(196e) High-Throughput Time-Series Metabolomic Analysis to Identify Regulation of Arabidopsis Thalina Response to Elevated Co2 by Sugar Signaling

Comprehensive understanding of regulation in the primary metabolism of a biological system is a pre-requisite for the systematic metabolic engineering of that system. To study regulation of Arabidopsis thaliana primary metabolism by sugar signaling and CO₂, we carried out high-throughput, quantitative, dynamic metabolomic analysis of systematically perturbed A. thaliana liquid culture system. Specifically, A. thaliana liquid cultures, grown for 12 days under constant light and temperature in B5 Gamborg media were subjected to perturbations for 30 hours of (1) Elevated CO₂ level and (2) Trehalose (sugar) signal - applied individually and in combination. Additionally, the effect of elevated CO₂ was also studied by replacing Sucrose with Glucose in the growth media. Metabolomic profiling using gas chromatography-mass spectrometry was used to monitor the dynamic metabolic response of A. thaliana to the perturbations. A new algorithm for metabolomic data normalization and validation was developed to ensure accuracy and reproducibility of the metabolomic data.

The data obtained in the current analysis was used to develop a structured data analysis strategy for studying metabolic regulation of eukaryotic system from the time-series metabolomic data. Further, data analysis methodology for identifying interaction between two simultaneously applied dynamic perturbations was also developed to study the interaction between elevated CO₂ response and sugar signaling. Using these data analysis techniques for the dynamic metabolomic profiles in combination with gene expression analysis, a comprehensive understanding of the regulation of primary metabolism of A. thaliana liquid cultures by sugar signaling and CO₂ was obtained. The results were validated in the context of the known A. thaliana physiology. Apart from providing information about the response of the of the A. thaliana system to perturbations, this analysis is expected to greatly contribute in enhancing the currently available experimental database and in advancing the systems biology computational and experimental toolbox.