Name: the terroir concept interpreted through grape berry metabolomics and transcriptomics
Uploaded: 2016-10-05T13:00:00
Description: Watch this Scientific Journal Video about The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics at JoVE.com

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 Grapevine in Genotype per Environment Interactions".

1. Select Appropriate Materials and Construct a Sampling Plan
<ol>
	<li>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. 
	&#8203;NOTE: In this case study, grape berries from a single clone (Vitis vinifera cv. Corvina, clone 48...

<ol>
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C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Can+we+trust+untargeted+metabolomics?+Results+of+the+metabo-ring+initiative,+a+large-scale,+multi-instrument+inter-laboratory+study.">Can we trust untargeted metabolomics? 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B., Zamboni, A., Zenoni, S., Delledonne, M., Pezzotti, M., Ger&#243;s, H., Chaves, M., Delrot, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Ch.+11.">Ch. 11.</a> The Biochemestry of the Grape Berry. 11, (2012).</li><li>Anesi, A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Towards+a+scientific+interpretation+of+the+terroir+concept:+plasticity+of+the+grape+berry+metabolome.">Towards a scientific interpretation of the terroir concept: plasticity of the grape berry metabolome.</a> BMC Plant Biol. 15, 1-17 (2015).</li><li>Berdeja, M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Water+limitation+and+rootstock+genotype+interact+to+alter+grape+berry+metabolism+through+transcriptome+reprogramming.">Water limitation and rootstock genotype interact to alter grape berry metabolism through transcriptome reprogramming.</a> Hort Res. 2, 1-13 (2015).</li><li>Carbonell-Bejerano, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Solar+ultraviolet+radiation+is+necessary+to+enhance+grapevine+fruit+ripening+transcriptional+and+phenolic+responses.">Solar ultraviolet radiation is necessary to enhance grapevine fruit ripening transcriptional and phenolic responses.</a> BMC Plant Biol. 14, 1-16 (2014).</li><li>Carbonell-Bejerano, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reducing+sampling+bias+in+molecular+studies+of+grapevine+fruit+ripening:+transcriptomic+assessment+of+the+density+sorting+method.">Reducing sampling bias in molecular studies of grapevine fruit ripening: transcriptomic assessment of the density sorting method.</a> Theor Exp Plant Phys. 28 (1), 109-129 (2016).</li><li>Carbonell-Bejerano, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Circadian+oscillatory+transcriptional+programs+in+grapevine+ripening+fruits.">Circadian oscillatory transcriptional programs in grapevine ripening fruits.</a> BMC Plant Biol. 14, 1-15 (2014).</li><li>Cavallini, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Functional+diversification+of+grapevine+MYB5a+and+MYB5b+in+the+control+of+flavonoid+biosynthesis+in+a+petunia+anthocyanin+regulatory+mutant.">Functional diversification of grapevine MYB5a and MYB5b in the control of flavonoid biosynthesis in a petunia anthocyanin regulatory mutant.</a> Plant &amp; Cell Physiol. 55 (3), 517-534 (2014).</li><li>Cramer, G. R., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Transcriptomic+analysis+of+the+late+stages+of+grapevine+(Vitis+vinifera+cv.+Cabernet+Sauvignon)+berry+ripening+reveals+significant+induction+of+ethylene+signaling+and+flavor+pathways+in+the+skin.">Transcriptomic analysis of the late stages of grapevine (Vitis vinifera cv. 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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...

