IC4R006-Metabolomics-2007-17556050 - Revision history

Xysj1990: /* The Background of This Projec */

2016-06-22T02:32:31Z

‎The Background of This Projec

Xysj1990: /* Plant Materials */

2016-06-22T02:31:57Z

‎Plant Materials

Xysj1990: /* The Background of This Projec */

2016-06-22T02:31:39Z

‎The Background of This Projec

Xysj1990 at 02:29, 22 June 2016

2016-06-22T02:29:45Z

Xysj1990: /* The Background of This Projec */

2016-06-22T02:28:29Z

‎The Background of This Projec

Xysj1990: /* Research Findings */

2016-06-22T02:26:11Z

‎Research Findings

Xysj1990: /* Research Findings */

2016-06-22T02:25:30Z

‎Research Findings

Xysj1990: /* Plant Materials */

2016-06-22T02:22:55Z

‎Plant Materials

Xysj1990: /* =Plant Materials */

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‎=Plant Materials

Xysj1990: /* The Background of This Projec */

2016-06-22T02:22:02Z

‎The Background of This Projec

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 * In order to develop a technique for studies in plant metabolomics, we have developed a combined method, namely, 1D- and GC × GC-TOF/MS. We applied this technique in the non-targeted metabolic profiling of brown rice seeds from the world rice core collection (WRC). The WRC is a representative set of Asian cultivated rice, Oryza sativa L., comprising 69 vari- eties selected from 3000 accessions stored in the Genebank of the National Institute of Agrobiological Sciences (NIAS), Japan, classified based on DNA polymorphism [28]. The WRC covers ca. 90% of the DNA polymorphisms detected in the original population and can be used as a convenient set to survey the genetic diversity of rice. Therefore, the metabolic phenotyping of the WRC varieties gives us an insight on the representative metabolite diversity of rice natural variants.
 * '''In this project, the researchers  describes a metabolomic method combining 1D-GC-TOF/MS and GC × GC-TOF/MS analyses and an example of its application to focus on the differences in the characteristics of data obtained from 1D-GC/MS and GC × GC/MS measurements of the WRC samples. This study presents the first report of the initial metabolic phenotyping in brown rice seeds of the WRC.'''<br><br>
-[[File:IC4R006-Metabolomics-2007-17556050-1.png|center|thumb|727px|]]
+[[File:IC4R006-Metabolomics-2007-17556050-1.png|center|thumb|727px|'''Figure 2. The PLS-DA score scatter plot for metabolite peaks obtained from the extracts of rice seeds of Nipponbare (brown dot), Kasalath (pale-green diamond)''']]
 ==Plant Materials==

