SE148:/S1/M4/D2

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
Comment

doi: 10.1007/s11306-014-0702-6

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.
Comment_of_details

Data Analysis Information

ID	D2
Title	Data analysis and statistics
Data Analysis Details ID	DS5
Recommended decimal places of m/z
Comment

Data Analysis Details Information

ID	DS5
Title	Data analysis and statistics
Description	Statistical data analysis for metabolite profile data The multi-platform data was summarized by unifying metabolite identifiers to a common referencing scheme using the MetMask tool (Redestig H, Kusano M, Fukushima A, Matsuda F, Saito K, Arita M: Consolidating metabolite identifiers to enable contextual and multi-platform metabolomics data analysis. BMC bioinformatics 2010, 11:214). The four matrices were then concatenated and correlated peaks with the same annotation were replaced by their first principal component. All data was log2 or log10 transformed prior to further data analysis. Principal component analysis (PCA) was performed on unit-variance scaled metabolite matrixes (observations, 81 samples; variables, 681 or 701 peaks) with log10 transformation using the pcaMethods package (Ref: Stackles) or SIMCA-P+ 13.0 software (Umetrics AB, Umeå, Sweden).
Comment_of_details