids. In a second step, the spatial localization of metabolites was investigated using both analytical techniques. Direct (1)H NMR profiling of juice or GC-EI-TOFMS profiling of tissue extracts collected from different locations in the fruit flesh provided information on advantages and drawbacks of each technique for the analysis of a sugar-rich matrix such as fruit. (1)H NMR and GC-EI-TOFMS data sets were compared using independently performed PCA and multiblock hierarchical PCA (HPCA), respectively. In addition a correlation-based multiblock HPCA was used for direct comparison of both analytical data sets. These data analyses revealed several gradients of metabolites in fruit flesh which can be related with differences in metabolism and indicated the suitability of multiblock HPCA for correlation of data from two (or potentially more) metabolomics platforms.

Tomato, an essential crop in terms of economic importance and nutritional quality, is also used as a model species for all fleshy fruits and for genomics of Solanaceae. Tomato fruit quality at harvest is a direct function of its metabolite content, which in turn is a result of many physiological changes during fruit development. The aim of the work reported here was to develop a global approach to characterize changes in metabolic profiles in two interdependent tissues from the same tomato fruits. Absolute quantification data of compounds in flesh and seeds from 8 days to 45 days post anthesis (DPA) were obtained through untargeted (proton nuclear magnetic resonance, 1 H-NMR) and targeted metabolic profiling (liquid chromatography with diode array detection (LC-DAD) or gas chromatography with flame ionization detection (GC-FID)). These data were analyzed with chemometric approaches. Kohonen self organizing maps (SOM) analysis of these data allowed us to combine multivariate (distribution of samples on Kohonen SOMs) and univariate information (component plane representation of metabolites) in a single analysis. This strategy confirmed published data and brought new insights on tomato flesh and seed composition, thus demonstrating its potential in metabolomics. The compositional changes were related to physiological processes occurring in each tissue. They pointed to (i) some parallel changes at early stages in relation to cell division and transitory storage of carbon, (ii) metabolites participating in the fleshy trait and (iii) metabolites involved in the specific developmental patterns of the seeds.

The biochemical composition of grape berries depends on the cultivar genome and is influenced by environmental conditions and growing practices, which vary according to origin and "terroir" (French word accounting for the factors of climate, soil, and cultural practices on grape and wine quality). The components currently measured to determine the potential quality of grapes for wine-making at harvest are sugars, acidity, pH, and total phenolics, referred to as "classic analysis". The aim of this work was to establish metabolic profiles using both conventional physicochemical analyses and 1H NMR spectrometry of the skin and pulp of mature berry extracts in order in four appellations situated in different locations in southern-western France (Bordeaux). Principal component analysis was applied to the physiochemical and 1H NMR data to investigate the variability of the grape composition and to characterize groups of samples. A significant clustering of the metabolic profile of pulps or skins in relation to their terroir was observed. Physicochemical analyses were more discriminant than 1H NMR data, but NMR spectroscopy allowed metabolic finger-printings using identified metabolites and some still nonattributed resonances.