Name: the terroir concept interpreted through grape berry metabolomics and transcriptomics
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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 Grapevine in Genotype per Environment Interactions".

1. Kies geschikte materialen en de bouw van een Sampling Plan <ol><li> Begin het experiment door het ontwikkelen van een passend bemonsteringsplan. Er is geen generieke en universele aanpak, zodat elk plan op een case-by-case basis te evalueren. Zorg ervoor dat het bemonsteringsplan stelt de sampling plaatsen, tijden en de precieze sampling procedure. Zie figuur 1 voor het bemonsteringsplan gebruikt in deze case study. LET OP: In dit geval studie werden druif bessen uit een enkele kloon (. ...

<ol>
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Dit artikel beschrijft de metabolomics, transcriptomics en statistische analyse protocollen die worden gebruikt om de druivenbes terroir begrip interpreteren. Metabolomics analyse met HPLC-ESI-MS is gevoelig genoeg om grote aantallen metabolieten gelijktijdig detecteren, maar de relatieve kwantificering wordt beïnvloed door de matrixeffect en ion onderdrukking / verbetering. Er is echter een soortgelijke aanpak reeds zijn gebruikt om de rijping en post-harvest verdorren van Corvina bessen te beschrijven, en de correcti...

Grootschalige data-analyse op basis van de genomen, transcriptomes, proteomen en metabolomes van planten biedt ongekend inzicht in het gedrag van complexe systemen, zoals de streek kenmerken van wijn die de interacties tussen grapevine planten en hun omgeving weerspiegelen. Omdat de streek van een wijn duidelijk kan zijn, zelfs wanneer identieke grapevine klonen worden gekweekt in verschillende wijngaarden, genomics-analyse is van weinig nut, omdat het klonen genomen zijn identiek. In plaats daarvan moet kijken naar correlaties tussen genexpressie en de metabolische eigenschappen van de druiven waarvan de kwaliteitskenmerken wijn bepalen. De analyse van genexpressie op het niveau van de transcriptoom voordelen van de vergelijkbare chemische eigenschappen van alle transcripten, kwantitatieve analyse vergemakkelijkt door gebruik algemene kenmerken zoals hybridisatie met geïmmobiliseerde probes op microarrays. Daarentegen universele analysemethoden in een proteomicsnd metabolomics zijn meer uitdagend vanwege de enorme fysieke en chemische diversiteit van individuele eiwitten en metabolieten. In het geval van metabolomics deze diversiteit is nog extremer omdat individuele metabolieten sterk verschillen in grootte, polariteit, overvloed en de volatiliteit, dus geen enkele extractieproces of analytische methode biedt een holistische benadering. Onder de analytische platforms geschikt voor niet-vluchtige metabolieten, die uit hoogwaardige vloeistofchromatografie gekoppeld aan massaspectrometrie (HPLC-MS) zijn veel gevoeliger dan alternatieven zoals HPLC met ultraviolet of diode array detector (HPLC-UV, HPLC-DAD ) of kernmagnetische resonantie (NMR) spectroscopie, maar kwantitatieve analyse met HPLC-MS kan worden beïnvloed door verschijnselen zoals matrixeffect en ion onderdrukking / verbetering 1-3. Het onderzoek naar dergelijke effecten bij de analyse van Corvina druivenbessen met HPLC-MS met behulp van een elektrospray ionisatiebron (HPLC-ESI-MS) toonde dat suikers of andere moleculen met de laagste retentietijden waren sterk underreported, waarschijnlijk mede als gevolg van het grote aantal moleculen in deze zone, en dat de overvloed aan andere moleculen kan worden onderschat, overschat of beïnvloed door de matrixeffect , maar de gegevens normalisering van de matrix effect leek te beperkte impact hebben op de algemene resultaten van 4,5 te hebben. De hierin beschreven werkwijze is geoptimaliseerd voor de analyse drager polariteit metabolieten die zich ophopen bij hoge niveaus druivenbessen tijdens het rijpen en die aanzienlijk worden beïnvloed door de streek. Zij omvatten anthocyanen, flavonolen, flavan-3-ols, procyanidins, andere flavonoïden, resveratrol, stilbenen, hydroxykaneelzuren en hydroxybenzoëzuur zuren, die samen de kleur, smaak en gezondheid-gerelateerde eigenschappen van wijnen te bepalen. Andere metabolieten zoals suikers en alifatische organische zuren, worden genegeerd omdat kwantificatie met HPLC-MS onbetrouwbaar door de matrix en effect en ion onderdrukking verschijnselen 5. Binnen het bereik polariteit geselecteerd door deze werkwijze is de benadering irrelevante omdat hij tot doel zoveel mogelijk verschillende metabolieten detecteren mogelijk 6. Transcriptomics methoden die het mogelijk maken duizenden grapevine transcripten gelijktijdig te bewaken worden vergemakkelijkt door de beschikbaarheid van de volledige grapevine genoomsequentie 7,8. Vroege transcriptomics methoden op basis van hoge doorvoer sequentiebepaling cDNA geëvolueerd met de komst van de volgende generatie sequencing in een verzameling procedures collectief omschreven als RNA-Seq technologie. Deze aanpak is hard op weg de methode van de keuze voor transcriptomics studies. Echter een grote hoeveelheid literatuur gebaseerd op microarray, waarmee duizenden transcripten parallel worden gekwantificeerd door hybridisatie is opgebouwd voor wijnstokken. Sterker nog, voor RNA-Seq werd een mainstream-technologie, hadden vele toegewijde commerciële microarray platformenontwikkeld waardoor grapevine transcriptoom in detail te inspecteren. Onder de grote verscheidenheid aan platforms, slechts twee toegestaan genoom-brede transcriptoom analyse 9. De verst ontwikkelde matrix kon de hybridisatie van tot 12 onafhankelijke monsters op een enkel apparaat, waardoor de kosten van elk experiment verminderen. De 12 sub-arrays elk omvatte 135.000 60-mer probes die 29.549 grapevine transcripten. Dit apparaat is gebruikt in een groot aantal studies 10-24. Deze twee platforms zijn inmiddels gestopt, maar een nieuwe aangepaste microarray is onlangs ontwikkeld en vertegenwoordigt een meer recente ontwikkeling als het een nog groter aantal probes die extra nieuw ontdekte genen grapevine 25 bevat. De grote verkoop datasets geproduceerd door transcriptomics en metabolomics analyse vereisen een geschikte statistische methoden voor data-analyse, met inbegrip van multivariate technieken om correlaties tussen de verschillende vorm bepalens data. De meest gebruikte multivariate technieken zijn die op basis van projectie, en deze kunnen zonder toezicht, zoals principale componenten analyse (PCA), of worden gecontroleerd, zoals bidirectionele orthogonale projectie latente structuren discriminerende analyse (O2PLS-DA) 26. Het protocol in dit artikel maakt gebruik van PCA voor de experimentele data-analyse en O2PLS-DA om de verschillen tussen groepen van de monsters te identificeren.

