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

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 ...

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.

the-terroir-concept-interpreted-through-grape-berry-metabolomics

Research

JoVE Journal

Genetics

In Vitro Transcription Assays and Their Application in Drug Discovery

In vitro transcription assays have been developed and widely used for many years to study the molecular mechanisms involved in transcription. This process requires multi-subunit DNA-dependent RNA polymerase (RNAP) and a series of transcription factors that act to modulate the activity of RNAP during gene expression. Sequencing gel electrophoresis of radiolabeled transcripts is used to provide detailed mechanistic information on how transcription proceeds and what parameters can affect it. In this paper we describe the protocol to study how the essential elongation factor NusA regulates transcriptional pausing, as well as a method to identify an antibacterial agent targeting transcription initiation through inhibition of RNAP holoenzyme formation. These methods can be used a as platform for the development of additional approaches to explore the mechanism of action of the transcription factors which still remain unclear, as well as new antibacterial agents targeting transcription which is an underutilized drug target in antibiotic research and development.

In Vitro Transcription Assays and Their Application in Drug Discovery

In this manuscript, we describe a protocol to functionally examine transcription and the inhibitory activity of antibacterial agents targeting bacterial transcription.

In vitro transcription assays have been developed and widely used for many years to study the molecular mechanisms involved in transcription. This process ...

Delivery of Nucleic Acids through Embryo Microinjection in the Worldwide Agricultural Pest Insect, Ceratitis capitata

The Mediterranean fruit fly (medfly) Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) is a pest species with extremely high agricultural relevance. This is due to its reproductive behavior: females damage the external surface of fruits and vegetables when they lay eggs and the hatched larvae feed on their pulp. Wild C. capitata populations are traditionally controlled through insecticide spraying and/or eco-friendly approaches, the most successful being the Sterile Insect Technique (SIT). The SIT relies on mass-rearing, radiation-based sterilization and field release of males that retain their capacity to mate but are not able to generate fertile progeny. The advent and the subsequent rapid development of biotechnological tools, together with the availability of the medfly genome sequence, has greatly boosted our understanding of the biology of this species. This favored the proliferation of new strategies for genome manipulation, which can be applied to population control.
In this context, embryo microinjection plays a dual role in expanding the toolbox for medfly control. The ability to interfere with the function of genes that regulate key biological processes, indeed, expands our understanding of the molecular machinery underlying medfly invasiveness. Furthermore, the ability to achieve germ-line transformation facilitates the production of multiple transgenic strains that can be tested for future field applications in novel SIT settings. Indeed, genetic manipulation can be used to confer desirable traits that can, for example, be used to monitor sterile male performance in the field, or that can result in early life-stage lethality. Here we describe a method to microinject nucleic acids into medfly embryos to achieve these two main goals.

Delivery of Nucleic Acids through Embryo Microinjection in the Worldwide Agricultural Pest Insect, Ceratitis capitata

The Mediterranean fruit fly (medfly) Ceratitis capitata (Diptera: Tephritidae) is a worldwide pest of agriculture. A deeper understanding of its biology is key to control medfly populations and thus reduce economic impact. Embryo microinjection is a fundamental tool allowing both germ-line transformation and reverse genetics studies in this species.

The Mediterranean fruit fly (medfly) Ceratitis capitata (Wiedemann) (Diptera: Tephritidae) is a pest species with extremely high agricultural relevance. ...

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved (Non-model) Organisms

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative transcriptomics. It also serves as a primer for biologists to develop data scientist computational skills, e.g. executing bash commands, visualization and management of large data sets. All command line code and further explanations of each command or step can be found on the wiki (<a href="https://wiki.cyverse.org/wiki/x/dgGtAQ">https://wiki.cyverse.org/wiki/x/dgGtAQ</a>). The Discovery Environment and Atmosphere platforms are connected together through the CyVerse Data Store. As such, once the initial raw sequencing data has been uploaded there is no more need to transfer large data files over an Internet connection, minimizing the amount of time needed to conduct analyses. This protocol is designed to analyze only two experimental treatments or conditions. Differential gene expression analysis is conducted through pairwise comparisons, and will not be suitable to test multiple factors. This workflow is also designed to be manual rather than automated. Each step must be executed and investigated by the user, yielding a better understanding of data and analytical outputs, and therefore better results for the user. Once complete, this protocol will yield de novo assembled transcriptome(s) for underserved (non-model) organisms without the need to map to previously assembled reference genomes (which are usually not available in underserved organism). These de novo transcriptomes are further used in pairwise differential gene expression analysis to investigate genes differing between two experimental conditions. Differentially expressed genes are then functionally annotated to understand the genetic response organisms have to experimental conditions. In total, the data derived from this protocol is used to test hypotheses about biological responses of underserved organisms.