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 ==Plant Materials==
-* Twenty-five rice seeds for each of the 68 WRC cultivars [28] and var. Dahonggu and Pokkari were sown on April 19, 2005, at a rice field in NIAS, Tsukuba, Japan. Seeds were harvested independently for each cultivar after 40 days, starting from the day on which the first panicle of rice was observed. The seeds were threshed from the panicles manually and then collected by each cultivar, after they were dried at 30 ◦ C for three days. All seeds in the husks were stored at 5 ◦ C under dark conditions until analysis. For each cultivar, 100 seeds were selected according to the average weight and length of seeds. After separating the husks from the seeds, the brown rice seeds obtained were bulked and crushed by using a Retsch mixer mill MM301 at a frequency of 20 Hz −1 for 2 min at 4 ◦ C. Successively, the obtained powder was divided into four pools for metabolic phenotyping. One hundred milligrams of each material was extracted with extraction buffer [methanol/chloroform/water (3:1:1, v/v/v)] at a concentration of 100 mg/ml and containing 10 stable isotope reference compounds. Each isotope compound was adjusted to a final concentration of 15 ng/ul for each 1-ul injection [20,29,30]. After centrifugation, a 200-ul aliquot of the supernatant was drawn and transferred into a glass insert vial. The extracts were evaporated to dryness in an SPD2010 SpeedVac ® concentrator from ThermoSavant.
+* Twenty-five rice seeds for each of the 68 WRC cultivars [28] and var. Dahonggu and Pokkari were sown on April 19, 2005, at a rice field in NIAS, Tsukuba, Japan. Seeds were harvested independently for each cultivar after 40 days, starting from the day on which the first panicle of rice was observed. The seeds were threshed from the panicles manually and then collected by each cultivar, after they were dried at 30 ◦ C for three days. All seeds in the husks were stored at 5 ◦ C under dark conditions until analysis. For each cultivar, 100 seeds were selected according to the average weight and length of seeds. After separating the husks from the seeds, the brown rice seeds obtained were bulked and crushed by using a Retsch mixer mill MM301 at a frequency of 20 Hz −1 for 2 min at 4 ◦ C. Successively, the obtained powder was divided into four pools for metabolic phenotyping. One hundred milligrams of each material was extracted with extraction buffer [methanol/chloroform/water (3:1:1, v/v/v)] at a concentration of 100 mg/ml and containing 10 stable isotope reference compounds. Each isotope compound was adjusted to a final concentration of 15 ng/ul for each 1-ul injection [20,29,30]. After centrifugation, a 200-ul aliquot of the supernatant was drawn and transferred into a glass insert vial. The extracts were evaporated to dryness in an SPD2010 SpeedVac ® concentrator from ThermoSavant.<br><br>
-[[File:IC4R006-Metabolomics-2007-17556050-2.png|center|thumb|927px|'''Figure 3. Modulated chromatogram (above) and the corresponding two-dimensional contour plot (below) for (A) Nipponbare and those of (B) Kasalath''']]
+[[File:IC4R006-Metabolomics-2007-17556050-2.png|center|thumb|727px|'''Figure 3. Modulated chromatogram (above) and the corresponding two-dimensional contour plot (below) for (A) Nipponbare and those of (B) Kasalath''']]
 ==Research Findings==
 * The PLS-DA models of the two controls Nipponbare and Kasalath and three selected WRC varieties were created and analyzed as shown in Fig. 2. For example, the loading of the principal components 1 (Anjana Dhan/Nipponbare separation) and 2 (Anjana Dhan/Kasalath separation) in Fig. 2 (A) was selected according to the value of the first weight vector (w ∗ 1) and second weight vector (w ∗ 2) together with the Fig. 2. The PLS-DA score scatter plot for metabolite peaks obtained from the extracts of rice seeds of Nipponbare (brown dot), Kasalath (pale-green dia- mond), and the following selected varieties: (A) Anjana Dhan (black diamond), number of significant components: 3, R 2 X = 0.75, R 2 Y = 1.00, and Q 2 Y = 0.99. (B) Ryou Suisan Koumai (black box), number of significant components: 3, R 2 X = 0.68, R 2 Y = 0.99, and Q 2 Y = 0.95. (C) Urasan 1 (red box), number of significant components: 3, R 2 X = 0.72, R 2 Y = 0.99, and Q 2 Y = 0.95. 75 95% confidence intervals calculated using jackknifing [36,37] to extract the loadings, which is a distinguishing factor between the controls and the selected varieties. Fifty-six variables were considered as significant in the separation of Anjana Dhan and Nipponbare, and 29 variables were considered as significant in the separation of Anjana Dhan and Kasalath.

@@ Line 6: / Line 6: @@
 * When non-targeted metabolic profiling data are subjected to multivariate statistical analysis such as principal component analysis (PCA) and partial least square-discriminate analysis (PLS-DA) toward the obtained data, a high throughput and high accuracy can be achieved for clustering according to the vectors of numerous metabolites.
 * In order to develop a technique for studies in plant metabolomics, we have developed a combined method, namely, 1D- and GC × GC-TOF/MS. We applied this technique in the non-targeted metabolic profiling of brown rice seeds from the world rice core collection (WRC). The WRC is a representative set of Asian cultivated rice, Oryza sativa L., comprising 69 vari- eties selected from 3000 accessions stored in the Genebank of the National Institute of Agrobiological Sciences (NIAS), Japan, classified based on DNA polymorphism [28]. The WRC covers ca. 90% of the DNA polymorphisms detected in the original population and can be used as a convenient set to survey the genetic diversity of rice. Therefore, the metabolic phenotyping of the WRC varieties gives us an insight on the representative metabolite diversity of rice natural variants.
-* '''In this project, the researchers  describes a metabolomic method combining 1D-GC-TOF/MS and GC × GC-TOF/MS analyses and an example of its application to focus on the differences in the characteristics of data obtained from 1D-GC/MS and GC × GC/MS measurements of the WRC samples. This study presents the first report of the initial metabolic phenotyping in brown rice seeds of the WRC.'''
+* '''In this project, the researchers  describes a metabolomic method combining 1D-GC-TOF/MS and GC × GC-TOF/MS analyses and an example of its application to focus on the differences in the characteristics of data obtained from 1D-GC/MS and GC × GC/MS measurements of the WRC samples. This study presents the first report of the initial metabolic phenotyping in brown rice seeds of the WRC.'''<br><br>
-[[File:IC4R006-Metabolomics-2007-17556050-1.png|center|thumb|927px|]]
+[[File:IC4R006-Metabolomics-2007-17556050-1.png|center|thumb|727px|]]
 ==Plant Materials==

