Systems genetics analysis of photosynthetic carbon metabolism in rice

这个项目的目的是发现水稻中增加光合作用效率的突变。植物通过光合作用获取阳光的能量，产生糖、淀粉和大量具有生物活性的高能量生命分子。提高植物光合作用的效率，使其在环境压力下保持稳定，将为我们提供可持续的食物和营养供应，以及可再生能源，以满足不断增长的人口的需求。这个跨学科的项目将为研究生和本科生提供实践训练。该项目还将提供动手实验练习，以提高本科生和研究生对影响粮食安全的植物多样性和环境挑战的认识，并扩大植物科学在STEM教育中的影响。此外，来自阿肯色州三角洲地区农业区的K-12学生将参与STEM扫盲外展计划，提供旨在关爱环境的实验性植物科学经验。该项目将使用综合系统遗传学方法来剖析植物光合作用驱动植物发育和生产力的复杂途径。对一个水稻群体进行全基因组关联分析，发现了与光合效率相关的单核苷酸多态性（SNP）标记。这些snp将用于鉴定决定光合作用重要自然变异的关键基因。为了理解这些相互作用过程的调控，有必要超越个体基因作用或生化途径。所采用的综合网络方法将有助于将多种基因定义的光合参数置于基因调控途径的背景下，这些途径支持对外部因素，生长和发育的反应。一组不同的计算公式将使用基于等级的协议集成到共识网络中，这种方法被证明对功能关系的预测是稳健的。预测的转录因子将在体内进行高通量测试，以验证其激活光合作用基因的能力，并在转基因植物中证实其下游调控途径。来自这些基因型的系统遗传信息将用于重建改良植物和作物，在基于光合作用的过程中进行模块化改进，以满足不同的需求。该项目由分子和细胞生物科学部的系统和合成生物学项目以及NSF EPSCoR项目共同资助。

The aim of this project is to discover mutations in rice that increase photosynthesis efficiency. Plants capture the energy of sunlight through photosynthesis to produce sugars, starch, and a multitude of biologically active energy-rich molecules of life. Improving the efficiency of photosynthesis in plants, making it stable to environmental stresses, would provide us with a sustainable supply of food and nutrition as well as renewable energy to maintain the needs of the growing population. This interdisciplinary project will offer practical training to graduate and undergraduate students. This project also will produce hands-on laboratory exercises to improve knowledge of undergraduate and graduate students on plant diversity and environmental challenges affecting food security, and broadening the impact of plant sciences in STEM education. In addition, K-12 students from the Arkansas agricultural areas in the Delta region will be engaged by a STEM literacy outreach program providing experience in experimental plant sciences aimed at caring for the environment. The project will use an integrated systems genetics approach to dissect the complex pathways of plant photosynthesis driving plant development and productivity. Genome wide association analysis of a diverse rice population identified single nucleotide polymorphism (SNP) markers associated with several parameters for photosynthetic efficiency. These SNPs will be used to identify the key genes determining important natural variation for photosynthesis. To understand the regulation of these interacting processes, it is essential to go beyond individual gene action or biochemical pathways. The integrated network approach employed will help place multiple genetically defined photosynthetic parameters in the context of gene regulatory pathways that underpin response to external factors, growth and development. A diverse set of computational formulations will be integrated into a consensus network using rank-based protocols, an approach proven to be robust for prediction of functional relationships. Predicted transcription factors will be tested in high throughput assays for their ability to activate photosynthesis genes in vivo, and confirmed in transgenic plants to unravel their downstream regulatory pathways. The systems genetic information from these genotypes will be used to reconstruct improved plants and crops with modular improvements in photosynthetic-based processes for diverse needs.This project is co-funded by the Systems and Synthetic Biology Program in the Division of Molecular and Cellular Biosciences and by the NSF EPSCoR Program.