SE148:/DS5
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 | 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 |
