|This graphic is a full-size view of a RiceNet layout, color-coded to indicate the likelihood of network links; red for higher and blue for lower likelihood scores. Image from Insuk Lee, Yonsei University. Click to enlarge.|
An experimentally tested genome-scale model for predicting the functions of genes and gene networks in a grass species has been developed by team of researchers from the US Department of Energy (DOE)’s Joint BioEnergy Institute (JBEI); Yonsei University (Korea); University of California, Davis; and University of Texas, Austin.
Called RiceNet, this systems-level model of rice gene interactions should help speed the development of new crops for the production of advanced biofuels, as well as help boost the production and improve the quality of one of the world’s most important food staples. An open-access paper on the work is published in the Proceedings of the National Academy of Sciences.
With RiceNet, instead of working on one gene at a time based on data from a single experimental set, we can predict the function of entire networks of genes, as well as entire genetic pathways that regulate a particular biological process. RiceNet represents a systems biology approach that draws from diverse and large datasets for rice and other organisms.—Dr. Pamela Ronald, JBEI and UC Davis
Rice is a staple food for half the world’s population and a model for monocotyledonous species—one of the two major groups of flowering plants. Rice is especially useful as a model for the perennial grasses, such as Miscanthus and switchgrass, that have emerged as prime feedstock candidates for the production of cellulosic biofuels.
Given the worldwide importance of rice, a network modeling platform that can predict the function of rice genes has been needed. However, until now the high number of rice genes—in excess of 41,000 compared to about 27,000 for Arabidopsis, a model for the other major group of flowering plants—along with several other important factors, has proven to be too great a challenge.
RiceNet builds upon 24 publicly available data sets derived from five different organisms including plants, animals, yeast, and humans, as well as an earlier mid-sized network of 100 rice stress response proteins constructed through protein interaction mapping.
Genes could be linked to traits by using guilt-by-association, predicting gene attributes on the basis of network neighbors. We applied RiceNet to an important agronomic trait, the biotic stress response. Using network guilt-by-association followed by focused protein–protein interaction assays, we identified and validated, in planta, two positive regulators...and one negative regulator.—Lee et al.
The team also showed that RiceNet can accurately predict gene functions in another important monocotyledonous crop species, maize.
A RiceNet website is now online. At JBEI, RiceNet will be used to identify genes that have not previously been known to be involved in cell wall synthesis and modification. JBEI researchers are looking for ways to increase the accessibility of fermentable sugars in the cell walls of feedstock plants.
The ability to identify key genes that control simple or complex traits in rice has important biological, agricultural, and economic consequences. RiceNet offers an attractive and potentially rapid route for focusing crop engineering efforts on the small sets of genes that are deemed most likely to affect the traits of interest.—Pamela Ronald
Co-authoring the PNAS paper with Ronald were Insuk Lee, Young-Su Seo, Dusica Coltrane, Sohyun Hwang, Taeyun Oh and Edward Marcotte. This research was supported in part by JBEI through the DOE Office of Science.
Insuk Lee, Young-Su Seo, Dusica Coltrane, Sohyun Hwang, Taeyun Oh, Edward M. Marcotte, and Pamela C. Ronald (2011) Genetic dissection of the biotic stress response using a genome-scale gene network for rice. PNAS DOI: 10.1073/pnas.1110384108