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The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

Published: October 5th, 2016



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

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.

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.

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....

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1. Select Appropriate Materials and Construct a Sampling Plan

  1. Begin the experiment by developing an appropriate sampling plan. There is no generic and universal approach so evaluate each plan on a case-by-case basis. Ensure that the sampling plan states the sampling places, times and the precise sampling procedure. See Figure 1 for the sampling plan used in this case study.
    ​NOTE: In this case study, grape berries from a single clone (Vitis vinifera cv. Corvina, clone 48.......

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The case study described in this article yielded a final data matrix comprising 552 signals (m/z features) including molecular ions plus their isotopes, adducts and some fragments, relatively quantified among 189 samples (7 vineyards x 3 ripening stages x 3 growing seasons x 3 biological replicates). The total number for data points was therefore 104,328. Fragmentation tree analysis resulted in the annotation of 282 m/z features, corresponding to metabolites plus adducts.......

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This article describes the metabolomics, transcriptomics and statistical analysis protocols used to interpret the grape berry terroir concept. Metabolomics analysis by HPLC-ESI-MS is sensitive enough to detect large numbers of metabolites simultaneously, but relative quantitation is affected by the matrix effect and ion suppression/enhancement. However, a similar approach has already been used to describe the ripening and post-harvest withering of Corvina berries, and the correction of matrix effects had a limited impact.......

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This work benefited from the networking activities coordinated within the EU-funded COST ACTION FA1106 "An integrated systems approach to determine the developmental mechanisms controlling fleshy fruit quality in tomato and grapevine". This work was supported by the 'Completamento del Centro di Genomica Funzionale Vegetale' project funded by the CARIVERONA Bank Foundation and by the 'Valorizzazione dei Principali Vitigni Autoctoni Italiani e dei loro Terroir (Vigneto)' project funded by the Italian Ministry of Agricultural and Forestry Policies. SDS was financed by the Italian Ministry of University and Research FIRB RBFR13GHC5 project "The Epigenomic Plasticity of Gr....

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Name Company Catalog Number Comments
Mill Grinder IKA IKA A11 basic
HPLC Autosampler Beckman Coulter  - System Gold 508 Autosampler
HPLC System Beckman Coulter  - System Gold 127 Solvent Module HPLC
C18 Guard Column Grace  - Alltima HP C18 (7.5x2.1mm; 5μm) Guard Column
C18 Column Grace  - Alltima HP C18 (150x2.1mm; 3μm) Column
Mass Spectometer Bruker Daltonics  - Bruker Esquire 6000; The mass spectometer was equipped with an ESI source and the analyzer was an ion trap.
Extraction solvents and HPLC buffers Sigma 34966 Methanol LC-MS grade
Sigma 94318 Formic acid LC-MS grade
Sigma 34967 Acetonitrile LC-MS grade
Sigma 39253 Water  LC-MS grade
Minisart RC 4 Syringe filters (0.2 μm) Sartorius 17764
Softwares for data collection (a) and processing (b) Bruker Daltonics - Bruker Daltonics Esquire 5.2 Control (a); Esquire 3.2 Data Analysis and MzMine 2.2 softwares (b)
Spectrum Plant Total RNA kit Sigma-Aldrich STRN250-1KT For total RNA extractino from grape pericarps
Nanodrop 1000 Thermo Scientific 1000
BioAnalyzer 2100 Agilent Technologies G2939A
RNA 6000 Nano Reagents Agilent Technologies 5067-1511
RNA Chips Agilent Technologies 5067-1511
Agilent Gene Expression Wash Buffer 1 Agilent Technologies 5188-5325
Agilent Gene Expression Wash Buffer 2 Agilent Technologies 5188-5326
LowInput QuickAmp Labeling kit One-Color Agilent Technologies 5190-2305
Kit RNA Spike In - One-Color Agilent Technologies 5188-5282
Gene Expression Hybridization Kit Agilent Technologies 5188-5242
RNeasy Mini Kit (50) Qiagen 74104 For cRNA Purification
Agilent SurePrint HD 4X44K 60-mer Microarray Agilent Technologies G2514F-048771 
eArray Agilent Technologies -
Gasket slides Agilent Technologies G2534-60012 Enable Agilent SurePrint Microarray 4-array Hybridization
Thermostatic bath Julabo -
Hybridization Chamber Agilent Technologies G2534-60001
Microarray Hybridization Oven Agilent Technologies G2545A
Hybridization Oven Rotator Rack Agilent Technologies G2530-60029
Rotator Rack Conversion Rod Agilent Technologies G2530-60030
Staining kit Bio-Optica 10-2000 Slide-staining dish and Slide rack
Magnetic stirrer device AREX Heating Magnetic Stirrer F20540163 
Thermostatic Oven Thermo Scientific Heraeus - 6030
Agilent Microarray Scanner Agilent Technologies G2565CA
Scanner Carousel, 48-position Agilent Technologies G2505-60502
Slide Holders Agilent Technologies G2505-60525
Feature extraction software v11.5 Agilent Technologies - inside the Agilent Microarray Scanner G2565CA
SIMCA + V13 Software Umetrics

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