(147g) Integrated Time-Series Metabolomic and Transcriptional Profiling Analyses of Arabidopsis Thaliana Response to Elevated Co2 and Osmotic Stress

Integrated time-series metabolomic and transcriptomic analyses of a systematically perturbed biological system can provide clues about gene and metabolic regulation, correlations between parallel occurring phenomena, the reconstruction of bio-reaction networks and even the function of unknown genes. Even further, comparing the system's response to individual perturbations with their combined effect might reveal information about the robustness of the regulatory networks, missed when studying individual perturbations only.

In this context, we subjected 12-day old Arabidopsis thaliana liquid cultures, grown at constant light and temperature, to elevated CO₂ levels (1%) and osmotic stress (50 mM NaCl), both individually and simultaneously, continuously for 30 hrs. The metabolomic and transcriptomic profiles were acquired by Gas Chromatography-Mass Spectrometry (GC-MS) and full genome cDNA microarrays, respectively. The GC-MS metabolomic data were corrected using a new normalization and validation strategy [1]. Both transcriptomic and metabolomic data were further analyzed using multivariate statistical analysis and a new rigorous technique [2] for the analysis of time-series ?omics? data. It has to be underlined that this is among first (if not the first) currently reported studies of plant physiology that concerns integrated metabolomic and transcriptomic analysis of dynamic plant response to such an extensive number of individual and simultaneously applied perturbations.

The results revealed a unique, identifiable response of A. thaliana to the individual and the combined stresses. The significant expression analysis at individual time points indicated the, number of expressions (both at the metabolic and transcriptional profiles) that remain at the same significance level throughout the entire period was largest for NaCl stress and smallest for CO₂ stress (the difference being more prominent in transcriptomic data). In case of average SV score (which measures the variability of significant expression with time)[2], for transcriptomic data was larger for CO₂ stress, while for metabolomic it was larger for the combined stress, showing differences in their dynamic response. The over-all significant analysis indicated a significant decrease in free metabolite concentrations of TCA cycle intermediates, Amino acids and phosphate derivatives, while a concurrent increase in sterols and precursors of secondary metabolites in response to NaCl treatment as well as for the combined stress.

This information is deemed extremely valuable for understanding the plant's regulatory network, because it provides clues about which parts of the network are robust for a particular stress and which become active under special circumstances. Further, the breadth and depth of the information that can be extracted from a biological system through integration of perturbations, biological datasets from different cellular levels and time-series analysis is quite large, thus demonstrating the usefulness of the systems level approach for gaining comprehensive information about any biological problem.

1. Kanani H and Klapa MI, 2006. ?Data Correction Strategy for Metabolomics Analysis using Gas Chromatography-Mass Spectrometry?, under review

2. Dutta B, Snyder R and Klapa MI, 2006. ?Significance Analysis for time-series transcriptomic data." under review

This work is funded by US NSF (QSB-0331312)