Period of performance:
Soil water availability is a major determinant of agricultural productivity worldwide; yet improving plant performance under soil drying and other abiotic stressors has proven to be a persistent challenge for conventional and biotechnological breeding. The challenge can be explained, in part, by the complex and myriad ways that plants respond to these environmental cues. From a practical breeding perspective, this complexity is manifested in strong correlation between stress response pathways and more fundamental aspects of plant growth and survival. Therefore breeding to improve performance under limiting environments invariably reduces crop yield under ideal conditions.
Plant molecular biologists have identified many of the cellular signaling and response pathways involved in abiotic stress response, but they remain largely ignorant of how these mechanisms interact with one another, when interactions at the molecular level very likely drive the trait correlations that confound breeding efforts. Therefore, a more mechanistic understanding of molecular and physiological interactions will allow for better design of stress-tolerant plants to improve yield to feed a growing population under increasingly unpredictable climactic conditions.
In the proposed study, we will develop new analytical tools to understand the structure and dynamics of gene regulatory networks (GRN). GRNs are the proximate mechanism through which plants perceive changes in the environment, transduce the signal of these perceptions between cells and tissues, and coordinate tissue and whole-plant responses to survive under stressful conditions.
Of particular interest for agricultural improvement is identifying and manipulating gene pathways that are expressed in environmentally dependent ways. For example, in response to soil drying a plant might coordinately activate a set of genes that together increase the efficiency with which water is used during growth. Current methods to detect this type of coordinated change in the activities of genes involve post-hoc analysis, i.e. compare the gene regulatory relationships in benign conditions with those that occur in stressful conditions, and compare the two. A more fruitful and powerful approach would be to integrate data from both conditions during the estimation of regulatory relationships, and then query the sets of parameters describing those relationships in order to better understand their functional basis on each condition.
Principal investigators Des Marais and Uhler plan to address this problem by developing novel tools of GRN estimation and comparison by using a new dataset generated in a model grass species, Brachypodium distachyon. This collaboration unites the extensive expertise in plant-environment interaction and sustainable agriculture that Des Marais possesses with that of Prof. Uhler, who is a leader in statistical approaches to studying networks.