SE136:/S1/M1/D1

Sample Set Information

ID	TSE1237
Title	Deciphering starch quality of rice kernels using metabolite profiling and pedigree network analysis.
Description	The physiological properties of rice grains are immediately obvious to consumers. High-coverage metabolomic characterization of the rice diversity research set predicted a negative correlation between fatty acid and lipid levels and amylose/total starch ratio (amylose ratio), but the reason for this is unclear. To obtain new insight into the relationships among the visual phenotypes of rice kernels, starch granule structures, amylose ratios, and metabolite changes, we investigated the metabolite changes of five Japonica cultivars with various amylose ratios and two knockout mutants (e1, a Starch synthase IIIa (SSIIIa)-deficient mutant and the SSIIIa/starch branching enzyme (BE) double-knockout mutant 4019) by using mass spectrometry-based metabolomics techniques. Scanning electron microscopy clearly showed that the two mutants had unusual starch granule structures. The metabolomic compositions of two cultivars with high amylose ratios (Hoshiyutaka and Yumetoiro) exhibited similar patterns, while that of the double-knockout mutant, which has an extremely high amylose ratio, differed. Rice pedigree network analysis of the cultivars and the mutants provided insight into the association between metabolic-trait properties and their underlying genetic basis in rice breeding in Japan. Multidimensional scaling analysis revealed that the Hoshiyutaka and Yumetoiro cultivars were Indica-like, yet they are classified as Japonica subpopulations. Exploring metabolomic traits is a powerful way to follow rice genetic traces and breeding history.
Authors	Kusano M, Fukushima A, Fujita N, Okazaki Y, Kobayashi M, Oitome NF, Ebana K, Saito K.
Reference	Mol Plant. 2012 Mar;5(2):442-51. doi: 10.1093/mp/ssr101. Epub 2011 Dec 15.
Comment

PubMed

Sample Information

ID	S1
Title	Rice plants
Organism - Scientific Name	Oryza sativa L.
Organism - ID	NCBI taxonomy 4530
Compound - ID
Compound - Source
Preparation	The five rice cultivars (Nipponbare, Kinmaze, Soft158, Hoshiyutaka, and Yumetoiro) and two knockout mutants (e1 and 4019) from RDRS were used for this study. Growth and harvesting were performed as previously described (Redestig et al., 2011).
Sample Preparation Details ID
Comment	Redestig et al. BMC Syst Biol. 2011 Oct 28;5:176.

PubMed

Analytical Method Information

ID	M1
Title	GC-TOF-MS
Method Details ID	MS1
Sample Amount	1 μL (ca. 277.8μg each sample)
Comment