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

This protocol outlines a comparative de novo transcriptome assembly and annotation workflow for novice bioinformaticians. The workflow is available for free entirely through CyVerse and connected by the Data Store. Command line and graphical user interfaces are used, but all code needed is available to copy and paste.

This workflow allows novice researchers to leverage advanced computational resources such as cloud computing to carry out pairwise comparative ...

Controllable Ion Channel Expression through Inducible Transient Transfection

Transfection, the delivery of foreign nucleic acids into a cell, is a powerful tool in protein research. Through this method, ion channels can be investigated through electrophysiological analysis, biochemical characterization, mutational studies, and their effects on cellular processes. Transient transfections offer a simple protocol in which the protein becomes available for analysis within a few hours to days. Although this method presents a relatively straightforward and time efficient protocol, one of the critical components is calibrating the expression of the gene of interest to physiological relevant levels or levels that are suitable for analysis. To this end, many different approaches that offer the ability to control the expression of the gene of interest have emerged. Several stable cell transfection protocols provide a way to permanently introduce a gene of interest into the cellular genome under the regulation of a tetracycline-controlled transcriptional activation. While this technique produces reliable expression levels, each gene of interest requires a few weeks of skilled work including calibration of a killing curve, selection of cell colonies, and overall more resources. Here we present a protocol that uses transient transfection of the Transient Receptor Potential cation channel subfamily V member 1 (TRPV1) gene in an inducible system as an efficient way to express a protein in a controlled manner which is essential in ion channel analysis. We demonstrate that using this technique, we are able to perform calcium imaging, whole cell, and single channel analysis with controlled channel levels required for each type of data collection with a single transfection. Overall, this provides a replicable technique that can be used to study ion channels structure and function.

Studying ion channels through a heterologously expressing system has become a core technique in biomedical research. In this manuscript, we present a time efficient method to achieve tightly controlled ion channel expression by performing transient transfection under the control of an inducible promoter.

Transfection, the delivery of foreign nucleic acids into a cell, is a powerful tool in protein research. Through this method, ion channels can be ...

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged as a powerful tool for gene-expression analysis and transcriptome mapping. However, handling RNA-Seq datasets requires sophisticated computational expertise and poses inherent challenges for biology researchers. This bottleneck has been mitigated by the open access Galaxy project that allows users without bioinformatics skills to analyze RNA-Seq data, and the Database for Annotation, Visualization, and Integrated Discovery (DAVID), a Gene Ontology (GO) term analysis suite that helps derive biological meaning from large data sets. However, for first-time users and bioinformatics' amateurs, self-learning and familiarization with these platforms can be time-consuming and daunting. We describe a straightforward workflow that will help C. elegans researchers to isolate worm RNA, conduct an RNA-Seq experiment and analyze the data using Galaxy and DAVID platforms. This protocol provides stepwise instructions for using the various Galaxy modules for accessing raw NGS data, quality-control checks, alignment, and differential gene expression analysis, guiding the user with parameters at every step to generate a gene list that can be screened for enrichment of gene classes or biological processes using DAVID. Overall, we anticipate that this article will provide information to C. elegans researchers undertaking RNA-Seq experiments for the first time as well as frequent users running a small number of samples.

Transcriptomic Analysis of C. elegans RNA Sequencing Data Through the Tuxedo Suite on the Galaxy Project

Galaxy and DAVID have emerged as popular tools that allow investigators without bioinformatics training to analyze and interpret RNA-Seq data. We describe a protocol for C. elegans researchers to perform RNA-Seq experiments, access and process the dataset using Galaxy and obtain meaningful biological information from the gene lists using DAVID.

