포도 베리 대사 체학 및 Transcriptomics을 통해 해석 떼루아 개념

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.