A.O.C. taureau de Camargue

In table grapes seedlessness is a crucial breeding target, mainly results from stenospermocarpy, linked to the Thompson Seedless variety. Several studies investigated the genetic control of seedlessness identifying AGL11, a MADS-box transcription factor, as a crucial gene.
We performed a deep investigation of the whole AGL11 gene sequence in a collection of grapevine varieties revealing three different promoter-CDS combinations. By investigating the expression of the three AGL11 alleles and evaluating their ability to activate the promoter region, we show that AGL11 regulates its transcription in a specific promoter-CDS manner. By a multi-AGL11 co-expression analysis we identified a methyl jasmonate esterase, an indole-3-acetate beta-glucosyltransferase, and an isoflavone reductase as top AGL11 candidate targets. In vivo experiments further confirmed AGL11 role in regulating these genes, demonstrating its significant influence in seed development and thus in seedlessness trait.

Toasted pruning vine-shoots represent a promising new enological tool for developing wines with chemical and organoleptic high quality, allowing that the resources of the vineyard to be returned to the wine through a “circular process”.

The evaluation of new grapevine genotypes regarding their potential to produce high quality wines is the time limiting factor in the process of grapevine breeding. Hence, the development of quality-related markers useable in marker-assisted selection (MAS) as well as in prediction models for this bottleneck trait will tremendously enhance breeding efficiency. In extensive studies a training set of a segregating white wine F1 population (150 F1 genotypes = POP150; `Calardis Musqué´ x `Villard Blanc´) was deeply phenotyped and genotyped for model development and QTL analysis.

Context and purpose of the study. Climate change is affecting wine production and induces a large variability in wine composition between vintages.

Grape harvest time is one of the most fundamental aspects that affect grape quality and thus wine quality. Many factors influence the decision of harvest; among them technological and phenolic maturity of grape. Technological ripeness is mainly related to sugar concentration, titratable acidity and pH. Conventional methods for chemical analysis of grapes are normally sample-destructive, time-consuming, include laborious sample preparation steps, and generate chemical waste, thereby limiting their utility in online/in-line quality monitoring. Moreover, destructive analyses can be performed only on a limited number of fruit pieces and, thus, their statistical relevance could be limited. This study evaluated the ability of a lab-scale hyperspectral imaging (HYP-IM) technique to predict titratable acidity, organic acid and sugar content of grapes. Samples of Cabernet franc and Chenin blanc grapes were consecutively collected six times at weekly intervals after veraison. The images were recorded thanks to the hyperspectral imaging camera Pica L (Resonon) in a spectral range from 400 to 1000 nm. Statistics were performed using Microsoft Xlstat software. Successively, the berries were analyzed for their sugar (glucose and fructose) and organic acid (malic and tartaric acid) content and titratable acidity according to usual methods.