SE148:/S1/M1/D1

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	M1
Title	LC-q-TOF-MS
Method Details ID	MS1
Sample Amount	1 μL
Comment

Analytical Method Details Information

ID	MS1
Title	LC-q-TOF-MS
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 polar metabolites Fifty-mg DW of each sample was extracted in 50 volumes of extraction medium (methanol/water [2:5 v/v]) containing two reference compounds (0.5 mg/l flavonol-2’-sulfonic acid and1.0 mg/l ampicilin) using a mixer mill MM301 (Retsch) at a frequency of 20 Hz for 5 min at 4°C. After centrifugation for 10 min at 15,000 × g, the supernatant was transferred into a 2 ml tube. Thirty volumes of methanol were added to the tube and then extracted again using the mixer mill at a frequency of 20 Hz for 5 min at 4°C. After centrifugation for 10 min at 15,000 x g, the resulting supernatant was transferred into the tube. One hundred twenty-μl aliquot of the extracts was filtered using an Oasis® HLB μelusion plate (30 μm, Waters Co., Massachusetts, US). The extracts (100 μl) were transferred into a 2 ml tube and were evaporated to dryness in an SPD2010 SpeedVac® concentrator from ThermoSavant (Thermo Fisher Scientific). The extracts were dissolved by 100 μl of 20% aqueous methanol containing 0.5 mg l−1 lidocaine and 10-camphorsulfonic acid. LC-q-TOF-MS conditions to detect polar metabolites After preparation of the extracts, the sample extracts (1 μl) were analyzed using an LC-MS system equipped with an electrospray ionization (ESI) interface (LC, Waters Acquity UPLC system; MS, Waters Xevo G2 Q-Tof). The analytical conditions were as follows. LC: column, Acquity bridged ethyl hybrid (BEH) C18 (pore size 1.7 μm, length 2.1 × 100 mm, Waters); solvent system, solvent A (water containing 0.1% formic acid) and solvent B (acetonitrile with 0.1% formic acid); gradient program, 0.5% of solvent B at 0 min, 0.5% of solvent B at 0.1 min, 99.5% of solvent B at 12.0 min, 99.5% of solvent A and 0.5%B at 12.0 min, 0.5% of solvent B at 12.1 min, and 0.5% of solvent B at 15.0 min; flow rate, 0.3 ml/min; temperature, 40°C; MS detection: capillary voltage, +3.0 keV, cone voltage, 25.0 V, source temperature, 120°C, desolvation temperature, 450°C, cone gas flow, 50 l per h; desolvation gas flow, 800 l per h; collision energy, 6 V; mass range, m/z 100‒1500; scan duration, 0.1 sec; interscan delay, 0.014 sec; mode, centroid; polarity, positive; Lockspray (Leucine enkephalin): scan duration, 1.0 sec; interscan delay, 0.1 sec. The data were recorded using MassLynx version 4.1 software (Waters).
Comment_of_details

Data Analysis Information

ID	D1
Title	Data processing for LC-q-TOF-MS
Data Analysis Details ID	DS1
Recommended decimal places of m/z
Comment

Data Analysis Details Information

ID	DS1
Title	Data processing for LC-q-TOF-MS
Description	Data processing for LC-q-TOF-MS data to detect polar metabolites The data matrix was aligned by MassLynx version 4.1 (Waters). The profiling data files were converted to the NetCDF format using the DataBridge function of the MassLynx software. After the processes of alignment and deisotope with the set of NetCDF data files, the data matrix was obtained. For normalization, intensity values of remained peaks was divided by those of the lidocaine ([M+H]+, m/z 235.1804) and 10-camphorsulfonic acid ([M-H]-, m/z 231.0691) after cutoff of the low-intensity peaks (less than 500 counts).
Comment_of_details