The Frommer lab uses high-throughput approaches to identify pairwise physical interactions among proteins, with a focus on membrane proteins and those involved in signaling. Over 65,000 interactions have been identified thus far. The team uses a screen called the mating-based protein complementation assay, or split ubiquitin system. Ubiquitin is a small protein. Candidate proteins are fused onto a version of ubiquitin that is split in half. When the two candidates interact, the two halves of the ubiquitin reassemble, triggering a process that liberates a transcription factor—a protein that switches a gene on—which then goes to the nucleus. When genes are turned on in the nucleus, the researchers are alerted to the successful interaction.
Genetically encoded metabolite sensors
The Frommer lab has constructed genetically encoded FRET sensors for a variety of important metabolites such as glucose, maltose, ribose and glutamate. Binding of the metabolite to the sensor induces a conformational change that modifies the fluorescence properties of the sensor. The FRET sensor thus allows a user to observe and measure dynamic changes in metabolite concentration, with high temporal and spatial resolution through a microscope. Spatial resolution is achieved by expression of sensors in specific cell types or genetic targeting of sensors to subcellular compartments. Time resolution in the millisecond range can be achieved.
For confocal imaging of FRET sensors, one of our spinning disk confocal systems is equipped with a Roper Dualview. As an alternative to ratiometric FRET analysis, the confocal FLIM system can be used.
In collaboration with the Quake lab, the Frommer lab is developing a platform for medium to high throughput sensor construction (EAGER grant link).
The Ehrhardt lab has developed a system for generating N-terminal fusions of GFP with random endogenous genes in the Arabidopsis genome, and used it to characterize the localizations and dynamics of 120 GFP-protein fusions. (Cutler et al, PNAS, 1999). See the website.
Gametophyte Gene Expression Data
The Evans lab is generating RNA-seq data to describe the transcriptome of the maize male and female gametophytes. In collaboration with John Fowler and Scott Givan this data is being mapped to the maize genome for identification of genes specific to the male and/or female gametophytes. This data can be visualized by the community on the project genome browser at http://maizegametophyte.org.