Highlight
Transcription Factor Library Enables Discovery of Novel Genes Involved in the Plant Circadian Clock
Achievement/Results
Research conducted in collaboration between the labs of Steve Kay at the University of California, San Diego and Joe Ecker at the Salk institute has led to the creation of a powerful platform for the discovery of novel genetic networks, consisting of a collection of all known transcription factors in the genome of the plant model organism, Arabidopsis thaliana. By using high-throughput screening techniques, this resource is enabling researchers to identify hundreds of novel molecular interactions that enrich our overall understanding of biological processes in plants and lead to the generation of predictive models of the genetic networks that underlie plant traits. NSF Integrative Graduate Education and Research Traineeship (IGERT) fellows Jeff Nelson and Tim Jobe are using this platform to uncover new genes involved in the generation and maintenance of circadian rhythms and heavy metal responses, respectively. The self-sustaining oscillations, driven by daily light and temperature cycles, govern diverse processes such as photosynthesis, growth, and flowering time. By gaining a greater understanding of these complex and agriculturally important traits, we may find new ways of addressing issues related to food supply and security that are increasingly important as farmers struggle to feed the rapidly growing human population.
Address Goals
Primary: Transcriptional networks mediate and control most biological outputs, but these complex networks remain poorly understood in multicellular eukaryotes, with Arabidopsis providing a powerful platform for discovery of principles. The transcription factor library and platforms provide a major new resource for illuminating transcriptional networks in plants.
Secondary: The cross disciplinary training and research opportunities provided to our IGERT students through this platform will prepare our students as potential leaders in plant systems biology. This platform has attracted funding from the highly competitive NIH challenge grant competition, which will leverage the initial investment and allow the study of many transcriptional networks in a multi-cellular eukaryote.
