Activating photosynthesis in non-photosynthetic cells for improved crop productivity

点击翻译按钮获取中文摘要

Theme: Agriculture and Food SecurityPhotosynthesis is the basis of life, and underpins crop productivity. Enhancing photosynthesis is high on the BBSRC agenda, as it is recognised that photosynthesis controls the yield potential of crops, and this needs to be increased to feed the future.The leaves of many of our most important crops develop to contain cells that fail to "green up" and accumulate photosynthetic apparatus. Why this is, and the mechanisms that repress photosynthesis gene expression in these cells, are not understood. Using rice as a model, we have identified when this repression occurs during leaf development. The project builds on these findings and will use state-of-the-art approaches developed by the lab to isolate cell-types that will either become fully green and photosynthetic (mesophyll) or fail to do so (bundle sheath). High throughput sequencing (eg RNA-SEQ, DNAaseI-SEQ) and bioinformatics analysis will be used to understand this repression of photosynthesis in cells of the leaf. The long-term aim is then to activate photosynthesis in these non-photosynthetic leaf cells to increase photosynthetic capacity of the leaf and increase productivity.This project is relevant to Agriculture and Food Security as increasing photosynthesis would enable the yield potential of crops to be increased. The work would also generated World Class Bioscience. Because rice is the model that is used for this research, core findings as well as those that are serendipitous can be translated into this major crop rapidly. Due to significant synteny within cereal genomes, findings from rice can also be used to guide improvements in other major cereal crops.The project is interdisciplinary, combining wet-lab analysis with training and use of bioinformatics and statistical analysis of big data. The student would be immersed in a laboratory that routinely uses these approaches, and contains members with expertise in both areas.