SE156:/DS2

Sample Set Information

ID	TSE1312
Title	Metabolic Profiling of Developing Pear Fruits Reveals Dynamic Variation in Primary and Secondary Metabolites, Including Plant Hormones.
Description	Metabolites in the fruits of edible plants include sweet sugars, visually appealing pigments, various products with human nutritional value, and biologically active plant hormones. Although quantities of these metabolites vary during fruit development and ripening because of cell division and enlargement, there are few reports describing the actual dynamics of these changes. Therefore, we applied multiple metabolomic techniques to identify the changes in metabolite levels during the development and ripening of pear fruits (Pyrus communis L. ‘La France’). We quantified and classified over 250 metabolites into six groups depending on their specific patterns of variation during development and ripening. Approximately half the total number of metabolites, including histidine and malate, accumulated transiently around the blooming period, during which cells are actively dividing, and then decreased either rapidly or slowly. Furthermore, the amounts of sulfur-containing amino acids also increased in pear fruits around 3–4 months after the blooming period, when fruit cells are enlarging, but virtually disappeared from ripened fruits. Some metabolites, including the plant hormone abscisic acid, accumulated particularly in the receptacle prior to blooming and/or fruit ripening. Our results show several patterns of variation in metabolite levels in developing and ripening pear fruits, and provide fundamental metabolomic data that is useful for understanding pear fruit physiology and enhancing the nutritional traits of new cultivars.
Authors	Oikawa A, Otsuka T, Nakabayashi R, Jikumaru Y, Isuzugawa K, Murayama H, Saito K, Shiratake K.
Reference	PLoS One. 2015 Jul 13;10(7):e0131408. doi: 10.1371/journal.pone.0131408. eCollection 2015.
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PubMed

Data Analysis Details Information

ID	DS2
Title	Data analysis and statistics
Description	Data describing the quantified or relative amounts of metabolites were integrated into one sheet. These data were standardized by subtraction of the averages from each amount and division of the resulting values by the standard deviations. The standardized data were subjected to principal component analysis (PCA) and hierarchical clustering analysis (HCA) using DrDMass (http://kanaya.naist.jp/DrDMASS/) and PermutMatrix (http://www.lirmm.fr/~caraux/PermutMatrix/), respectively. In HCA, Euclidean distance and Ward’s minimum variance were used as a dissimilarity and a linkage rule, respectively.
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