From Metabolonote
jump-to-nav Jump to: navigation, search

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

Link icon article.png

Sample Information

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)

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

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

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

Analytical Method Details Information

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).


Data Analysis Information

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

Data Analysis Details Information

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).

Personal tools
View and Edit Metadata