The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Silvia Dal Santo; Mauro Commisso; Erica D'Incà; Andrea Anesi; Matteo Stocchero; Sara Zenoni; Stefania Ceoldo; Giovanni B. Tornielli; Mario Pezzotti; Flavia Guzzo

doi:10.3791/54410

Method Article

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

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DOI:

10.3791/54410

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October 5th, 2016

Silvia Dal Santo^*¹ , Mauro Commisso^*¹ , Erica D'Incà¹ , Andrea Anesi¹^,² , Matteo Stocchero³ , Sara Zenoni¹ , Stefania Ceoldo¹ , Giovanni B. Tornielli¹ , Mario Pezzotti¹ , Flavia Guzzo¹

¹Biotechnology Department, University of Verona, ²Lab. of Bioorganic Chemistry, Physics Department, University of Trento, ³S-IN Soluzioni Informatiche

^* These authors contributed equally

Summary

This article describes the application of untargeted metabolomics, transcriptomics and multivariate statistical analysis to grape berry transcripts and metabolites in order to gain insight into the terroir concept, i.e., the impact of the environment on berry quality traits.

Abstract

Terroir refers to the combination of environmental factors that affect the characteristics of crops such as grapevine (Vitis vinifera) according to particular habitats and management practices. This article shows how certain terroir signatures can be detected in the berry metabolome and transcriptome of the grapevine cultivar Corvina using multivariate statistical analysis. The method first requires an appropriate sampling plan. In this case study, a specific clone of the Corvina cultivar was selected to minimize genetic differences, and samples were collected from seven vineyards representing three different macro-zones during three different growing seasons. An untargeted LC-MS metabolomics approach is recommended due to its high sensitivity, accompanied by efficient data processing using MZmine software and a metabolite identification strategy based on fragmentation tree analysis. Comprehensive transcriptome analysis can be achieved using microarrays containing probes covering ~99% of all predicted grapevine genes, allowing the simultaneous analysis of all differentially expressed genes in the context of different terroirs. Finally, multivariate data analysis based on projection methods can be used to overcome the strong vintage-specific effect, allowing the metabolomics and transcriptomics data to be integrated and analyzed in detail to identify informative correlations.

Introduction

Large-scale data analysis based on the genomes, transcriptomes, proteomes and metabolomes of plants provides unprecedented insight into the behavior of complex systems, such as the terroir characteristics of wine which reflect the interactions between grapevine plants and their environment. Because the terroir of a wine can be distinct even when identical grapevine clones are grown in different vineyards, genomics analysis is of little use because the clonal genomes are identical. Instead it is necessary to look at correlations between gene expression and the metabolic properties of the berries, which determine the quality traits of wine. The analysis of gene expressi....