This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2020. The fellowship supports a research and training plan in a host laboratory for the Fellow who also presents a plan to broaden participation in biology. The title of the research and training plan for this fellowship to Dr. Trevor M. Nolan is "Reconstructing spatial-temporal responses to drought in Arabidopsis and rice using time series single-cell transcriptomics" The host institution for the fellowship is Duke University and the sponsoring scientist is Dr. Philip N. Benfey.
Drought is a devastating environmental stress that causes billions of dollars in damages and contributes to weather-related instability in crop yield. A great deal of research has been devoted to identifying strategies that can improve plant survival during drought; however, the majority of factors that have been shown to promote drought resistance are associated with decreased plant growth and productivity. To engineer plants that grow well and withstand drought, it is important to understand when and where drought responses occur and how plants recover once drought subsides. This project will address this challenge through the use of innovative technology that allows gene activity to be monitored in individual cells. The Fellow will receive training in developmental biology, single-cell genomics, bioinformatics and lab management from this project. To increase broader participation in science, the Fellow will also mentor students from underrepresented groups at the host-lab institution.
In order to define the spatio-temporal responses to drought and recovery the Fellow will use an integrated approach involving single-cell genomics, computational modeling, and phenotyping. First, gene expression will be profiled at single-cell resolution at different times after drought stress and recovery. This will allow construction of spatially and temporally resolved gene regulatory networks that will be used to prioritize genes involved in drought resistance and recovery. These predictions will be tested at several levels of biological organization including cells and tissues via confocal and light sheet microscopy, whole plant drought phenotypes using time lapse imaging, and across two species: Arabidopsis and rice. The inclusion of a model plant, Arabidopsis, will allow predictions to be quickly and comprehensively tested. Parallel analysis of rice, an important crop plant, will enable the direct application of the identified genes to improve crop performance during drought. The resulting datasets, predictive models and plant lines will be made publicly available and serve as a valuable resource to guide future studies on drought.
This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