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 expression at the level of the transcriptome benefits from the similar chemical properties of all transcripts, which facilitates quantitative analysis by exploiting universal characteristics such as hybridization to immobilized probes on microarrays. In contrast, universal analytical methods in proteomics and metabolomics are more challenging because of the huge physical and chemical diversity of individual proteins and metabolites. In the case of metabolomics this diversity is even more extreme because individual metabolites differ vastly in size, polarity, abundance and volatility, so no single extraction process or analytical method offers a holistic approach.
Among the analytical platforms suitable for non-volatile metabolites, those based on high performance liquid chromatography coupled to mass spectrometry (HPLC-MS) are much more sensitive than alternatives such as HPLC with ultraviolet or diode array detectors (HPLC-UV, HPLC-DAD) or nuclear magnetic resonance (NMR) spectroscopy, but quantitative analysis by HPLC-MS can be influenced by phenomena such as the matrix effect and ion suppression/enhancement1-3. The investigation of such effects during the analysis of Corvina grape berries by HPLC-MS using an electrospray ionization source (HPLC-ESI-MS), showed that sugars and other molecules with the lowest retention times were strongly underreported, probably also reflecting the large number of molecules in this zone, and that the abundance of other molecules could be underestimated, overestimated or unaffected by the matrix effect, but the data normalization for the matrix effect seemed to have limited impact on the overall results4,5. The method described herein is optimized for the analysis of medium-polarity metabolites that accumulate at high levels in grape berries during ripening, and which are significantly impacted by the terroir. They include anthocyanins, flavonols, flavan-3-ols, procyanidins, other flavonoids, resveratrol, stilbenes, hydroxycinnamic acids and hydroxybenzoic acids, which together determine the color, taste and health-related properties of wines. Other metabolites, such as sugars and aliphatic organic acids, are ignored because quantitation by HPLC-MS is unreliable due to the matrix effect and ion suppression phenomena5. Within the polarity range selected by this method, the approach is untargeted in that it aims to detect as many different metabolites as possible6.
Transcriptomics methods that allow thousands of grapevine transcripts to be monitored simultaneously are facilitated by the availability of the complete grapevine genome sequence7,8. Early transcriptomics methods based on high-throughput cDNA sequencing have evolved with the advent of next-generation sequencing into a collection of procedures collectively described as RNA-Seq technology. This approach is rapidly becoming the method of choice for transcriptomics studies. However, a large body of literature based on microarray, which allow thousands of transcripts to be quantified in parallel by hybridization, has accumulated for grapevine. Indeed, before RNA-Seq became a mainstream technology, many dedicated commercial microarray platforms had been developed allowing grapevine transcriptome to be inspected in great detail. Among the vast variety of platforms, only two allowed genome-wide transcriptome analysis9. The most evolved array allowed the hybridization of up to 12 independent samples on a single device, thus reducing the costs of each experiment. The 12 sub-arrays each comprised 135,000 60-mer probes representing 29,549 grapevine transcripts. This device has been used in a large number of studies10-24. These two platforms have now been discontinued but a new custom microarray has recently been designed and represents a more recent development as it contains an even greater number of probes representing additional newly discovered grapevine genes25.
The large-sale datasets produced by transcriptomics and metabolomics analysis require suitable statistical methods for data analysis, including multivariate techniques to determine correlations between different forms of data. The most widely used multivariate techniques are those based on projection, and these can be unsupervised, such as principal component analysis (PCA), or supervised, such as bidirectional orthogonal projection to latent structures discriminant analysis (O2PLS-DA)26. The protocol presented in this article utilizes PCA for exploratory data analysis and O2PLS-DA to identify differences between groups of samples.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>Mill Grinder</td>
			<td>IKA</td>
			<td>IKA A11 basic</td>
			<td></td>
		</tr>
		<tr>
			<td>HPLC Autosampler</td>
			<td>Beckman Coulter</td>
			<td>&#160;-</td>
			<td>System Gold 508 Autosampler</td>
		</tr>
		<tr>
			<td>HPLC System</td>
			<td>Beckman Coulter</td>
			<td>&#160;-</td>
			<td>System Gold 127 Solvent Module HPLC</td>
		</tr>
		<tr>
			<td>C18 Guard Column</td>
			<td>Grace</td>
			<td>&#160;-</td>
			<td>Alltima HP C18 (7.5x2.1mm; 5&#956;m) Guard Column</td>