Next generation sequencing (NGS) technologies have revolutionized the nature of biological investigation. Of these, RNA Sequencing (RNA-Seq) has emerged ...

Robust DNA Isolation and High-throughput Sequencing Library Construction for Herbarium Specimens

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with a number of challenges including sample preservation quality, degraded DNA, and destructive sampling of rare specimens. In order to more effectively use herbarium material in large sequencing projects, a dependable and scalable method of DNA isolation and library preparation is needed. This paper demonstrates a robust, beginning-to-end protocol for DNA isolation and high-throughput library construction from herbarium specimens that does not require modification for individual samples. This protocol is tailored for low quality dried plant material and takes advantage of existing methods by optimizing tissue grinding, modifying library size selection, and introducing an optional reamplification step for low yield libraries. Reamplification of low yield DNA libraries can rescue samples derived from irreplaceable and potentially valuable herbarium specimens, negating the need for additional destructive sampling and without introducing discernible sequencing bias for common phylogenetic applications. The protocol has been tested on hundreds of grass species, but is expected to be adaptable for use in other plant lineages after verification. This protocol can be limited by extremely degraded DNA, where fragments do not exist in the desired size range, and by secondary metabolites present in some plant material that inhibit clean DNA isolation. Overall, this protocol introduces a fast and comprehensive method that allows for DNA isolation and library preparation of 24 samples in less than 13 h, with only 8 h of active hands-on time with minimal modifications.

This article demonstrates a detailed protocol for DNA isolation and high-throughput sequencing library construction from herbarium material including rescue of exceptionally poor-quality DNA.

Herbaria are an invaluable source of plant material that can be used in a variety of biological studies. The use of herbarium specimens is associated with ...

Isolating, Sequencing and Analyzing Extracellular MicroRNAs from Human Mesenchymal Stem Cells

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, milk and urine. One subset of these RNAs are the posttranscriptional regulators &#8211; microRNAs (miRNAs). To delineate the miRNAs produced by specific cell types, in vitro culture systems can be used to harvest and profile exRNAs derived from one subset of cells. The secreted factors of mesenchymal stem cells are implicated in alleviating numerous diseases and is used as the in vitro model system here. This paper describes the process of collection, purification of small RNA and library generation to sequence extracellular miRNAs. ExRNAs from culture media differ from cellular RNA by being low RNA input samples, which calls for optimized procedures. This protocol provides a comprehensive guide to small exRNA sequencing from culture media, showing quality control checkpoints at each step during exRNA purification and sequencing.

This protocol demonstrates how to purify extracellular microRNAs from cell culture media for small RNA library construction and next generation sequencing. Various quality control checkpoints are described to allow readers to understand what to expect when working with low input samples like exRNAs.

Extracellular and circulating RNAs (exRNA) are produced by many cell types of the body and exist in numerous bodily fluids such as saliva, plasma, serum, ...

Using In Vitro and In-cell SHAPE to Investigate Small Molecule Induced Pre-mRNA Structural Changes

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences is desired. We herein provide a detailed in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) protocol to study the RNA structural change in the presence of an experimental drug for spinal muscular atrophy (SMA), survival of motor neuron (SMN)-C2, and in exon 7 of the pre-mRNA of the SMN2 gene. In in vitro SHAPE, an RNA sequence of 140 nucleotides containing SMN2 exon 7 is transcribed by T7 RNA polymerase, folded in the presence of SMN-C2, and subsequently modified by a mild 2&#39;-OH acylation reagent, 2-methylnicotinic acid imidazolide (NAI). This 2&#39;-OH-NAI adduct is further probed by a 32P-labeled primer extension and resolved by polyacrylamide gel electrophoresis (PAGE). Conversely, 2&#39;-OH acylation in in-cell SHAPE takes place in situ with SMN-C2 bound cellular RNA in living cells. The pre-mRNA sequence of exon 7 in the SMN2 gene, along with SHAPE-induced mutations in the primer extension, was then amplified by PCR and subject to next-generation sequencing. Comparing the two methodologies, in vitro SHAPE is a more cost-effective method and does not require computational power to visualize results. However, the in vitro SHAPE-derived RNA model sometimes deviates from the secondary structure in a cellular context, likely due to the loss of all interactions with RNA-binding proteins. In-cell SHAPE does not need a radioactive material workplace and yields a more accurate RNA secondary structure in the cellular context. Furthermore, in-cell SHAPE is usually applicable for a larger range of RNA sequences (~1,000 nucleotides) by utilizing next-generation sequencing, compared to in vitro SHAPE (~200 nucleotides) that usually relies on PAGE analysis. In case of exon 7 in SMN2 pre-mRNA, the in vitro and in-cell SHAPE derived RNA models are similar to each other.

