Systems genetics analysis of photosynthetic carbon metabolism in rice

University Of Arkansas, Fayetteville AR

Investigators

Abstract

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.