Data from: Assessing metabolomic and chemical diversity of a soybean lineage representing 35 years of breeding

<p>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 (<em>Glycine max</em>) 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. </p><div><br>Resources in this dataset:</div><br><ul><li><p>Resource Title: Pointer to Electronic Supplementary Material.</p> <p>File Name: Web Page, url: <a href="https://link.springer.com/article/10.1007/s11306-014-0702-6#Sec17">https://link.springer.com/article/10.1007/s11306-014-0702-6#Sec17</a> </p><p>Link to Electronic Supplementary Material at <em>Metabolomics</em>. Files are:</p>
<p>Supplementary material 1: Full metabolite profile and ionomic dataset - Download Excel</p>
<p>Supplementary material 2: List of annotated metabolites - Download Excel</p>
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<li>List of the 681 annotated metabolites obtained by using 4 platforms after data summarization and removal of 50% missing value in the metabolite profile data. Some metabolite peaks could not be summarized and thus kept in the list.</li>
</ul>
<p>Supplementary material 3: Monsanto_ionomics_Data_Baxterla - Download Excel</p>
<p>Supplementary material 4: Metabolomics metadata - Download .docx</p>
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<li>Plant context metadata; Chemical analysis metadata.</li>
</ul>
<p>Supplementary material 5: Spearman correlations between yield and metabolites/ions - Download Excel</p>
<p>Supplementary material 6: Supporting Tables and Figures - Download .docx</p>
<ul>
<li>Supporting Table 1.Similarity matrix: Genetic similarity of different soybean varieties based on genetic fingerprint data.</li>
<li>Supporting Table 2. Metabolite Coverage of Analytical Platforms.</li>
<li>Supporting Table 3. Summary of Statistically Significant Differences in Ionomic Profiles.</li>
<li>Supporting Figure A. PCA (principal components one and two) based on the genotypic data of 1,484 pre-commercial and commercial proprietary Monsanto lines.</li>
<li>Supporting Fig. 1. Evaluation of the achieved coverage of metabolite profile data.</li>
<li>Supporting Fig. 2. Principal component analysis of the identified or annotated metabolites/ peaks.</li>
<li>Supporting Fig. 3. Principal component analysis of the identified or annotated metabolites/peaks and including the ionomics data.</li>
<li>Supporting Fig. 4. The score scatter plot of OPLS-DA using the identified or annotated metabolites/ peaks and including the ionomics data.</li>
<li>Supporting Fig. 5. Graphic representation of nodes of the first neighbors in the yield-to-metabolite correlation networks of samples harvested at ILJA and ILJE.</li>
</ul>
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Tags: ars, chemical-diversity, data-gov, metabolomic-diversity
Organization: Department of Agriculture