Quantitative metabolic profiles of tomato flesh and seeds during fruit development: complementary analysis with ANN and PCA

Abstract

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.

Gene and Metabolite Regulatory Network Analysis of Early Developing Fruit Tissues Highlights New Candidate Genes for the Control of Tomato Fruit Composition and Development

“…The 35-DPA mesocarp and locular fruit tissues characterized here were collected from green fruit harvested before the mature green stage (i.e. before the onset of ripening; Mounet et al, 2007). Although these tissues were not yet engaged in the ripening process, they displayed major metabolic and transcriptomic changes, especially between 20 and 35 DPA (Figs.…”

“…Surprisingly, other hub genes were related to ethylene synthesis/signaling (Giovannoni, 2001), such as ACC synthase 2 (spot ID 6.2.3.14) and RIN transcription factor (spot ID 8.2.16.2). Since the biological material used in this work corresponded to green growing fruit (Mounet et al, 2007), the upregulation of these genes as soon as 20 DPA (Fig. 9) could correspond to early preparation of the ripening phase.…”

“…For real-time RT-PCR, one pool of mesocarp and locular tissue was collected at 12, 20, and 35 DPA in an independent culture, prepared, and stored in the same way as described above. Fresh and dry matter contents of each sample were measured as described by Mounet et al (2007).…”

“…For NMR analysis, polar metabolites were extracted with a series of hot ethanol/water and quantified by 1 H-NMR at 500.162 MHz on a Bruker Avance spectrometer using a 5-mm inverse probe as described previously (Mounet et al, 2007). For the extract preparation and NMR acquisition parameters, special care was taken to allow absolute quantification of individual metabolites through the addition of EDTA sodium salt solution to improve the resolution and quantification of organic acids such as malate and citrate, adequate choice of the NMR acquisition parameters, and use of an electronic reference for quantification.…”

“…Coexpression analyses allowed the identification of genes regulating tissue and cell development (Persson et al, 2005;Gifford et al, 2008), environmental responses , and plant metabolism Gutiérrez et al, 2008) and were further validated by reverse genetics strategies. With the current availability of large-scale gene expression and metabolite profiling techniques in many plants, including tomato (Alba et al, 2004(Alba et al, , 2005Lemaire-Chamley et al, 2005;Moco et al, 2006;Mounet et al, 2007;Saito et al, 2007;Mintz-Oron et al, 2008;Schauer et al, 2008), large gene expression and metabolite data sets can be combined through correlation and clustering analyses (Urbanczyk-Wochniak et al, 2003) and further represented as a network of relationships between genes and metabolites Saito et al, 2007;Hoefgen and Nikiforova, 2008). These approaches have been successfully applied to the discovery of regulatory and biosynthetic genes involved in the control of metabolite production, such as the glucosinolate metabolism (Hirai et al, 2004.…”