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...

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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 ...

The overall goal of these experimental procedures is to observe the different terroir signatures in the berry metabolome and transcriptome of the grapevine cultivar Corvina. The case study described here involved the collection and analysis of samples at three ripening stages from seven vineyards in three macro zones over three growing seasons. The techniques described reveal the prevalent ripening in vintage effect on the berry metabolome.Clear specific chemical signatures representing the three macro zones were also identified in the ripe berries. The terroir signature at the macro zone level was revealed using suitable statistical techniques such as the PCA, which is the principal component analysis, and the auto BLSDA, which is the bidirectional orthogonal projection to leaden structures, discrete analysis, to analyze both the transcriptomics, and the metabalomics data matrix. On the other side, the differences among individual vineyards are more subtle, and also require more sophisticated and sensitive statistical approaches.Begin the experiment by developing an appropriate sampling plan. Ensure that the sampling plan states the sampling places, times, and the precise sampling procedure. After harvesting 30 clusters from different positions along two vine rows, select three berries randomly from each cluster, avoiding those with visible damage and or signs of infection.Repeat this process to obtain three independent pools for each ripening stage. De-seed the berries, and freeze the pericarp immediately in liquid nitrogen. Crush all the frozen berries from each pool with an automatic mill grinder.Divide each powdered sample into two equal parts, one for transcriptomics analysis, and one for metabolomics analysis. Then, close and remove the duer, and store the powders at minus 80 degrees celsius. Prepare the metabolic extracts of the berry samples at room temperature in three volumes of methanol, acidified with 0.1%formic acid, in an ultrasonic bath at 40 kilohertz for 15 minutes.Centrifuge the extracts at 16, 000 times g for 10 minutes at four degrees celsius. Dilute the supernatent in two volumes of deionized water, and pass through a 0.2 micron filter. Set up the high performance liquid chromatography, coupled to mass spectrometry, using an electro spray ionization source or HPLC-ESI-MS system, as described in the text protocol.Run the HPLC separation with a linear gradient of solvents A and B at a constant flow rate of 0.2 milliliters per minute, using a sample injection volume of 30 microliters. Acquire mass spectra in alternate negative and positive ionization modes, with the parameters listed in the text protocol. To process the LCMS data, implement the peak detection alignment, gap-filling, and peak filtering procedures using suitable software, such as the open source software M Zeta Mine, version 2.14.Import the resulting text files into a spreadsheet. This produces a data matrix in which all the detected metabolites recognized by an identification number, mass-to-charge ratio value, and retention time, are quantified in all samples in terms of their peak area values. Prepare the hybridization assembly based on four pack format specifications, by placing 1.65 micrograms of cyanine-labeled complementary RNA, in a final volume of 41.8 microliters of deionized RNAse free water.Add 11 microliters of 10X blocking agent, and 2.2 microliters of 25X fragmentation buffer. Incubate at 60 degrees celsius for 30 minutes in a thermostatic bath, to fragment the RNA. After 30 minutes, immediately cool on ice for one minute.Finally, add 55 microliters of 2X hybridization buffer, and mix well by pipetting. After spinning for one minute at 15, 500 times g at room temperature, promptly place the micro-centrifuge tube on ice. To load the custom micro-array, load a gasket slide with the label facing up, into the base of a hybridization chamber.Slowly load 100 microliters of hybridization sample onto each gasket well, dispensing the liquid with the tip of a pipette, avoiding bubbles. Slowly place the custom micro-array, facing down, ensuring that the numeric barcode is facing up. Make sure that the sandwich pair is properly aligned.Finally, place the cover of the hybridization chamber onto the sandwiched slides, and hand-tighten the clamp onto the chamber. Rotate the assembled chamber, to assess the mobility of bubbles. Place the assembled slide chamber in a balanced rotisserie, in a hybridization oven set to 65 degrees celsius.Set the rotator to rotate at 10 RPM. Allow the hybridization to proceed for 17 hours. To proceed with the micro-array slide wash, prepare three slide-staining dishes and fill them with the appropriate washing buffers.Fill two dishes with wash buffer one, at room temperature, and one dish with pre-warmed wash buffer two. Disassemble the hybridization chamber, and remove the sandwich. With