A multi-Ievel omic approach of tomato fruit quality
2012
Xu, Jiaxin | Pascual, Laura | Desplat, Nelly | Faurobert, Mireille | Gibon, Yves, Y. | Moing, Annick | Maucourt, Mickaël | Ballias, Patricia | Deborde, Catherine | Liang, Yan | Bouchet, Jean-Paul | Brunel, Dominique, D. | Le Paslier, Marie-Christine | Causse, Mathilde, M. | Génétique et Amélioration des Fruits et Légumes (GAFL) ; Institut National de la Recherche Agronomique (INRA) | Northwest A & F University | College of horticulture ; Northwest A & F University | Biologie du fruit et pathologie (BFP) ; Université Bordeaux Segalen - Bordeaux 2-Institut National de la Recherche Agronomique (INRA)-Université Sciences et Technologies - Bordeaux 1 (UB) | Etude du Polymorphisme des Génomes Végétaux (EPGV) ; Institut National de la Recherche Agronomique (INRA) | Centre National de Génotypage (CNG) ; Commissariat à l'énergie atomique et aux énergies alternatives (CEA)
National audience
Show more [+] Less [-]English. Tomato fruit quality is an important trait for tomato consumers, but complex to improve due to the number of components involved and by their polygenic nature. ln order to decipher the genetic diversity and the inheritance of fruit qua lity components at a global level, we conducted a large multi-Ievel omic experiment. A set of 8 contrasted lines and 4 of their F1 hybrids were phenotyped for fruit development traits. Fruits were harvested and pericarp samples analysed at 2 stages (cell expansion and orange) and different scales: (1) primary and secondary metabolome profiles, (2) activities of 28 enzymes involved in primary metabolism, (3) proteome profiles revealed by 2D-PAGE and sequencing of 470 spots showing quantitative variations and (4) gene expression analysis by Digital Gene Expression. ln parallel, the 8 lines were resequenced and more than 3 millions SNPs identified when aligned on the reference tomato genome. This experiment allowed us to address several questions: the range of variability for the metabolic traits and expression data. Correlation networks can be constructed within and between levels of analysis to identify regulatory networks. Diversity of chosen candidate genes can be analysed, relating the polymorphisms at the sequence levels with their expression. Some examples will be presented.
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