SE146:/S1/M1/D1

Sample Set Information

ID	TSE1303
Title	Exploring molecular backgrounds of quality traits in rice by predictive models based on high-coverage metabolomics
Description	BACKGROUND: Increasing awareness of limitations to natural resources has set high expectations for plant science to deliver efficient crops with increased yields, improved stress tolerance, and tailored composition. Collections of representative varieties are a valuable resource for compiling broad breeding germplasms that can satisfy these diverse needs. RESULTS: Here we show that the untargeted high-coverage metabolomic characterization of such core collections is a powerful approach for studying the molecular backgrounds of quality traits and for constructing predictive metabolome-trait models. We profiled the metabolic composition of kernels from field-grown plants of the rice diversity research set using 4 complementary analytical platforms. We found that the metabolite profiles were correlated with both the overall population structure and fine-grained genetic diversity. Multivariate regression analysis showed that 10 of the 17 studied quality traits could be predicted from the metabolic composition independently of the population structure. Furthermore, the model of amylose ratio could be validated using external varieties grown in an independent experiment. CONCLUSIONS: Our results demonstrate the utility of metabolomics for linking traits with quantitative molecular data. This opens up new opportunities for trait prediction and construction of tailored germplasms to support modern plant breeding.
Authors	Redestig H, Kusano M, Ebana K, Kobayashi M, Oikawa A, Okazaki Y, Matsuda F, Arita M, Fujita N, Saito K
Reference	BMC Syst Biol. 2011 Oct 28;5:176.
Comment

PubMed

Sample Information

ID	S1
Title	Oryza sativa L.
Organism - Scientific Name	Oryza sativa L.
Organism - ID	NCBI taxonomy:4530
Compound - ID
Compound - Source
Preparation	Genotype The rice diversity research set (RDRS) consists of 68 accessions and Nipponbare and Kasalath as reference cultivars. We furthermore chose to include the salinity resistant cultivar Pokkari. Four additional varieties outside the RDRS (Yumetoiro, Hoshiyutaka, Kinmaze and Soft158) and two amylose hyper accumulating Starch synthase IIIa (SSIIIa) knock-out lines (Tos17 retrotransposon insert): e1, a single knock-out (Nipponbare background) [9] and 4019, double knockout, with Nipponbare/Kinmaze backgroundwere used for the validation experiment. Organ Kernel Organ specification Brown rice
Sample Preparation Details ID	SS1
Comment

Sample Preparation Details Information

ID	SS1
Title	Sample Preparation
Description	Growth condition Twenty-five rice seeds for each of RDRS were sown at a rice field in NIAS, Tsukuba (Lat., 36.030753; Long. 140.099858), Japan in Spring. For the external set of samples, seeds were grown at a rice field in Akita (Lat. 39.803897; Long. 140.046451), Japan. Sampling and sampling date For RDRS, seeds were harvested independently for each variety after 40 days, starting from the day on which the first panicle of rice was observed in 2005 and 2006. For others, seeds were also harvested independently for each variety in 2005 (for Yumetoiro, Hoshiyutaka, Kinmaze, Soft158, el and Nipponbare) and 2008 (for 4019 and Nipponbare).
Comment_of_details

Analytical Method Information

ID	M1
Title	GC-TOF MS
Method Details ID	MS1
Sample Amount	1µl
Comment

Analytical Method Details Information

ID	MS1
Title	Extraction and derivatization for GC-MS
Instrument	GC Agilent 6890N gas chromatograph / MS Pegasus IV TOF mass spectrometer
Instrument Type
Ionization	EI
Ion Mode	Positive
Description	BioSource amount For RDRS and Hoshiyutaka, 100 seeds of each variety 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 for 2 min at 4 °C. Successively, the obtained powder was divided into three to four pools. For external set of samples harvested in Akita, 100 seeds of each biological replicate were selected and crushed in the same way as RDRS. Sample processing and extraction 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 15 ng/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 SpeedVac® concentrator from ThermoSavant (Thermo electron corporation, Waltham, MA, USA). For methoximation, 30 µl 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 30 µl 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, 30 µl of methoxyamine hydrochloride (20 mg ml−1 in pyridine) was added to the sample. After 24 h of derivatization at room temperature, the sample was trimethylsilylated for 1 h using 30 µl 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 x 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−1. 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) . 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

Data Analysis Information

ID	D1
Title	Data processing (GC-MS)
Data Analysis Details ID	DS1
Recommended decimal places of m/z
Comment

Data Analysis Details Information

ID	DS1
Title	Data processing (GC-MS)
Description	Nonprocessed MS data from GC-TOF/MS analysis were exported in NetCDF format generated by chromatography processing and mass spectral deconvolutionsoftware, Leco ChromaTOF version 2.32 and 3.22 (LECO, St. Joseph, MI, USA) to MATLAB 6.5 and 7.0 (Mathworks, Natick, MA, USA), where all data-pretreatment procedures, such as smoothing, alignment, timewindow setting, and H-MCR, were carried out [13]. The resolved MS spectra were matched against reference mass spectra using the NIST mass spectral search program for the NIST/EPA/NIH mass spectral library (version 2.0) and our custom software for peak annotation written in JAVA. Peaks were identified or annotated based on RIs and the reference mass spectra comparison to the Golm Metabolome Database (GMD) released from CSB.DB1 and our in-house spectral library. The metabolites were identified by comparison with RIs from the library databases (GMD and our own library) and with those of authentic standards, and the metabolites were defined as annotated metabolites on comparison with mass spectra and RIs from these two libraries.
Comment_of_details