Plants are essential to life on Earth and provide us with food, fuel, clothing, and shelter. Despite all this, we know very little about how they do what they do. Even for the best-studied species, such as Arabidopsis thaliana --a wild mustard studied in the lab--we know about less than 20% of what its genes do and how or why they do it. And understanding this evolution can help develop new crop strains to adapt to climate change.
Sue Rhee wants to uncover the molecular mechanisms underlying adaptive traits in plants to understand how these traits evolved. A bottleneck has been the limited understanding of the functions of most plant genes. Rhee’s group is building genome-wide molecular networks of genes and proteins using a combination of computational and laboratory approaches to understand the functions of uncharacterized genes rapidly and systematically. Ultimately they are interested in finding patterns of network evolution to identify the evolutionary innovations for adaptation.
Rhee and team employs several methods: computational modeling and targeted laboratory testing; the collection of large-scale data needed for modeling by collaborating with other labs; and robust, quantitative analysis of the data and the models.
One example of faster identification and manipulation method to identify crop genes resistant or tolerant to environmental extremes is a computational model, called AraNet. It can predict gene function of uncharacterized plant genes with unprecedented speed and accuracy. Rhee, with colleagues, used it to predict a drought-related function of one previously uncharacterized gene and found with follow-up experiments that it is involved in the drought response.
AraNet encompasses over 19,600 genes of the tiny, experimental Arabidopsis thaliana plant, which are associated to with each other by over 1 million links. AraNet can increase the discovery rate of new genes affiliated with a trait tenfold. It is based on the notion that genes near each other, or that turn on in concert with one another, are probably associated with similar traits. The model is based on the evidence gathered from over 50 million scientific observations, which enables the map of associations to be made. Researchers propose that uncharacterized genes are linked to specific traits based on the strength of their associations with genes already known to be linked to those characteristics. They then follow up with experiments that suppress the activity of the uncharacterized gene to see what normal characteristics in the plant go awry. For more see
Sue Rhee received her B.A. in biology from Swarthmore College and her Ph. D. in biological sciences from Stanford University. She was the database curator of the Arabidopsis thaliana Database at Stanford before joining Carnegie as a staff associate in in 1999 when she was the director of one of the most widely used biological databases in the world the Arabidopsis Information Resource (TAIR). She became a Carnegie staff scientist in 2005 and was appointed director March 1, 2016. For more see http://dpb.carnegiescience.edu/labs/rhee-lab