<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>
		</tr>
	</tbody>
</table>

the terroir concept interpreted through grape berry metabolomics and transcriptomics

Streek verwijst naar de combinatie van omgevingsfactoren die de eigenschappen van gewassen beïnvloeden, zoals grapevine (Vitis vinifera) volgens bepaalde habitats en beheerspraktijken. Dit artikel laat zien hoe bepaalde terroir handtekeningen kunnen worden gedetecteerd in de bessen metaboloom en transcriptoom van de wijnstok cultivar Corvina met behulp van multivariate statistische analyse. De methode vereist eerst een geschikte bemonsteringsplan. In deze case study, een specifieke kloon van de Corvina cultivar werd geselecteerd om genetische verschillen te minimaliseren, en monsters werden verzameld van zeven wijngaarden vertegenwoordigen drie verschillende macro-zones tijdens drie verschillende groeiseizoenen. Een ongerichte LC-MS metabolomics aanpak wordt aanbevolen vanwege de hoge gevoeligheid, begeleid door een efficiënte verwerking van gegevens met behulp van MZmine software en een metaboliet identificatie strategie gebaseerd op fragmentatie boom analyse. Uitgebreide transcriptoom analyse kan worden bereikt met behulp van microarraysprobes die ~ 99% van de voorspelde genen grapevine, waardoor de gelijktijdige analyse van differentieel tot expressie gebrachte genen in de context van verschillende terroirs. Tenslotte kunnen multivariate data analyse gebaseerd op de projectie methoden worden toegepast om de uitstekende sterke-specifiek effect te overwinnen, waardoor de metabolomics en transcriptomics data te integreren en in detail geanalyseerd om informatieve correlaties te identificeren.

De case studie in dit artikel beschreven leverde een definitieve gegevens matrix omvattende 552 signalen (m / z functies), met inbegrip van moleculaire ionen plus hun isotopen, adducten en een aantal fragmenten, relatief gekwantificeerd onder 189 monsters (7 wijngaarden x 3 rijpen stadia x 3 groeiseizoenen x 3 biologische herhalingen). Het totale aantal voor data punten was dan ook 104.328. Fragmentatie boom analyse resulteerde in de annotatie van 282 m / z kenmerken ov...

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

Dit artikel beschrijft de toepassing van ongerichte metabolomics, transcriptomics en multivariate statistische analyse om druivenbes transcripties en metabolieten om inzicht te krijgen in het terroir concept, dat wil zeggen, de invloed van de omgeving op bes kwaliteitskenmerken.

De Terroir Concept Uitgelegd door de Bes van de Druif Metabolomics en Transcriptomics

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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