@@ Line 11: / Line 11: @@
 ==Plant Materials==
 * Twenty-five rice seeds for each of the 68 WRC cultivars [28] and var. Dahonggu and Pokkari were sown on April 19, 2005, at a rice field in NIAS, Tsukuba, Japan. Seeds were harvested independently for each cultivar after 40 days, starting from the day on which the first panicle of rice was observed. The seeds were threshed from the panicles manually and then collected by each cultivar, after they were dried at 30 ◦ C for three days. All seeds in the husks were stored at 5 ◦ C under dark conditions until analysis. For each cultivar, 100 seeds were selected according to the average weight and length of seeds. After separating the husks from the seeds, the brown rice seeds obtained were bulked and crushed by using a Retsch mixer mill MM301 at a frequency of 20 Hz −1 for 2 min at 4 ◦ C. Successively, the obtained powder was divided into four pools for metabolic phenotyping. One hundred milligrams of each material was extracted with extraction buffer [methanol/chloroform/water (3:1:1, v/v/v)] at a concentration of 100 mg/ml and containing 10 stable isotope reference compounds. Each isotope compound was adjusted to a final concentration of 15 ng/ul for each 1-ul injection [20,29,30]. After centrifugation, a 200-ul aliquot of the supernatant was drawn and transferred into a glass insert vial. The extracts were evaporated to dryness in an SPD2010 SpeedVac ® concentrator from ThermoSavant.
 ==Research Findings==
 * The PLS-DA models of the two controls Nipponbare and Kasalath and three selected WRC varieties were created and analyzed as shown in Fig. 2. For example, the loading of the principal components 1 (Anjana Dhan/Nipponbare separation) and 2 (Anjana Dhan/Kasalath separation) in Fig. 2 (A) was selected according to the value of the first weight vector (w ∗ 1) and second weight vector (w ∗ 2) together with the Fig. 2. The PLS-DA score scatter plot for metabolite peaks obtained from the extracts of rice seeds of Nipponbare (brown dot), Kasalath (pale-green dia- mond), and the following selected varieties: (A) Anjana Dhan (black diamond), number of significant components: 3, R 2 X = 0.75, R 2 Y = 1.00, and Q 2 Y = 0.99. (B) Ryou Suisan Koumai (black box), number of significant components: 3, R 2 X = 0.68, R 2 Y = 0.99, and Q 2 Y = 0.95. (C) Urasan 1 (red box), number of significant components: 3, R 2 X = 0.72, R 2 Y = 0.99, and Q 2 Y = 0.95. 75 95% confidence intervals calculated using jackknifing [36,37] to extract the loadings, which is a distinguishing factor between the controls and the selected varieties. Fifty-six variables were considered as significant in the separation of Anjana Dhan and Nipponbare, and 29 variables were considered as significant in the separation of Anjana Dhan and Kasalath.

