NSF Postdoctoral Fellowship in Biology FY 2016

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This action funds an NSF National Plant Genome Initiative Postdoctoral Research Fellowship in Biology for FY 2015. 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 David Mitchell is "A Genome-wide Investigation of Light Intensity Effects on the Maize RNA Structurome". The host institution for the fellowship is the Pennsylvania State University and the sponsoring scientists are Dr. Philip C. Bevilacqua and Dr. Sarah M. Assmann.As maize is a vital food and fuel crop, efforts have been made to increase maize yields by increasing planting density. Dense planting increases the canopy cover and blocks sunlight from reaching the lower leaves, thus stimulating shade avoidance responses which limits crop yield. Information obtained from this research can contribute to genetic engineering of maize to improve yield under high planting densities. Powerful tools used and developed in this research will revolutionize how RNA structure and function are studied. Through this project, the researcher will gain novel expertise in plant biology techniques and in computational and bioinformatics analysis. Furthermore, for six weeks annually underrepresented high school students will be mentored in laboratory research projects.This research will adapt Structure-seq to maize to perform high-throughput, genome-wide in vivo investigation of gene expression, RNA secondary structures, and RNA-protein interactions in response to low-light stress. By applying new structure-probing chemical reagents that allow Structure-seq to probe all four RNA bases, this research will better enable Structure-seq to distinguish RNA-RNA from RNA-protein interactions. It will examine the use of in vivo crosslinking via photoactivatable nucleoside analogues to provide direct proximity information on these interactions. Furthermore, it will modify the Structure-seq RNA structure prediction algorithm to incorporate constraints from proximity information, sequence complementarity, and known protein binding motifs. Data generated from this research will be uploaded upon processing to the appropriate community databases, including the NCBI Sequence Reads archive (SRA) for RNA structure data (http://www.ncbi.nlm.nih.gov/sra) and GEO for RNA-seq data (www.ncbi.nlm.nih.gov/geo).