SE148:/S1/M3/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	M3
Title	CE-TOF-MS
Method Details ID	MS3
Sample Amount	11.25 μl of extracts, ca. 0.6 μg of each sample
Comment

Analytical Method Details Information

ID	MS3
Title	CE-TOF-MS
Instrument	CE-TOF MS: Agilent CE capillary electrophoresis system
Instrument Type
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 CE-TOF-MS Seventy mg DW of each sample was extracted in 20 volumes of methanol containing 8 μM of two reference compounds (methionine sulfone for cation and camphor 10-sulfonic acid for anion analyses) using a Retsch mixer mill MM310 at a frequency of 27 Hz for 1 min. The extracts were then centrifuged at 15,000 x g for 3 min at 4 °C. Five hundred-μl aliquot of the supernatant was transferred into a tube. Five hundred μl of chloroform and 200 μl of water was added into the tube to perform liquid-liquid distribution. The upper layer was evaporated for 30 min at 45°C by a centrifugal concentrator to obtain two layers. For removing high-molecular-weight compounds such as oligo-sugars, the upper layer was centrifugally filtered through a Millipore 5-kDa cutoff filter at 9,100 g for 120 min at 4°C. The filtrate was dried for 120 min by a centrifugal concentrator. The residue (ca. 25 mg of each sample) was dissolved into 20 μl of water containing 200 μM of internal standards (3-aminopyrrolidine for cation and trimesic acid for anion analyses) that were used for compensation of migration time in the peak annotation step. CE-TOF-MS conditions All CE-TOFMS experiments were performed using an Agilent G7100A CE Instrument (Agilent Technologies, Sacramento, CA), an Agilent G6224A TOF LC/MS system, an Agilent 1200 Infinity series G1311C Quad Pump VL, and the G1603A Agilent CE-MS adapter and G1607A Agilent CE-ESI-MS sprayer kit. The G1601BA 3D-CE ChemStation software for CE and G3335-64002 MH Workstation were used. Separation column and electrolytes: Separations were carried out using a fused silica capillary (50 μm i.d. × 100 cm total length) filled with 1 M formic acid for cation analyses or with 20 mM ammonium formate (pH 10.0) for anion analyses as the electrolyte. The capillary temperature was maintained at 20°C. Sample injection: The sample solutions (11.25 μl of extracts, ca. 0.6 μg of each sample) were injected at 50 mbar for 15 sec (15 nl). The sample tray was cooled below 4 °C. Separation parameters: Prior to each run the capillary was flushed with electrolyte for 5 min. The applied voltage for separation was set at 30 kV. Fifty percent (v/v) methanol/water containing 0.5 μM reserpine was delivered as the sheath liquid at 10 μl/min. Ionization: ESI-TOFMS was conducted in the positive ion mode for cation analyses or in the negative ion mode for anion analyses, and the capillary voltage was set at 4 kV. Dry gas condition: A flow rate of heated dry nitrogen gas (heater temperature 300 °C) was maintained at 10 psig. Voltage settings in TOF-MS: The fragmentor, skimmer, and Oct RFV voltage were set at 110V, 50V, and 160V for cation analyses or at 120V, 60V, and 220V for anion analyses, respectively. Mass calibration: Automatic recalibration of each acquired spectrum was performed using reference masses of reference standards. The methanol dimer ion ([2M+H]+, m/z 65.0597) and reserpine ([M+H] +, m/z 609.2806) for cation analyses or the formic acid dimer ion ([2M-H]-, m/z 91.0037) and reserpine ([M-H] -, m/z 607.2661) for anion analyses provided the lock mass for exact mass measurements. Mass data acquirement: Exact mass data were acquired at a rate of 1.5 cycles/sec over a 50-1000 m/z range. Quality control: In an every single sequence analysis (maximum 36 samples) on our CE-TOF-MS system, we analyzed the standard compound mixture at the first and the end of sample analyses. The detected peak area of standard compound mixture was checked in point of reproducible sensitivity. Standard compound mixture composed of major detectable metabolites including amino acids and organic acids, and this mixture was newly prepared at least once a half year. In all analyses in this study, there were no differences in the sensitivity of standard compounds mixture.
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