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Sample Set Information

ID TSE1352
Title Metabolic Reprogramming in Leaf Lettuce Grown Under Different Light Quality and Intensity Conditions Using Narrow-Band LEDs
Description Light-emitting diodes (LEDs) are an artificial light source used in closed-type plant factories and provide a promising solution for a year-round supply of green leafy vegetables, such as lettuce (Lactuca sativa L.). Obtaining high-quality seedlings using controlled irradiation from LEDs is critical, as the seedling health affects the growth and yield of leaf lettuce after transplantation. Because key molecular pathways underlying plant responses to a specific light quality and intensity remain poorly characterised, we used a multi-omics–based approach to evaluate the metabolic and transcriptional reprogramming of leaf lettuce seedlings grown under narrow-band LED lighting. Four types of monochromatic LEDs (one blue, two green and one red) and white fluorescent light (control) were used at low and high intensities (100 and 300 μmol·m−2·s−1, respectively). Multi-platform mass spectrometry-based metabolomics and RNA-Seq were used to determine changes in the metabolome and transcriptome of lettuce plants in response to different light qualities and intensities. Metabolic pathway analysis revealed distinct regulatory mechanisms involved in flavonoid and phenylpropanoid biosynthetic pathways under blue and green wavelengths. Taken together, these data suggest that the energy transmitted by green light is effective in creating a balance between biomass production and the production of secondary metabolites involved in plant defence.
Authors Kazuyoshi Kitazaki, Atsushi Fukushima, Ryo Nakabayashi, Yozo Okazaki, Makoto Kobayashi, Tetsuya Mori, Tomoko Nishizawa, Sebastian Reyes-Chin-Wo, Richard W. Michelmore, Kazuki Saito, Kazuhiro Shoji & Miyako Kusano
Reference Scientific Reports, volume 8, Article number: 7914 (2018)

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Data Analysis Details Information

Title Statistical data analysis for metabolite profile data
Description The multi-platform data was summarized by unifying metabolite identifiers to a common referencing scheme using the MetMask tool. The three matrices were then concatenated and correlated peaks with the same annotation were replaced by their first principal component. Principal component analysis (PCA) and orthogonal partial least square discriminant analysis (O2PLS-DA) were performed with log10 transformation and autoscaling using SIMCA-P 14.0 software (Umetrics AB, Umeå, Sweden).

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