@@ Line 7: / Line 7: @@
 * In order to develop a technique for studies in plant metabolomics, we have developed a combined method, namely, 1D- and GC × GC-TOF/MS. We applied this technique in the non-targeted metabolic profiling of brown rice seeds from the world rice core collection (WRC). The WRC is a representative set of Asian cultivated rice, Oryza sativa L., comprising 69 vari- eties selected from 3000 accessions stored in the Genebank of the National Institute of Agrobiological Sciences (NIAS), Japan, classified based on DNA polymorphism [28]. The WRC covers ca. 90% of the DNA polymorphisms detected in the original population and can be used as a convenient set to survey the genetic diversity of rice. Therefore, the metabolic phenotyping of the WRC varieties gives us an insight on the representative metabolite diversity of rice natural variants.
 * '''In this project, the researchers  describes a metabolomic method combining 1D-GC-TOF/MS and GC × GC-TOF/MS analyses and an example of its application to focus on the differences in the characteristics of data obtained from 1D-GC/MS and GC × GC/MS measurements of the WRC samples. This study presents the first report of the initial metabolic phenotyping in brown rice seeds of the WRC.'''
 ==Plant Materials==
 * Twenty-five rice seeds for each of the 68 WRC cultivars [28] and var. Dahonggu and Pokkari were sown on April 19, 2005, at a rice field in NIAS, Tsukuba, Japan. Seeds were harvested independently for each cultivar after 40 days, starting from the day on which the first panicle of rice was observed. The seeds were threshed from the panicles manually and then collected by each cultivar, after they were dried at 30 ◦ C for three days. All seeds in the husks were stored at 5 ◦ C under dark conditions until analysis. For each cultivar, 100 seeds were selected according to the average weight and length of seeds. After separating the husks from the seeds, the brown rice seeds obtained were bulked and crushed by using a Retsch mixer mill MM301 at a frequency of 20 Hz −1 for 2 min at 4 ◦ C. Successively, the obtained powder was divided into four pools for metabolic phenotyping. One hundred milligrams of each material was extracted with extraction buffer [methanol/chloroform/water (3:1:1, v/v/v)] at a concentration of 100 mg/ml and containing 10 stable isotope reference compounds. Each isotope compound was adjusted to a final concentration of 15 ng/ul for each 1-ul injection [20,29,30]. After centrifugation, a 200-ul aliquot of the supernatant was drawn and transferred into a glass insert vial. The extracts were evaporated to dryness in an SPD2010 SpeedVac ® concentrator from ThermoSavant.

@@ Line 12: / Line 12: @@
 * The PLS-DA models of the two controls Nipponbare and Kasalath and three selected WRC varieties were created and analyzed as shown in Fig. 2. For example, the loading of the principal components 1 (Anjana Dhan/Nipponbare separation) and 2 (Anjana Dhan/Kasalath separation) in Fig. 2 (A) was selected according to the value of the first weight vector (w ∗ 1) and second weight vector (w ∗ 2) together with the Fig. 2. The PLS-DA score scatter plot for metabolite peaks obtained from the extracts of rice seeds of Nipponbare (brown dot), Kasalath (pale-green dia- mond), and the following selected varieties: (A) Anjana Dhan (black diamond), number of significant components: 3, R 2 X = 0.75, R 2 Y = 1.00, and Q 2 Y = 0.99. (B) Ryou Suisan Koumai (black box), number of significant components: 3, R 2 X = 0.68, R 2 Y = 0.99, and Q 2 Y = 0.95. (C) Urasan 1 (red box), number of significant components: 3, R 2 X = 0.72, R 2 Y = 0.99, and Q 2 Y = 0.95. 75 95% confidence intervals calculated using jackknifing [36,37] to extract the loadings, which is a distinguishing factor between the controls and the selected varieties. Fifty-six variables were considered as significant in the separation of Anjana Dhan and Nipponbare, and 29 variables were considered as significant in the separation of Anjana Dhan and Kasalath.
 * Finally, based on the interpretation of the results of the multivariate analysis for each model as shown in the Fig. 2A–C, 10 metabolite peaks (GABA, glycerol-3-phosphate, myristate, fructose, IAA, inositol-1-phosphate, trehalose, alpha-tocopherol, cholesterol, and raffinose) were selected as representatives of metabolites, which contributed to significant differences between the controls and the three varieties in the PLS-DA models. The two con- trols and the three selected cultivars were further analyzed using the GC × GC-TOF/MS analysis for high-resolution metabolic profiling.
 ==Labs working on this Project==