SE148:/S1/M4/D1

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Sample Set Information

ID TSE1321
Title Assessing metabolomic and chemical diversity of a soybean lineage representing 35 years of breeding
Description Information on crop genotype- and phenotype-metabolite associations can be of value to trait development as well as to food security and safety. The unique study presented here assessed seed metabolomic and ionomic diversity in a soybean lineage representing ~35 years of breeding (launch years 1972–2008) and increasing yield potential. Selected varieties included six conventional and three genetically modified (GM) glyphosate-tolerant lines. A metabolomics approach utilizing capillary electrophoresis (CE)-time-of-flight-mass spectrometry (TOF-MS), gas chromatography (GC)-TOF-MS and liquid chromatography (LC)-quadrupole (q)-TOFMS resulted in measurement of a total of 732 annotated peaks. Ionomics through inductively-coupled plasma (ICP)-MS profiled twenty mineral elements. Orthogonal partial least squares-discriminant analysis (OPLS-DA) of the seed data successfully differentiated newer higher-yielding soybean from earlier lower-yielding accessions at both field sites. This result reflected genetic fingerprinting data that demonstrated a similar distinction between the newer and older soybean. Correlation analysis also revealed associations between yield data and specific metabolites. There were no clear metabolic differences between the conventional and GM lines. Overall, observations of metabolic and genetic differences between older and newer soybean varieties provided novel and significant information on the impact of varietal development on biochemical variability. Proposed applications of omics in food and feed safety assessments will need to consider that GM is not a major source of metabolite variability and that trait development in crops will, of necessity, be associated with biochemical variation.
Authors Miyako Kusano, Ivan Baxter, Atsushi Fukushima, Akira Oikawa, Yozo Okazaki, Ryo Nakabayashi, Denise J. Bouvrette, Frederic Achard, Andrew R. Jakubowski, Joan M. Ballam, Jonathan R. Phillips, Angela H. Culler, Kazuki Saito, George G. Harrigan
Reference Metabolomics April 2015, Volume 11, Issue 2, pp 261–270
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Sample Information

ID S1
Title Soybean (Glycine max L.)
Organism - Scientific Name Glycine max
Organism - ID NCBI taxonomy: 3847
Compound - ID
Compound - Source
Preparation BioSource Species

Soybean Glycine max (9 varieties)

Genotypes/Varieties
Williams, A3127, A3469, A3555, A3733/CX329 (CX375), AG3701, AG3803, CX366, and AG3705

Organ specification
Mature seeds

Growth conditions
Nine soybean varieties representing a genetic lineage from Williams (1972) to A3555 (2008) were grown at two sites in Illinois (Jerseyville [ILJA] and Jacksonville [ILJA]) during the 2011 season. Varieties included six conventional and three glyphosate-tolerant lines. Starting seeds were planted in a randomized complete block design with six replicates. Soybean plants were treated with maintenance pesticides as necessary throughout the growing season at both sites. The three Roundup Ready varieties were not treated with glyphosate.

Experimental conditions
Same as the growth conditions. Soybean seeds of 5-6 biological replications were harvested at maturity on 2011. Seeds for each replicate was homogenized by grinding with dry ice to a fine powder, lyophilized and stored frozen at approximately -20°C prior to analysis. We weighed 70 mg dry weight (DW) for CE-TOF-MS analysis, 5 mg DW for GC-TOF-MS analysis, 50 mg DW for LC-q-TOF-MS analysis to detect polar metabolites, and 15 mg DW for lipid profiling.

Sample Preparation Details ID
Comment


Table 1
Launch year and average yield of each variety

Variety Launch year Yield at ILJA Yield at ILJE
Williams 1972 66.5 65.3
A3127 1979 68.2 61.0
CX366 1986 71.9 66.6
CX375(A3733/CX329) 1996 71.8 66.4
A3469 1997 80.3 73.5
AG3701 1999 72.8 71.1
AG3705 2006 80.6 77.2
A3555 2008 85.9 74.2
AG3803 2008 78.8 76.4


Bushels/acre. ILJA represents the Jacksonville, Illinois site and ILJE represents the Jerseyville, Illinois site

Analytical Method Information

ID M4
Title GC-TOF-MS
Method Details ID MS4
Sample Amount 1 μl of each sample (equivalent to 1.4 µg DW)
Comment

Analytical Method Details Information

ID MS4
Title GC-TOF-MS
Instrument GC:Agilent 6890N gas chromatograph (Agilent Technologies, Wilmingston, USA)
MS:Pegasus IV TOF mass spectrometer (LECO, St. Joseph, MI, USA)
Instrument Type
Ionization EI
Ion Mode Positive
Description BioSource amount

We weighed 70 mg dry weight (DW) of the lyophilized samples for CE-TOF-MS analysis, 5 mg DW for GC-TOF-MS analysis, 50 mg DW for LC-q-TOF-MS analysis to detect polar metabolites, and 15 mg DW for lipid profiling.

