Effect of differences in light source environment on transcriptome of leaf lettuce (Lactuca sativa L.) to optimize cultivation conditions.

When used in closed-type plant factories, light-emitting diode (LED) illumination systems have the particular advantages of low heat emission and high luminous efficiency. The effects of illumination quality and intensity on the growth and morphogenesis of many plant species have been examined, but improvements are needed to optimize the illumination systems for better plant products with lower resource investments. In particular, new strategies are needed to reduce the wastage of plant products related to leaf senescence, and to better control the ingredients and appearance of leafy vegetables. Although the quality of light is often altered to change the characteristics of plant products, the transcriptional status underlying the physiological responses of plants to light has not been established. Herein, we performed a comprehensive gene expression analysis using RNA-sequencing to determine how red, blue, and red/blue LEDs and fluorescent light sources affect transcriptome involved in the leaf aging of leaf lettuce. The RNA-sequencing profiling revealed clear differences in the transcriptome between young and old leaves. Red LED light caused large variation between the two age classes, while a pure or mixed blue LED light spectrum induced fewer transcriptome differences between young and old leaves. Collectively, the expression levels of genes that showed homology with those of other model organisms provide a detailed physiological overview, incorporating such characteristics as the senescence, nutrient deficiency, and anthocyanin synthesis of the leaf lettuce plants. Our findings suggest that transcriptome profiles of leaf lettuce grown under different light sources provide helpful information to achieve better growth conditions for marketable and efficient green-vegetable production, with improved wastage control and efficient nutrient inputs.

High-throughput classification of images of cells transfected with cDNA clones.

The sequence of the human genome has been determined. The next task is to determine the function of the genes. Classifying cellular forms of proteins encoded by human cDNA clones is a primary step toward understanding the biological role of proteins and their coding genes. We report here our ongoing work on an automatic system to facilitate this classification. Our system handles the transfection, incubation, acquisition of microscopic images of the cells, and the classification of forms there appearing in the images. Our system correctly classified proteins by their forms at a rate of 90% in feasibility studies.