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P-11<br />

Drawing links from transcriptome to metabolites: the evolution of Muscat aroma in the<br />

ripening berry<br />

L. Costantini* 1 , J. Battilana 1 , C. Kappel 2 , F. Emanuelli 1 , M. Sordo 1 , S. Delrot 2 , M.S. Gr<strong>and</strong>o 1<br />

1<br />

IASMA Research Centre, Fondazione Edmund Mach, San Michele all’Adige, Trento, Italy;<br />

2 UMR Ecophysiology <strong>and</strong> Grape Functional Genomics, University of Bordeaux, INRA, Institut<br />

des Sciences de la Vigne et du Vin, Villenave d’Ornon, France<br />

*Corresponding author : laura.costantini@iasma.it<br />

Owing to the role that aroma plays in the perceived quality of grapes <strong>and</strong> wines, there is a<br />

tremendous interest in underst<strong>and</strong>ing how the accumulation of sensory compounds is regulated at<br />

the molecular level. To this purpose, we applied an integrative functional genomics approach by<br />

simultaneously monitoring the abundance of transcripts <strong>and</strong> metabolites in ripening berries of<br />

Moscato Bianco (Vitis vinifera L.).Gas chromatography-mass spectrometry analysis of thirty-two<br />

out of the most important ‘impact’ compounds (monoterpenoids, C 13 -norisoprenoids, benzenoids<br />

<strong>and</strong> C 6 -aliphatic compounds) in their free <strong>and</strong> bound forms was combined <strong>with</strong> a large-scale<br />

transcriptome analysis based on the use of the AROS 1.0 oligonucleotide microarray (14,562<br />

probes) at five developmental stages from pre-véraison to over-ripening. Several exploratory<br />

methods were tested for the integration of transcript <strong>and</strong> metabolite profiles <strong>with</strong> the final goal to<br />

identify genes or groups of genes which are linked <strong>with</strong> the accumulation of specific metabolites<br />

or groups of metabolites. Pair-wise metabolite-to-metabolite <strong>and</strong> gene-to-metabolite correlations<br />

were calculated. Various types of clustering were performed by incorporating both differentially<br />

expressed genes <strong>and</strong> metabolites. Gene network analysis was assayed in order to identify items<br />

<strong>with</strong> similar patterns of co-regulation. Finally, modulated transcripts were checked for their<br />

potential co-localization <strong>with</strong> previously identified QTLs controlling the level of aromatic<br />

compounds. This work is expected to provide insight into the regulatory network of grapevine<br />

secondary metabolism <strong>and</strong> is aimed at identifying genes relevant to aroma determination both at<br />

the regulatory <strong>and</strong> catalytic levels.<br />

87


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