Research

October 28, 2014 at 10:13 am

Wyatt: Unlocking the Black Box of Plant Response to Gravity

Dr. Sarah Wyatt presented her lab’s work on developing a network of gene expression data that is helping them to expand the knowledge of how plants respond to gravity.

Wyatt, Professor of Environmental & Plant Biology, presented Plant Gravitropic Signal Transduction: A Network Analysis Leads to Gene Discovery at the annual meeting of the American Society for Gravitational and Space Research Oct. 25 in Pasadena, CA.

Several students on Wyatt’s Team Gravitron also presented at the conference:

Read more about their experiment that will fly on the International Space Station.

Abstract: Gravity plays a fundamental role in plant growth and development. Although a significant body of research has helped define the events of gravity perception, the role of the plant growth regulator auxin, and the mechanisms resulting in the gravity response, the events of signal transduction, those that link the biophysical action of perception to a biochemical signal that results in auxin redistribution, those that regulate the gravitropic effects on plant growth, remain, for the most part, a “black box.”

Using a cold affect, dubbed the gravity persistent signal (GPS) response, we developed a mutant screen to specifically identify components of the signal transduction pathway. Cloning of the GPS genes have identified new proteins involved in gravitropic signaling. We have further exploited the GPS response using a multi-faceted approach including gene expression microarrays, proteomics analysis, and bioinformatics analysis and continued mutant analysis to identified additional genes, physiological and biochemical processes.

Gene expression data provided the foundation of a regulatory network for gravitropic signaling. Based on these gene expression data and related data sets/information from the literature/repositories, we constructed a gravitropic signaling network for Arabidopsis inflorescence stems. To generate the network, both a dynamic Bayesian network approach and a time-lagged correlation coefficient approach were used. The dynamic Bayesian network added existing information of protein-protein interaction while the time-lagged correlation coefficient allowed incorporation of temporal regulation and thus could incorporate the time-course metric from the data set. Thus the methods complemented each other and provided us with a more comprehensive evaluation of connections.

Each method generated a list of possible interactions associated with a statistical significance value. The two networks were then overlaid to generate a more rigorous, intersected network with shared genes and interactions. This network is flexible and can be updated with new data from the original research. The network allows identification of hubs/additional components and processes that are involved in gravitropic signal transduction to provide further hypotheses for testing. This research is partially supported by NSF IOS #1147087.

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