SE148:/S1/M2/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	M2
Title	LC-TOF-MS (Lipid profiling)
Method Details ID	MS2
Sample Amount	1 μL
Comment

Analytical Method Details Information

ID	MS2
Title	LC-TOF-MS (Lipid profiling)
Instrument	LC, Waters Acquity UPLC system; MS, Waters Xevo G2 Q-Tof
Instrument Type	UPLC-QTOF-MS
Ionization	ESI
Ion Mode	positive and negative
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 for LC-q-TOF-MS to detect lipids Each sample (15 mg DW) was extracted with 80 volume of methyl tert-butyl ether /methanol (3:1, v/v) containing 20 μM of 1,2-dioctanoyl-sn-glycero-3-phosphocholine (SIGMA. After adding the extraction solvent, samples were vigorously mixed using a vortex mixture. To each sample, 25 volume of water was added, and then vigorously mixed for 5 min at room temperature. After standing for 15 min on ice, the samples were centrifuged at 1,000 × g at 5°C for 5 min. The supernatant (50μl) was transferred to a 2 ml tube. Each extract was evaporated to dryness by SPD2010 SpeedVac® concentrator (Thermo Fisher Scientific). The residue was dissolved in 1,250 μl of ethanol, and centrifuged at 10,000 x g at 45°C for 15 min. Two hundred microliter of the supernatant was transferred to a glass tube for lipid analysis. LC-q-TOF-MS conditions to detect lipids Sample extracts (1 μl) were analyzed using an LC-MS system equipped with an electrospray ionization (ESI) interface (HPLC, Waters Acquity UPLC system; MS, Waters Xevo G2 Qtof). Two-solvent (A and B) system was used for separation of each metabolite. Compositions of these solvents were as follows: solvent A, acetonitrile: water:1 M ammonium acetate:formic acid = (158 g:800g:10 ml:1 ml); solvent B, acetonitrile:2-propanol:water:1 M ammonium acetate:formic acid = (79 g:711 g:10 ml:1 ml). The analytical conditions were as follows. HPLC: column, Acquity UPLC HSS T3 (pore size 1.8 μm, 1.0 i.d × 50 mm long, Waters); gradient program, 35% B at 0 min, 70% B at 3 min, 85% B at 7 min, 90% B at 10 min, 90% B at 12 min and 35% B at 12.5 min; flow rate, 0.15 ml/min; temperature, 55°C; MS detection: capillary voltage, +3.0 kV; cone voltage, 20 V for positive mode and 40 V for negative mode; source temperature, 120°C; desolvation temperature, 450°C; cone gas flow, 50 l/h; desolvation gas flow, 450 l/h; collision energy, 6 V; detection mode, scan (m/z 100–2000; scan time, 0. 5 sec; centroid). The scans were repeated for 15 min in a single run. The data were recorded using MassLynx version 4.1 software (Waters).
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