SE136:/DS1

Sample Set Information

ID	TSE1237
Title	Deciphering starch quality of rice kernels using metabolite profiling and pedigree network analysis.
Description	The physiological properties of rice grains are immediately obvious to consumers. High-coverage metabolomic characterization of the rice diversity research set predicted a negative correlation between fatty acid and lipid levels and amylose/total starch ratio (amylose ratio), but the reason for this is unclear. To obtain new insight into the relationships among the visual phenotypes of rice kernels, starch granule structures, amylose ratios, and metabolite changes, we investigated the metabolite changes of five Japonica cultivars with various amylose ratios and two knockout mutants (e1, a Starch synthase IIIa (SSIIIa)-deficient mutant and the SSIIIa/starch branching enzyme (BE) double-knockout mutant 4019) by using mass spectrometry-based metabolomics techniques. Scanning electron microscopy clearly showed that the two mutants had unusual starch granule structures. The metabolomic compositions of two cultivars with high amylose ratios (Hoshiyutaka and Yumetoiro) exhibited similar patterns, while that of the double-knockout mutant, which has an extremely high amylose ratio, differed. Rice pedigree network analysis of the cultivars and the mutants provided insight into the association between metabolic-trait properties and their underlying genetic basis in rice breeding in Japan. Multidimensional scaling analysis revealed that the Hoshiyutaka and Yumetoiro cultivars were Indica-like, yet they are classified as Japonica subpopulations. Exploring metabolomic traits is a powerful way to follow rice genetic traces and breeding history.
Authors	Kusano M, Fukushima A, Fujita N, Okazaki Y, Kobayashi M, Oitome NF, Ebana K, Saito K.
Reference	Mol Plant. 2012 Mar;5(2):442-51. doi: 10.1093/mp/ssr101. Epub 2011 Dec 15.
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PubMed

Data Analysis Details Information

ID	DS1
Title	Statistical Data Analysis
Description	Statistical analyses were performed using R v2.12.1 (www.r-project.org/) and Microsoft Office Excel 2007. Differences in the morphological traits of rice seeds and kernels between Nipponbare and each cultivar or mutant were determined using Welch’s t-test (p < 0.05). The fold changes of all cultivars and mutants were calculated by dividing by the mean value of Nipponbare. The differentially accumulated metabolites between a cultivar and Nipponbare and between a mutant and Nipponbare were detected using the LIMMA package (Smyth, 2004), which includes false discovery rate (FDR) correction for multiple testing (Benjamini and Hochberg, 1995). We identified metabolites with significant changes in metabolite levels (the log2-fold change > |1|) and the corresponding FDR-corrected p-values that were <0.05. We used the log2-fold change matrix for HCA and MDS analysis. HCA was performed using Cluster 3.0 (de Hoon et al., 2004), and the results of HCA were visualized using Java TreeView v1.1.6 (http://jtreeview.sourceforge.net/). We applied Euclidean distance as similarity matrices for the metabolites and cultivars or mutants and the average linkage for clustering. Using Euclidean distance as implemented in the ‘cmdscale’ function of the R software, we performed MDS analysis, which tries to demonstrate the underlying structure of empirically acquired data. We also used the log2-fold change values for this analysis.
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