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Very berry study aims to improve wine quality

Posted on June 7, 2013

A gene expression study of grapevine berries grown in different Italian vineyards has highlighted genes that help buffer the plants against environmental change and may explain the different quality performances of grapevine when grown in different “terroirs”. The research, reported in the open access journal Genome Biology, could be used to help identify and breed grapevine varieties better suited to climate change and improve berry and wine quality.

 

Much to the inconvenience of winemakers and drinkers, grapevine berries vary within berries on vines grown in different vineyards and in different vintages. This means they may ripen unevenly, affecting the quality of wine from place to place and from vintage to vintage. But although the differences are known to reflect environmental change and differing grape-growing practices, the molecular mechanisms underlying this variability are unclear.

To address the issue, Silvia Dal Santo from the Plant Genetics Lab headed by Mario Pezzotti of the University of Verona, Italy grew a single grapevine clone (Vitis vinifera, cultivar Corvina) in eleven very different vineyards across the Verona region. They then harvested berries at various stages of ripening, across three consecutive years, and used microarrays to study patterns of gene expression across the genome, linking interesting finds to the grapevine’s DNA sequence.

The team were able to highlight various environmentally-sensitive genes thought to influence berry quality. These included genes regulating metabolic pathways – such as the production of phenolic compounds which contribute to taste, colour and ‘mouth-feel’ of wine – that were highly sensitive towards different climates.

It was hard to pick out generalised gene expression signatures reflecting common environmental conditions or viticulture practices, instead patterns of gene expressionclustered by year of growth. However, during a typical climate vintage, the authors were able to link sets of differentially-expressed transcripts to particular environmental attributes or specific agronomical parameters, such as the vine trelling system.

Read more at: Phys.org

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