Systems genetics analysis of photosynthetic carbon metabolism in rice

这个项目的目的是发现水稻中提高光合作用效率的突变。植物通过光合作用获取阳光的能量，以产生糖、淀粉和大量具有生物活性的富含能量的生命分子。提高植物的光合作用效率，使其对环境胁迫保持稳定，将为我们提供可持续的食物和营养供应以及可再生能源，以维持不断增长的人口的需求。这个跨学科项目将为研究生和本科生提供实践培训。该项目还将制作动手实验室练习，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知识，并扩大植物科学在STEM教育中的影响。此外，来自三角洲地区阿肯色州农业区的K-12学生将参加STEM扫盲推广计划，提供旨在保护环境的实验植物科学经验。该项目将使用集成的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。对不同水稻群体的全基因组关联分析确定了与几个光合作用效率参数相关的单核苷酸多态(SNP)标记。这些SNPs将被用来识别决定光合作用重要自然变异的关键基因。要了解这些相互作用过程的调节，必须超越单个基因的作用或生化途径。所采用的集成网络方法将有助于将多个基因定义的光合作用参数置于支持对外部因素、生长和发育的反应的基因调控途径的背景下。一套不同的计算公式将被整合到一个共识网络中，使用基于排名的协议，这一方法被证明对于预测函数关系是稳健的。预测的转录因子将在高通量测试中被测试它们在体内激活光合作用基因的能力，并在转基因植物中得到证实，以解开它们的下游调控途径。来自这些基因类型的系统遗传信息将用于重建基于光合作用过程的模块化改进以满足不同需求的改良植物和作物。该项目由分子和细胞生物科学部门的系统和合成生物学计划以及NSF EPSCoR计划共同资助。

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.