		</tr>
		<tr>
			<td>C18 Column</td>
			<td>Grace</td>
			<td>&#160;-</td>
			<td>Alltima HP C18 (150x2.1mm; 3&#956;m) Column</td>
		</tr>
		<tr>
			<td>Mass Spectometer</td>
			<td>Bruker Daltonics</td>
			<td>&#160;-</td>
			<td>Bruker Esquire 6000; The mass spectometer was equipped with an ESI source and the analyzer was an ion trap.</td>
		</tr>
		<tr>
			<td rowspan="4">Extraction solvents and HPLC buffers</td>
			<td>Sigma</td>
			<td>34966</td>
			<td>Methanol LC-MS grade</td>
		</tr>
		<tr>
			<td>Sigma</td>
			<td>94318</td>
			<td>Formic acid LC-MS grade</td>
		</tr>
		<tr>
			<td>Sigma</td>
			<td>34967</td>
			<td>Acetonitrile LC-MS grade</td>
		</tr>
		<tr>
			<td>Sigma</td>
			<td>39253</td>
			<td>Water&#160; LC-MS grade</td>
		</tr>
		<tr>
			<td>Minisart RC 4 Syringe filters (0.2 &#956;m)</td>
			<td>Sartorius</td>
			<td>17764</td>
			<td></td>
		</tr>
		<tr>
			<td>Softwares for data collection (a) and processing (b)</td>
			<td>Bruker Daltonics</td>
			<td>-</td>
			<td>Bruker Daltonics Esquire 5.2 Control (a); Esquire 3.2 Data Analysis and MzMine 2.2 softwares (b)</td>
		</tr>
		<tr>
			<td>Spectrum Plant Total RNA kit</td>
			<td>Sigma-Aldrich</td>
			<td>STRN250-1KT</td>
			<td>For total RNA extractino from grape pericarps</td>
		</tr>
		<tr>
			<td>Nanodrop 1000</td>
			<td>Thermo Scientific</td>
			<td>1000</td>
			<td></td>
		</tr>
		<tr>
			<td>BioAnalyzer 2100</td>
			<td>Agilent Technologies</td>
			<td>G2939A</td>
			<td></td>
		</tr>
		<tr>
			<td>RNA 6000 Nano Reagents</td>
			<td>Agilent Technologies</td>
			<td>5067-1511</td>
			<td></td>
		</tr>
		<tr>
			<td>RNA Chips</td>
			<td>Agilent Technologies</td>
			<td>5067-1511</td>
			<td></td>
		</tr>
		<tr>
			<td>Agilent Gene Expression Wash Buffer 1</td>
			<td>Agilent Technologies</td>
			<td>5188-5325</td>
			<td></td>
		</tr>
		<tr>
			<td>Agilent Gene Expression Wash Buffer 2</td>
			<td>Agilent Technologies</td>
			<td>5188-5326</td>
			<td></td>
		</tr>
		<tr>
			<td>LowInput QuickAmp Labeling kit One-Color</td>
			<td>Agilent Technologies</td>
			<td>5190-2305</td>
			<td></td>
		</tr>
		<tr>
			<td>Kit RNA Spike In - One-Color</td>
			<td>Agilent Technologies</td>
			<td>5188-5282</td>
			<td></td>
		</tr>
		<tr>
			<td>Gene Expression Hybridization Kit</td>
			<td>Agilent Technologies</td>
			<td>5188-5242</td>
			<td></td>
		</tr>
		<tr>
			<td>RNeasy Mini Kit (50)</td>
			<td>Qiagen</td>
			<td>74104</td>
			<td>For cRNA Purification</td>
		</tr>
		<tr>
			<td>Agilent SurePrint HD 4X44K 60-mer Microarray</td>
			<td>Agilent Technologies</td>
			<td>G2514F-048771&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>eArray</td>
			<td>Agilent Technologies</td>
			<td>-</td>
			<td><a href="https://earray.chem.agilent.com/earray/">https://earray.chem.agilent.com/earray/</a></td>
		</tr>
		<tr>
			<td>Gasket slides</td>
			<td>Agilent Technologies</td>
			<td>G2534-60012</td>
			<td>Enable Agilent SurePrint Microarray 4-array Hybridization</td>
		</tr>
		<tr>
			<td>Thermostatic bath</td>
			<td>Julabo</td>
			<td>-</td>
			<td></td>
		</tr>
		<tr>
			<td>Hybridization Chamber</td>
			<td>Agilent Technologies</td>
			<td>G2534-60001</td>
			<td></td>
		</tr>
		<tr>
			<td>Microarray Hybridization Oven</td>
			<td>Agilent Technologies</td>
			<td>G2545A</td>
			<td></td>
		</tr>
		<tr>
			<td>Hybridization Oven Rotator Rack</td>
			<td>Agilent Technologies</td>
			<td>G2530-60029</td>
			<td></td>
		</tr>
		<tr>
			<td>Rotator Rack Conversion Rod</td>
			<td>Agilent Technologies</td>
			<td>G2530-60030</td>
			<td></td>
		</tr>
		<tr>
			<td>Staining kit</td>
			<td>Bio-Optica</td>
			<td>10-2000</td>
			<td>Slide-staining dish and Slide rack</td>
		</tr>
		<tr>
			<td>Magnetic stirrer device</td>
			<td>AREX Heating Magnetic Stirrer</td>
			<td>F20540163&#160;</td>
			<td></td>
		</tr>
		<tr>
			<td>Thermostatic Oven</td>
			<td>Thermo Scientific</td>
			<td>Heraeus - 6030</td>
			<td></td>
		</tr>
		<tr>
			<td>Agilent Microarray Scanner</td>
			<td>Agilent Technologies</td>
			<td>G2565CA</td>
			<td></td>
		</tr>
		<tr>
			<td>Scanner Carousel, 48-position</td>
			<td>Agilent Technologies</td>
			<td>G2505-60502</td>
			<td></td>
		</tr>
		<tr>
			<td>Slide Holders</td>
			<td>Agilent Technologies</td>
			<td>G2505-60525</td>
			<td></td>
		</tr>
		<tr>
			<td>Feature extraction software v11.5</td>
			<td>Agilent Technologies</td>
			<td>-</td>
			<td>inside the Agilent Microarray Scanner G2565CA</td>
		</tr>
		<tr>
			<td>SIMCA + V13 Software</td>
			<td>Umetrics</td>
			<td></td>
			<td></td>
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the terroir concept interpreted through grape berry metabolomics and transcriptomics

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.

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...

Watch this Scientific Journal Video about The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics at JoVE.com

The Terroir Concept Interpreted through Grape Berry Metabolomics and Transcriptomics

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 ...

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