Analytical Method Details Information

ID	MS1
Title	GC-TOF-MS
Instrument	GC:Agilent 6890N MS:LECO Pegasus 3 and 4
Instrument Type
Ionization	EI
Ion Mode	Positive
Description	Extraction and derivatization for GC-MS One hundred milligrams of each sample was extracted with extraction buffer [methanol/chloroform/water(3:1:1,v/v/v)] at a concentration of 100 mg/ml containing 10 stable isotope reference compounds as follows: ・[2H4]-succinic acid, ・[13C5,15N]-glutamic acid, ・[2H7]-cholesterol, ・[13C3]-myristic acid, ・[13C5]-proline, ・[13C12]-sucrose, ・[13C4]-hexadecanoic acid, ・[2H4]-1,4-butanediamine, ・[2H6]-2-hydoxybenzoic acid and ・[13C6]-glucose. Each isotope compound was adjusted to a final concentration of 15ng/μl for each 1-μl injection. After centrifugation, a 200-μl aliquot of the supernatant (ca. 25 mg of each sample) was drawn and transferred into a glass insert vial. The extracts were evaporated to dryness in an SPD2010 Speed-Vac® concentrator from ThermoSavant (Thermo electron corporation, Waltham, MA, USA). For methoximation, 30μl of methoxyamine hydrochloride (20mg/ml in pyridine) was added to the sample. After 24 h of derivatization at room temperature, the sample was trimethylsilylated for 1h using 30μl of MSTFA with 1% TMCS at 37°C with shaking. Thirty μl of n-heptane was added　following silylation. All the derivatization steps were performed in the vacuum glove box VSC-100(Sanplatec, Japan) filled with 99.9995% (G3 grade) of dry nitrogen. For methoximation, 30μl of methoxyamine hydrochloride (20 mg/ml in pyridine) was added to the sample. After 24 h of derivatization at room temperature, the sample was trimethylsilylated for 1 h using 30μl of MSTFA with 1% TMCS at 37°C with shaking. Thirty μl of n-heptane was added following silylation. All the derivatization steps were performed in the vacuum glove box VSC-100 (Sanplatec, Japan) filled with 99.9995% (G3 grade) of dry nitrogen. GC-TOF-MS conditions One microliter of extracts (ca. 277.8μg each sample) was injected in the splitless mode by an CTC CombiPAL autosampler (CTC analytics, Zwin-gen, Switzerland) into an Agilent 6890N gas chromatograph (Agilent Technologies, Wilmingston, USA) equipped with a 30 m × 0.25 mm inner diameter fused-silica capillary column with a chemically bound 0.25-μl film Rtx-5 Sil MS stationary phase (RESTEK, Bellefonte, USA) for metabolome analysis. Helium was used as the carrier gas at a constant flow rate of 1 ml/min. The temperature program for metabolome analysis started with a 2-min isothermal step at 80°C and this was followed by temperature ramping at 30°C to a final temperature of 320°C, which was maintained for 3.5 min. The transfer line and the ion source temperatures were 250 and 200°C, respectively. Ions were generated by a 70-eV electron beam at an ionization current of 2.0 mA. The acceleration voltage was turned on after a solvent delay of 222 and 237 s. Data acquisition was performed on a Pegasus III and Pegasus IV TOF mass spectrometers (LECO, St. Joseph, MI, USA) with an acquisition rate of 30 spectra s−1 in the mass range of a mass-to-charge ratio of m/z = 60–800. Alkane standard mixtures (C8–C20 and C21–C40) were purchased from Sigma–Aldrich (Tokyo,Japan) and were used for calculating the retention index (RI) [10, 11]. The normalized response for the calculation of the signal intensity of each metabolite from the mass-detector response was obtained by each selected ion current that was unique in each metabolite MS spectrum to normalize the peak response. For quality control, we injected methylstearate in every 6 samples. Data was normalized using the CCMN algorithm.
Comment_of_details	Redestig et al. BMC Syst Biol. 2011 Oct 28;5:176.

PubMed

Data Analysis Information

ID	D1
Title	Statistical Data Analysis
Data Analysis Details ID	DS1
Recommended decimal places of m/z
Comment

Data Analysis Details Information

ID	DS1
Title	Statistical Data Analysis
Description	Statistical analyses were performed using R v2.12.1 (www.r-project.org/) and Microsoft Office Excel 2007. Differences in the morphological traits of rice seeds and kernels between Nipponbare and each cultivar or mutant were determined using Welch’s t-test (p < 0.05). The fold changes of all cultivars and mutants were calculated by dividing by the mean value of Nipponbare. The differentially accumulated metabolites between a cultivar and Nipponbare and between a mutant and Nipponbare were detected using the LIMMA package (Smyth, 2004), which includes false discovery rate (FDR) correction for multiple testing (Benjamini and Hochberg, 1995). We identified metabolites with significant changes in metabolite levels (the log2-fold change > |1|) and the corresponding FDR-corrected p-values that were <0.05. We used the log2-fold change matrix for HCA and MDS analysis. HCA was performed using Cluster 3.0 (de Hoon et al., 2004), and the results of HCA were visualized using Java TreeView v1.1.6 (http://jtreeview.sourceforge.net/). We applied Euclidean distance as similarity matrices for the metabolites and cultivars or mutants and the average linkage for clustering. Using Euclidean distance as implemented in the ‘cmdscale’ function of the R software, we performed MDS analysis, which tries to demonstrate the underlying structure of empirically acquired data. We also used the log2-fold change values for this analysis.
Comment_of_details