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Sample Set Information

ID SE137
Title Metabolite Signature during Short-Day Induced Growth Cessation in Populus.
Description The photoperiod is an important environmental signal for plants, and influences a wide range of physiological processes. For woody species in northern latitudes, cessation of growth is induced by short photoperiods. In many plant species, short photoperiods stop elongational growth after a few weeks. It is known that plant daylength detection is mediated by Phytochrome A (PHYA) in the woody hybrid aspen species. However, the mechanism of dormancy involving primary metabolism remains unclear. We studied changes in metabolite profiles in hybrid aspen leaves (young, middle, and mature leaves) during short-day-induced growth cessation, using a combination of gas chromatography–time-of-flight mass spectrometry, and multivariate projection methods. Our results indicate that the metabolite profiles in mature source leaves rapidly change when the photoperiod changes. In contrast, the differences in young sink leaves grown under long and short-day conditions are less distinct. We found short daylength induced growth cessation in aspen was associated with rapid changes in the distribution and levels of diverse primary metabolites. In addition, we conducted metabolite profiling of leaves of PHYA overexpressor (PHYAOX) and those of the control to find the discriminative metabolites between PHYAOX and the control under the short-day conditions. The metabolite changes observed in PHYAOX leaves, together with those in the source leaves, identified possible candidates for the metabolite signature (e.g., 2-oxo-glutarate, spermidine, putrescine, 4-amino-butyrate, and tryptophan) during short-day-induced growth cessation in aspen leaves.
Authors Kusano M, Jonsson P, Fukushima A, Gullberg J, Sjöström M, Trygg J, Moritz T.
Reference Front Plant Sci. 2011 Jul 12;2:29. doi: 10.3389/fpls.2011.00029. eCollection 2011.

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Data Analysis Details Information

Title Statistical Data Analysis
Description Multivariate statistical investigations were performed using SIMCA-P + 12 software (Umetrics, Umeå, Sweden). All variables were log10-transformed, centered, and scaled to unit variance for the analysis. To connect the information of two-block variables (X and Y) to each other, we used an orthogonal projection to latent structures (OPLS). OPLS is one of the supervised methods which is commonly applied in metabolomics. An OPLS regression model (Trygg and Wold, 2002) was calculated to investigate potential relationships between the metabolic compositions (X) of the aspen leaves and their positions (Y) on the stem. Peak areas under the resolved GC–MS peaks were used as descriptors (X) and the leaf positions as the response (Y) in the OPLS model. R2X is the cumulative modeled variation in X, R2Y is the cumulative modeled variation in Y, and Q2Y is the cumulative predicted variation in Y, according to cross-validation. The range of these parameters is 0–1, where 1 indicates a perfect fit.

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