Genomics resources for poplar
Access project data: VISIT Poplar genome browser
Project Title: Poplar Interactome for Bioenergy Research
INVESTIGATORS: Pankaj Jaiswal, Palitha Dharmawardhana, Amy Brunner, Eric Beers
INSTITUTIONS: Oregon State University, Virginia Tech University
NON-TECHNICAL SUMMARY: Poplars have many characteristics which make them suitable as a bioenergy feedstock. They are fast growing woody perennials where the biomass can be stored in the trunk. Poplars also have heating values and cellulose content that are comparable to corn, wheat and switchgrass. A key constraint to the expansion of cellulosic bioenergy sources such as in poplar is the negative consequence of converting land use from food crops to energy crops. Therefore, understanding the biological mechanisms and development of operational poplar varieties that can thrive under abiotic stress on marginal land unsuitable for food crops is imperative.
OBJECTIVES: (1) To identify a whole genome-wide functional gene network (an interaction network of genes) for poplar using gene orthology based projections and identify subnetworks associated to abiotic stress tolerance and bioenergy related traits, (2) To identify a set of candidate genes which interact to produce abiotic stress resistant phenotypes, and to identify diagnostic genetic markers associated with the sub-networks, (3) To create an interactive online resource of regulatory and metabolic networks of abiotic stress associated candidate genes and genetic markers for the poplar tree improvement community.
APPROACH: The objectives will be accomplished through a combination of computational projections, gene expression analysis, and validation from 1) gene orthology based projections from probabilistic functional gene networks that have been developed for Arabidopsis and rice, 2) supplementation of derived network with existing experimentally validated interactions, 3) RNA-seq method based gene expression analysis of poplar plants inheriting extreme abiotic stress phenotypes, and 4) yeast two-hybrid (Y2H) system based experimental validation of computationally derived gene-gene interactions.