SE148:/DS3
From Metabolonote
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 |
Data Analysis Details Information
| ID | DS3 |
|---|---|
| Title | Data processing (CE-MS) |
| Description | Data processing for CE-TOF-MS data An original data file (.wiff) was converted to an unique binary file (.kiff) using the in-house software (nondisclosure). Peak picking and alignment were performed using another in-house software (nondisclosure), peaks were picked and aligned among samples automatically. By contrast with the detected m/z and migration time values of standard compounds including internal standards, peaks were annotated automatically using the same software. For normalization, the individual area of the detected peaks was divided by the peak area of the internal reference standards. Based on the calibration curves for standard compounds, peak area values were converted into values corresponding to amounts. |
| Comment_of_details |
