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: Ronald, et. al |
The first genome-scale model for predicting the
functions of genes and gene networks in a grass species has been developed by
an international team of researches that includes scientists with the U.S.
Department of Energy (DOE)’s Joint BioEnergy Institute (JBEI), a
multi-institutional partnership led by Lawrence Berkeley National Laboratory
(Berkeley Lab). 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.
“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,” says Pamela Ronald, a plant geneticist who
holds joint appointments with JBEI, where she directs the grass genetics
program, and with the University of California (UC) Davis, where she is a
professor in the Department of Plant Pathology and at The Genome Center. “RiceNet represents a systems biology approach that draws from diverse and
large datasets for rice and other organisms.”
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 clean, green and renewable
cellulosic biofuels.
Given the worldwide importance of rice, a network
modeling platform that can predict the function of rice genes has been sorely
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.
Ronald is the corresponding author of a paper in
the Proceedings of the National Academy of Sciences (PNAS) that
describes how JBEI researchers, working with researchers at the University of Texas
in Austin, and Yonsei
University in Seoul, Korea,
overcame the challenge and developed a network that encompasses nearly half of
all rice genes. The paper is titled “Genetic dissection of the biotic stress
response using a genome-scale gene network for rice.”
“RiceNet builds upon 24 publicly available data
sets from five species as well as an earlier mid-sized network of 100 rice
stress response proteins that my group constructed through protein interaction
mapping,” Ronald says. “We have conducted experiments that validated RiceNet’s
predictive power for genes involved in the rice innate immune response.”
Ronald and her team also showed that RiceNet can
accurately predict gene functions in another important monocotyledonous crop
species, maize.
A RiceNet Website is now available that allows
researchers from all over the world to use it. 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,” Ronald says. “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.”
