|
|
|
| Abstract Title:
|
| Metabolic profiling unravels complex biological phenomena in the model plant A. thaliana
|
| Graduate Student Presenter:
|
Kayla Kaiser
|
| Name of the Author(s) and Affiliation(s):
|
Kayla Kaiser, Dept. of Chemistry, UC Riverside; Julia Bailey-Serres, Dept. of Botany and Plant Sciences, UC Riverside; Charles Jang, Genetics, Genomics and Bioinformatics Dept., UC Riverside; Cynthia Larive, Dept. of Chemistry, UC Riverside
|
|
A multi-faceted approach was used to dissect metabolic responses to environmental and chemical stress in the flowering plant Arabidopsis thaliana, a member of the mustard family closely related to cauliflower, cabbage and broccoli. This project employs chemistry, multivariate statistics, computer science, bioinformatics, plant biology and biochemistry to examine and interpret the complex metabolic profile and how it responds to stress. A. thaliana mutants were grown for seven days, harvested, and freeze dried. Small molecule metabolites were extracted from dry tissue and subjected to analysis by nuclear magnetic resonance spectroscopy (NMR). NMR is capable of providing information about which metabolites are present in the plant extract and their relative amounts. Using a non-targeted analysis known as metabolic fingerprinting, we gained insight into unexpected metabolic adjustments that occur in the plants in response to a low oxygen environment. Metabolite data were compared to transcript profiling data. These experiments generate large datasets with hundreds of highly correlated variables, therefore a battery of statistical tests were employed to extract biologically meaningful patterns from the data. Metabolic profiling and flux balancing experiments were used to quantify the direction and magnitude of these adjustments. The data from these experiments can be used to construct predictive models, which put the results into context at the molecular, cellular and organismal levels. Experiments in progress will probe response to herbicides in wildtype (sensitive) and mutant (resistant) plants. This work allows us to build an arsenal of knowledge about the plant metabolome and its dynamic nature within a model system.
|
|
|