Extraction and derivatization for GC-TOF-MS
Each sample with a 5-mm zirconia bead was extracted with a concentration of 100 mg DW of powder per ml extraction medium (methanol/chloroform/water [3:1:1 v/v/v]) containing 10 stable isotope reference compounds at 4°C in a mixer mill (MM301; Retsch, Haan, Germany) at a frequency of 15 Hz. Each isotope compound was adjusted to a final concentration of 15 ng per 1-μl injection volume. After 5-min centrifugation at 15,100 x g, a 200-μl aliquot of the supernatant was transferred to a glass insert vial. The extracts were evaporated to dryness in an SPD2010 SpeedVac® concentrator (Thermo Fisher, Scientific, Waltham, MA, USA). We used extracts from 1-mg DW samples for derivatization, i.e., methoxymation and silylation. For methoxymation, 30 μl of methoxyamine hydrochloride (20 mg/ml in pyridine) were added to the sample. After 17 h of derivatization at room temperature the sample was trimethylsilylated for 1 h using 30 µl of MSTFA at 37°C with shaking. All derivatization steps were performed in a vacuum glove box VSC-100 (Sanplatec, Osaka, Japan) filled with 99.9995% (G3 grade) dry nitrogen.

GC-TOF-MS conditions
Using the splitless mode of a CTC CombiPAL autosampler (CTC Analytics, Zwingen, Switzerland), 1 μl of each sample (equivalent to 1.4 µg DW) was injected into an Agilent 6890N gas chromatograph (Agilent Technologies, Wilmingston, DE, USA) featuring a 30 m × 0.25 mm inner diameter fused-silica capillary column and a chemically bound 0.25-μl film Rxi-5 Sil MS stationary phase (RESTEK, Bellefonte, PA, USA) with a tandem connection to a fused silica tube (1 m, 0.15 mm). An MS column change interface (ms NoVent-J; SGE, Yokohama, Japan) was used to prevent air and water from entering the MS during column change-over. Helium was the carrier gas at a constant flow rate of 1 ml min-1. The temperature program for GC-MS analysis started with a 2-min isothermal step at 80°C followed by 30°C temperature-ramping to a final temperature of 320°C that 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 sec. Data acquisition was on a Pegasus IV TOF mass spectrometer (LECO, St. Joseph, MI, USA); the acquisition rate was 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) purchased from Sigma-Aldrich (Tokyo, Japan) were used for calculating the retention index (RI) (Schauer N, et al. (2005) GC-MS libraries for the rapid identification of metabolites in complex biological samples. FEBS lett 579(6):1332-1337). For quality control we injected methylstearate into every 6th sample. The sample run order was randomized in single-sequence analyses. We analyzed the standard compound mixtures using the same sequence analysis procedures.

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Data Analysis Information

ID D1
Title Data processing (GC-MS)
Data Analysis Details ID DS4
Recommended decimal places of m/z
Comment


Data Analysis Details Information

ID DS4
Title Data processing (GC-MS)
Description Data processing for GC-TOF-MS data

Nonprocessed MS data from GC-TOF-MS analysis were exported in NetCDF format generated by chromatography processing- and mass spectral deconvolution software (Leco ChromaTOF version 3.22; LECO, St. Joseph, MI, USA) to MATLAB 6.5 or MATLAB2011b (Mathworks, Natick, MA, USA) for the performance of all data-pretreatment procedures, e.g. smoothing, alignment, time-window setting H-MCR, and RDA (Jonsson P, et al. (2006) Predictive metabolite profiling applying hierarchical multivariate curve resolution to GC-MS data--a potential tool for multi-parametric diagnosis. J Proteome Res 5(6):1407-1414.). 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 their RIs, a comparison of the reference mass-spectra with the Golm Metabolome Database (GMD) released from CSB.DB (Kopka J, et al. (2005) GMD@CSB.DB: the Golm Metabolome Database. Bioinformatics 21(8):1635-1638), and our in-house spectral library. The metabolites were identified by comparison with RIs from the library databases (GMD and our own library) and the RIs of authentic standards. The metabolites were defined as annotated metabolites after comparison with the mass spectra and the RIs from these two libraries. The data matrix was normalized using the CCMN algorithm for further analysis (Redestig H, et al. (2009) Compensation for systematic cross-contribution improves normalization of mass spectrometry based metabolomics data. Anal Chem 81(19):7974-7980).

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