the micro-array slide numeric barcode facing up, submerge the sandwich into the first slide staining dish filled with wash buffer one at room temperature, and with the help of clean forceps, separate the gasket from the micro-array slide.Quickly transfer the micro-array slide into a slide rack, and place it in the second slide-staining dish filled with wash buffer one at room temperature. Put the slide-staining dish onto a magnetic stirrer, and wash for one minute with moderate stirring. Quickly transfer the slide rack into the third slide-staining dish, filled with pre-warmed wash buffer two, and wash for one minute with moderate stirring.Slowly remove the rack from the slide-staining dish, and carefully remove the slide from the rack, avoiding droplets. Place the micro-array slide into a suitable scanner, and scan each array using the parameter settings recommended in the micro-array manufacturer's instruction manual. To prepare the statistical analysis software, first import the metabolomics and transcriptomics data.Go to File, New Regular Project, New Regular Project, to import the obtained data matrix. Then click on Edit. Transpose the whole matrix, home, and assign the appropriate primary and secondary ID.And finish.To carry out the multivariate statistical analysis, navigate to the Workset window, select PCAX as the model type, and click on AutoFit. Select Scores and then Scatter, to see the plot that shows the possible grouping of the samples. Inspect the PCA score plot.If a good model is obtained, use the same classes of samples to build an O2PLS discriminant analysis model. To build two O2PLS discriminant analysis models using the samples classified by MacroZone, assign the classes by going to Home window, New As, M1, and Observations. Set the desired classes.Then, change the model type from PCAX to O2PLS discriminant analysis. Press Autofit. Proceed to Select M2 in the Project window, and click on Scores, and then Scatter, to see the plot and the position of the sample classes.Go to Select Data Type, and select Observations and Loadings. Then click Add Series, and select the items PQ correlation one, and PQ correlation two, or further components when present. With the mouse's right button pressed on the plot, go to Property, select Color, and then select By Terms, to distinguish the plot symbols among molecules and classes.Go to Layout, Format Plot, Access and or Styles, to modify the plot with the desired characteristics. To observe what metabolites characterize one or more specific classes, go to Plot List, and then Scatter. Exploratory analysis of the whole data matrix by PCA, showed that samples clustered according to the ripening stage, including veraison, mid-ripening, and ripe, along the first and second principal components.The samples clustered according to the growing season along the third principal component. O2PLS discriminant analysis of mature berries allows for identification of specific terroir signatures, representing the three macrozones, Valpolicella, Garda Lake, and Soave. The loading plots of these models are expressed as PQ correlation, the correlation between P, the class of samples, and Q, the metabolites.In the loading plots, the red squares represent the class of samples or macrozones, and the colored triangles represent the individual metabolites. The metabolites are color coded according to their chemical class, and the distance between the metabolites and the samples reflects their relationships. The Lake Garda macrozone was characterized by stilbenes, whereas Soave and Valpolicella was marked by flavonoids.Within the anthocyanins, peonidin and its derivatives were more prevalent in the Lake Garda macrozone, while the other anthocyanins were more prevalent in the Valpolicella macrozone. Moreover, anthocyanin three-O-glucosides were more prevalent in the Valpolicella macrozone, whereas acylated and cumerated derivatives were more prevalent in the Soave macrozone. Transcriptomics, RA, and RNA sig methods, allow thousands of grape vine transcript to be monitored simultaneously.A new custom micro-array has recently been designed and represents an even greater number of probes, representing additional, newly discovered grapevine genus. Given the rapid obsolescence of transcriptomic platforms, we described an alternative procedure using a new platform that contains more probes than the old one, including 249 more peno predicted transcripts, for 1392 newly identified peno loci, and 179 corvina prava genes. Metabolomics analysis by HPLC-ESI-MS, is sensitive enough to detect large numbers of metabolites simultaneously.The ability to create a sampling plain is critical. The choice of one single clone to minimize genetic differences, and the multiple duration of the sampling, allowed identification of the real differences among the terroirs. It would be interesting to compare the same cultivar grown in less optimal zones, but unfortunately, these vineyards were not available.

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