Detailed protocols for both in vitro and in-cell selective 2&#39;-hydroxyl acylation analyzed by primer extension (SHAPE) experiments to determine the secondary structure of pre-mRNA sequences of interest in the presence of an RNA-targeting small molecule are presented in this article.

In the process of drug development of RNA-targeting small molecules, elucidating the structural changes upon their interactions with target RNA sequences ...

Inducible, Cell Type-Specific Expression in Arabidopsis thaliana Through LhGR-Mediated Trans-Activation

Inducible, tissue-specific expression is an important and powerful tool to study the spatio-temporal dynamics of genetic perturbation. Combining the flexible and efficient GreenGate cloning system with the proven and benchmarked LhGR system (here termed GR-LhG4) for the inducible expression, we have generated a set of transgenic Arabidopsis lines that can drive the expression of an effector cassette in a range of specific cell types in the three main plant meristems. To this end, we chose the previously developed GR-LhG4 system based on a chimeric transcription factor and a cognate pOp-type promoter ensuring tight control over a wide range of expression levels. In addition, to visualize the expression domain where the synthetic transcription factor is active, an ER-localized mTurquoise2 fluorescent reporter under control of the pOp4 or pOp6 promoter is encoded in driver lines. Here, we describe the steps necessary to generate a driver or effector line and demonstrate how cell type specific expression can be induced and followed in the shoot apical meristem, the root apical meristem and the cambium of Arabidopsis. By using several or all driver lines, the context specific effect of expressing one or multiple factors (effectors) under control of the synthetic pOp promoter can be assessed rapidly, for example in F1 plants of a cross between one effector and multiple driver lines. This approach is exemplified by the ectopic expression of VND7, a NAC transcription factor capable of inducing ectopic secondary cell wall deposition in a cell autonomous manner.

Inducible, Cell Type-Specific Expression in Arabidopsis thaliana Through LhGR-Mediated Trans-Activation

Here, we describe the generation and application of a set of transgenic Arabidopsis thaliana lines enabling inducible, tissue-specific expression in the three main meristems, the shoot apical meristem, the root apical meristem, and the cambium.

Inducible, tissue-specific expression is an important and powerful tool to study the spatio-temporal dynamics of genetic perturbation. Combining the ...

Improving Small RNA-seq: Less Bias and Better Detection of 2'-O-Methyl RNAs

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as plant microRNAs (miRNAs) carry a 2&#39;-O-methyl (2&#39;-OMe) modification at their 3&#39;&#160;terminal nucleotide. This modification adds another difficulty as it inhibits 3&#39;&#160;adapter ligation. We previously demonstrated that modified versions of the &#39;TruSeq (TS)&#39;&#160;protocol have less bias and an improved detection of 2&#39;-OMe RNAs. Here we describe in detail protocol &#39;TS5&#39;, which showed the best overall performance. TS5 can be followed either using homemade reagents or reagents from the TS kit, with equal performance.

Improving Small RNA-seq: Less Bias and Better Detection of 2&#039;-O-Methyl RNAs

We present a detailed small RNA library reparation protocol with less bias than standard methods and an increased sensitivity for 2&#39;-O-methyl RNAs. This protocol can be followed using homemade reagents to save cost or using kits for convenience.

The study of small RNAs (sRNAs) by next-generation sequencing (NGS) is challenged by bias issues during library preparation. Several types of sRNA such as ...