Published: October 5th, 2016
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....
1. Select Appropriate Materials and Construct a Sampling Plan
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.......
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.......
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....
|IKA A11 basic
|System Gold 508 Autosampler
|System Gold 127 Solvent Module HPLC
|C18 Guard Column
|Alltima HP C18 (7.5x2.1mm; 5μm) Guard Column
|Alltima HP C18 (150x2.1mm; 3μm) Column
|Bruker Esquire 6000; The mass spectometer was equipped with an ESI source and the analyzer was an ion trap.
|Extraction solvents and HPLC buffers
|Methanol LC-MS grade
|Formic acid LC-MS grade
|Acetonitrile LC-MS grade
|Water LC-MS grade
|Minisart RC 4 Syringe filters (0.2 μm)
|Softwares for data collection (a) and processing (b)
|Bruker Daltonics Esquire 5.2 Control (a); Esquire 3.2 Data Analysis and MzMine 2.2 softwares (b)
|Spectrum Plant Total RNA kit
|For total RNA extractino from grape pericarps
|RNA 6000 Nano Reagents
|Agilent Gene Expression Wash Buffer 1
|Agilent Gene Expression Wash Buffer 2
|LowInput QuickAmp Labeling kit One-Color
|Kit RNA Spike In - One-Color
|Gene Expression Hybridization Kit
|RNeasy Mini Kit (50)
|For cRNA Purification
|Agilent SurePrint HD 4X44K 60-mer Microarray
|Enable Agilent SurePrint Microarray 4-array Hybridization
|Microarray Hybridization Oven
|Hybridization Oven Rotator Rack
|Rotator Rack Conversion Rod
|Slide-staining dish and Slide rack
|Magnetic stirrer device
|AREX Heating Magnetic Stirrer
|Heraeus - 6030
|Agilent Microarray Scanner
|Scanner Carousel, 48-position
|Feature extraction software v11.5
|inside the Agilent Microarray Scanner G2565CA
|SIMCA + V13 Software
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