Poster abstracts
Poster number 99 submitted by David Mitchell III
A genome-wide investigation of light intensity effects on the maize RNA structurome
David Mitchell (Department of Chemistry, Pennsylvania State University), Philip C. Bevilacqua (Department of Chemistry, Pennsylvania State University), Sarah M. Assmann (Department of Biology, Pennsylvania State University), David H. Mathews (Department of Biochemistry & Biophysics, University of Rochester Medical Center)
Abstract:
Besides being a primary staple food for human consumption and the most important crop in terms of world production, maize is a vital source of animal feed and biofuel. Efforts have been made to increase maize yields by increasing planting density. Dense planting of maize increases the canopy cover and blocks sunlight from reaching the lower leaves, thus stimulating shade avoidance responses in these plants. While shade avoidance increases a plant’s survivability, it does so at a cost to reproductive fitness and potentially yield. As RNAs are involved in a diverse set of essential biological functions, shade avoidance responses may derive from in altered RNA structure. We propose to improve and apply Structure-seq to reveal, genome-wide, in vivo changes in RNA structure and protein interactions caused by low light environments in maize. Structure-seq has been used previously to reveal novel in vivo RNA structural features in Arabidopsis thaliana such as triplet periodicity within the gene coding sequence. However, Structure-seq can only probe adenine and cytosine bases, limiting the amount of information obtained by this method. Thus, we seek to establish capability within Structure-seq to distinguish RNA secondary structure formation from protein binding to RNA by applying new structure-probing chemical reagents to study guanine and uracil bases. Furthermore, we propose the use of in vivo crosslinking via photoactivatable nucleoside analogues to obtain direct proximity information on RNA structure formation and protein binding to RNA. As Structure-seq requires computational structure prediction to infer secondary structure from chemical probing data, we further propose altering the RNA-prediction algorithm to incorporate constraints from proximity information, sequence complementarity, and known protein-binding motifs. Information obtained from the use of Structure-seq can contribute to genetic engineering of maize to improve yield under high planting densities.
References:
Ding, Y., et al., Genome-wide profiling of in vivo RNA structure at single-nucleotide resolution using structure-seq. Nat Protoc, 2015. 10(7): p. 1050-66.
Ding, Y., et al., In vivo genome-wide profiling of RNA secondary structure reveals novel regulatory features. Nature, 2014. 505(7485): p. 696-700.
Aphalo, P., C. Ballare, and A. Scopel, Plant-plant signalling, the shade-avoidance response and competition. J. Exp. Bot., 1999. 50(340): p. 1629-1634.
Dubois, P.G. and T.P. Brutnell, Topology of a maize field: distinguishing the influence of end-of-day far-red light and shade avoidance syndrome on plant height. Plant Signal Behav, 2011. 6(4): p. 467-70.
Keywords: Structure-seq, RNA Structure, Genome-wide