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.选择合适的材料和构造一个抽样计划<ol><li>通过制定适当的采样计划，开始实验。有没有通用的和普遍的方法，使评价对案件逐案基础上每一项计划。确保抽样方案规定的抽样地点，时间和精确的抽样程序。参见图1在这种情况下，研究中使用的抽样方案。 注：在这个案例中，从单一的克隆（ 葡萄品种科维纳，克隆48）葡萄浆果从七个商业葡萄园收集于维罗纳省三个不?...

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本文介绍了用于解释葡萄果实风土理念代谢组学，转录和统计分析的协议。通过HPLC-ESI-MS代谢分析是不够敏感，同时检测大量的代谢产物，但相对定量是由基体效应和离子抑制/增强的影响。然而，类似的做法已经被用来形容科维纳浆果的成熟和收获后枯萎，和基体效应的修正对结果5的影响有限。此外，最近的大型多仪器实验室间研究，以确定NMR和LC-MS的用于使用相同样品非靶向代谢物组?...

根据植物的基因组，转录组，蛋白质组和代谢组大规模数据分析提供了前所未有的洞察复杂系统，如葡萄酒的风土特性反映葡萄植物及其环境之间的相互作用的行为。因为即使在相同的葡萄无性系在不同的葡萄园种植葡萄酒的风土可以是不同的，基因组学分析是没有多大用处，因为克隆的基因组是相同的。相反，它是必要看看基因表达和浆果，决定葡萄酒的品质性状的代谢特性之间的相关性。基因表达在所有转录物的化学性质相似，它通过在芯片开发通用的特点，如杂交固定的探针有利于定量分析转录福利水平的分析。与此相反，普遍在蛋白质组学分析方法第二代谢是由于个别蛋白质和代谢物的巨大的物理和化学多样性更具挑战性。在代谢组学的情况下，这种多样性是更为极端，因为个体代谢物的大小，极性，丰度和波动性大大不同，所以不存在单一的提取工艺或分析方法提供了一个全面的方法。 之间合适的非易失性代谢物的分析平台，那些基于高效液相色谱耦合质谱（HPLC-MS）比等替代性HPLC用紫外线或二极管阵列检测器（HPLC-UV，HPLC-DAD敏感得多）或核磁共振（NMR）谱，但是通过HPLC-MS定量分析可以通过的现象，如基质效应和离子抑制/增强1-3的影响。这种影响通过HPLC-MS的科维纳葡萄果实的分析过程中使用电喷雾离子源（HPLC-E的调查SI-MS），表明糖和其他分子具有最低保留时间强烈低估，也可能反映在这一区域中的大数目的分子，而其它分子的丰度可能被低估，由基体效应高估或未受影响的的，但对基体效应的数据归一化似乎对总体结果4,5-影响有限。本文所描述的方法是成熟期间积聚在高水平葡萄浆果中等极性代谢物的分析优化，这是由风土显著影响。它们包括花青素，黄酮醇，黄烷-3-醇，花青素，类黄酮等，白藜芦醇，二苯乙烯，羟基酸和羟基苯甲酸，它们共同决定了颜色，口味和葡萄酒的健康相关的属性。其它代谢物，如糖和脂族有机酸，是由于基质短跑运动员忽略，因为定量通过HPLC-MS是不可靠T和离子抑制现象5。内通过该方法选择的极性范围，该方法是在它的目的是检测许多不同的代谢物尽可能6未定位。 转录方法，使成千上万的小道消息成绩单可以同时监控由完整的葡萄基因组序列7,8的可用性提供便利。基于高通量测序的cDNA转录早期方法已经发展下一代测序的到来进入统称为RNA测序技术的过程的集合。这种方法正迅速成为首选转录研究方法。然而，基于芯片，这也让成千上万的成绩单并行通过杂交来量化大量文献，积累了葡萄。事实上，在此之前RNA测序成为一种主流技术，很多专门的商用微阵列平台已经开发允许小道消息转录，并详细检查。在广阔的各种平台，只有两个允许的全基因组转录组分析9。最进化阵列允许在单个设备上多达12个独立样品的杂交，从而减少每个实验的成本。 12个子阵列的每个组成代表29549小道消息成绩单135000 60-mer的探针。该装置已在大量的研究10-24被使用。现在这两个平台已经停产，但一个新的定制芯片最近已经设计并表示最近的发展，因为它包含代表附加新发现的基因葡萄树25探头的更大的数字。 通过转录组和代谢组学分析产生的大数据集销售需要对数据进行分析合适的统计方法，包括多元技术来确定不同形式之间的相关性s的数据的。最广泛使用的多元技术是那些基于投影，并且这些可以是无监督，如主成分分析（PCA），或监督，如双向正交投影到潜结构判别分析（O2PLS-DA）26。在这篇文章中介绍的协议采用PCA进行探索性数据分析和O2PLS-DA来确定样本的群体之间的差异。

<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

风土指的是，根据特定的生境和管理实践影响作物的特性，如葡萄（ 葡萄 ）的环境因素的结合。本文将介绍如何某些风土签名可以使用多元统计分析小道消息科维纳品种的浆果代谢和转录组进行检测。该方法首先需要一个适当的采样计划。在这个案例研究中，科维纳品种的特定克隆选择遗传差异最小化，并从代表在三个不同的生长季节三种不同的宏观区域7葡萄园收集样品。一个不相关的LC-MS代谢物组学的方法是，由于其高灵敏度，建议使用MZmine软件和基于碎片树分析的代谢物的识别策略伴有有效的数据处理。可以使用微阵列可以实现全面的转录组分析含探针涵盖〜所有预测葡萄基因的99％，允许所有的差异表达的基因在不同风土的上下文中同时分析。最后，基于投影的方法多元数据分析可以被用来克服强特异性复古效果，允许代谢和转录数据被集成并详细分析以识别信息相关性。

本文中介绍的案例研究产生了最终的数据矩阵包括552信号（M / Z功能），包括分子离子以及它们的同位素，加合物和一些片断，其中189样本相对量化（7葡萄园×3成熟期×3生长季点¯x 3生物学重复）。因此104328用于数据点的总数为。碎片树分析导致282 M / Z功能注释，相应的代谢产物加合物，同位素和片段。由主成分分析整个数据矩阵的探索性分析表明根据熟化?...

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

本文描述了为了深入了解风土概念， 即非靶向代谢组学，转录和多元统计分析，以葡萄果实成绩单和代谢产物的应用中，环境对果实品质性状的影响。

风土通过概念葡萄果实代谢组学和转录组解释的

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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