W867
Quantifying Transcriptome-Wide Levels of Divergent Gene Expression Following Speciation By Autopolyploidy in Tolmiea (Saxifragaceae)

Date: Saturday, January 9, 2016
Time: 9:00 AM
Room: Towne - Meeting House
Clayton J Visger , University of Florida, Gainsville, FL
Gane Ka-Shu Wong , Department of Biological Sciences, University of Alberta, Edmonton, AB, AB, Canada
Pamela S. Soltis , University of Florida, Gainesville, FL
Douglas E. Soltis , University of Florida, Gainesville, FL
Allopolyploidy has historically been the focal point of researchers interested in the effects of whole-genome duplication.  This focus has come at the near exclusion of autopolyploidy and reflects the traditional view of autopolyploidy being evolutionarily unimportant and rare in nature. Recently, the ever-broadening interest in the evolutionary implications of polyploidy has led to the discovery that autopolyploidy is frequent and perhaps a major evolutionary force, particularly in certain clades. Unfortunately, we know little about the ecological, physiological, and genomic implications of speciation by autopolyploidy. The angiosperm genus Tolmiea (Saxifragaceae) is an emerging evolutionary system for the study of speciation by autopolyploidy, comprising two species, the autotetraploid T. menziesii (2n = 28) and its diploid progenitor, T. diplomenziesii (2n = 14). We present the results of our study of transcriptome-wide divergence in gene expression following autopolyploidy in Tolmiea. We also discuss our methods for normalizing expression count data of different ploidal levels through the use of spike-in RNA standards. Using this approach, we report on the differences in total transcriptome size, the proportion of the transcriptome expressed additively versus non-additively (dosage-insensitive), and patterning with respect to ontology.  Additionally, we leverage results from our recent and ongoing investigation of ecological and physiological divergence following speciation by autopolyploidy in Tolmiea to place the gene expression data in an ecological framework.